In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-sp...In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.展开更多
Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays...Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.展开更多
Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contam...Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contamination,leading to elevated levels of disinfection byproducts(DBPs).Epidemiological studies have associated increased risks of adverse health effects with frequent exposure to DBPs in swimming pools.Zhang et al.(2023b)investigated the occurrence of trihalomethanes(THMs),haloacetic acids(HAAs),haloacetonitriles(HANs),and haloacetaldehydes(HALs)in eight swimming pools and the corresponding input water in a city in Eastern China.The concentrations of THMs,HAAs,HANs,and HALs in swimming poolswere 1–2 orders of magnitude higher than those detected in the input water.The total lifetime cancer and non-cancer health risks of swimmers through oral,dermal,inhalation,buccal,and aural exposure pathways were assessed using the United States Environmental Protection Agency’s(USEPA)standard model and Swimmer Exposure Assessment Model(SWIMODEL).The results showed that dermal and inhalation pathways were the most significant for the associated cancer and non-cancer risks.This article provides an overview and perspectives of DBPs in swimming pools,the benefits of swimming,the need to improve the monitoring of DBPs,and the importance of swimmers’hygiene practices to keep swimming pools clean.The benefits of swimming outweigh the risks from DBP exposure for the promotion of public health.展开更多
Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regard...Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.展开更多
Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for th...Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.展开更多
Vertical position changes of ground-based Global Navigation Satellite System(GNSS) stations have been used to study regional terrestrial water storage(TWS) changes. However, the feasibility is still unclear in many ar...Vertical position changes of ground-based Global Navigation Satellite System(GNSS) stations have been used to study regional terrestrial water storage(TWS) changes. However, the feasibility is still unclear in many areas due to diverse local effects. This study aims to evaluate the capability of GNSS vertical displacements in monitoring hydrological variations in four climate settings over Chinese mainland. The spatial and temporal variations of hydrological load-induced(HYDL) vertical displacements at 208 GNSS sites during 2011-2020 were analyzed by comparing with Gravity Recovery and Climate Experiment(GRACE)/GRACE Follow-On(GFO) and Global Land Data Assimilation System(GLDAS) derived TWS changes. The results indicate that GNSS vertical positions show different capabilities in capturing seasonal and non-seasonal hydrological dynamics in different climate regions. Among the four climatic settings, the subtropical monsoon climate(SMC) region, with the largest deformation fluctuation(the regional mean root mean square(RMS) is 7.97 mm), has the highest regional mean HYDL-GRACE and HYDL-GLDAS anti-correlation coefficients(CCs) of-0.47 and-0.45 at the seasonal scale, respectively. For the individual GNSS site, the number of the sites with CC <-0.40 between HYDL and GRACE/GLDASderived TWS changes accounts for 55.1% and 55.1%(SMC), 13.0% and 7.4%(temperate monsoon climate, TMC), 6.7% and 13.3%(temperate continental climate, TCC), 32.3% and 38.7%(plateau climate,PC), respectively. For the non-seasonal term, although the proportion with CC <-0.40 in each climate type decreases mainly due to the influence of local geodynamic and human activities, especially in the SMC and PC regions, GNSS site vertical deformations still show good capability in monitoring hydrological extremes. The results provide valuable information for better application of GNSS to hydrology.展开更多
BACKGROUND High-resolution optical coherence tomography(HR-OCT)has become an essential instrument in the screening and diagnosis of ocular surface neoplasms.Research demonstrates that HR-OCT possesses a diagnostic sen...BACKGROUND High-resolution optical coherence tomography(HR-OCT)has become an essential instrument in the screening and diagnosis of ocular surface neoplasms.Research demonstrates that HR-OCT possesses a diagnostic sensitivity ranging from 85%to 90%for ocular surface squamous neoplasia(OSSN).The connections between HR-OCT features and histological findings have consistently shown robustness,hence increasing the reliability of clinical diagnosis.AIM To examine the existing HR-OCT indicators employed in the identification of common non-benign ocular surface tumors,namely,basal cell carcinoma,OSSN,and melanocytic conjunctival lesions,and to assess their diagnostic efficacy,benefits,and prospective developments.METHODS A thorough literature review was performed to assess the published research on HR-OCT in the diagnosis of ocular surface cancers.Significant attention was given to research that compares HR-OCT characteristics with histopath-ologic validation,as well as on publications addressing the