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With the surge in mobile data traffic, leveraging Wi-Fi has become pivotal to alleviate the strain on LTE networks. An essential challenge lies in selecting the optimal Wi-Fi access point during Handover (HO), impacting Quality of Experience (QoE) and connection stability. This study introduces an innovative approach to enhance VoIP call Quality of Service (QoS) during HOs from LTE to Wi-Fi. Our contribution integrates two Mean Opinion Score (MOS) prediction models, multilayer perceptron (MLP) and recurrent neural networks (LSTMs) into the software-defined network controller (SDN), optimized by the Grey Wolf Optimizer (GWO). This solution provides accurate predictions for the SDN to forward mobile devices (MEs) and traffic to the nearest optimal Wi-Fi access points. Simulation results affirm the effectiveness of our approach. The LSTM-based approach performs well in the long run and provides better stability in terms of QoS parameters, especially when the metric of choice for mobile users is based on the Signal-to-Noise and Interference Ratio (SNIR), rather than the Received Signal Strength (RSSI) indicator. These results explore opportunities for the real-world deployment of the implemented models, particularly in urban areas experiencing cellular network congestion. [ABSTRACT FROM AUTHOR]

• We explore the moderating role of bus satisfaction based on the examination of the antecedents and consequences of affective motivation for private cars. • The moderating effect of bus satisfaction on the relationship between symbolic motivation and affective motivation is significantly negative. • Bus satisfaction evinces a significantly positive moderating effect on the relationship between affective motivation and private car ownership and use. • The decision to own and use a private car is heavily influenced by bus satisfaction. China started implementing a public transport priority policy in 2004 to encourage people to use public transit, especially buses, and reduce reliance on private cars. This paper used 334 questionnaire survey data from Changzhou City, China, and explored the moderating role of bus satisfaction based on the examination of the antecedents and consequences of affective motivation for private cars through two studies. Study 1 explored the antecedents of affective motivation for private cars and the moderating effect of bus satisfaction. The empirical results revealed that instrumental motivation is the most relevant predictor of affective motivation favoring private cars, followed by symbolic motivation. The moderating effect of bus satisfaction on the relationship between symbolic motivation and affective motivation was significantly negative. Study 2 explored the consequences of affective motivation for private cars and the moderating effect of bus satisfaction. Empirical results indicate that decisions regarding private car ownership and use are significantly negatively influenced by bus satisfaction. Bus satisfaction has a significantly positive moderating effect on the relationship between affective motivation and private car ownership and use. The marginal effect of bus satisfaction on the probability of simultaneously owning and using private cars is significantly negative, and it strengthens the influence of affective motivation in promoting both ownership and use of private cars. These results demonstrate that for individuals with experience using both buses and private cars, the public transport priority policy focused on improving bus service quality can effectively reduce private car ownership and use. These results can serve as evidence to support policymakers in continuing to enhance the public transport priority policy. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area network (WBAN) is a potential low‐cost technology for privacy‐sensitive telemedicine and e‐health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency‐aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the Nth primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing has several benefits over traditional systems, such as scalability and high availability. However, these benefits, to be eventuated, require efforts in the area of resource provisioning and scaling, to match resources to current and future demand, and this is not always trivial to achieve. Since workload may fluctuate substantially in cloud environments, over-provisioning is a common practice to avoid abrupt quality of service (QoS) drops that may result in service level agreement (SLA) violations, but at the price of increased provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a clouds can be improved. Therefore, in this paper, we show the potential benefits of a proactive resource provisioning scheme augmented by three of the most promising machine learning prediction techniques in this context, namely ARIMA, MLP, and GRU, that are known to be able to cope with the dynamic behavior of our target applications. We analyze the trade-off between resource consumption and quality of service using SLA violations in web workloads, considering real case provisioning requirements and constraints, extensively simulating and analyzing the impact of prediction and scaling intervals, and publishing all used tools and datasets to allow reproducibility. Simulation experiments with a proactive approach are executed using real traces from NASA and Wikipedia workloads and achieved a reduction of 40% in SLA violations on average when compared to a reactive approach, while reducing the provisioned resources by almost 3%. [ABSTRACT FROM AUTHOR]

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user's received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability. • Maximizes the LEO system throughput while ensuring the GEO system service quality. • Builds a PPO algorithm based deep reinforcement learning framework. • Constructs two benchmark schemes: improved FO (IFO) and the DNN-accelerated IFO. • Achieves the highest LEO system throughput with the lowest computational complexity. • Yields the best system stability compared with the benchmarks. [ABSTRACT FROM AUTHOR]

