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We aimed to investigate differences in patients' anxiety and satisfaction between patients undergoing paper-based patient decision aid (PDA) for shared decision-making (SDM) and those receiving computer-based PDA. We retrospectively collected questionnaires before and after SDM. Basic demographic data as well as anxiety, satisfaction, knowledge acquisition, and participation in SDM were recorded. We divided our population into subgroups according to use of paper-based or computer-based PDA. In addition, Pearson correlation analysis was applied to assess the relationships among variables. In total, 304 patients who visited our Division of Nephrology were included in the final analysis. Overall, over half of the patients felt anxiety (n = 217, 71.4%). Near half of the patients felt a reduction in anxiety after SDM (n = 143, 47.0%) and 281 patients (92.4%) were satisfied with the whole process of SDM. When we divided all the patients based on use of paper-based or computer-based PDA, the reduction of anxiety level was greater in the patients who underwent paper-based PDA when compared with that of those who underwent computer-based PDA. However, there was no significant difference in satisfaction between the two groups. Paper-based PDA was as effective as computer-based PDA. Further studies comparing different types of PDA are warranted to fill the knowledge gaps in the literature. [ABSTRACT FROM AUTHOR]

In a dense IEEE 802.11 WLAN, arranging multiple access points (APs) to achieve suitable area coverage is an urgent problem that needs to be addressed. This paper proposes an elaborate AP selection strategy based on three types of criteria. Furthermore, to effectively implement the selection process, the AP selection decision is modeled as a limited non-cooperative game scheme, and a two-tier approximation game is adopted to determine the final selection decision for a centralized framework. Specifically, a criterion for guaranteeing wireless station (WS) fairness is proposed to realize AP selection. This algorithm takes into account the varying throughput requirements of WSs and maximizes the total throughput Quality of Service (throughput-QoS) of all WSs, thereby enhancing WS satisfaction. Additionally, this paper introduces a load-balancing AP selection algorithm aimed at enhancing network load balancing. In this algorithm, the load balancing index is optimized to distribute the load evenly among APs, thus enhancing resource utilization within the network. Moreover, a joint optimization AP selection algorithm is presented. It employs a joint optimization index to measure the comprehensive performance of the network, striking a balance between average network throughput-QoS and load balancing. Numerical simulations prove the effectiveness of the proposed AP selection strategy incorporating three different criteria. Under larger number of WS, they can improve total throughput-QoS 10%, make load balancing and multi-attribute weighted index reach 0.9770 and 0.6356, respectively. [ABSTRACT FROM AUTHOR]

Moving objects databases (MODs) have been extensively studied due to their wide variety of applications including traffic management, tourist service and mobile commerce. However, queries in natural languages are still not supported in MODs. Since most users are not familiar with structured query languages, it is essentially important to bridge the gap between natural languages and the underlying MODs system commands. Motivated by this, we design a natural language interface for moving objects, named NALMO. In general, we use semantic parsing in combination with a location knowledge base and domain-specific rules to interpret natural language queries. We design a corpus of moving objects queries for model training, which is later used to determine the query type. Extracted entities from parsing are mapped through deterministic rules to perform query composition. NALMO is able to well translate moving objects queries into structured (executable) languages. We support five kinds of queries including time interval queries, range queries, nearest neighbor queries, trajectory similarity queries and join queries. We develop the system in a prototype system SECONDO and evaluate our approach using 280 natural language queries extracted from popular conference and journal papers in the domain of moving objects. Four volunteers give the system satisfaction and related suggestions through three rounds of independent tests. Experimental results show that (i) NALMO achieves accuracy and precision 96.8% and 81.1%, respectively, (ii) the average time cost of translating a query is 1.49s, and (iii) the average satisfaction is 95.5%. [ABSTRACT FROM AUTHOR]

This paper introduces a new framework for cooperative games with restricted permutations, focusing on situations where not all possible player permutations occur. The framework allows players within an ordered coalition to exchange positions if it benefits them. To ensure stable connections among players, we introduce the concept of stably connected ordered coalitions, which incorporates the notions of objection and counter-objection. We then define an order-restricted game, a generalized characteristic function game derived from the original game. Additionally, inspired by the Shapley value, we propose an allowable value and provide its axiomatization. Finally, we explore several extensions of this value to further investigate its potential applications. [ABSTRACT FROM AUTHOR]

The rapid pace of ageing raises concerns regarding financial security, health and long-term care and place for older people to live as they age. This paper examined the factors associated with preference to age in place (AIP) among 2231 respondents aged 60 and older from the Malaysia Ageing and Retirement Survey (MARS). The Categorical Principal Component Analysis (CATPCA) was utilized to identify variables that represent family supports, social connectedness and social participation. 75% of the respondents indicated that they would like to age in place with the binary logistic regression showing a greater likelihood among respondents with strong family and social connectedness, having a spouse, being non-Chinese and being in a good health condition. Urban respondents, with a high individual capacity and social participation, are less likely to age in place. There is a need to strengthen the family institutions and communities, providing incentives and support to promote ageing in place and empower them to remain sustainable for future generations. [ABSTRACT FROM AUTHOR]