integration of emerging technologies and artificial intelligence in ocular oncology imaging.RESULTS HR-OCT exhibits elevated diagnostic sensitivity(85%-90%)for identifying OSSN and presents distinct imaging patterns that align closely with histology results.This approach has substantial clinical advantages due to its non-invasive characteristics,improved axial resolution,and real-time imaging capabilities.HR-OCT has demonstrated potential in assessing various lesions,including basal cell carcinoma and melanocytic conjunctival malignancies.CONCLUSION HR-OCT assumes an increasingly vital role in the early identification and clinical management of ocular surface malignancies.With advancements in imaging technology and the integration of artificial intelligence,HR-OCT is anticipated to enhance individualized diagnosis and treatment planning in ocular oncology,hence improving patient outcomes.展开更多
Brain organoids encompass a large collection of in vitro stem cell–derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and function.First,this review provides a brief int...Brain organoids encompass a large collection of in vitro stem cell–derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and function.First,this review provides a brief introduction to the current state-of-the-art for neuroectoderm brain organoid development,emphasizing their biggest advantages in comparison with classical two-dimensional cell cultures and animal models.However,despite their usefulness for developmental studies,a major limitation for most brain organoid models is the absence of contributing cell types from endodermal and mesodermal origin.As such,current research is highly investing towards the incorporation of a functional vasculature and the microglial immune component.In this review,we will specifically focus on the development of immune-competent brain organoids.By summarizing the different approaches applied to incorporate microglia,it is highlighted that immune-competent brain organoids are not only important for studying neuronal network formation,but also offer a clear future as a new tool to study inflammatory responses in vitro in 3D in a brainlike environment.Therefore,our main focus here is to provide a comprehensive overview of assays to measure microglial phenotype and function within brain organoids,with an outlook on how these findings could better understand neuronal network development or restoration,as well as the influence of physical stress on microglia-containing brain organoids.Finally,we would like to stress that even though the development of immune-competent brain organoids has largely evolved over the past decade,their full potential as a pre-clinical tool to study novel therapeutic approaches to halt or reduce inflammation-mediated neurodegeneration still needs to be explored and validated.展开更多
BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches....BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when impl...Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.展开更多
Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in ...Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.展开更多
This paper introduces damping amplifier friction vibration absorbers(DAFVAs),compound damping amplifier friction vibration absorbers(CDAFVAs),nested damping amplifier friction vibration absorbers(NDAFVAs),and levered ...This paper introduces damping amplifier friction vibration absorbers(DAFVAs),compound damping amplifier friction vibration absorbers(CDAFVAs),nested damping amplifier friction vibration absorbers(NDAFVAs),and levered damping amplifier friction vibration absorbers(LDAFVAs)for controlling the structural vibrations and addressing the limitations of conventional tuned mass dampers(TMDs)and frictiontuned mass dampers(FTMDs).The closed-form analytical solution for the optimized design parameters is obtained using the H_(2)and H_(∞)optimization approaches.The efficiency of the recently established closed-form equations for the optimal design parameters is confirmed by the analytical examination.The closed form formulas for the dynamic responses of the main structure and the vibration absorbers are derived using the transfer matrix formulations.The foundation is provided by the harmonic and random-white noise excitations.Moreover,the effectiveness of the innovative dampers has been validated through numerical analysis.The optimal DAFVAs,CDAFVAs,NDAFVAs,and LDAFVAs exhibit at least 30%lower vibration reduction capacity compared with the optimal TMD.To demonstrate the effectiveness of the damping amplification mechanism,the novel absorbers are compared with a conventional FTMD.The results show that the optimized novel absorbers achieve at least 91%greater vibration reduction than the FTMD.These results show how the suggested designs might strengthen the structure's resilience to dynamic loads.