According to survey data released by the National Bureau of Statistics, from 2011 to 2022, the total output value of China's construction industry showed an increasing trend year by year. From 2018 to 2021, infrastructure investment has always maintained a positive growth, indicating that the current construction industry is in a rapid development stage. In order to improve the development quality of the construction industry as much as possible, it is of great significance to develop the whole Process engineering consulting service model. The quality evaluation of whole process engineering consulting service modes is MAGDM. The single-valued neutrosophic sets (SVNSs) is useful tool to cope with uncertain information during the quality evaluation of whole process engineering consulting service modes. In this paper, the single-valued neutrosophic number combined TOPSIS (SVNN-CTOPSIS) model based on single-valued neutrosophic number Hamming distances (SVNNHD) and single-valued neutrosophic number Euclidean distance (SVNNED) is formed to cope with the MAGDM. The CRITIC model is utilized to obtain the weight numbers in light with the SVNNHD and SVNNED under SVNSs. Finally, numerical example and comparative analysis for quality evaluation of whole process engineering consulting service modes is utilized to verify SVNN-CTOPSIS model. The main contributions of this study are formed: (1) the CRITIC model is formed to obtain the weight numbers in light with SVNNHD and SVNNED; (2) the SVNN-CTOPSIS model is formed in light with SVNNHD and SVNNED under SVNNs; (3) Finally, numerical example and comparative analysis for quality evaluation of whole process engineering consulting service modes is employed to verify SVNN-CTOPSIS model. [ABSTRACT FROM AUTHOR]

This study aimed to measure the service quality of China's high-speed electric multiple unit (EMU) trains and examine empirically the correlations among service quality, passenger satisfaction, and loyalty. Using a service quality scale developed based on passenger ticket, inbound, waiting, boarding, and outbound data, a survey was conducted in southeast China with 615 respondents. Using exploratory factor analysis, mean value, and correlation analysis, the authors constructed a multiple regression analysis model of high-speed EMU train service quality, passenger satisfaction, and loyalty. The results indicate that passengers were most satisfied with inbound service, followed by outbound and boarding services. They registered lower satisfaction with ticket and waiting services. The average mean values showed that passengers were most satisfied with the ID card swiping service and least satisfied with the free Wi-Fi service in waiting rooms. In addition, ticket, inbound, boarding, and outbound services exerted a valid positive impact on passenger satisfaction and loyalty. These discoveries demonstrate that passenger satisfaction partially mediates the influence of the service quality of high-speed EMU trains on loyalty. Recommendations are made to improve the service quality of China's high-speed EMU trains to enhance tourism in China, particularly in the four surveyed cities. [ABSTRACT FROM AUTHOR]

Quality of Service is one of the most important research topics in cloud computing, both from the customer's point of view and the cloud service provider's point of view, due to the increasing number of cloud services and applications, along with the significant increase in users and workloads. Task scheduling has been a topic of discussion in many researches, some of which have proposed new ways to improve the Quality of Service in cloud systems. Recently, metaheuristic algorithms have been employed to improve job execution efficiency in such systems, which has proven effective in finding optimal task scheduling solutions. During this research, the latest metaheuristic algorithm Whale Optimization Algorithm (WOA) will be applied for optimizing task scheduling in cloud systems. Additionally, a multi-objective model for optimization will be used to achieve a balance between user satisfaction with these systems, and the requirements needed by service providers. Research results indicate WOA superiority in minimizing the execution time cost, price cost, and total cost parameters as contrasted with the current metaheuristic algorithms, which ultimately result in higher service quality. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Management & Business Economics is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of the study was to examine the understanding of tax regulations, the quality of fiscal services, and tax sanctions on individual taxpayer compliance, as well as risk preferences that act as moderating variables, will be examined in this study. Primary data was used in this study through a survey using a questionnaire. Primary data consists of respondents' responses from external sources through filling out questionnaires. This study aims to comprehed tax regulation, the quality of service provided by tax authorities, tax penalties, and risk preferences at the Ciayumajakuning Tax Office. The population for this study consists of 1,054,320. The sample size calculation method used in this study is the total number of samples by using the sample size calculator function available on the www.raosoft.com website, yielding a total of 271 individuals. The partial least square (PLS) data analysis technique uses the software smartPLS version 4 to apply the Structural Equation Model (SEM) based on component or variation data. Although risk preferences have no effect on taxpayer compliance, understanding tax regulations, the quality of tax authorities' services and tax sanctions affect tax compliance. [ABSTRACT FROM AUTHOR]

Train delays can significantly impact the punctuality and service quality of high‐speed trains, which also play a crucial role in affecting dispatchers with their decision‐making. In this study, a data‐driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine‐tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost‐Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision‐making and rescheduling. [ABSTRACT FROM AUTHOR]

