Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
The objectives of this work were to evaluate the surgical activities carried out in the general surgery department of the Reference Health Center of Commune I of Bamako, to describe the sociodemographic characteristic...The objectives of this work were to evaluate the surgical activities carried out in the general surgery department of the Reference Health Center of Commune I of Bamako, to describe the sociodemographic characteristics of the operated patients, to determine the main pathologies encountered and to evaluate qualitatively the result of the treatment. In order to improve performance, and the quality of care, and to identify common pathologies in the surgical department, we undertook a retrospective study on surgical activities from January 2009 to December 2010. At the end of this study, out of 474 men and 187 women (equal sex ratio 2.53);we were able to determine the frequency of surgical pathologies. Farmers, housewives and pupils/students were the most represented with 25.9% respectively;20% and 13.3%. The most frequently observed pathologies were wall hernia (44.8%), prostate adenoma (12%) and acute appendicitis (10.5%). The average length of hospitalization was 3.43 days. Infectious complications affected 25 patients (3.8% of cases) and a death rate of 0.45% (i.e. 3 patients). The average cost of care was 53,500 FCFA. Indeed, the reality of surgical practice in health centers was not the same because of the level of skills of practicing surgeons.展开更多
Introduction: Superior mesenteric artery syndrome (SMAS), a rare diagnosis due to compression of the third duodenum between the superior mesenteric artery (SMA) and the aorta resulting in bowel obstruction, may lead t...Introduction: Superior mesenteric artery syndrome (SMAS), a rare diagnosis due to compression of the third duodenum between the superior mesenteric artery (SMA) and the aorta resulting in bowel obstruction, may lead to severe malnutrition. We report two cases of patients hospitalised in the Internal Medicine, Endocrinology, Diabetology, and Nutrition Department of the National Hospital Center (NHC) of Pikine. Observations: Patient 1: A 35-year-old female was referred for an aetiological diagnosis due to a rapid weight loss of 15 kilograms in one month, accompanied by persistent vomiting, following an appendectomy performed a month before admission. Upon clinical examination, she presented severe malnutrition (Buzby index of 76%), early post-prandial chronic vomiting, and a poor general condition. An abdominal CT scan revealed aortomesenteric clamp syndrome (AMCS) with an angulation between the aorta and the SMA of 13˚. The underlying cause in this patient was severe malnutrition. Fortunately, her condition improved with medical treatment. Patient 2: We report the case of a 30-year-old female hospitalized due to unusual weight-bearing post-prandial epigastric pain and intermittent vomiting over the past six months. Upon physical examination at admission, she exhibited severe malnutrition with a body mass index (BMI) of 14 kg/m<sup>2</sup>, a Buzby index of 71%, trophic disorders, and a stage IV general condition assessment according to the World Health Organization (WHO). An abdominal CT scan revealed AMCS with an angle between the aorta and the SMA of 22˚ and an aortomesenteric space of 4 mm. The outcome was poor with medical treatment failure and, unfortunately, the patient died before surgery. Conclusion: SMAS is rarely evoked in clinical practice despite the presence of contributing factors and suggestive clinical signs. The prognosis depends on management time.展开更多
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded...Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded organic frameworks(HOFs)have promising application potential for embedding enzymes.In fact,no metal involvement is required,and HOFs exhibit superior biocompatibility,and free access to substrates in mesoporous channels.Herein,a facile in situ growth approach was proposed for the self-assembly of alcohol dehydrogenase encapsulated in HOF.The micron-scale bio-catalytic composite was rapidly synthesized under mild conditions(aqueous phase and ambient temperature)with a controllable embedding rate.The high crystallinity and periodic arrangement channels of HOF were preserved at a high enzyme encapsulation efficiency of 59%.This bio-composite improved the tolerance of the enzyme to the acid-base environment and retained 81%of its initial activity after five cycles of batch hydrogenation involving NADH coenzyme.Based on this controllably synthesized bio-catalytic material and a common lipase,we further developed a two-stage cascade microchemical system and achieved the continuous production of chiral hydroxybutyric acid(R-3-HBA).展开更多