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voir...CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voirs.However,the use of these gels in high-temperature CCUS applications is limited due to their rever-sible swelling behavior at elevated temperatures.In this study,a novel dispersed particle gel(DPG)suspension is developed for high-temperature profile control in CCUS applications.First,we synthesize a double-network hydrogel consisting of a crosslinked polyacrylamide(PAAm)network and a crosslinked sodium alginate(SA)network.The hydrogel is then sheared in water to form a pre-prepared DPG suspen-sion.To enhance its performance,the gel particles are modified by introducing potassium methylsilan-etriolate(PMS)upon CO_(2) exposure.Comparing the particle size distributions of the modified and pre-prepared DPG suspension reveals a significant swelling of gel particles,over twice their original size.Moreover,subjecting the new DPG suspension to a 100℃ environment for 24 h demonstrates that its gel particle sizes do not decrease,confirming irreversible swelling,which is a significant advantage over the traditional CO_(2)-responsive gels.Thermogravimetric analysis further indicates improved thermal sta-bility compared to the pre-prepared DPG particles.Core flooding experiments show that the new DPG suspension achieves a high plugging efficiency of 95.3%in plugging an ultra-high permeability sandpack,whereas the pre-prepared DPG suspension achieves only 82.8%.With its high swelling ratio,irreversible swelling at high temperatures,enhanced thermal stability,and superior plugging performance,the newly developed DPG suspension in this work presents a highly promising solution for profile control in high-temperature CCUS applications.展开更多
The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled ...The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled by individuals,businesses,enterprises,and public administrations.There are several advantages for creating these RECs and participating in them,which include social,environmental,and financial.Nonetheless,according to the Renewable Energy Directive(RED II),the idea of RECs has given opportunities for researchers to investigate the behavior from all aspects.These RECs are characterized by energy fluxes corresponding to self-consumption,energy sales,and energy sharing.Our work focuses on amathematical time-dependentmodel on an hourly basis that considers the optimization of photovoltaic-based RECs tomaximize profit based on the number of prosumers and consumers,as well as the impact of load profiles on the community’s technical and financial aspects usingMATLAB software.In this work,REC’s users can install their plant and become prosumers or vice versa,and users could change their consumption habits until the optimumconfiguration of REC is obtained.Moreover,this work also focuses on the financial analysis of the plant by comparing the Net Present Value(NPV)as a function of plant size,highlighting the advantage of creating a REC.Numerical results have been obtained investigating the case studies of RECs as per the Italian framework,which shows an optimal distribution of prosumers and consumers and an optimal load profile in which the maximum profitability is obtained.Optimization has been performed by considering different load profiles.Moreover,starting from the optimized configurations,an analysis based on the plant size is also made to maximize the NPV.This work has shown positive outcomes and would be helpful for the researchers and stakeholders while designing the RECs.展开更多
The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to ...The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.展开更多
The National Gas Hydrate Program expeditions(NGHP-01 and-02)have conclusively proven the presence of hydrate deposits on the eastern coast of India.The novelty of the present study lies in its investigation of the ric...The National Gas Hydrate Program expeditions(NGHP-01 and-02)have conclusively proven the presence of hydrate deposits on the eastern coast of India.The novelty of the present study lies in its investigation of the richest gas hydrate deposit(hydrate saturation[Sh]>0.75),NGHP-01-10D,in the Krishna-Godavari(KG)Basin,India.The study presents a first look at the long-term gas production viability using a single vertical well,subjected to variations in production interval and bottom hole pressure.Specifically,we compared the gas production at bottom hole pressures of 3-6 MPa and production intervals of 20-40 m.The results indicate production rates that are technically feasible but lower than commercially acceptable standards.Increasing the bottom hole pressure drawdown from 6 MPa to 3 MPa increased the gas production from 1297 m^(3)/d to 4902 m^(3)/d(i.e.,more than tripling the average daily gas production).Meanwhile,while expanding the production interval from 20 m to 40 m led to an increase in gas production,it also resulted in higher water production.As a result,the average gas-to-water ratio(RGW)decreased from 9.5 to 5.3 with the expansion of the production interval,thereby highlighting the need to optimize the interval length.Furthermore,the spatial evolution of certain thermodynamic parameters,including pressure,temperature,and phase saturation(methane,water,and hydrate),underscores the critical role of heat transfer from the UB.Our study findings offer valuable insights for long-term production forecasting,the delineation of phase evolution patterns,and the identification of potential flow barriers that may impede deliverability.展开更多