The Hong Kong - Shenzhen Western Railway is important infrastructure supporting the construction of the Hong Kong-Shenzhen Western Corridor. This study aims to determine the optimal target speed value, focusing on facilitating fast travel for passengers between the two regions, adapting to the technical standards of both regions, and ensuring economic feasibility. The research is significant for improving service quality and maximizing project's economic returns. Comprehensive comparisons of target speeds was conducted using survey methods, experimental methods, and both quantitative and qualitative analysis. First, the technical standards of the Hong Kong rail network and the Greater Bay Area intercity railways were reviewed. An initial range of target speeds was determined based on the functional positioning of the railway. Simulations were then performed to calculate the interval running time for different target speed ranges, analyzing their adaptability to time and station spacing. Comparisons were made from the perspective of train operations. Additionally, the number of facilities, system compatibility, engineering feasibility, and construction investment for different power supply systems were analyzed, leading to recommendations on the optimal power supply system. Further analysis involved calculating the vehicle procurement costs, depot size, construction expenses, and property development revenue associated with different train model platforms, allowing for comparisons of target speeds based on power supply and vehicle model considerations. Based on comprehensive comparisons, the advantages and disadvantages of each target speed were summarized, and a value of 160 km/ h was recommended for this project. The research conclusions comprehensively consider the technical standards, time-space distance, project functionality, power supply systems, vehicle platform, engineering economics and other influencing factors from both Hong Kong and mainland China. This provides strong support for the reasonable determination of the project's target speed and also offers useful references for the selection of target speeds for intercity railways within the Hong Kong - Shenzhen metropolitan area. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Explore stakeholder perspectives of the benefits of continuously training general practitioners in the same rural or remote practice in distributed locations via the Remote Vocational Training Scheme (RVTS). Design, setting, participants: Online one‐hour semi‐structured interviews were conducted with 27 RVTS staff, participants and supervisors from all states and territories between 16 October and 24 November 2023. Data were deductively and inductively coded by stakeholder type and the range of benefits, and the findings were informed by insights from a project reference group and a stakeholder advisory group. Questions explored the benefits of the RVTS — a program which supports doctors already working in rural, remote and First Nations communities to train towards general practice or rural generalist fellowship while remaining in the same practice. Main outcomes measures: Perspectives on the nature and spread of benefits. Results: Broad benefits were perceived to flow to four system‐level stakeholders: communities, health services, participants and policy makers. Perceived participant and community benefits were doctors staying longer in distributed locations with tailored place‐based supports and training, doctors building relationships with patients, and doctors learning through longitudinal care. Health service benefits included reduced reliance on locums, improved continuity of accessible and appropriate services in areas otherwise facing major recruitment and retention issues, and the doctors having more time to contribute to improving service quality and upskilling local staff. Policy‐maker benefits were sustaining safe and high quality services for distributed populations with high needs. Conclusion: The RVTS model was perceived to offer diverse benefits for different system stakeholders which could improve quality of learning, service delivery and community care. It also aligned with key policy directions for a distributed and sustainable generalist workforce under the goals of the National Medical Workforce Strategy 2021–2031 and the directions set by the independent review of overseas health practitioner regulatory settings led by Robyn Kruk. However, models like the RVTS largely rely on distribution levers to recruit more doctors to the locations it supports. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the moderating role of dimensions of brand authenticity (continuity, credibility, integrity and symbolism) in the relationship between evaluations of imperfect experiences and satisfaction within a service experience context. Building on prior research arguing for the subjective evaluation of service experiences as well as for the use of signals during dynamic experiences that build a shield to protect a brand, it assumes that service experiences range across a continuum of experiential perfection/imperfection. Design/methodology/approach: Two Web-based surveys, one pilot (N = 231) and one main (N = 349) have been conducted. The survey methodology was complemented by a (moderated) structural equation modelling approach taken for the data analysis. Findings: The findings indicate that imperfect experiences in a restaurant context have a negative influence on satisfaction. Brand continuity, credibility, integrity and symbolism authenticity weaken the negative relationship between imperfect experiences and satisfaction, but this effect seems to depend on the type of experiential imperfections. Originality/value: The current study provides theoretical and preliminary empirical evidence explaining how dynamic interactions with customers can impact subsequent static experiences. The authors found that the strength of the relationship between imperfect experiential elements and satisfaction does not only depend on perceived brand authenticity and does not always weaken when brand authenticity is present. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the impact of virtual museum tours on intentions to visit on-site museums. Furthermore, the role of an authentic virtual tour experience and its drivers is examined. Design/methodology/approach: The results of two studies, a field study in collaboration with one of the most renowned museums in Austria (n = 227) and an online survey (n = 153), were analyzed with a series of mediation models. Findings: Visual appeal and narrative quality were significant drivers of an authentic virtual tour experience. Curiosity mediated the positive effect of virtual tour usage intention on on-site museum visit intention. Originality/value: Although virtual reality has been considered a promising marketing tool in tourism, no research has explored the drivers of an authentic virtual tour experience. The findings of this study not only add new insights into the role of a virtual tour's visual appeal and narrative quality in generating authentic experience and thus prompting virtual tour usage intentions but also demonstrate that virtual tours positively impact on-site visit intentions driven by curiosity. [ABSTRACT FROM AUTHOR]

Purpose: Based on the stereotype content model (SCM), this study aims to enrich comprehension of virtual service assistant (VSA)-enabled service delivery through two pivotal avenues. Firstly, it aims to conceptualize and test the fundamental mechanism underlying how businesses deploy services using VSAs. Secondly, this study explores whether the paradigms of service co-creation versus service recovery differentially impact customers' perceptions of warmth and competence in VSA-enabled service delivery. Design/methodology/approach: This study used a one-factorial (service paradigm: service co-creation vs service recovery) field experiment. Covariance-based structural equation modelling was used to analyze the data. Findings: The results show that VSA service quality dimensions impact satisfaction for service co-creation and both trust and satisfaction for service recovery. The stronger link that mediates this effect is competence for service co-creation and warmth for service recovery. Originality/value: This research extends the understanding of SCM to VSA-enabled services and shows the asymmetries of mediation between the paradigms of service co-creation versus service recovery to suggest the most effective approach for VSAs to successfully meet customer requirements for establishing trust and satisfaction. [ABSTRACT FROM AUTHOR]

Copyright of REICE. Ibero-American Journal on Quality, Effectiveness & Change in Education / REICE. Revista Iberoamericana Sobre Calidad, Eficacia y Cambio en Educación is the property of Red Iberoamericana de Investigacion sobre Cambio y Eficacia en Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework. Design/methodology/approach: Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge. Findings: The results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE. Originality/value: This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies. [ABSTRACT FROM AUTHOR]

