Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major...Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.展开更多
This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to ...This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to becoming carbon sources under compounded bioclimatic stress.BEAT synthesizes satellite-derived data from 2001 to 2022 and integrates temperature anomalies,vapor pressure deficit(VPD),fire activity,and vegetation degradation into a Compound Stress Index(CSI).Methodologically,the study applies piecewise regression,changepoint analysis,and early warning signal(EWS)metrics,including rolling variance and lag-1 autocorrelation,to identify nonlinear emission tipping points and ecological resilience loss.Machine learning models such as XGBoost and SHAP were employed to evaluate the predictive relevance of CSI components and enhance model interpretability.Results reveal a critical CSI threshold(≥0.6),beyond which Net Ecosystem Exchange(NEE)exhibits abrupt positive anomalies,indicating carbon emission amplification.EWS metrics significantly increased prior to emission spikes,validating BEAT’s predictive capacity for ecological destabilization.In addition,spatial clustering and time-lagged correlation analysis confirmed the alignment between compound stress hotspots and emission anomalies,and when compared to traditional Earth System Models(ESMs),BEAT uniquely captures synergistic stress interactions and nonlinearity.The findings underscore BEAT’s potential to improve early warning systems,REDD+monitoring frameworks,and climate adaptation planning.Its scalable design enables application across vulnerable biomes globally and offers a transformative tool for anticipating biosphere-climate tipping points and informing proactive ecosystem governance.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting...Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.展开更多
The recent surge in global financial and patent innovations and rising CO_(2) emissions in the global energy sector have drawn significant attention to China’s transportation industry.This study examines how financia...The recent surge in global financial and patent innovations and rising CO_(2) emissions in the global energy sector have drawn significant attention to China’s transportation industry.This study examines how financial innovations(FINI),patent innovations(PTIN),and bioenergy(BIOE)affect CO_(2) emissions in China’s transportation sector(TBCO_(2))using quarterly data from 2000 to 2018.This study employed a novel wavelet local multiple correlation(WLMC)methodology,alongside the time-varying causality test,to examine the time–frequency nexus,addressing a critical gap in the current literature.The WLMC bivariate analyses revealed a negative long-term relationship between PTIN and FINI with TBCO_(2).At the same time,BIOE showed only a short-term mitigating effect,with PTIN playing a dominant role in this nexus at various frequency levels.Furthermore,the three-and four-variate assessments highlight the consistent positive influence of all included factors on TBCO_(2).A timevarying causality test also demonstrated significant causal relationships between FINI,PTIN,BIOE,and TBCO_(2) across different periods,confirming the robustness of our WLMC results.This study provides crucial insights,emphasizing the urgency of promoting FINIs,technological advancement,and bioenergy usage to reduce transportation emissions and pursue sustainable solutions to address China’s environmental challenges.展开更多
This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transporta...This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.展开更多
Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and contro...Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.展开更多
The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range comm...The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.展开更多
A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data....A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.展开更多
BACKGROUND Unilateral patellofemoral pain syndrome(PFPS)is the most frequently diagnosed knee condition in populations aged<50 years old.Although the treatment of myofascial trigger points(MTrPs)is a common and eff...BACKGROUND Unilateral patellofemoral pain syndrome(PFPS)is the most frequently diagnosed knee condition in populations aged<50 years old.Although the treatment of myofascial trigger points(MTrPs)is a common and effective tool for reducing pain,previous studies showed no additional benefits compared with placebo in populations with PFPS.Percutaneous electrolysis is a minimally invasive approach frequently used in musculotendinous pathologies which consists of the application of a galvanic current through dry needling(DN).AIM To evaluate changes in sensitivity,knee pain perception and perceived pain during the application of these three invasive techniques.METHODS A triple-blinded,pilot randomized controlled trial was conducted on fifteen patients with unilateral PFPS who were randomized to the high-intensity percutaneous electrolysis(HIPE)experimental group,low-intensity percutaneous electrolysis(LIPE)experimental group or DN active control group.All interventions were conducted in the most active MTrP,in the rectus femoris muscle.The HIPE group received a 660 mA galvanic current for 10 s,the LIPE group 220 mA×30 s and the DN group received no galvanic current.The MTrP and patellar