PURPOSE:To investigate the differences in gut microbial characteristics between two traditional Chinese syndromes of premature ovarian insufficiency(POI).METHODS:Forty women with POI were recruited from the Department...PURPOSE:To investigate the differences in gut microbial characteristics between two traditional Chinese syndromes of premature ovarian insufficiency(POI).METHODS:Forty women with POI were recruited from the Department of Traditional Chinese Medicine at Shenzhen Maternity and Child Healthcare Hospital between June and December 2020.Women with POI were divided into the kidney deficiency and blood stasis syndrome(SDBS)and Qi and blood deficiency syndrome(QBDS)groups.Gut microbial community profiles were analyzed by 16S rRNA gene sequencing using an Illumina Mi Seq system.A retrospective study comparing hormone levels and gut microbiota information was performed between the SDBS and QBDS groups.RESULTS:Compared with the QBDS group,the serum levels of estradiol(E2)and anti-Müllerian hormone(AMH)were significantly decreased in the SDBS group.The quantities of Adlercreutzia,Eggerthella,Klebsiella,and Paraprevotella significantly increased in the SDBS group,whereas Lactobacillus decreased significantly.Moreover,alterations in the microbiome in the SDBS and QBDS groups were closely related to the levels of E2 and AMH.The area under the receiver operating characteristic curve for the classification of the two syndromes by the gut microbiome was 0.71.CONCLUSIONS:There were significant differences in the dominant microbiota between the SDBS and QBDS groups,and the change in Proteobacteria in the QBDS group was more significant.The characteristics of gut microbiota help us differentiate between the SDBS and QBDS groups,which may provide a basis for the objectification of TCM syndrome types.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of user...The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.展开更多
Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhance...Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.展开更多
Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive s...Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.展开更多
This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV i...This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
Background: The incidence of intracranial metastases (ICMET) has been steadily rising, and its frequency with respect to primary brain tumours is relatively high. Objective: The objectives of this study were to elucid...Background: The incidence of intracranial metastases (ICMET) has been steadily rising, and its frequency with respect to primary brain tumours is relatively high. Objective: The objectives of this study were to elucidate the current epidemiology and describe the clinical, diagnostic and therapeutic features of ICMET in Yaounde. Method and findings: A descriptive cross-sectional study was done in the neurosurgery departments of the General and Central Hospitals of Yaounde during the period from January 2016 to December 2022. We included all medical booklets of patients admitted for a tumoral intracranial expansive process with our target population being patients with histological evidence of ICMET, and did a retrospective inclusion of data using a pre-established technical form aimed at collecting sociodemographic data, clinical data, paraclinical data, and the treatment procedures. Analysis was done using the SPSS statistical software. A total of 614 cases of intracranial tumors were included among whom 35 presented histological evidence of ICMET. This gives a frequency of 5.7%. The sex ratio was 0.94, the mean age was 55.68 +/- 14.4 years, extremes 28 and 86 years and the age range 50 - 59 was affected in 28.57% of cases. The clinical presentation included signs of raised intracranial pressure (headache, blurred vision, vomiting) in 26 cases (74.3%), motor deficit 48.6%, seizures 17.1%. The mode of onset was metachronous in 71.4% and synchronous in 28.6%. The imaging techniques were cerebral CT scan in 82.9%, cerebral MRI in 40%, TAP scan in 22.9%. The metastatic lesions were supratentorial in 94.3% and single in 62.9%. The primary cancers found were breast cancer (31.4%), lung cancer (25.7%), prostate cancer (17.1%), thyroid cancer (5.7%), colon cancer (2.9%), and melanoma (2.9%). The therapeutic modalities were total resection (68.6%), radiotherapy (37.1%). Conclusion: Intracranial metastases are relatively frequent. There is a female sex predominance and the age group 50 - 59 years is the most affected. Brain metastases mostly occur in patients with a history of known primary tumor. The clinical signs mainly include signs of raised intracranial pressure, motor deficit, seizures and mental confusion. Cerebral CT Scan is the main imaging technique used. Most of the lesions are single and supratentorially located. The primary cancers most represented include breast cancer, lung cancer and prostate cancer. Surgery is the main treatment procedure. The adjuvant treatment (radiotherapy, chemotherapy) was limited.展开更多