The low-Earth-orbit satellite internet supports the transmission of multiple business types. With increasing business volume and advancements in encryption technology, the quality of service faces challenges. Traditional models lack flexibility in optimizing network performance and ensuring service quality, particularly showing poor performance in identifying encrypted traffic. Therefore, designing a model that can accurately identify multiple business scenarios as well as encrypted traffic with strong generalization capabilities is a challenging issue to resolve. In this paper, addressing the characteristics of diverse low-Earth-orbit satellite traffic and encryption, the authors propose STC-BERT (satellite traffic classification-BERT). During the pretraining phase, this model learns contextual relationships of large-scale unlabeled traffic data, while in the fine-tuning phase, it utilizes a semantic-enhancement algorithm to highlight the significance of key tokens. Post semantic enhancement, a satellite traffic feature fusion module is introduced to integrate tokens into specific low-dimensional scales and achieve final classification in fully connected layers. The experimental results demonstrate our approach's outstanding performance compared to other models: achieving 99.31% (0.2%↑) in the USTC-TFC task, 99.49% in the ISCX-VPN task, 98.44% (0.9%↑) in the Cross-Platform task, and 98.19% (0.8%↑) in the CSTNET-TLS1.3 task. [ABSTRACT FROM AUTHOR]

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively. [ABSTRACT FROM AUTHOR]

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion. [ABSTRACT FROM AUTHOR]

Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients' gradients to reconstruct the original training data, compromising the security of federated learning. Thus, there has been an increasing number of studies aiming to protect gradients using different techniques. One common technique is secret sharing. However, it has been shown in previous research that when using secret sharing to protect gradient privacy, the original gradient cannot be reconstructed when one share is lost or a server is damaged, causing federated learning to be interrupted. In this paper, we propose an approach that involves using additive secret sharing for federated learning gradient aggregation, making it difficult for attackers to easily access clients' original gradients. Additionally, our proposed method ensures that any server damage or loss of gradient shares are unlikely to impact the federated learning operation, within a certain probability. We also added a membership level system, allowing members of varying levels to ultimately obtain models with different accuracy levels. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are characterized by heterogeneous traffic types (audio, video, data) and diverse application traffic requirements. This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs. The requirements for each class regarding sensitivity to QoS (Quality of Service) parameters, such as loss, delay, and jitter, are described. These classes encompass real-time and delay-tolerant traffic. Given that QoS evaluation is a multi-criteria decision-making problem, we employed the AHP (Analytical Hierarchy Process) method for multi-criteria optimization. As a result of this approach, we derived weight values for different traffic classes based on key QoS factors and requirements. These weights are assigned to individual traffic classes to determine transmission priority. This study provides a thorough comparative analysis of the proposed model against existing methods, demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications. The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions, offering insights into practical deployments in real-world scenarios. Additionally, the paper includes an analysis of energy consumption, underscoring the trade-offs between QoS performance and energy efficiency. This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks, considering the appropriate QoS framework supported by experimental analyses. [ABSTRACT FROM AUTHOR]

Occupational licensing laws are designed to improve service quality and protect consumers from harm by establishing entry requirements and oversight of practising professionals. Evidence on the effects of licensing is mixed. The quality of services is difficult to measure objectively, complicating attempts to compare costs and benefits of regulation. A 2017 executive order in the US state of Idaho required licensing boards to report disciplinary actions against professionals. This new source of data provides some insight into the type of enforcement undertaken by licensing boards. We find that disciplinary actions directly related to quality are rare. Between 0.01 and 0.38 per cent of licensees are disciplined for quality‐related issues; 36 to 53 per cent of total disciplinary actions are directly related to quality, with the rest covering technical breaches. [ABSTRACT FROM AUTHOR]

Institutional excellence is considered one of the main frameworks to help planning institutions to enhance their ability and potential to achieve excellence in performance, and to keep pace with global developments and changes in leadership, planning, creativity and innovation in order to improve the quality of services provided by these institutions. The importance of electronic management is highlighted in that it constitutes one of the main frameworks that play an important role in helping institutions enhance their capabilities and achieve the required institutional excellence. The pursuit of institutional excellence is one of the most important and modern topics, as the excellence standards were clarified at the forefront of the goals that institutions seek to achieve to support more excellence and uniqueness in their institutional performance, which required administrative leaders to make more efforts to achieve success and progress, relying on speed, flexibility and innovation. This study sought to clarify the impact of electronic management on achieving institutional excellence in planning institutions in the Gaza Strip. Therefore, it was necessary for planning institutions to strive for excellence in institutional performance by encouraging competition between these institutions, so that they can provide a high-quality and distinctive service to beneficiaries. Therefore, the current study focused on determining the impact of electronic management in achieving institutional excellence in planning institutions by applying it to workers in the Gaza Strip. To achieve the objectives of the study, the descriptive analytical approach was used, and a simple random sample was used, as 132 questionnaires were distributed to the study sample, and the SPSS statistical program was used to analyze the data. The study recommended the need to work on training human cadres, especially training related to modern technologies and programs, which contributes to keeping pace with development and achieving the goals of the institution, and working on continuous evaluation of performance. Workers in addition to evaluating the devices and taking the necessary development procedures, which achieves rapid development and growth. [ABSTRACT FROM AUTHOR]