tendon pain pressure thresholds(PPTs)and subjective anterior knee pain perception(SAKPP)were assessed before,after and 7 d after the single intervention.In addition,perceived pain during the intervention was also assessed.RESULTS Both groups were comparable at baseline as no significant differences were found for age,height,weight,body mass index,PPTs or SAKPP.No adverse events were reported during or after the interventions.A significant decrease in SAKPP(both HIPE and LIPE,P<0.01)and increased patellar tendon PPT(all,P<0.001)were found,with no differences between the groups(VAS:F=0.30;η2=0.05;P>0.05;tendon PPT immediate effects:F=0.15;η2=0.02;P>0.05 and tendon PPT 7-d effects:F=0.67;η2=0.10;P>0.05).A significant PPT increase in rectus femoris MTrP was found at follow-up in both the HIPE and LIPE groups(both,P<0.001)with no differences between the groups(immediate effects:F=1.55;η2=0.20;P>0.05 and 7-d effects:F=0.71;η2=0.10;P>0.05).Both HIPE and LIPE interventions were considered less painful compared with DN(F=8.52;η2=0.587;P<0.01).CONCLUSION HIPE and LIPE induce PPT changes in MTrPs and patellar tendon and improvements in SAKPP,and seem to produce less pain during the intervention compared with DN.展开更多
Most loan evaluation methods in peer-to-peer(P2P)lending mainly exploit the borrowers’credit information.However,the present study presents the maturity-based lender composition score,which exploits the investment ca...Most loan evaluation methods in peer-to-peer(P2P)lending mainly exploit the borrowers’credit information.However,the present study presents the maturity-based lender composition score,which exploits the investment capability of a group of lenders who fund the same loan,to enhance the P2P loan evaluation.More specifically,we extract lenders’profiles in terms of performance,risk,and experience by quantifying their investment history and develop our loan evaluation indicator by aggregating the profiles of lenders in the composition.To measure the ability of a lender for continuous improvement in P2P investment,we introduce lender maturity to capture this evolvement and incorporate it into the aggregation process.Our empirical study demonstrates that the maturity-based lender composition score can serve as an effective indicator for identifying loan quality and be included in other commonly used loan evaluation models for accuracy improvement.展开更多
Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities.Recent advances in information and communications technology,the smartphone,the Internet and Wi-Fi have g...Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities.Recent advances in information and communications technology,the smartphone,the Internet and Wi-Fi have given a boost to the tourism industry.The city bus tour(CBT)service is one of the most successful businesses in the tourism industry.However,there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT.In this research,we report on the ongoing development of a mobile application(app)and a website for tourists,hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time.Firstly,the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem.All places of interest(PoI)and pickup/dropout points are identified with arcs of the network(instead of nodes),each of which can be visited at least once(instead of exactly once).Secondly,the resulting pure integer programming(IP)problem is solved using a leading optimization soft-ware known as General Algebraic Modeling System(GAMS).The GAMS code developed for this project returns:(1)the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network;(2)the augmenting paths corre-sponding to the pickup stage,the PoI visiting stage and the drop-off stage.Finally,we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne(Australia).All the computations relative to the initial tests show that the ability of the app to answer users'inquiries in a fraction of a minute.展开更多
Intelligent assembly of large-scale,complex structures using an intelligent manufacturing platform represents the future development direction for industrial manufacturing.During large-scale structural assembly proces...Intelligent assembly of large-scale,complex structures using an intelligent manufacturing platform represents the future development direction for industrial manufacturing.During large-scale structural assembly processes,several bottleneck problems occur in the existing auxiliary assembly technology.First,the traditional LiDARbased assembly technology is often limited by the openness of the manufacturing environment,in which there are blind spots,and continuous online assembly adjustment thus cannot be realized.Second,for assembly of large structures,a single-station LiDAR system cannot achieve complete coverage,which means that a multi-station combination method must be used to acquire the complete three-dimensional data;many more data errors are caused by the transfer between stations than by the measurement accuracy of a single station,which means that the overall system's measurement and adjustment errors are increased greatly.Third,because of the large numbers of structural components contained in a large assembly,the accumulated errors may lead to assembly interference,but the LiDAR-assisted assembly process does not have a feedback perception capability,and thus assembly component loss can easily be caused when assembly interference occurs.Therefore,this paper proposes to combine