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
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
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
Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoret...Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.展开更多
Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two prima...Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.展开更多
In fields like astronomy and radar technology, high-gain antennas are required for long-distance communication. Due to its relatively large gain, the use of parabolic antennas has become very popular over time, becaus...In fields like astronomy and radar technology, high-gain antennas are required for long-distance communication. Due to its relatively large gain, the use of parabolic antennas has become very popular over time, because they can easily achieve gains of above 30 dB at microwave and higher frequencies. Today, most systems’ success depends on how well the antennas perform. These antennas are available in different types and sizes. Each antenna’s effective area usually has less than the actual physical area of the antenna surface. This means that the unused area of the antenna is massive, and a waste. The aim of the research is to show that the actual physical aperture of a parabolic antenna can be reduced as much as possible to equal the effective area, as given by the antenna formula, thereby saving manufacturing costs, improve the aesthetics. In other words, the focus of this work is to experimentally show that reflector antenna can be made of smaller sizes but better performance. Measurements were taken from different positions from a parabolic antenna, the signal level measured and compared with signal levels for optimal performance.展开更多
The advancement of science and technology has introduced the concept of big data, which has significantly transformed the business management environment of enterprises. Currently, most administrative tasks in compani...The advancement of science and technology has introduced the concept of big data, which has significantly transformed the business management environment of enterprises. Currently, most administrative tasks in companies heavily rely on human resources, with skilled management staff using their expertise to oversee business operations. However, this approach is susceptible to human subjective biases. The method assists managers in formulating efficient strategies for implementing management measures and enhancing the effectiveness of production, sales, financial, and people organization structure management. This ultimately leads to a more evidence-based approach to corporate management. This technique expands the utilization of Web services from a strategy focused on integrating services to a comprehensive framework for Service-Oriented Computing (SOC). The primary focus is implementing WS-session to manage sessions in general Web services applications, defining a bidirectional entire duplex interface for communication in Web services, and developing the Web Services Initiation Protocol, presenting WIP, a thorough multimedia and voice communication framework constructed using Web services and Service-Oriented Architecture (SOA). The office automation management system, created utilizing ASP.net and SQL Server technology, encompasses the evaluation of viability, analysis of needs, and system design. Office automation refers to using equipment with computing capabilities to carry out various office tasks and utilize associated tools and applications. Office automation uses computer-based systems to collect, organize, and modify visual and auditory information to enhance business energy efficiency and time management. Office automation refers to auto-mating essential tasks employees perform, including identification, automatic appointment reminders, and automatic power management for personal computers. The employee image is obtained through the utilization of the Java media framework. Attendance data for all employees are collected and methodically examined. The database enables the retrieval of these records monthly or weekly. An office automation system offers organizations enhanced features for managing office information, significantly improving office efficiency and quality. Moreover, it improves office administration and decision-making procedures by automating and strengthening the scientific elements of office functions. The ongoing progress of organizational information technology has led to a strong focus on sharing information resources in scientific research initiatives. Conventional firms exhibit intricate company processes, inadequate managerial efficacy, and unnecessarily elevated operational expenses, resulting in diminished competitiveness. Thus, using an ERP management system is the optimal decision for organizations to restore their crucial competitive advantage. This technology enhances their operational methods, streamlines their operations, and enhances transparency in their operational approaches. The system comprises six modules: buy plan, purchase order, purchase contract, purchase document inquiry, purchase to order, and purchase return. These modules effectively meet the operational needs of enterprises.展开更多
ChatGPT,launched on November 30,2022,has been adopted by the industries and academia alike.Students have started using ChatGPT for classroom assignments.In the search for the use of this tool for constructive purposes...ChatGPT,launched on November 30,2022,has been adopted by the industries and academia alike.Students have started using ChatGPT for classroom assignments.In the search for the use of this tool for constructive purposes,the professor of a graduate class(MBA)on Supply Chain and Operations Management decided to require students to use ChatGPT to first generate some material for their term papers.Typically,ChatGPT generates one to two pages of original material for the student who is not well trained in using it,which was the case for the students in this class.Then,the students were asked to use the ChatGPT-generated material as a guide to writing a 10-page long paper with new references and citations added.A comparative study is conducted to determine the usefulness of ChatGPT on this project.The preliminary results indicate that students found ChatGPT useful in generating their own papers.Meanwhile,our analysis shows beginners of ChatGPT have limited capacity to generate high-quality content based on ChatGPT.Also,text mining is conducted to compare the readability and information density of ChatGPT-generated content and student-generated content.展开更多
Drawing on emotional contagion theory and language-mediated association theory,this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance.We use computer...Drawing on emotional contagion theory and language-mediated association theory,this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance.We use computer-aided techniques to extract and measure specific textual and facial emotions in pitches.The proposed model is tested via regression analysis with a sample of 1372 campaigns collected from the largest charitable crowdfunding platform in China—Tencent Gongyi.Moreover,we conducted a fuzzy-set qualitative comparative analysis to examine the complementarity of textual and facial emotions,which supplements the regression analysis results.Our findings show that both textual and facial emotions can impact funding outcomes.However,the effects of specific emotions vary:some(e.g.,textual sadness and facial anger)are positive,some(e.g.,textual anger and facial fear)are negative,and others(e.g.,textual fear,textual disgust,and facial sadness)are insignificant.Moreover,facial emotions complement textual emotions in their effects on funding outcomes.This research outlines a framework to offer a more detailed and comprehensive understanding of emotions in charitable crowdfunding.It also contributes to existing research by revealing the vital but complex role of emotions in the persuasive process of prosocial behaviors and by uncovering the different cognitive mechanisms underlying the impacts of textual and facial emotions.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 hav...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
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
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.