This editorial is a commentary on the case report by Furuya et al focusing on the challenging diagnosis of early pancreatic adenocarcinoma and new tools for an earlier diagnosis.Currently,pancreatic cancer still has a...This editorial is a commentary on the case report by Furuya et al focusing on the challenging diagnosis of early pancreatic adenocarcinoma and new tools for an earlier diagnosis.Currently,pancreatic cancer still has a poor prognosis,mainly due to late diagnosis in an advanced stage.Two main precancerous routes have been identified as pathways to pancreatic adenocarcinoma:The first encompasses a large group of mucinous cystic lesions:intraductal papillary mucinous neoplasm and mucinous cystic neoplasm,and the second is pancreatic intraepithelial neoplasia.In the last decade the focus of research has been to identify high-risk patients,using advanced imaging techniques(magnetic resonance cholangiopancreatography or endoscopic ultrasonography)which could be helpful in finding“indirect signs”of early stage pancreatic lesions.Nevertheless,the survival rate still remains poor,and alternative screening methods are under investigation.Endoscopic retrograde cholangiopancreatography followed by serial pancreatic juice aspiration cytology could be a promising tool for identifying precursor lesions such as intraductal papillary mucinous neoplasm,but confirming data are still needed to validate its role.Probably a combination of cross-sectional imaging,endoscopic techniques(old and new ones)and genetic and biological biomarkers also in pancreatic juice)could be the best solution to reach an early diagnosis.Biomarkers could help to predict and follow the progression of early pancreatic lesions.However,further studies are needed to validate their diagnostic reliability and to establish diagnostic algorithms to improve prognosis and survival in patients with pancreatic cancer.展开更多
基金the National Science Foundation of China (Grant No. NSFC: 92162213)the Geology Department Faculty of Science of Al-Azhar University (Assiut Branch)+2 种基金the China Scholarship CouncilChang'an UniversityIstanbul Technical University's Scientific Research Project (BAP Project ID: 45396, code: FHD-2024-45396)
文摘In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2022YFA1404204 and 2022YFA1403700)the National Natural Science Foundation of China (Grant Nos. 12274086, 11534001 and 11925402)+5 种基金funding from the National Science Foundation of China (Grant Nos. 12274046, 11874094, 12147102, and 12347101)Chongqing Natural Science Foundation (Grant No. CSTB2022NSCQ-JQX0018)the Fundamental Research Funds for the Central Universities (Grant No. 2021CDJZYJH-003)Xiaomi Foundation/Xiaomi Young Talents Programthe supports of the start-up funding of Westlake Universitysupport from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants。
文摘Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.
文摘Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contamination,leading to elevated levels of disinfection byproducts(DBPs).Epidemiological studies have associated increased risks of adverse health effects with frequent exposure to DBPs in swimming pools.Zhang et al.(2023b)investigated the occurrence of trihalomethanes(THMs),haloacetic acids(HAAs),haloacetonitriles(HANs),and haloacetaldehydes(HALs)in eight swimming pools and the corresponding input water in a city in Eastern China.The concentrations of THMs,HAAs,HANs,and HALs in swimming poolswere 1–2 orders of magnitude higher than those detected in the input water.The total lifetime cancer and non-cancer health risks of swimmers through oral,dermal,inhalation,buccal,and aural exposure pathways were assessed using the United States Environmental Protection Agency’s(USEPA)standard model and Swimmer Exposure Assessment Model(SWIMODEL).The results showed that dermal and inhalation pathways were the most significant for the associated cancer and non-cancer risks.This article provides an overview and perspectives of DBPs in swimming pools,the benefits of swimming,the need to improve the monitoring of DBPs,and the importance of swimmers’hygiene practices to keep swimming pools clean.The benefits of swimming outweigh the risks from DBP exposure for the promotion of public health.
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
文摘Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.
基金supported by the National Natural Science Foundation of China(Grant No.52125307)the National Key R&D Program of China(Grant No.2021YFB3501500)the support from the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.
基金supported by National Natural Science Foundation of China(42064002,42004013,42204006)the GuangxiNatural Science Foundation of China(2024GXNSFDA010041)+5 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515010469)Guangxi Key Laboratory of Spatial Information and Geomatics(Grant no.21-238-21-05)the Open Fund of Hubei Luojia Laboratory(230100019,230100020)The GNSS observation data are provided by Crustal Movement Observation Network of China(CMONC)The GRACE/GFO mascon gravimetry data products are provided by NASA Jet Propulsion Laboratory/California Institute of TechnologyThe GLDAS data products are provided by NASA Earthdata.