BACKGROUND Small cell lung cancer(SCLC)is the most malignant type of lung cancer.Even in the latent period and early stage of the tumor,SCLC is prone to produce distant metastases with complex and diverse clinical man...BACKGROUND Small cell lung cancer(SCLC)is the most malignant type of lung cancer.Even in the latent period and early stage of the tumor,SCLC is prone to produce distant metastases with complex and diverse clinical manifestations.SCLC is most closely related to paraneoplastic syndrome,and some cases present as paraneoplastic peripheral neuropathy(PPN).PPN in SCLC appears early,lacks specificity,and often occurs before diagnosis of the primary tumor.It is easy to be misdiagnosed as a primary disease of the nervous system,leading to missed diagnosis and delayed diagnosis and treatment.CASE SUMMARY This paper reports two cases of SCLC with limb weakness as the first symptom.The first symptoms of one patient were rash,limb weakness,and abnormal electromyography.The patient was repeatedly referred to the hospital for limb weakness and rash for>1 year,during which time,treatment with hormones and immunosuppressants did not lead to significant improvement,and the condition gradually aggravated.The patient was later diagnosed with SCLC,and the dyskinesia did not worsen as the dermatomyositis improved after antineoplastic and hormone therapy.The second case presented with limb numbness and weakness as the first symptom,but the patient did not pay attention to it.Later,the patient was diagnosed with SCLC after facial edema caused by tumor thrombus invading the vein.However,he was diagnosed with extensive SCLC and died 1 year after diagnosis.CONCLUSION The two cases had PPN and abnormal electromyography,highlighting its correlation with early clinical indicators of SCLC.展开更多
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘The objectives of this work were to evaluate the surgical activities carried out in the general surgery department of the Reference Health Center of Commune I of Bamako, to describe the sociodemographic characteristics of the operated patients, to determine the main pathologies encountered and to evaluate qualitatively the result of the treatment. In order to improve performance, and the quality of care, and to identify common pathologies in the surgical department, we undertook a retrospective study on surgical activities from January 2009 to December 2010. At the end of this study, out of 474 men and 187 women (equal sex ratio 2.53);we were able to determine the frequency of surgical pathologies. Farmers, housewives and pupils/students were the most represented with 25.9% respectively;20% and 13.3%. The most frequently observed pathologies were wall hernia (44.8%), prostate adenoma (12%) and acute appendicitis (10.5%). The average length of hospitalization was 3.43 days. Infectious complications affected 25 patients (3.8% of cases) and a death rate of 0.45% (i.e. 3 patients). The average cost of care was 53,500 FCFA. Indeed, the reality of surgical practice in health centers was not the same because of the level of skills of practicing surgeons.
文摘Introduction: Superior mesenteric artery syndrome (SMAS), a rare diagnosis due to compression of the third duodenum between the superior mesenteric artery (SMA) and the aorta resulting in bowel obstruction, may lead to severe malnutrition. We report two cases of patients hospitalised in the Internal Medicine, Endocrinology, Diabetology, and Nutrition Department of the National Hospital Center (NHC) of Pikine. Observations: Patient 1: A 35-year-old female was referred for an aetiological diagnosis due to a rapid weight loss of 15 kilograms in one month, accompanied by persistent vomiting, following an appendectomy performed a month before admission. Upon clinical examination, she presented severe malnutrition (Buzby index of 76%), early post-prandial chronic vomiting, and a poor general condition. An abdominal CT scan revealed aortomesenteric clamp syndrome (AMCS) with an angulation between the aorta and the SMA of 13˚. The underlying cause in this patient was severe malnutrition. Fortunately, her condition improved with medical treatment. Patient 2: We report the case of a 30-year-old female hospitalized due to unusual weight-bearing post-prandial epigastric pain and intermittent vomiting over the past six months. Upon physical examination at admission, she exhibited severe malnutrition with a body mass index (BMI) of 14 kg/m<sup>2</sup>, a Buzby index of 71%, trophic disorders, and a stage IV general condition assessment according to the World Health Organization (WHO). An abdominal CT scan revealed AMCS with an angle between the aorta and the SMA of 22˚ and an aortomesenteric space of 4 mm. The outcome was poor with medical treatment failure and, unfortunately, the patient died before surgery. Conclusion: SMAS is rarely evoked in clinical practice despite the presence of contributing factors and suggestive clinical signs. The prognosis depends on management time.