The study sought to establish the moderating effect of information quality on the relationship between corporate IT strategy and service delivery of state corporations in Kenya. The unit of analysis was 178 state corporations in Kenya. Descriptive methods were used to collect and analyze data on the study variables. Structured questionnaire was used to collect primary data while secondary data was collected from annual performance reports of the state corporations. Out of 178targeted response only 120 positively responded making an adequate response rate of 67.4% for investigation. The analysis established that 68.2% of changes in service delivery were accounted for the changes in information quality, corporate IT strategy and interaction term. In conclusion this study found statistically significant effect of information quality on the relationship between corporate information Technology and service delivery of state corporations in Kenya. The findings of this study added to the body of knowledge in the area of service delivery of state corporations by emphasizing on information quality and corporate IT strategy. On the basis of these findings it is important that national government to develop appropriate policies to support corporate information quality that is embedded on new technologies to foster customized services for profitability and growth. [ABSTRACT FROM AUTHOR]

The purpose of this study was to find the effect of customer satisfaction on the relationship between perceived service quality and customer loyalty among passengers of railway transport services in Kenya. A descriptive cross-sectional survey design and a pragmatism paradigm were used. Proportionate stratified random sampling technique was used to pick samples from each railway service provider. Simple random sampling was used to select a representative sample size of 357 respondents for the study. The study population comprised all railway customers who had frequently used Madaraka Express, Nairobi Commuter Rail, Kisumu Safari Train and Nairobi-Nanyuki Train passenger services in Kenya. Primary data was collected by a selfadministered semi-structured questionnaire. Data was analyzed using descriptive and inferential statistics. Stepwise regression analysis was carried out to test the research hypothesis. The results showed that customer satisfaction significantly and partially mediated the relationship between perceived service quality and customer loyalty (p-value<0.05, ß= 0.400). The study contributes to the existing literature by revealing the partial mediating influence of customer satisfaction on the relationship between perceived service quality and customer loyalty. The study recommends that policy makers should embrace perceived service quality to enhance customer loyalty and gain competitive advantage. Managers should use the study results to establish perceived service quality dimensions and customer satisfaction attributes for customer loyalty realization. Future studies may use longitudinal research design to establish causal relationship between perceived service quality and customer loyalty over a period of time. [ABSTRACT FROM AUTHOR]

In Pakistan's intensely competitive mobile market, cellular service providers' profitability and long-term viability are largely dependent on customer satisfaction. As a result, this study looks into the various factors that influence customer satisfaction with Pakistan's cellular service providers, with a particular emphasis on factors like brand image, network coverage and quality, price fairness, sales promotion, and service quality. For this study, data from 500 potential customers of Pakistan's four mobile phone providers--Jazz, Telenor, Zong, and U-Fone--was acquired using a stratified sampling technique. Further data analysis included a twostage structural modeling methodology. The structural model was used to evaluate the relationship between the underlying variables, while the measurement model was utilized to evaluate the validity and reliability. According to the survey, customer satisfaction is significantly and favorably impacted by brand image, network coverage and quality, sales promotion, and service quality. Pricing, on the other hand, had a negative and considerable impact on customer satisfaction among Pakistani cellular providers. This study examines how multi-dimensional metrics might be used to measure customer satisfaction in the Pakistani cellular industry. [ABSTRACT FROM AUTHOR]

The new paradigm of the Internet of Things (IoT) has attractiveness in healthcare so that it is used for medical purposes such as health monitoring for chronic diseases and caring for the elderly. The use of IoT technology can reduce costs, increase the quality of life and increase the Quality of Service (QoS). Remote monitoring applications that utilize technological advances in the IoT can deal with a large amount of real-time data. For this purpose, the cloud service attributes (e.g., high storage space and data processing capabilities at the edge) can be very valuable. Therefore, the integration of the cloud and IoT (a paradigm called CloudIoT or CoT) in improving healthcare delivery is becoming essential. In this article, we intend to express the challenges of service quality in remote care systems, to facilitate tourists' access to medical and specialized services by designing different scenarios using an intelligent routing system in different scenarios. Because usually, the people who need quick access to health and emergency services are tourists who do not know their current situation in advance. By designing three scenarios, we propose an approach to improve QoS challenges in the remote healthcare field. These three scenarios include routing without emergency situations, routing by considering healthcare services, and routing by considering emergency situations. This intelligent routing helps users choose the optimal treatment path or the optimal time path according to their needs. Finally, by analyzing the routing system in different scenarios, we suggest how much the service quality can improve in the field of intelligent healthcare through the cloud and IoT integration, and how the challenges of service quality can be addressed. These analyzes using the AHP (Analytical Hierarchy Process) program show that by using the intelligent routing program, improvements in real-time data processing, and data consistency can be achieved. [ABSTRACT FROM AUTHOR]

