Conduct Performance Evaluation Research Articles

Enhancing SCI Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance healthcare by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may impact those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

Hydrodynamic Thrust Bearing Test Rig with Novel Bearing Alignment System

Abstract This study presents the design and development of a new hydrodynamic thrust bearing test rig with a novel (patent pending) hydrodynamic pressure feedback bearing alignment control system. The bearing alignment system maintains even distribution of thrust load across the bearing thrust pads using active hydrodynamic pressure as input to manipulate bearing orientation with stepper motors. The test rig is used to conduct performance evaluation of fixed geometry hydrodynamic thrust bearings with variable taper depths. The influence of helical taper angle, rotational speed, and applied load on key performance characteristics including minimum oil film thickness (MOFT), hydrodynamic pressure distribution, and bearing temperature is presented and analyzed. A numerical model based on the Reynolds equation is used to support the experimental results obtained. Trends in performance established by the numerical analysis show mutually agreeable results compared to experimental data. The average percent deviation of the experimentally gathered change in MOFT as load is increased with respect to the numerically predicted values is 24%. Comparison of experimental to numerical pressure distribution data shows an overall average percent deviation of 32%.

Multiple-Junction-Based Traffic-Aware Routing Protocol Using ACO Algorithm in Urban Vehicular Networks.

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.

Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.

Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing

Mobile edge computing has been a promising solution to enable real-time service in vehicular networks. However, due to high dynamics of mobile environment and heterogeneous features of vehicular services, traditional expert-based or learning-based strategies has to update handcrafted parameters or retrain learning model, which leads to intolerant overhead. Therefore, this paper investigates the problem of multi-task offloading (MTO), where there exist multiple offloading scenarios with varying parameters, such as task topology, resource requirement and transmission/computation capability. The objective is to design a unified solution to minimize task execution time under different MTO scenarios. Accordingly, we develop a Seq2seq-based Meta Reinforcement Learning algorithm for MTO (SMRL-MTO). Specifically, a bidirectional gated recurrent units integrated with attention mechanism is designed to determine offloading action by encoding sequential offloading actions and showing different preferences to different parts of input sequence. Particularly, a meta reinforcement learning framework is designed based on model-agnostic meta learning, which trains a meta policy offline and fast adapts to new MTO scenario within a few training steps. Finally, we conduct performance evaluation based on task generator DAGGEN and realistic vehicular traces, which shows that the SMRL-MTO reduces task execution time by 11.36% on average compared with greedy algorithm.

ODRAD: An optical wireless DCN dynamic-bandwidth reconfiguration with AWGR and deep reinforcement learning

The rapid growth of Data Center Network (DCN) traffic has brought new challenges, such as limited bandwidth, high latency, and packet loss to existing DCNs based on electrical switches. Because of its theoretically unlimited bandwidth and faster data transmission speeds, optical switching can overcome the problems of electrically switched DCNs. Additionally, numerous research works have been devoted to optical wired DCNs. However, static and fixed-topology DCNs based on optical interconnects significantly limit their flexibility, scalability, and reconfigurability to provide adaptive bandwidth for traffic with heterogeneous characteristics. In this study, we propose and conduct performance evaluations on a reconfigurable optical wireless DCN architecture based on distributed Software-Defined Networking (SDN), Deep Reinforcement Learning (DRL), Semiconductor Optical Amplifier (SOA), and Arrayed Waveguide Grating Router (AWGR). Our architecture is called ODRAD (which stands for Optical Wireless DCN Dynamic-bandwidth Reconfiguration with AWGR and Deep Reinforcement Learning). A Mininet simulation model is established to further verify the reconfigurability of the ODRAD network for various server scales. Based on experimental verification, ODRAD achieves an average end-to-end server latency of 5.2μs under a load of 99%. Compression results demonstrate a 17.36% improvement in packet rate latency performance compared to RotorNet and a 15.21% improvement compared to OPSquare at a load of 99% as the ODRAD network scales from 2,560 to 40,960 servers. Furthermore, ODRAD exhibits effective throughput across different routing protocols, DCN scales and loads.