文摘Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported by the National Key Research and Development Program of China(2019YFA0905100)the National Natural Science Foundation of China(21991102,22378227).
文摘Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded organic frameworks(HOFs)have promising application potential for embedding enzymes.In fact,no metal involvement is required,and HOFs exhibit superior biocompatibility,and free access to substrates in mesoporous channels.Herein,a facile in situ growth approach was proposed for the self-assembly of alcohol dehydrogenase encapsulated in HOF.The micron-scale bio-catalytic composite was rapidly synthesized under mild conditions(aqueous phase and ambient temperature)with a controllable embedding rate.The high crystallinity and periodic arrangement channels of HOF were preserved at a high enzyme encapsulation efficiency of 59%.This bio-composite improved the tolerance of the enzyme to the acid-base environment and retained 81%of its initial activity after five cycles of batch hydrogenation involving NADH coenzyme.Based on this controllably synthesized bio-catalytic material and a common lipase,we further developed a two-stage cascade microchemical system and achieved the continuous production of chiral hydroxybutyric acid(R-3-HBA).
基金Sanming Project of Medicine in Shenzhen:the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine,Luo Songping National Famous Chinese Medicine Practitioner Female Reproductive Disorders Prevention and Treatment Team(SZZYSM202311010)Guangdong Provincial Administration of Traditional Chinese Medicine:Investigation of the Mechanism of Regulating Ren-Tong-Du Acupuncture on Ovarian Granulosa Cells in Polycystic Ovary Syndrome based on Activin A/Smads Signalling Pathway(No.20181229)+1 种基金Guangdong Provincial Administration of Traditional Chinese Medicine:Evaluation of the Efficacy of Menstrual Regulation and Pregnancy Promotion by Acupuncture in the Treatment of Premature Ovarian Insufficiency(No.20201294)Shenzhen Science and Innovation Commission:Investigating the Mechanism of Action of Acupuncture in Regulating the Gut Microbiome to Inhibit Apoptosis of Ovarian Granulosa Cells in Premature Ovarian Insufficiency Mice based on the Rictor/Torepamycin Target Protein C2 Pathway(No.JCYJ20210324130001004)。
文摘PURPOSE:To investigate the differences in gut microbial characteristics between two traditional Chinese syndromes of premature ovarian insufficiency(POI).METHODS:Forty women with POI were recruited from the Department of Traditional Chinese Medicine at Shenzhen Maternity and Child Healthcare Hospital between June and December 2020.Women with POI were divided into the kidney deficiency and blood stasis syndrome(SDBS)and Qi and blood deficiency syndrome(QBDS)groups.Gut microbial community profiles were analyzed by 16S rRNA gene sequencing using an Illumina Mi Seq system.A retrospective study comparing hormone levels and gut microbiota information was performed between the SDBS and QBDS groups.RESULTS:Compared with the QBDS group,the serum levels of estradiol(E2)and anti-Müllerian hormone(AMH)were significantly decreased in the SDBS group.The quantities of Adlercreutzia,Eggerthella,Klebsiella,and Paraprevotella significantly increased in the SDBS group,whereas Lactobacillus decreased significantly.Moreover,alterations in the microbiome in the SDBS and QBDS groups were closely related to the levels of E2 and AMH.The area under the receiver operating characteristic curve for the classification of the two syndromes by the gut microbiome was 0.71.CONCLUSIONS:There were significant differences in the dominant microbiota between the SDBS and QBDS groups,and the change in Proteobacteria in the QBDS group was more significant.The characteristics of gut microbiota help us differentiate between the SDBS and QBDS groups,which may provide a basis for the objectification of TCM syndrome types.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金funding from King Saud University through Researchers Supporting Project number(RSP2024R387),King Saud University,Riyadh,Saudi Arabia.