The use of the learning management system (LMS) at the Malaysian Polytechnic is constantly changing according to the current situation. In addition, the relatively low acceptance of LMS in technical and vocational education training (TVET) institutions requires further study. This paper will discuss accurate construct of measurement for LMS TVET using expert consensus through Aiken's V analysis. Based on the analysis coefficient and the reliability of the content, several important constructs have been identified involving system quality, information quality, service quality, motivation, user satisfaction, intention-to-use, self-discipline, practical training, and actual use. Through quantitative analysis, every item in constructs is calculated and reviewed by an expert in order to validate the items. The minimum validity value accepted in this study is 0.75 based on Aiken's V table, thus, two items were rejected. These items were rejected due to the same meaning and being inappropriate. This study proves the instrument's content validity based on expert agreement using the Aiken agreement index. This study contributes to a suitable instrument for measuring LMS in TVET for use in subsequent studies. [ABSTRACT FROM AUTHOR]

Service robots could provide benefits for hotels in terms of productivity and even service quality. However, hotel managers in Spain (and Europe) have not implemented them as yet or are doing so only tentatively. This could be due to the fact that current literature is fragmented (i.e., dominated by hybrid models) and inconclusive and does not provide consistent guidelines for practitioners. This research narrows this gap by showing that a parsimonious application of the Theory of Planned Behavior is useful to predict attitude and buying intention toward robot-served hotels. Through an empirical analysis of 600 consumers in Spain, our findings indicate that hotel managers could focus on intrinsic motivations (enjoyment and challenge). They should also communicate the advantages of hybrid human–robot teams, in which the robots supplement humans instead of substituting them. The anthropomorphism of robots should not be emphasized as it seems to be perceived as a threat. Although utilitarian benefits matter, they seem to have a subordinate salience. [ABSTRACT FROM AUTHOR]

Wine tourism, traditionally understood as an activity practised by adults, has been suggested as a tourism form also adequate and interesting for families with children. However, both literature and practice do not consider this segment to the extent it, arguably, may deserve. Many challenges are associated with the creation of wine tourism offerings for children. Even so, wine estates and destinations attract families, not only for their wine-related attributes but also for their rural and cultural appeal. In Central Portugal, the Dão and Bairrada wine routes cover tourist regions strongly linked to family tourism. Nevertheless, there is a lack of wine tourism offerings targeting this segment. Likewise, there are no know studies on wineries' managers perceptions of this market. Thus, this study aims to identify and analyse how wine estates and wineries from these wine routes cater to families with children, to understand the managers' perception of benefits and challenges of targeting this market, and to examine the existing child-friendly tourism supply. The results, derived from the content analysis of 29 interviews with wineries' managers, reveal that, despite an inclusive and receptive discourse towards the presence of children, the attractions still do not offer services and experiences specifically designed for this segment. Except for a few innovative attractions, families are welcomed with improvisations to entertain the little ones while their parents carry out wine-related activities. [ABSTRACT FROM AUTHOR]

This study assesses the influences of organizational ambidexterity and new public management (NPM) innovations on public service quality in the under-researched and changing context of the Arabian Peninsula Gulf States. The data used were obtained from government organizations in the Sultanate of Oman and were analyzed using structural equation modeling. The findings indicate that exploitation is associated with public service quality and this relationship is partially mediated by public service innovation. The results show an indirect relationship between NPM practices and service quality mediated by service innovation. Moreover, the relationship between exploration and service quality is fully mediated by service innovation. This study extends the existing research on ambidexterity in government organizations and contributes to the international public administration reform and innovation literature by examining the applicability of Western practices in changing societal and politico-administrative cultures. [ABSTRACT FROM AUTHOR]

This study explores the impact of distinct characteristics of theme restaurants on service quality and behavioral intentions. As the unique attributes of themed restaurants continue to gain in popularity, this study gathered data from a face-to-face survey of 776 diners who visited various themed restaurants across different regions of Taiwan. The study validated the evaluation of customers' service quality, affective experience, sensory experience, and entertainment in relation to behavioral intention within a proposed conceptual model, which was then tested using covariance-based structural equation modeling (CB-SEM) analysis. The findings reveal that affective experiences, the perception of entertainment and the sensory experience mediate between service quality and behavioral intention. Furthermore, the study underscores that the perception of a servicescape acts as a moderating factor influencing the associations between service quality and sensory experience, thereby reinforcing the links between affective experience and behavioral intentions. The study's robustness is confirmed by implementing alternative models. The final section of the study discusses both theoretical and managerial implications derived from these outcomes. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) are becoming important with the advancement of connected technologies, yet they grapple with the pivotal challenge of ensuring quality of service (QoS) and quality of experience (QoE). This stems from the inherently erratic nature and huge data volumes. While reliability and efficiency remain paramount, the need to address QoS and QoE emerges as a critical motivation. Most current algorithms that address streaming data fall short in terms of QoS performance metrics. Thus this work strives to improve upon QoS metrics and further improve QoE. Our proposed method uses deep learning to address these problems in a realistic software-defined vehicular network (SDVN) based on the QoS and QoE. Our research aims to combine SDVN with a recurrent neural network (RNN) in Ryu SDVN controller. The RNN model encompasses a sophisticated architecture comprising multiple layers of recurrent units designed to capture temporal dependencies in data. Through a meticulously crafted training methodology utilizing techniques such as backpropagation through time, it learns to predict future network states based on historical data. Fine-tuning hyperparameters such as the number of epochs and the batch size enables optimal model convergence. We examine this method in a realistic simulation and compare its effectiveness with conventional approaches. The results show significant gains, i.e., marginal to 28% better than the nearest rival and far better than VANET. We also evaluate the network's resilience by varying transmission rate and packet size. Our method functions well in high-density situations, suggesting that real-world deployments can benefit from it. [ABSTRACT FROM AUTHOR]