文摘Vertical position changes of ground-based Global Navigation Satellite System(GNSS) stations have been used to study regional terrestrial water storage(TWS) changes. However, the feasibility is still unclear in many areas due to diverse local effects. This study aims to evaluate the capability of GNSS vertical displacements in monitoring hydrological variations in four climate settings over Chinese mainland. The spatial and temporal variations of hydrological load-induced(HYDL) vertical displacements at 208 GNSS sites during 2011-2020 were analyzed by comparing with Gravity Recovery and Climate Experiment(GRACE)/GRACE Follow-On(GFO) and Global Land Data Assimilation System(GLDAS) derived TWS changes. The results indicate that GNSS vertical positions show different capabilities in capturing seasonal and non-seasonal hydrological dynamics in different climate regions. Among the four climatic settings, the subtropical monsoon climate(SMC) region, with the largest deformation fluctuation(the regional mean root mean square(RMS) is 7.97 mm), has the highest regional mean HYDL-GRACE and HYDL-GLDAS anti-correlation coefficients(CCs) of-0.47 and-0.45 at the seasonal scale, respectively. For the individual GNSS site, the number of the sites with CC <-0.40 between HYDL and GRACE/GLDASderived TWS changes accounts for 55.1% and 55.1%(SMC), 13.0% and 7.4%(temperate monsoon climate, TMC), 6.7% and 13.3%(temperate continental climate, TCC), 32.3% and 38.7%(plateau climate,PC), respectively. For the non-seasonal term, although the proportion with CC <-0.40 in each climate type decreases mainly due to the influence of local geodynamic and human activities, especially in the SMC and PC regions, GNSS site vertical deformations still show good capability in monitoring hydrological extremes. The results provide valuable information for better application of GNSS to hydrology.
文摘BACKGROUND High-resolution optical coherence tomography(HR-OCT)has become an essential instrument in the screening and diagnosis of ocular surface neoplasms.Research demonstrates that HR-OCT possesses a diagnostic sensitivity ranging from 85%to 90%for ocular surface squamous neoplasia(OSSN).The connections between HR-OCT features and histological findings have consistently shown robustness,hence increasing the reliability of clinical diagnosis.AIM To examine the existing HR-OCT indicators employed in the identification of common non-benign ocular surface tumors,namely,basal cell carcinoma,OSSN,and melanocytic conjunctival lesions,and to assess their diagnostic efficacy,benefits,and prospective developments.METHODS A thorough literature review was performed to assess the published research on HR-OCT in the diagnosis of ocular surface cancers.Significant attention was given to research that compares HR-OCT characteristics with histopath-ologic validation,as well as on publications addressing the integration of emerging technologies and artificial intelligence in ocular oncology imaging.RESULTS HR-OCT exhibits elevated diagnostic sensitivity(85%-90%)for identifying OSSN and presents distinct imaging patterns that align closely with histology results.This approach has substantial clinical advantages due to its non-invasive characteristics,improved axial resolution,and real-time imaging capabilities.HR-OCT has demonstrated potential in assessing various lesions,including basal cell carcinoma and melanocytic conjunctival malignancies.CONCLUSION HR-OCT assumes an increasingly vital role in the early identification and clinical management of ocular surface malignancies.With advancements in imaging technology and the integration of artificial intelligence,HR-OCT is anticipated to enhance individualized diagnosis and treatment planning in ocular oncology,hence improving patient outcomes.
基金funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No.813263(PMSMat Train,granted to UF,PP,MV,and DP)provided by the Fund for Scientific Research Flanders(FWO-Vlaanderen)of the Flemish Government(FWO sabbatical bench fee K800224N granted to PP)and ERA-NET Re Park(granted to PP)。
文摘Brain organoids encompass a large collection of in vitro stem cell–derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and function.First,this review provides a brief introduction to the current state-of-the-art for neuroectoderm brain organoid development,emphasizing their biggest advantages in comparison with classical two-dimensional cell cultures and animal models.However,despite their usefulness for developmental studies,a major limitation for most brain organoid models is the absence of contributing cell types from endodermal and mesodermal origin.As such,current research is highly investing towards the incorporation of a functional vasculature and the microglial immune component.In this review,we will specifically focus on the development of immune-competent brain organoids.By summarizing the different approaches applied to incorporate microglia,it is highlighted that immune-competent brain organoids are not only important for studying neuronal network formation,but also offer a clear future as a new tool to study inflammatory responses in vitro in 3D in a brainlike environment.Therefore,our main focus here is to provide a comprehensive overview of assays to measure microglial phenotype and function within brain organoids,with an outlook on how these findings could better understand neuronal network development or restoration,as well as the influence of physical stress on microglia-containing brain organoids.Finally,we would like to stress that even though the development of immune-competent brain organoids has largely evolved over the past decade,their full potential as a pre-clinical tool to study novel therapeutic approaches to halt or reduce inflammation-mediated neurodegeneration still needs to be explored and validated.