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.
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Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.
文摘Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.
文摘In fields like astronomy and radar technology, high-gain antennas are required for long-distance communication. Due to its relatively large gain, the use of parabolic antennas has become very popular over time, because they can easily achieve gains of above 30 dB at microwave and higher frequencies. Today, most systems’ success depends on how well the antennas perform. These antennas are available in different types and sizes. Each antenna’s effective area usually has less than the actual physical area of the antenna surface. This means that the unused area of the antenna is massive, and a waste. The aim of the research is to show that the actual physical aperture of a parabolic antenna can be reduced as much as possible to equal the effective area, as given by the antenna formula, thereby saving manufacturing costs, improve the aesthetics. In other words, the focus of this work is to experimentally show that reflector antenna can be made of smaller sizes but better performance. Measurements were taken from different positions from a parabolic antenna, the signal level measured and compared with signal levels for optimal performance.
文摘The advancement of science and technology has introduced the concept of big data, which has significantly transformed the business management environment of enterprises. Currently, most administrative tasks in companies heavily rely on human resources, with skilled management staff using their expertise to oversee business operations. However, this approach is susceptible to human subjective biases. The method assists managers in formulating efficient strategies for implementing management measures and enhancing the effectiveness of production, sales, financial, and people organization structure management. This ultimately leads to a more evidence-based approach to corporate management. This technique expands the utilization of Web services from a strategy focused on integrating services to a comprehensive framework for Service-Oriented Computing (SOC). The primary focus is implementing WS-session to manage sessions in general Web services applications, defining a bidirectional entire duplex interface for communication in Web services, and developing the Web Services Initiation Protocol, presenting WIP, a thorough multimedia and voice communication framework constructed using Web services and Service-Oriented Architecture (SOA). The office automation management system, created utilizing ASP.net and SQL Server technology, encompasses the evaluation of viability, analysis of needs, and system design. Office automation refers to using equipment with computing capabilities to carry out various office tasks and utilize associated tools and applications. Office automation uses computer-based systems to collect, organize, and modify visual and auditory information to enhance business energy efficiency and time management. Office automation refers to auto-mating essential tasks employees perform, including identification, automatic appointment reminders, and automatic power management for personal computers. The employee image is obtained through the utilization of the Java media framework. Attendance data for all employees are collected and methodically examined. The database enables the retrieval of these records monthly or weekly. An office automation system offers organizations enhanced features for managing office information, significantly improving office efficiency and quality. Moreover, it improves office administration and decision-making procedures by automating and strengthening the scientific elements of office functions. The ongoing progress of organizational information technology has led to a strong focus on sharing information resources in scientific research initiatives. Conventional firms exhibit intricate company processes, inadequate managerial efficacy, and unnecessarily elevated operational expenses, resulting in diminished competitiveness. Thus, using an ERP management system is the optimal decision for organizations to restore their crucial competitive advantage. This technology enhances their operational methods, streamlines their operations, and enhances transparency in their operational approaches. The system comprises six modules: buy plan, purchase order, purchase contract, purchase document inquiry, purchase to order, and purchase return. These modules effectively meet the operational needs of enterprises.
文摘ChatGPT,launched on November 30,2022,has been adopted by the industries and academia alike.Students have started using ChatGPT for classroom assignments.In the search for the use of this tool for constructive purposes,the professor of a graduate class(MBA)on Supply Chain and Operations Management decided to require students to use ChatGPT to first generate some material for their term papers.Typically,ChatGPT generates one to two pages of original material for the student who is not well trained in using it,which was the case for the students in this class.Then,the students were asked to use the ChatGPT-generated material as a guide to writing a 10-page long paper with new references and citations added.A comparative study is conducted to determine the usefulness of ChatGPT on this project.The preliminary results indicate that students found ChatGPT useful in generating their own papers.Meanwhile,our analysis shows beginners of ChatGPT have limited capacity to generate high-quality content based on ChatGPT.Also,text mining is conducted to compare the readability and information density of ChatGPT-generated content and student-generated content.
基金supported by the National Social Science Fund of China(Grant No.21AGL008).
文摘Drawing on emotional contagion theory and language-mediated association theory,this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance.We use computer-aided techniques to extract and measure specific textual and facial emotions in pitches.The proposed model is tested via regression analysis with a sample of 1372 campaigns collected from the largest charitable crowdfunding platform in China—Tencent Gongyi.Moreover,we conducted a fuzzy-set qualitative comparative analysis to examine the complementarity of textual and facial emotions,which supplements the regression analysis results.Our findings show that both textual and facial emotions can impact funding outcomes.However,the effects of specific emotions vary:some(e.g.,textual sadness and facial anger)are positive,some(e.g.,textual anger and facial fear)are negative,and others(e.g.,textual fear,textual disgust,and facial sadness)are insignificant.Moreover,facial emotions complement textual emotions in their effects on funding outcomes.This research outlines a framework to offer a more detailed and comprehensive understanding of emotions in charitable crowdfunding.It also contributes to existing research by revealing the vital but complex role of emotions in the persuasive process of prosocial behaviors and by uncovering the different cognitive mechanisms underlying the impacts of textual and facial emotions.
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.
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.