an optical fiber sensor network with digital twin technology,which will allow the test data from the assembly entity state in the real world to be applied to the"twin"model in the virtual world and thus solve the problems with test openness and data transfer.The problem of station and perception feedback is also addressed and represents the main innovation of this work.The system uses an optical fiber sensor network as a flexible sensing medium to monitor the strain field distribution within a complex area in real time,and then completes real-time parameter adjustment of the virtual assembly based on the distributed data.Complex areas include areas that are laser-unreachable,areas with complex contact surfaces,and areas with large-scale bending deformations.An assembly condition monitoring system is designed based on the optical fiber sensor network,and an assembly condition monitoring algorithm based on multiple physical quantities is proposed.The feasibility of use of the optical fiber sensor network as the real-state parameter acquisition module for the digital twin intelligent assembly system is discussed.The offset of any position in the test area is calculated using the convolutional neural network of a residual module to provide the compensation parameters required for the virtual model of the assembly structure.In the model optimization parameter module,a correction data table is obtained through iterative learning of the algorithm to realize state prediction from the test data.The experiment simulates a largescale structure assembly process,and performs virtual and real mapping for a variety of situations with different assembly errors to enable correction of the digital twin data stream for the assembly process through the optical fiber sensor network.In the plane strain field calibration experiment,the maximum error among the test values for this system is 0.032 mm,and the average error is 0.014 mm.The results show that use of visual calibration can correct the test error to within a very small range.This result is equally applicable to gradient curvature surfaces and freeform surfaces.Statistics show that the average measurement accuracy error for regular surfaces is better than 11.2%,and the average measurement accuracy error for irregular surfaces is better than 14.8%.During simulation of large-scale structure assembly experiments,the average position deviation accuracy is 0.043 mm,which is in line with the designed accuracy.展开更多
With recent attention to high power energy and its interaction with materials of different types,both in industry and military application,this paper covers a short review course into subject of materials response in ...With recent attention to high power energy and its interaction with materials of different types,both in industry and military application,this paper covers a short review course into subject of materials response in respect to such high power energy lasers.In this paper,we are covering laser interaction with solid and going through steps of phase changes,from solid to liquid and finally vapor stage.As we indicated in this part of short course mainly Part I,we have stated of series of article on the subject of Materials Responses to High Power Energy Lasers and continue these series by starting to introduce the Laser Light Propagation either in vacuum or through atmosphere by also introducing thermal blooming effects as well,then we cover,subjects such as Optical Reflectivity,thermal responses of materials by looking at Latent Heat of Fusion as well as Vaporization,No Phase Changes in both Semi-Infinite Solid or Slab of Finite Thickness,Melting and Vaporization and then move on to Effects of Pulsed or Continuous Laser Radiation as well,throughout of next few parts that we report them as further Short Courses content.展开更多
With recent attention to high power energy and its interaction with materials of different type,both in industry and military application,this paper covers a short review course into subject of materials response in r...With recent attention to high power energy and its interaction with materials of different type,both in industry and military application,this paper covers a short review course into subject of materials response in respect to such high power energy lasers.In this paper,we are covering laser interaction with solid and going through steps of phase changes,from solid to liquid and finally vapor stage.As we indicated in this part of short course mainly Part I and Part II,we have started a series of articles on the subject of Materials Responses to High Power Energy Lasers and continue these series by starting to introduce the Laser Light Propagation into materials.In this part namely Part III,we are discussing,one of the most important effects of intense laser irradiation is the conversion of the optical energy in the beam into thermal energy in the material.This is the basis of many applications of lasers,such as welding and cutting.We shall summarize here this thermal response.It is basically a classical problem,namely heat flow,in a usual manner of heat conduction,we show solutions to the equation which governs the flow of heat and discuss change of phases in targeting material from solid to liquid and finally vapor and plasma stages step by step.展开更多