Background: In the 21st century's digital age, health organizations face challenges from reduced public healthcare spending, creating a competitive market that impacts healthcare management. The struggle is hardest for small organizations, such as private clinics. Competing under unfavorable conditions, these small businesses must run as independent, profitable units in a government-regulated market where they are subject to numerous restrictions yet receive no financial support. In a world of increasing dependence on digital technologies, these small businesses must adopt competitive business models and be adaptive and flexible in embracing change. Methods: This qualitative study is based on a thematic qualitative analysis of 20 in-depth, 45-minute-long interviews with dentists and owners of private dental clinics in various specialties. The study employs the strategic change model to examine how dentists who run private dental clinics implement new strategies and technologies to adjust to changes and create a competitive edge. Results: Six main categories emerged from the analysis of the interviews: changes in the organization's environment; instituting and assimilating changes; obstacles in embracing change; added value obtained from embracing the change; quality of care and service; and cost-benefit considerations. The categories were map and evaluated in light of the strategic change model. The analysis indicated that digital strategies have been only partially adopted, suggesting an absence of a cohesive, long-term strategic vision for the organizations. Conclusions: The study explored the actions, perceptions, and challenges of adapting to a competitive digital market in dental private clinics. Based on these insights, recommendations have been provided for global change management, aiming for a sustainable and stable healthcare system that benefits the broader community. [ABSTRACT FROM AUTHOR]

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Critical infrastructure (CI) services are crucial to the functioning of modern communities, and a single CI failure can cause cascading effects through a network of services, impacting an entire city. Challenges such as climate change and rapid urbanisation are increasing the risk of cascading effects of CI failure, particularly impacting the urban poor in peri-urban and informal settlements. This study uses a systems thinking approach to validate the hypothesis if CI services are susceptible to generating severe cascading effects in informal than formal settlements. Qualitative data was collected on cascading effects via interviews and focus group discussion in a formal and an informal settlement in Medellin, Colombia. The responses were analysed and structured using a service chain framework and a Causal Loop Diagram. Our findings show that cascading effects vary between the formal and informal contexts concerning CI service availability and accessibility and may be attributed to differences in demography, geography, socio-economic development, risk regulations, community coping capacities and other factors associated with urban development. [ABSTRACT FROM AUTHOR]

In the 1980s, New Zealand famously adopted a high-specification view of performance management. Departments and ministers would agree ex-ante to the quantity and quality of goods and services to be produced in the coming year. This change helped inspire an international "performance movement," but no country ever went as far as New Zealand in reducing all activities to specifiable outputs. What has happened since? Successive efforts to refine the model reached a peak in 2004, but requirements were significant relaxed in 2013 without replacement, with a variety of possible explanations. [ABSTRACT FROM AUTHOR]

An accident occurred at Tokyo Electric Power Company's Fukushima Daiichi Nuclear Power Station (FDNPS) in March 2011, following an earthquake and tsunami. Local government staff members who worked in the area had a variety of experiences due to the accident, including trauma. This cross-sectional study was conducted to clarify the relationship between years of service and radiation-related traumatic experiences among 490 staff members in municipalities around the FDNPS exactly a decade after the accident. They were classified as <5 years, 5–10 years, and >10 years, relative to their lengths of employment. The experienced staff (>10 years of service) had a rich array of traumatic experiences, compared to those with less experience (<5 years of service). By contrast, the experienced staff had a lower SOC-13 score than those with less experience, and a higher percentage of them reported that their workload was greater than that imposed on other local government employees. The study results suggested that the experienced staff members who worked in the affected towns prior to the FDNPS accident have been burdened with a prolonged heavier workload, including radiation-related duties. [ABSTRACT FROM AUTHOR]

Purpose: This research seeks to better understand the impact of employee-customer identification on critical job outcomes such as customer orientation and commitment to service quality. Design/methodology/approach: A sample of 316 hotel/motel employees was used for the study. Structural equation modeling was used to analyze the data. Findings: Results show a positive relationship between ethical values person-organization fit and employee-customer identification indicating that when customer-contact service employees' ethical values align with those of the organization, they identify with customers more closely. Results also suggest that when employees identify with customers they are likely to be more customer-oriented and committed to providing service quality. Originality/value: We learn how the relationship between employee and organization impacts employee-customer identification. Furthermore, we better comprehend the impact of employee-customer identification on critical outcomes in the hospitality industry such as customer orientation and commitment to service quality. [ABSTRACT FROM AUTHOR]