Financial Performance Evaluation of Leading Fisheries Enterprises Based on Entropy-Weighted TOPSIS Method

Fishery is a strategic and fundamental core industry in China. Since the 18th National Congress of the Communist Party of China, the central government has attached great importance to the fishery industry and has issued a series of policies to promote its revitalization and vigorous development. In the favorable policy environment, it is crucial for leading fisheries enterprises to pay attention to financial performance evaluation and strive to improve their own operational efficiency. In this study, we selected seven leading fisheries enterprises as research subjects and used the entropy-weighted TOPSIS method to construct a financial performance evaluation system from four dimensions: debt-paying ability, operational capability, profitability, and development potential. We then conducted performance evaluations on the seven sample companies. The research findings indicate that enterprises should focus on their development capabilities to improve their potential for sustained development, enhance the operational efficiency of fisheries enterprises, and promote their continuous and healthy growth.

Temporal-channel cascaded transformer for imagined handwriting character recognition

Neuroelectric signals recorded by micro-electrodes reflect the spontaneous and rhythmic activities of brain neurons. Numerous deep learning frameworks have been designed for various neuroelectric signal decoding tasks, most of which are based on convolutional neural network (CNN) and recurrent neural network (RNN). However, neither CNNs or RNNs can perceive the global dependencies of neural activities in both time and channel dimensions. To address this issue, this paper presents a temporal-channel cascaded transformer network to decode the neural activities of imagined handwriting movements, which can perform imagined handwriting character recognition from spiking activity recorded by two micro-electrode arrays (MEAs). Specifically, we design a temporal-channel cascaded framework and a dense residual transformer encoder structure, which can promote the hierarchical learning and fusion of the temporal and channel features. In addition, a mutual learning strategy of multiple class tokens is proposed to improve classification performance. We conduct performance evaluation experiments on a single-character handwriting-imagination dataset and a sentence handwriting-imagination dataset, which are collected from the public Handwriting BCI dataset. The comparison results demonstrate the superiority of the proposed framework and strategy. Especially in the imagined single-character recognition task, the recognition accuracy of our model can achieve 95.78%, which provides an improvement of +2% over the existing state of the art models.

Generating Task Reallocation Suggestions to Handle Contingencies in Human-Supervised Multi-Robot Missions

In a mission with significant uncertainty due to intermittent communications, delayed information flow, and robotic failures, the role of human supervisors is extremely challenging. As and when any new information arrives, humans must infer both the existing and predicted future states, identify potential contingencies, and update task assignments to robots rapidly. We propose methodologies for automated generation of task reallocation suggestions to humans to assist in the decision-making process. Our generated robot retasking plan minimizes a modified makespan of the mission, which incorporates task criticality and penalty for incomplete tasks. The plan considers the effects of potential mission contingencies on the tasks, the robots, and the future performance of the robots operating based on the previous task plans. Our method includes the incorporation of two optional tasks, i.e., relay and robot rescue, for performance improvement. The rescue task has probabilistic outcomes affecting the team size. One or more rescues are incorporated in a way that can minimize the expected value of the modified makespan overall possibilities of rescue outcomes. We have conducted performance evaluation using simulation, demonstrating the value of the optional tasks and performance enhancement using our method of incorporating them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The work reported in this paper will be useful in applications where a team of agents is deployed to carry on a large-scale mission with communication constraints where the number of functional agents can change probabilistically. Typically, such uncertainty is encountered in applications that are challenging or dangerous in nature. Agents can have non-zero probabilities to fail while doing certain risky tasks and to get recovered by other agents. The proposed centralized multi-agent task reallocation method can help in proactively addressing potential contingencies in surveillance, search and rescue, or disaster relief to support resilient operations while having a supervisor in the higher chain of command.