BACKGROUND: To determine if elderly frequent attenders are associated with increased 30-day mortality, assess resource utilization by the elderly frequent attenders and identify associated characteristics that contrib...BACKGROUND: To determine if elderly frequent attenders are associated with increased 30-day mortality, assess resource utilization by the elderly frequent attenders and identify associated characteristics that contribute to mortality. METHODS: Retrospective observational study of electronic clinical records of all emergency department(ED) visits over a 10-year period to an urban tertiary general hospital in Singapore. Patients aged 65 years and older, with 3 or more visits within a calendar year were identified. Outcomes measured include 30-day mortality, admission rate, admission diagnosis and duration spent at ED. Chi-square-tests were used to assess categorical factors and Student t-test was used to assess continuous variables on their association with being a frequent attender. Univariate and multivariate logistic regressions were conducted on all significant independent factors on to the outcome variable(30-day mortality), to determine factor independent odds ratios of being a frequent attender.RESULTS: 1.381 million attendance records were analyzed. Elderly patients accounted for 25.5% of all attendances, of which 31.3% are frequent attenders. Their 30-day mortality rate increased from 4.0% in the first visit, to 8.8% in the third visit, peaking at 10.2% in the sixth visit. Factors associated with mortality include patients with neoplasms, ambulance utilization, male gender and having attended the ED the previous year.CONCLUSION: Elderly attenders have a higher 30-day mortality risk compared to the overall ED population, with mortality risk more marked for frequent attenders. This study illustrates the importance and need for interventions to address frequent ED visits by the elderly, especially in an aging society.展开更多
In this paper we employ artificial neural networks for predictive approximation of generalized functions having crucial applications in different areas of science including mechanical and chemical engineering, signal ...In this paper we employ artificial neural networks for predictive approximation of generalized functions having crucial applications in different areas of science including mechanical and chemical engineering, signal processing, information transfer, telecommunications, finance, etc. Results of numerical analysis are discussed. It is shown that the known Gibb’s phenomenon does not occur.展开更多
The purpose of this study was to investigate the clinical efficacy of photodynamic combined freezing in patients with non-melanoma skin cancer(NMSC).First,according to the treatment regimen,96 patients with NMSC were ...The purpose of this study was to investigate the clinical efficacy of photodynamic combined freezing in patients with non-melanoma skin cancer(NMSC).First,according to the treatment regimen,96 patients with NMSC were divided into study group(n=50)and control group(n=46).The control group was treated with 5-amino-ketovalic acid photodynamic therapy(ALAPDT),while the study group was treated with ala-PDT combined with cryotherapy.Visual analogue scale(VAS)scores,visual satisfaction,clinical efficacy,adverse reactions,and progression-free survival were compared between the two groups.The results showed that VAS score in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).The appearance satisfaction and total effective rate of patients in the study group were higher than those in the control group,and the difference was statistically significant(P<0.05).The total incidence of adverse reactions in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).3 years progressionfree survival time and 3 years progression-free survival rate were compared between the two groups,and the difference was not statistically significant(P>0.05).Therefore,the combination of PDT and cryotherapy for non-melanoma skin cancer has a good clinical effect,which is conducive to the recovery of skin lesions,high patient satisfaction,fewer adverse reactions,and longer progression-free survival.In addition,the combined therapy can provide a new treatment idea for non-melanoma skin cancer patients who are not suitable for surgical treatment.展开更多
We consider the efficacy of a proposed linear-dimension-reduction method to potentially increase the powers of five hypothesis tests for the difference of two high-dimensional multivariate-normal population-mean vecto...We consider the efficacy of a proposed linear-dimension-reduction method to potentially increase the powers of five hypothesis tests for the difference of two high-dimensional multivariate-normal population-mean vectors with the assumption of homoscedastic covariance matrices. We use Monte Carlo simulations to contrast the empirical powers of the five high-dimensional tests by using both the original data and dimension-reduced data. From the Monte Carlo simulations, we conclude that a test by Thulin [1], when performed with post-dimension-reduced data, yielded the best omnibus power for detecting a difference between two high-dimensional population-mean vectors. We also illustrate the utility of our dimension-reduction method real data consisting of genetic sequences of two groups of patients with Crohn’s disease and ulcerative colitis.展开更多
The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network t...The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.展开更多
文摘Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.