文摘BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金funded by the McGill University Graduate Excellence Fellowship Award(00157)the Mitacs Accelerate Program(IT13369)the McGill Engineering Doctoral Award(MEDA).
文摘Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.
文摘Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.
基金the postdoctoral research grant received from the University of Glasgow for the partial financial support for this research work。
文摘This paper introduces damping amplifier friction vibration absorbers(DAFVAs),compound damping amplifier friction vibration absorbers(CDAFVAs),nested damping amplifier friction vibration absorbers(NDAFVAs),and levered damping amplifier friction vibration absorbers(LDAFVAs)for controlling the structural vibrations and addressing the limitations of conventional tuned mass dampers(TMDs)and frictiontuned mass dampers(FTMDs).The closed-form analytical solution for the optimized design parameters is obtained using the H_(2)and H_(∞)optimization approaches.The efficiency of the recently established closed-form equations for the optimal design parameters is confirmed by the analytical examination.The closed form formulas for the dynamic responses of the main structure and the vibration absorbers are derived using the transfer matrix formulations.The foundation is provided by the harmonic and random-white noise excitations.Moreover,the effectiveness of the innovative dampers has been validated through numerical analysis.The optimal DAFVAs,CDAFVAs,NDAFVAs,and LDAFVAs exhibit at least 30%lower vibration reduction capacity compared with the optimal TMD.To demonstrate the effectiveness of the damping amplification mechanism,the novel absorbers are compared with a conventional FTMD.The results show that the optimized novel absorbers achieve at least 91%greater vibration reduction than the FTMD.These results show how the suggested designs might strengthen the structure's resilience to dynamic loads.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金Lin Du acknowledges the financial support provided by China Scholarship Council(CSC)via a Ph.D.Scholarship(202008510128)supported by Core Technology Project of China National Petroleum Corporation(CNPC)"Research on Thermal Miscible Flooding Technology"(2023ZG18)。
文摘CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voirs.However,the use of these gels in high-temperature CCUS applications is limited due to their rever-sible swelling behavior at elevated temperatures.In this study,a novel dispersed particle gel(DPG)suspension is developed for high-temperature profile control in CCUS applications.First,we synthesize a double-network hydrogel consisting of a crosslinked polyacrylamide(PAAm)network and a crosslinked sodium alginate(SA)network.The hydrogel is then sheared in water to form a pre-prepared DPG suspen-sion.To enhance its performance,the gel particles are modified by introducing potassium methylsilan-etriolate(PMS)upon CO_(2) exposure.Comparing the particle size distributions of the modified and pre-prepared DPG suspension reveals a significant swelling of gel particles,over twice their original size.Moreover,subjecting the new DPG suspension to a 100℃ environment for 24 h demonstrates that its gel particle sizes do not decrease,confirming irreversible swelling,which is a significant advantage over the traditional CO_(2)-responsive gels.Thermogravimetric analysis further indicates improved thermal sta-bility compared to the pre-prepared DPG particles.Core flooding experiments show that the new DPG suspension achieves a high plugging efficiency of 95.3%in plugging an ultra-high permeability sandpack,whereas the pre-prepared DPG suspension achieves only 82.8%.With its high swelling ratio,irreversible swelling at high temperatures,enhanced thermal stability,and superior plugging performance,the newly developed DPG suspension in this work presents a highly promising solution for profile control in high-temperature CCUS applications.