Influence of Contact Pressure and Flow Rate on the Performance of Anion Exchange Membrane Electrolysis

Hydrogen production by electrolysis is a key aspect of a climate-neutral economy. However, there are still weak points in the established low-temperature technologies such as high material costs in case of PEM electrolysis and poor dynamic operations in case of alkaline electrolysis. The emerging electrolysis technology, the anion exchange membrane (AEM) electrolysis, addresses these weaknesses and combines their advantages. Due to the solid polymer electrolyte and the alkaline environment, AEM water electrolysis is featured by a compact zero-gap cell design and inexpensive materials like non-noble metal catalysts. Hence, it offers the potential to a cost-effective and efficient electrolysis technology.To enable the breakthrough of this technology, we contribute by conducting performance evaluations of membrane electrode assemblies (MEAs) under application-oriented conditions. One focus is on non-noble metal MEAs with an active area of 25 cm2 operated with pure water and low concentrated potassium hydroxide solution.To ensure comparability of different MEAs, it is crucial to compare them at their performance optimum. This is achieved by determining the optimal set-up parameters like contact pressure and operation parameters like flow rate for each MEA type. Otherwise, deviation from the optimal operating point by up to 50 % may occur.The influence of the parameters contact pressure and flow rate on the MEA performance is discussed in this presentation. To investigate their impact, we analysed the MEAs in an electrolyser test station using polarisation curves, electrochemical impedance spectroscopy and load tests at constant current and constant voltage.Some of the results presented are obtained within the project “REVAL – reversible anion exchange membrane electrolysis” (funding code 03ZZ0732D) funded by the German Federal Ministry of Education and Research (BMBF).

Inappropriate financial performance evaluation: based on statistical analysis of employee anxiety disorders

BackgroundIn modern enterprise management, financial performance evaluation is one of the main evaluation tools for employees and teams. However, when these evaluation methods are inappropriate or too strict, they may have a negative impact on employees. In addition, improper management and unclear systems in enterprises may lead to negative emotions among employees, especially anxiety disorders. Based on this, this study explores how inappropriate financial performance evaluation can affect employees’ mental health, with the expectation of obtaining reasonable results.Subjects and MethodsFive large companies were selected for the experiment, and 500 employees were randomly selected for anonymous surveys. The questionnaire includes experiences in financial performance evaluation, personal opinions on the evaluation, and scores on the Self-Rating Anxiety Scale (SAS). Use Excel and SPSS24.0 to statistically process the data.ResultsThe survey results showed that 60% of employees who experienced inappropriate financial performance evaluations reported significant anxiety symptoms during and after the evaluation period. In contrast, only 20% of employees who believe that performance evaluation is appropriate reported experiencing similar anxiety experiences. In addition, for employees who believe that the evaluation is unfair or too strict, the severity of their anxiety symptoms is significantly higher than that of other populations.ConclusionsImproper financial performance evaluation may increase employees’ mental health risks, especially the risk of anxiety disorders. It is recommended that enterprises ensure fairness, transparency, and rationality when conducting performance evaluations to maintain the psychological health of employees.

Integrating generative parametric design workflow in the terraced concert hall design process

Compared to shoe box halls, terraced concert halls are popular due to their proximity to the stage and expressive design. However, its complex geometry requires delicate adjustments during the design process to effectively deliver a design that satisfies both the needs of acoustics and creativity. The study aims to explore the integration of generative parametric design workflow into the design of terraced concert halls and apply the workflow to an actual competition project. This workflow consists of three steps: defining basic parameters and reference geometries, generating and composing geometric elements with synchronized acoustic and geometric verification, and conducting performance evaluation of the results. The workflow enables integration of diverse viewpoints for the competition project, covering both the acoustics of the concert hall and the spatial integration with the surroundings. The outcome is a multi-layered, terraced concert hall with acoustic performance comparable to a traditional shoebox hall, and a metaphorical image of the topography of the city in which the concert hall is located.

MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing

With the rapid development and popularity of Internet of Things (IoT) technology, IoT devices can generate MultiMedia Big Data (MMBD) as multimedia devices. MMBD is often encrypted and stored on an untrusted cloud server. Searchable encryption is an effective way which can finish a controllable keyword search of ciphertext. However, there are two limitations to these existing works. On the one hand, it is hard to independently protect the data privacy of each IoT device. On the other hand, when faced with an untrusted cloud server, it is difficult to implement more flexible fine-grained verification for search results. To overcome these limitations, we propose a secure and verifiable MultiMedia Data Search (MMDS) scheme for cloud-assisted edge computing. To protect the data of IoT devices independently, we designed a secure, flexible, and efficient keyword search mechanism based on bilinear pairings. To achieve a more flexible and practical search result verification mechanism, we design a fine-grained verification algorithm combining blockchain and hashing techniques. We have conducted performance evaluations and security proof and analysis for MMDS scheme. Finally, we applied MMDS scheme to solve real problems in intelligent multimedia systems. Security proof and analysis prove the security of MMDS. Performance analyses and evaluations further confirm that MMDS is efficient and feasible for practical applications.

Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices

Handwritten digit recognition has been used in many consumer electronic devices for a long time. However, we found that the recognition system used in current consumer electronics is sensitive to image or character rotations. To address this problem, this study builds a low-cost and light computation consumption handwritten digit recognition system. A Principal Component Analysis (PCA)-based logistic regression classifier is presented, which is able to provide a certain degree of robustness in the digit subject to rotations. To validate the effectiveness of the developed image recognition algorithm, the popular MNIST dataset is used to conduct performance evaluations. Compared to other popular classifiers installed in MATLAB, the proposed method is able to achieve better prediction results with a smaller model size, which is 18.5% better than the traditional logistic regression. Finally, real-time experiments are conducted to verify the efficiency of the presented method, showing that the proposed system is successfully able to classify the rotated handwritten digit.

Inkjet Printing of PrBa0.5Sr0.5Co1.5Fe0.5O5+ δ Cathode for High-Performance Proton Ceramic Fuel Cells