文摘This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to becoming carbon sources under compounded bioclimatic stress.BEAT synthesizes satellite-derived data from 2001 to 2022 and integrates temperature anomalies,vapor pressure deficit(VPD),fire activity,and vegetation degradation into a Compound Stress Index(CSI).Methodologically,the study applies piecewise regression,changepoint analysis,and early warning signal(EWS)metrics,including rolling variance and lag-1 autocorrelation,to identify nonlinear emission tipping points and ecological resilience loss.Machine learning models such as XGBoost and SHAP were employed to evaluate the predictive relevance of CSI components and enhance model interpretability.Results reveal a critical CSI threshold(≥0.6),beyond which Net Ecosystem Exchange(NEE)exhibits abrupt positive anomalies,indicating carbon emission amplification.EWS metrics significantly increased prior to emission spikes,validating BEAT’s predictive capacity for ecological destabilization.In addition,spatial clustering and time-lagged correlation analysis confirmed the alignment between compound stress hotspots and emission anomalies,and when compared to traditional Earth System Models(ESMs),BEAT uniquely captures synergistic stress interactions and nonlinearity.The findings underscore BEAT’s potential to improve early warning systems,REDD+monitoring frameworks,and climate adaptation planning.Its scalable design enables application across vulnerable biomes globally and offers a transformative tool for anticipating biosphere-climate tipping points and informing proactive ecosystem governance.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project Grant Number(BFP/RGP/ICT/22/490).
文摘Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.
基金supported by Key Project of National Social Fund of China(21AGL014).
文摘The recent surge in global financial and patent innovations and rising CO_(2) emissions in the global energy sector have drawn significant attention to China’s transportation industry.This study examines how financial innovations(FINI),patent innovations(PTIN),and bioenergy(BIOE)affect CO_(2) emissions in China’s transportation sector(TBCO_(2))using quarterly data from 2000 to 2018.This study employed a novel wavelet local multiple correlation(WLMC)methodology,alongside the time-varying causality test,to examine the time–frequency nexus,addressing a critical gap in the current literature.The WLMC bivariate analyses revealed a negative long-term relationship between PTIN and FINI with TBCO_(2).At the same time,BIOE showed only a short-term mitigating effect,with PTIN playing a dominant role in this nexus at various frequency levels.Furthermore,the three-and four-variate assessments highlight the consistent positive influence of all included factors on TBCO_(2).A timevarying causality test also demonstrated significant causal relationships between FINI,PTIN,BIOE,and TBCO_(2) across different periods,confirming the robustness of our WLMC results.This study provides crucial insights,emphasizing the urgency of promoting FINIs,technological advancement,and bioenergy usage to reduce transportation emissions and pursue sustainable solutions to address China’s environmental challenges.
文摘This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.
基金supported by the Qatar National Research Fund(NPRP5-364-2-142NPRP7-1040-2-293)
文摘Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.
文摘The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.
基金sponsored by the National Basic Research Program of China (Grant No. 2012CB955202)the China Scholarship Council under the Joint-PhD program for conducting research at CSIROsupported by the Indian Ocean Climate Initiative
文摘A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.