文摘The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled by individuals,businesses,enterprises,and public administrations.There are several advantages for creating these RECs and participating in them,which include social,environmental,and financial.Nonetheless,according to the Renewable Energy Directive(RED II),the idea of RECs has given opportunities for researchers to investigate the behavior from all aspects.These RECs are characterized by energy fluxes corresponding to self-consumption,energy sales,and energy sharing.Our work focuses on amathematical time-dependentmodel on an hourly basis that considers the optimization of photovoltaic-based RECs tomaximize profit based on the number of prosumers and consumers,as well as the impact of load profiles on the community’s technical and financial aspects usingMATLAB software.In this work,REC’s users can install their plant and become prosumers or vice versa,and users could change their consumption habits until the optimumconfiguration of REC is obtained.Moreover,this work also focuses on the financial analysis of the plant by comparing the Net Present Value(NPV)as a function of plant size,highlighting the advantage of creating a REC.Numerical results have been obtained investigating the case studies of RECs as per the Italian framework,which shows an optimal distribution of prosumers and consumers and an optimal load profile in which the maximum profitability is obtained.Optimization has been performed by considering different load profiles.Moreover,starting from the optimized configurations,an analysis based on the plant size is also made to maximize the NPV.This work has shown positive outcomes and would be helpful for the researchers and stakeholders while designing the RECs.
基金supported by the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2021B0301030001)the National Key Research and Development Program of China(Grant No.2021YFB3802300)the Foundation of National Key Laboratory of Shock Wave and Detonation Physics(Grant No.JCKYS2022212004)。
文摘The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.
文摘The National Gas Hydrate Program expeditions(NGHP-01 and-02)have conclusively proven the presence of hydrate deposits on the eastern coast of India.The novelty of the present study lies in its investigation of the richest gas hydrate deposit(hydrate saturation[Sh]>0.75),NGHP-01-10D,in the Krishna-Godavari(KG)Basin,India.The study presents a first look at the long-term gas production viability using a single vertical well,subjected to variations in production interval and bottom hole pressure.Specifically,we compared the gas production at bottom hole pressures of 3-6 MPa and production intervals of 20-40 m.The results indicate production rates that are technically feasible but lower than commercially acceptable standards.Increasing the bottom hole pressure drawdown from 6 MPa to 3 MPa increased the gas production from 1297 m^(3)/d to 4902 m^(3)/d(i.e.,more than tripling the average daily gas production).Meanwhile,while expanding the production interval from 20 m to 40 m led to an increase in gas production,it also resulted in higher water production.As a result,the average gas-to-water ratio(RGW)decreased from 9.5 to 5.3 with the expansion of the production interval,thereby highlighting the need to optimize the interval length.Furthermore,the spatial evolution of certain thermodynamic parameters,including pressure,temperature,and phase saturation(methane,water,and hydrate),underscores the critical role of heat transfer from the UB.Our study findings offer valuable insights for long-term production forecasting,the delineation of phase evolution patterns,and the identification of potential flow barriers that may impede deliverability.
文摘This editorial is a commentary on the case report by Furuya et al focusing on the challenging diagnosis of early pancreatic adenocarcinoma and new tools for an earlier diagnosis.Currently,pancreatic cancer still has a poor prognosis,mainly due to late diagnosis in an advanced stage.Two main precancerous routes have been identified as pathways to pancreatic adenocarcinoma:The first encompasses a large group of mucinous cystic lesions:intraductal papillary mucinous neoplasm and mucinous cystic neoplasm,and the second is pancreatic intraepithelial neoplasia.In the last decade the focus of research has been to identify high-risk patients,using advanced imaging techniques(magnetic resonance cholangiopancreatography or endoscopic ultrasonography)which could be helpful in finding“indirect signs”of early stage pancreatic lesions.Nevertheless,the survival rate still remains poor,and alternative screening methods are under investigation.Endoscopic retrograde cholangiopancreatography followed by serial pancreatic juice aspiration cytology could be a promising tool for identifying precursor lesions such as intraductal papillary mucinous neoplasm,but confirming data are still needed to validate its role.Probably a combination of cross-sectional imaging,endoscopic techniques(old and new ones)and genetic and biological biomarkers also in pancreatic juice)could be the best solution to reach an early diagnosis.Biomarkers could help to predict and follow the progression of early pancreatic lesions.However,further studies are needed to validate their diagnostic reliability and to establish diagnostic algorithms to improve prognosis and survival in patients with pancreatic cancer.