文摘The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.
文摘Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.
文摘Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23066).
文摘This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
文摘Background: The incidence of intracranial metastases (ICMET) has been steadily rising, and its frequency with respect to primary brain tumours is relatively high. Objective: The objectives of this study were to elucidate the current epidemiology and describe the clinical, diagnostic and therapeutic features of ICMET in Yaounde. Method and findings: A descriptive cross-sectional study was done in the neurosurgery departments of the General and Central Hospitals of Yaounde during the period from January 2016 to December 2022. We included all medical booklets of patients admitted for a tumoral intracranial expansive process with our target population being patients with histological evidence of ICMET, and did a retrospective inclusion of data using a pre-established technical form aimed at collecting sociodemographic data, clinical data, paraclinical data, and the treatment procedures. Analysis was done using the SPSS statistical software. A total of 614 cases of intracranial tumors were included among whom 35 presented histological evidence of ICMET. This gives a frequency of 5.7%. The sex ratio was 0.94, the mean age was 55.68 +/- 14.4 years, extremes 28 and 86 years and the age range 50 - 59 was affected in 28.57% of cases. The clinical presentation included signs of raised intracranial pressure (headache, blurred vision, vomiting) in 26 cases (74.3%), motor deficit 48.6%, seizures 17.1%. The mode of onset was metachronous in 71.4% and synchronous in 28.6%. The imaging techniques were cerebral CT scan in 82.9%, cerebral MRI in 40%, TAP scan in 22.9%. The metastatic lesions were supratentorial in 94.3% and single in 62.9%. The primary cancers found were breast cancer (31.4%), lung cancer (25.7%), prostate cancer (17.1%), thyroid cancer (5.7%), colon cancer (2.9%), and melanoma (2.9%). The therapeutic modalities were total resection (68.6%), radiotherapy (37.1%). Conclusion: Intracranial metastases are relatively frequent. There is a female sex predominance and the age group 50 - 59 years is the most affected. Brain metastases mostly occur in patients with a history of known primary tumor. The clinical signs mainly include signs of raised intracranial pressure, motor deficit, seizures and mental confusion. Cerebral CT Scan is the main imaging technique used. Most of the lesions are single and supratentorially located. The primary cancers most represented include breast cancer, lung cancer and prostate cancer. Surgery is the main treatment procedure. The adjuvant treatment (radiotherapy, chemotherapy) was limited.
基金Supported by Science and Technology Plan Project of Jiaxing,No.2021AD30044Supporting Discipline of Neurology in Jiaxing,No.2023-ZC-006Affiliated Hospital of Jiaxing University,No.2020-QMX-16.
文摘BACKGROUND Small cell lung cancer(SCLC)is the most malignant type of lung cancer.Even in the latent period and early stage of the tumor,SCLC is prone to produce distant metastases with complex and diverse clinical manifestations.SCLC is most closely related to paraneoplastic syndrome,and some cases present as paraneoplastic peripheral neuropathy(PPN).PPN in SCLC appears early,lacks specificity,and often occurs before diagnosis of the primary tumor.It is easy to be misdiagnosed as a primary disease of the nervous system,leading to missed diagnosis and delayed diagnosis and treatment.CASE SUMMARY This paper reports two cases of SCLC with limb weakness as the first symptom.The first symptoms of one patient were rash,limb weakness,and abnormal electromyography.The patient was repeatedly referred to the hospital for limb weakness and rash for>1 year,during which time,treatment with hormones and immunosuppressants did not lead to significant improvement,and the condition gradually aggravated.The patient was later diagnosed with SCLC,and the dyskinesia did not worsen as the dermatomyositis improved after antineoplastic and hormone therapy.The second case presented with limb numbness and weakness as the first symptom,but the patient did not pay attention to it.Later,the patient was diagnosed with SCLC after facial edema caused by tumor thrombus invading the vein.However,he was diagnosed with extensive SCLC and died 1 year after diagnosis.CONCLUSION The two cases had PPN and abnormal electromyography,highlighting its correlation with early clinical indicators of SCLC.