With technological advancements, the availability of low-cost smartphones and internet connectivity, the online retail industry has gained traction. A pandemic-related lockdown has fuelled the fire. Consequently, it becomes important to identify the factors defining online retail service quality (ORSQ), which affect customer satisfaction and customer engagement behaviour. First, a qualitative study was conducted to identify ORSQ factors and reliability, ease of use, customer-friendly policies, problem handling and security were found to be the most vital ones for Indian consumers. Further, a quantitative study was attempted to determine the impact of ORSQ factors on consumer satisfaction and customer engagement behaviour. While testing these relationships, website familiarity was considered as a moderating variable between ORSQ factors and customer satisfaction. The findings of the study suggest that the identified ORSQ factors influence customer satisfaction and customer engagement behaviour in turn. The moderating effect of website familiarity was also established on customer satisfaction. For the analysis, Smart PLS 3.0 was used. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore how customers perceive service providers' flexibility and evaluate the healthcare experience. The study applied a machine learning technique to develop a classification framework of service flexibility in healthcare where the studies are limited. The data mining was applied to 3600 stories from a healthcare firm using the Weka software. The study presents several facets of service flexibility in healthcare through topic modeling using Latent Dirichlet Allocation (LDA) theory. The relationship was gauged by utilizing structural equation modeling (PLS-SEM). This paper is a first step to designing the marketing strategies from the flexibility perspective by adopting a customer's lens. The article demystifies a novel perspective on customer-perceived service flexibility by illuminating the underlying mechanisms through a data-driven approach. The implications are then discussed, emphasizing that healthcare service providers need to develop flexible competence to understand customer idiosyncrasies and enhance the service experience. [ABSTRACT FROM AUTHOR]

In vehicular edge computing, both edge and cloud can provide computing services (i.e., tasks). The edge can reduce vehicular task delay by processing data nearby, but is resource‐constrained and cannot handle too many tasks simultaneously. The resource‐rich cloud can handle massive tasks, but is far from users and has low quality of service and energy efficiency. Currently, some work has been done on task offloading for edge‐cloud collaboration. However, either the collaboration among multiple devices is not considered and the load is easily imbalanced; or edge‐cloud collaboration is required for offloading decisions with too many parameters, affecting tasks to be processed efficiently. To address these issues, we learn from the swarm intelligence evolution of sparrow foraging, improve a sparrow search algorithm by integrating three strategies of flyer refine producer update, sin/cos perturbation follower update, and adaptive adjustment agitator update, to optimize the vehicular task offloading location for edge‐cloud collaboration. Furthermore, we combine delay relaxation variables and delay‐energy penalty operator to design a lightweight heuristic task offloading algorithm, and greedily compare the task preoffload location sets with different delay constraints based on the improved sparrow search algorithm, to obtain the optimal task offloading with integrated delay and energy. Simulation experiments verify that our approach can improve the algorithm's optimization accuracy, convergence speed, and robustness. Moreover, the average task delay, total task energy consumption, and load balance degree of our approach outperform the five benchmark algorithms, and can comprehensively optimize task delay and energy consumption, and achieve edge devices load balancing. [ABSTRACT FROM AUTHOR]

Social commerce has become a recent phenomenon and is poised to grow rapidly in the next few years. To better address customer behavior on social commerce platforms, it is imperative to acquire a comprehensive understanding of social commerce from the perspective of service quality. The objective of this research is to examine the dimensions of social commerce service quality and to reveal the factors influencing purchase intentions among the expanding user population. This study identified seven critical dimensions of social commerce service quality (website design, fulfilment, customer service, communication, contact, credibility, and security) that influence purchase intention. This research adopts a questionnaire survey method to collect data from social commerce users. Using PLS-SEM, the findings from an empirical analysis, conducted with a sample of 411 social commerce users, demonstrate that all measured dimensions significantly impact the intention to purchase. The findings also demonstrate that e-commerce has considerable influence on customer purchase intention as a partial mediator in social commerce. The findings hold significant implications for social commerce enterprises to increase customer attraction by identifying the motivations behind their purchasing decisions. [ABSTRACT FROM AUTHOR]

Product innovation and service improvement have become essential or crucial for organisations, including public service organisations. The Indonesia Immigration Directorate released the m-passport application to enhance its quality of service. The m-passport application is considered good as it has been downloaded over a million times. Like immigration officers, this application seems to be at the forefront, reflecting an increasingly better service. However, there was still a need for significant improvement in the application. Improvements can be made to the application by considering user feedback or reviews. Reviews provided by users, approximately 12K, will serve as input for improving or enhancing the application. This was made possible as users interacti directly with the application. The most common issues are one-time password or OTP verification code with a probability value of 0.044, errors when logging in with a probability value of 0.283, and slow response applications with a probability value of 0.125. [ABSTRACT FROM AUTHOR]

In recommender systems, the user items are offered tailored to users' requirements. Because there are multiple cloud services, recommending a suitable service for users' requirements is of paramount importance. Cloud recommender systems are qualified depending on the extent to which they accurately predict service quality values. Because no service was chosen by the user beforehand, and no record of the user's selections is available, it became challenging to recommend it to users. To promote the recommender system quality, to accurately predict service quality values by offering various procedures, including collaborative filtering, matrix factorization, and clustering. This review article first mentions the general problem and states the need for research, followed by examining and expressing the kinds of recommender systems along with their problems and challenges. In the present review, various approaches, platforms, and solutions are reviewed to articulate the pros and cons of individual approaches, simulation models, and evaluation metrics employed in the reviewed techniques. The measured values in various approaches of the papers are compared with one another in several diagrams. This review paper reviews and introduces the entire datasets applied in the studies. [ABSTRACT FROM AUTHOR]

This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation's performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks' performance. The work's main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together. Article Highlights: Dynamic cell selection improves performance, reducing call drops and enhancing throughput. Simulation framework adapts to real-time conditions, offering flexibility for 4G and 5G networks. Scalable method provides valuable insights for optimizing cellular network efficiency and QoS. [ABSTRACT FROM AUTHOR]