文摘BACKGROUND Unilateral patellofemoral pain syndrome(PFPS)is the most frequently diagnosed knee condition in populations aged<50 years old.Although the treatment of myofascial trigger points(MTrPs)is a common and effective tool for reducing pain,previous studies showed no additional benefits compared with placebo in populations with PFPS.Percutaneous electrolysis is a minimally invasive approach frequently used in musculotendinous pathologies which consists of the application of a galvanic current through dry needling(DN).AIM To evaluate changes in sensitivity,knee pain perception and perceived pain during the application of these three invasive techniques.METHODS A triple-blinded,pilot randomized controlled trial was conducted on fifteen patients with unilateral PFPS who were randomized to the high-intensity percutaneous electrolysis(HIPE)experimental group,low-intensity percutaneous electrolysis(LIPE)experimental group or DN active control group.All interventions were conducted in the most active MTrP,in the rectus femoris muscle.The HIPE group received a 660 mA galvanic current for 10 s,the LIPE group 220 mA×30 s and the DN group received no galvanic current.The MTrP and patellar tendon pain pressure thresholds(PPTs)and subjective anterior knee pain perception(SAKPP)were assessed before,after and 7 d after the single intervention.In addition,perceived pain during the intervention was also assessed.RESULTS Both groups were comparable at baseline as no significant differences were found for age,height,weight,body mass index,PPTs or SAKPP.No adverse events were reported during or after the interventions.A significant decrease in SAKPP(both HIPE and LIPE,P<0.01)and increased patellar tendon PPT(all,P<0.001)were found,with no differences between the groups(VAS:F=0.30;η2=0.05;P>0.05;tendon PPT immediate effects:F=0.15;η2=0.02;P>0.05 and tendon PPT 7-d effects:F=0.67;η2=0.10;P>0.05).A significant PPT increase in rectus femoris MTrP was found at follow-up in both the HIPE and LIPE groups(both,P<0.001)with no differences between the groups(immediate effects:F=1.55;η2=0.20;P>0.05 and 7-d effects:F=0.71;η2=0.10;P>0.05).Both HIPE and LIPE interventions were considered less painful compared with DN(F=8.52;η2=0.587;P<0.01).CONCLUSION HIPE and LIPE induce PPT changes in MTrPs and patellar tendon and improvements in SAKPP,and seem to produce less pain during the intervention compared with DN.
基金supported by the Natural Science Foundation of China(Nos.71974031,71771034)the Chinese Universities Scientific Fund(No.DUT19RW216)+1 种基金the Economic and Social Development Project of Liaoning Province(No.20201slktyb-019)supported in part by the National Science Foundation(NSF)via the Grant Number IIS-1648664.
文摘Most loan evaluation methods in peer-to-peer(P2P)lending mainly exploit the borrowers’credit information.However,the present study presents the maturity-based lender composition score,which exploits the investment capability of a group of lenders who fund the same loan,to enhance the P2P loan evaluation.More specifically,we extract lenders’profiles in terms of performance,risk,and experience by quantifying their investment history and develop our loan evaluation indicator by aggregating the profiles of lenders in the composition.To measure the ability of a lender for continuous improvement in P2P investment,we introduce lender maturity to capture this evolvement and incorporate it into the aggregation process.Our empirical study demonstrates that the maturity-based lender composition score can serve as an effective indicator for identifying loan quality and be included in other commonly used loan evaluation models for accuracy improvement.
文摘Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities.Recent advances in information and communications technology,the smartphone,the Internet and Wi-Fi have given a boost to the tourism industry.The city bus tour(CBT)service is one of the most successful businesses in the tourism industry.However,there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT.In this research,we report on the ongoing development of a mobile application(app)and a website for tourists,hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time.Firstly,the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem.All places of interest(PoI)and pickup/dropout points are identified with arcs of the network(instead of nodes),each of which can be visited at least once(instead of exactly once).Secondly,the resulting pure integer programming(IP)problem is solved using a leading optimization soft-ware known as General Algebraic Modeling System(GAMS).The GAMS code developed for this project returns:(1)the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network;(2)the augmenting paths corre-sponding to the pickup stage,the PoI visiting stage and the drop-off stage.Finally,we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne(Australia).All the computations relative to the initial tests show that the ability of the app to answer users'inquiries in a fraction of a minute.