As the impacts of climate change intensify globally, scholars and policymakers are increasingly interested in determining the factors that lead to the success or failure of climate adaptation strategies. This paper investigates the well-being outcomes of ex-pastoralists in northern Kenya who have migrated to towns in response to severe droughts. Focusing on Marsabit Town, the study employs a comparative design with primary survey data to analyze the well-being outcomes resulting from migration as an adaptation strategy. We contrast two heterogeneous groups of former pastoralists: a "settled group" that was already residing in Marsabit Town before ending their pastoral activities and a "migrant group" that relocated to Marsabit Town at the time of abandoning pastoralism. Our analysis reveals significant differences in well-being outcomes between these groups, with the migrant group often experiencing deterioration in their well-being levels. Key predictors of poorer well-being outcomes include the loss of all livestock, informal housing, and failure to transition into agricultural work, which often results in dependence on casual labor. Additionally, many migrants continue to experience poor subjective well-being—referring to their personal satisfaction with the quality of life—years after their livelihood transition. These insights offer a nuanced understanding of the well-being outcomes of migration-as-adaptation among heterogeneous groups of ex-pastoralists and underscore the need for customized livelihood support strategies for the most at-risk populations. [ABSTRACT FROM AUTHOR]

The accurate due-date reliability evaluation of demand satisfaction is vital for due-time performance prediction of production systems. This paper proposes an improved multi-state reliability evaluation approach for production systems with finite buffers subject to time-dependent failures (TDFs), operation-dependent failures (ODFs). Instead of TDF machine model or ODF machine model, a modified machine reliability model considering both TDFs and ODFs is presented as a basis. And then an equivalent workstation reliability model is proposed by steady probability analysis of finite buffers. A united reliability calculating framework is constructed for the multi-state production system based on universal generating function. A case study of a piston production line is conducted to prove the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Purpose: Total knee arthroplasty (TKA) is a common orthopedic surgery, yet postoperative dissatisfaction persists in around 20% of cases. Robotic total knee arthroplasty (rTKA) promises enhanced precision, but its impact on patient satisfaction compared to conventional TKA remains controversial (cTKA). This systematic review aims to evaluate patient satisfaction post-rTKA and compare outcomes with cTKA. Methods: Papers from the following databases were identified and reviewed: PubMed, Scopus, Web of Science, and the Cochrane Online Library, using keywords like "Knee replacement," "Total knee arthroplasty," "Robotic," and "Patient satisfaction." Extracted data included patient satisfaction measures, Knee Society Score, Oxford Knee Score, Forgotten Joint Score, SF-36, HSS, and KOOS. Statistical analysis, including odds ratio and 95% CI was performed using R software. Heterogeneity was assessed using Cochrane's Q test. Results: The systematic review included 17 articles, involving 1148 patients (571 in the rTKA group and 577 in the cTKA group) assessing patient satisfaction following rTKA. An analysis of proportions reveals rTKA satisfaction rate was 95%, while for cTKA, it was 91%. A meta-analysis comparing rTKA and cTKA found no statistically significant difference in patient satisfaction. Additionally, various patient-reported outcome measures (PROMs) were examined, showing mixed results across different studies and follow-up periods. Conclusions: The results of this study found no difference in patient satisfaction outcomes in the short to mid-term for rTKA compared to conventional methods. This study does not assert superiority for the robotic approach, highlighting the need for careful consideration of various factors influencing outcomes in knee arthroplasty. [ABSTRACT FROM AUTHOR]

Cloud federation has been proposed as a new model enabling Cloud providers to cooperate and exchange resources among multiple Clouds to provide uninterrupted services and fulfil the requirements of Cloud users. To sustain the many benefits offered by this environment, it is crucial to have an accurate performance evaluation model. However, evaluating the performance of the federated Clouds environment is challenging for several reasons: the dynamic nature of this environment, the exponential growth of users and the diversity of requests. Therefore, to better satisfy user requests in the federated Clouds environment, we propose in this paper a performance evaluation model using an open Jackson network. Our model considers the key characteristics of the Cloud, such as the diversity of Cloud services and their exponential growth (IaaS, PaaS, and SaaS), as well as the increasing number of user requests, to obtain essential performance metrics, including response time and utilization. These two parameters are obtained analytically and are used to quantify the elasticity of the federated Clouds environment. Several experiments were carried out to evaluate the proposed solution. The results showed that our model outperforms the state-of-the-art and it is usefulness for designing real cloud computing systems. [ABSTRACT FROM AUTHOR]