Ceramic fuel cells have been actively studied and are in the commercialization stage because of their economic and efficiency advantages, including the lack of precious metal catalysts, including Pt, fuel flexibility, and high efficiency. Solid oxide fuel cells (SOFCs) are the most typical type of ceramic fuel cell that conduct oxide ions. However, the operating temperature of SOFCs is high (>700 °C) owing to the high activation energy required for ion conduction. Prolonged operation at high temperatures can cause chemical and mechanical degradation in materials and peripheral parts, discouraging their use as long-time stationary generators, which is the main target market for SOFCs. For these reasons, expectations and interest in proton ceramic fuel cells (PCFCs) have recently increased owing to excellent power and durability exhibited at intermediate temperatures (<600 °C). According to recent reports, the durability of PCFC is superior to that of SOFC, even when methane fuel or city gas is used. The cathode reaction accounts for the highest resistance in PCFC operation. Therefore, the development of a high-performance cathode is the biggest factor for the success of PCFCs. To develop a high-performance cathode, the following factors must be optimized: 1) surface and interface shapes and components that can maximize the charge transfer reaction, and 2) porous microstructure that can effectively deliver oxygen gas to the reaction sites, and 3) electrolyte-electrode adhesion at the interface. The potential of inkjet printing (IJP) as a fabrication technology capable of optimizing these structures and components was recently confirmed by our group. IJP has attracted attention as a thin-film deposition technology that can produce complex and sophisticated microstructures, such as various multi-component ceramic materials, while controlling them at the nano-level. Recently, solution processes such as spin coating, tape casting, and screen printing have been frequently adopted in device manufacturing owing to their advantages such as simple accessibility and convenient management. However, fabricating films with nanoscale thickness is very difficult. Moreover, the precision with which a microstructure with the desired shape can be fabricated is limited. Conventional thin-film processes, such as physical vapor deposition and chemical vapor deposition, require complex and difficult deposition conditions, such as high-temperature or high-vacuum maintenance. In addition, equipment management is difficult, a large number of parts and a large system are required, and large-area deposition is highly challenging. Meanwhile, in the case of IJP technology, it is feasible to deposit high-quality nanoscale thin films relatively conveniently without requiring vacuum or temperature maintenance. Furthermore, they can be manufactured over large areas. IJP can be used to conveniently fabricate various patterns in the desired shape without masking or lithography. Moreover, it can precisely control the concentration and combination of printing inks to produce thin films with desired material compositions and microstructures. We successfully fabricated perovskite-type PBSCF cathodes for PCFCs using thermal inkjet printing (T-IJP) on a BaZrxCe0.8-xY0.1Yb0.1O3-δ electrolyte using an inexpensive desktop printer. Ceramic ink solutions were formulated using PBSCF nanopowder as a solid material and dispersed in anhydrous ethanol/propylene glycol methyl ether (PGME)-based solvents. The ink properties were improved by adding various polymers such as surfactants and dispersants. Printability was verified by analyzing the viscosity, surface tension, and particle size of the pigments. The shape and microstructure of the PBSCF cathode layer were successfully adjusted by controlling the number of printing cycles and grayscale value (color level) using commercial printing software. We verified the tendency of these specimens by observing the images using a scanning electron microscope (SEM) equipped with energy dispersive spectra. We also conducted performance evaluations and electrochemical impedance spectroscopy (EIS). Finally, we obtained an optimized PBSCF cathode (G-80) with adequate thickness and a favorable number of pores and cracks. The samples had identical thicknesses of 6–7 μm. This enabled a more reliable and accurate comparison and examination of the tendencies of the specimen microstructures by using grayscale values. We enhanced the performance compared to that of our previous T-IJP work. Among the four specimens measured in this study, G-80 attained a high power output of 728 mWcm-2 in the sub-IT regime (< 600 °C). This performance is higher than that achieved in previous similar studies.

Attentional factorization machine with review-based user–item interaction for recommendation

In recommender systems, user reviews on items contain rich semantic information, which can express users’ preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user–item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user–item feature interactions to distinguish the importance of different user–item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics.

Prime: Privacy-preserving video anomaly detection via Motion Exemplar guidance

Video anomaly detection (VAD) involves identifying events or behaviours in video sequences that deviate from expected patterns. Most VAD models to date focus on seeking continuous improvement by directly learning identifiable visual cues from information-rich appearance data, regardless of the critical issue of privacy and data security in public places. This paper explores the possibility of addressing privacy-preserving VAD by privacy-independent data, such as human body skeleton and optical flow. However, due to the imbalanced nature of normality and anomaly, direct learning of the consistency of heterogeneous data may result in normality bias. To address the issues, we propose a novel motion exemplar-guided approach (a.k.a. Prime) that explicitly incorporates the support set of human skeleton poses into the VAD framework for breaking through the usefulness-versus-privacy dilemma. The support set containing diverse motion exemplars from the large-scale human skeleton-based action database enables our model to disentangle the coarsely defined anomalies. To learn the abnormal consistency between poses and optical flow, we introduce a Non-Minimum Suppression (NMS) strategy that adaptively highlights the correlation of anomalous pairs. The proposed architecture allows us to train our model with both fully and weakly-supervised paradigms in an end-to-end manner. We conducted performance evaluations of our method on three well-established datasets for VAD tasks: UCSD Ped2, Avenue, and ShanghaiTech. These evaluations were carried out in both privacy and non-privacy settings to assess the effectiveness of our approach. The results demonstrate that our approach surpasses the performance of most state-of-the-art (SOTA) methods, both in fully-supervised and weakly-supervised paradigms.