基金supported by the National Science Foundation of China(Theoretical Model and Experimental Research on the Novel FBG Sensing System based on the Fusion Algorithm,No.61703056)the Jilin Province Science and Technology Development Plan Project(No.20190103154JH)。
文摘Intelligent assembly of large-scale,complex structures using an intelligent manufacturing platform represents the future development direction for industrial manufacturing.During large-scale structural assembly processes,several bottleneck problems occur in the existing auxiliary assembly technology.First,the traditional LiDARbased assembly technology is often limited by the openness of the manufacturing environment,in which there are blind spots,and continuous online assembly adjustment thus cannot be realized.Second,for assembly of large structures,a single-station LiDAR system cannot achieve complete coverage,which means that a multi-station combination method must be used to acquire the complete three-dimensional data;many more data errors are caused by the transfer between stations than by the measurement accuracy of a single station,which means that the overall system's measurement and adjustment errors are increased greatly.Third,because of the large numbers of structural components contained in a large assembly,the accumulated errors may lead to assembly interference,but the LiDAR-assisted assembly process does not have a feedback perception capability,and thus assembly component loss can easily be caused when assembly interference occurs.Therefore,this paper proposes to combine an optical fiber sensor network with digital twin technology,which will allow the test data from the assembly entity state in the real world to be applied to the"twin"model in the virtual world and thus solve the problems with test openness and data transfer.The problem of station and perception feedback is also addressed and represents the main innovation of this work.The system uses an optical fiber sensor network as a flexible sensing medium to monitor the strain field distribution within a complex area in real time,and then completes real-time parameter adjustment of the virtual assembly based on the distributed data.Complex areas include areas that are laser-unreachable,areas with complex contact surfaces,and areas with large-scale bending deformations.An assembly condition monitoring system is designed based on the optical fiber sensor network,and an assembly condition monitoring algorithm based on multiple physical quantities is proposed.The feasibility of use of the optical fiber sensor network as the real-state parameter acquisition module for the digital twin intelligent assembly system is discussed.The offset of any position in the test area is calculated using the convolutional neural network of a residual module to provide the compensation parameters required for the virtual model of the assembly structure.In the model optimization parameter module,a correction data table is obtained through iterative learning of the algorithm to realize state prediction from the test data.The experiment simulates a largescale structure assembly process,and performs virtual and real mapping for a variety of situations with different assembly errors to enable correction of the digital twin data stream for the assembly process through the optical fiber sensor network.In the plane strain field calibration experiment,the maximum error among the test values for this system is 0.032 mm,and the average error is 0.014 mm.The results show that use of visual calibration can correct the test error to within a very small range.This result is equally applicable to gradient curvature surfaces and freeform surfaces.Statistics show that the average measurement accuracy error for regular surfaces is better than 11.2%,and the average measurement accuracy error for irregular surfaces is better than 14.8%.During simulation of large-scale structure assembly experiments,the average position deviation accuracy is 0.043 mm,which is in line with the designed accuracy.
文摘With recent attention to high power energy and its interaction with materials of different types,both in industry and military application,this paper covers a short review course into subject of materials response in respect to such high power energy lasers.In this paper,we are covering laser interaction with solid and going through steps of phase changes,from solid to liquid and finally vapor stage.As we indicated in this part of short course mainly Part I,we have stated of series of article on the subject of Materials Responses to High Power Energy Lasers and continue these series by starting to introduce the Laser Light Propagation either in vacuum or through atmosphere by also introducing thermal blooming effects as well,then we cover,subjects such as Optical Reflectivity,thermal responses of materials by looking at Latent Heat of Fusion as well as Vaporization,No Phase Changes in both Semi-Infinite Solid or Slab of Finite Thickness,Melting and Vaporization and then move on to Effects of Pulsed or Continuous Laser Radiation as well,throughout of next few parts that we report them as further Short Courses content.