PERCEIVED EASE AND SECURITY OF USING QRIS TOWARDS CASHLESS SOCIETY

Purpose: The purpose of the research journal is to investigate the perceptions of accounting students at UPN "Veteran" East Java regarding the implementation of a cashless society. Specifically, the study aims to explore the variables of Perception of Ease of Use of QRIS (Quick Response Code Indonesian Standard) and Perception of Security of QRIS. The researchers want to understand how these perceptions influence the adoption of a cashless society among accounting students.&#x0D; Design/methodology/approach: The research study employed a quantitative methodology and was placed during the academic year 2019-2020. The participants in the research were accounting students at UPN "Veteran" East Java. The researchers gathered data from these students and applied the SPSS Ver.26 software for analysis. To explore the connections between the variables of Perception Ease of Using QRIS, Perception of Security QRIS, and adoption a cashless society using multiple linear regression analysis.&#x0D; Findings: The study's results demonstrate that the adoption of a cashless society among accounting students at UPN "Veteran" East Java is affected by the perceived easeof using QRIS. That indicates a higher likelihood of embracing a cashless society observed when students perceive QRIS as user-friendly. Furthermore, the research reveals that the perception of QRIS security also play a role in influencing the adoption of a cashless society among accounting students. This suggests that a stronger inclination to embrace a cashless society is associated with perceiving QRIS as a secure payment method.&#x0D; Practical implications: The author recommends the government of Bank Indonesia and banking companies in conducting performance evaluations on the importance of QRIS-based payments accompanied by increased promotion and efficiency.&#x0D; Originality/value: This research is original and adds new value to the Cashless Society in Indonesia which is influenced by perceptions of convenience and perceptions of security

Using normalized echo state network to detect abnormal ECG patterns

AbstractCardiovascular disease (CVD) stands as a prominent contributor to human mortality. Electrocardiogram (ECG) represents a widely adopted noninvasive method employed by clinicians to detect and diagnose CVDs. Nonetheless, conventional ECG‐based detection approaches for cardiac disorders tend to be time‐consuming and inefficient, necessitating the need for more effective solutions. Recent studies have highlighted the effectiveness of the echo state network (ESN) in detecting abnormal ECG patterns. However, traditional ESN models often face challenges such as unstable training and convergence difficulties due to variations in the range of reservoir state values. To address this issue, this study introduces a novel approach called the normalized echo state network (NESN). The NESN method normalizes the states of all neurons within the reservoir before applying the nonlinear activation function. In our study, we conducted performance evaluations of the proposed model using the MIT‐BIH arrhythmia database. We performed a synergistical analysis to investigate the impact of reservoir parameters on the network performance. The experimental results demonstrated promising outcomes, with an accuracy of 99.1% and an F1‐score of 96.4%. Specifically, for detecting abnormal ECG patterns, our model achieved a sensitivity of 90.2%, a positive predictive value of 96.6%, and a specificity of 99.8%. These results highlight the superior performance of our classifier compared to most traditional mainstream heartbeat detection methods and ring topology ESN model.

An integrated Multi-Criteria Decision Making and Multi-Choice Conic Goal Programming approach for customer evaluation and manager assignment

Companies adopt a customer-centric approach to maintain their competitive position or dominate the market. In this context, managers are pivotal in navigating the competitive markets in accomplishing these goals. Setting targets and conducting performance evaluations are vital for managers’ long-term success. This study aims to optimise customer evaluation and manager assignment in the defence industry. An integrated approach utilising the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to conduct a comprehensive market analysis and conduct this evaluation. A bi-objective integer programming model is formulated to maximise​ total integrated scores and potential returns. The efficient number of visits is determined by combining Multi-Criteria Decision Making (MCDM) and Multi-Choice Conic Goal Programming (MCCGP). This study offers an objective alternative to traditional survey-based evaluations by proposing a novel decision support system for Goal-Based Performance Appraisals (GBPA) of managers. The proposed approach yields performance values that are considerably more reliable than questionnaire results. Moreover, the proposed decision support system can effectively benefit employees across diverse sectors, extending beyond the confines of the defence industry.