文摘With recent attention to high power energy and its interaction with materials of different type,both in industry and military application,this paper covers a short review course into subject of materials response in respect to such high power energy lasers.In this paper,we are covering laser interaction with solid and going through steps of phase changes,from solid to liquid and finally vapor stage.As we indicated in this part of short course mainly Part I and Part II,we have started a series of articles on the subject of Materials Responses to High Power Energy Lasers and continue these series by starting to introduce the Laser Light Propagation into materials.In this part namely Part III,we are discussing,one of the most important effects of intense laser irradiation is the conversion of the optical energy in the beam into thermal energy in the material.This is the basis of many applications of lasers,such as welding and cutting.We shall summarize here this thermal response.It is basically a classical problem,namely heat flow,in a usual manner of heat conduction,we show solutions to the equation which governs the flow of heat and discuss change of phases in targeting material from solid to liquid and finally vapor and plasma stages step by step.
文摘BACKGROUND: To determine if elderly frequent attenders are associated with increased 30-day mortality, assess resource utilization by the elderly frequent attenders and identify associated characteristics that contribute to mortality. METHODS: Retrospective observational study of electronic clinical records of all emergency department(ED) visits over a 10-year period to an urban tertiary general hospital in Singapore. Patients aged 65 years and older, with 3 or more visits within a calendar year were identified. Outcomes measured include 30-day mortality, admission rate, admission diagnosis and duration spent at ED. Chi-square-tests were used to assess categorical factors and Student t-test was used to assess continuous variables on their association with being a frequent attender. Univariate and multivariate logistic regressions were conducted on all significant independent factors on to the outcome variable(30-day mortality), to determine factor independent odds ratios of being a frequent attender.RESULTS: 1.381 million attendance records were analyzed. Elderly patients accounted for 25.5% of all attendances, of which 31.3% are frequent attenders. Their 30-day mortality rate increased from 4.0% in the first visit, to 8.8% in the third visit, peaking at 10.2% in the sixth visit. Factors associated with mortality include patients with neoplasms, ambulance utilization, male gender and having attended the ED the previous year.CONCLUSION: Elderly attenders have a higher 30-day mortality risk compared to the overall ED population, with mortality risk more marked for frequent attenders. This study illustrates the importance and need for interventions to address frequent ED visits by the elderly, especially in an aging society.
文摘In this paper we employ artificial neural networks for predictive approximation of generalized functions having crucial applications in different areas of science including mechanical and chemical engineering, signal processing, information transfer, telecommunications, finance, etc. Results of numerical analysis are discussed. It is shown that the known Gibb’s phenomenon does not occur.
文摘The purpose of this study was to investigate the clinical efficacy of photodynamic combined freezing in patients with non-melanoma skin cancer(NMSC).First,according to the treatment regimen,96 patients with NMSC were divided into study group(n=50)and control group(n=46).The control group was treated with 5-amino-ketovalic acid photodynamic therapy(ALAPDT),while the study group was treated with ala-PDT combined with cryotherapy.Visual analogue scale(VAS)scores,visual satisfaction,clinical efficacy,adverse reactions,and progression-free survival were compared between the two groups.The results showed that VAS score in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).The appearance satisfaction and total effective rate of patients in the study group were higher than those in the control group,and the difference was statistically significant(P<0.05).The total incidence of adverse reactions in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).3 years progressionfree survival time and 3 years progression-free survival rate were compared between the two groups,and the difference was not statistically significant(P>0.05).Therefore,the combination of PDT and cryotherapy for non-melanoma skin cancer has a good clinical effect,which is conducive to the recovery of skin lesions,high patient satisfaction,fewer adverse reactions,and longer progression-free survival.In addition,the combined therapy can provide a new treatment idea for non-melanoma skin cancer patients who are not suitable for surgical treatment.
文摘We consider the efficacy of a proposed linear-dimension-reduction method to potentially increase the powers of five hypothesis tests for the difference of two high-dimensional multivariate-normal population-mean vectors with the assumption of homoscedastic covariance matrices. We use Monte Carlo simulations to contrast the empirical powers of the five high-dimensional tests by using both the original data and dimension-reduced data. From the Monte Carlo simulations, we conclude that a test by Thulin [1], when performed with post-dimension-reduced data, yielded the best omnibus power for detecting a difference between two high-dimensional population-mean vectors. We also illustrate the utility of our dimension-reduction method real data consisting of genetic sequences of two groups of patients with Crohn’s disease and ulcerative colitis.
文摘The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.