This paper proposes a wireless network-based machine intelligence model to enhance the predictive capability of green tourism satisfaction in regions with cultural differences. The model combines big data analysis techniques and machine learning algorithms to collect tourist data through intelligent machines and devices. Analyzing tourists' behavior and preferences establishes a reliable prediction model to effectively assess tourists' satisfaction with green tourism. Additionally, the paper constructs a distributed integration scheduling and feature mining model of green tourism satisfaction, which extracts relevant information features of big statistical data using an integration feature detection method. Furthermore, the paper establishes a fuzzy network structure model and designs the objective function of green tourism satisfaction prediction through decision scheduling and parameter optimization. The model's parameter-weighted learning enhances the optimization prediction ability of green tourism satisfaction under regional cultural differences. The paper also carries out green tourism satisfaction prediction and tourists' preference feature analysis using the fuzzy big data matching feature clustering method. The fuzzy network block fusion and clustering results also enable tourists' preference behavior feature analysis, further improving the predictive capability of green tourism satisfaction under regional cultural differences. The simulation results demonstrate high accuracy and good optimization ability, ultimately improving green tourism satisfaction prediction. [ABSTRACT FROM AUTHOR]

On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product's merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product's quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model's performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures. [ABSTRACT FROM AUTHOR]

For the global-scale multimedia live streaming services, both of the cost-efficiency at the service provider side and the Quality of Experience (QoE) satisfaction at the viewer side need to be achieved. This is a difficult challenge because the request patterns of global live-streaming services are highly dynamic. In this paper, we solve this issue by cloud-based adaptive resource allocation. We first present a cloud-based multi-tier architecture, called MaaS (Media as a Service), which consists of four types of modules. The main issue that we focus on is the deployment of properly dimensioned MaaS modules in proper geographical regions. We take the QoE of the Dynamic Adaptive Streaming over HTTP viewers into account for this decision. We propose a combination of deep-learning based demand prediction scheme and a dynamic-programming based heuristic to make a good tradeoff between viewers' QoE and the cloud resource cost. Extensive evaluation shows that the proposed scheme clearly outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

The verification of model transformations is important for realizing robust model-driven engineering technologies and quality-assured automation. Many approaches for checking properties of model transformations have been proposed. Most of them have focused on the effective and efficient detection of property violations by contract checking. However, there remains the fault localization step between identifying a failing contract for a transformation based on verification feedback and precisely identifying the faulty rules. While there exist fault localization approaches in the model transformation verification literature, these require the creation and maintenance of test cases, which imposes an additional burden on the developer. In this paper, we combine transformation verification based on symbolic execution with spectrum-based fault localization techniques for identifying the faulty rules in DSLTrans model transformations. This fault localization approach operates on the path condition output of symbolic transformation checkers instead of requiring a set of test input models. In particular, we introduce a workflow for running the symbolic execution of a model transformation, evaluating the defined contracts for satisfaction, and computing different measures for tracking the faulty rules. We evaluate the effectiveness of spectrum-based analysis techniques for tracking faulty rules and compare our approach to previous works. We evaluate our technique by introducing known mutations into five model transformations. Our results show that the best spectrum-based analysis techniques allow for effective fault localization, showing an average EXAM score below 0.30 (less than 30% of the transformation needs to be inspected). These techniques are also able to locate the faulty rule in the top-three ranked rules in 70% of all cases. The impact of the model transformation, the type of mutation and the type of contract on the results is discussed. Finally, we also investigate the cases where the technique does not work properly, including discussion of a potential pre-check to estimate the prospects of the technique for a certain transformation. [ABSTRACT FROM AUTHOR]

Most past research on positive affect and emotion has focused exclusively on high-arousal positive affect (HAPA: e.g., excited), however, low-arousal positive affect (LAPA: e.g., calm) increasingly is included in emotion research. As such, there is a need to synthesize knowledge about the similarities and differences between LAPA and HAPA, the operationalization of LAPA and HAPA, and the distinct characteristics and importance of LAPA within emotional life. A systematic search identified 226 research papers comparing LAPA with HAPA from a broad spectrum of research topics; this review provides a narrative summary of their findings. Indications of differences between LAPA and HAPA were found in 89% of comparisons, with LAPA having a consistently distinguishable relationship to variables such as brain activity, cardiovascular health, decision-making, memory, mindfulness, personality, and solitude, among others. Other notable aspects of LAPA were found, including its role in stress, work, positive sociality, and well-being, as well as its importance in older adults and women. An analysis of items used to measure LAPA and HAPA revealed nuanced differences in conceptualizations, as well as emerging consensus around specific item usage. While considering item use in light of approach-avoidance motivation, we identified three possible LAPA subtypes: calm (a steady state of neither approach nor avoidance), satisfaction (having successfully approached), and relief (having successfully avoided). This review clarifies LAPA's role in affective life, underscoring that LAPA's differences from HAPA should be considered in research involving positive affect. [ABSTRACT FROM AUTHOR]

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST. [ABSTRACT FROM AUTHOR]

Managers and experts today realize that maintaining customer satisfaction is impossible without addressing customers' needs and turning them into necessities. In this type of research, three methods are employed, including quality function deployment, hierarchical analysis, and TOPSIS technique for order preference by similarity to the ideal solution with the fuzzy logic approach at each stage. In this paper, a fuzzy-multi-criterion decision making approach is conceived to develop an identifying and prioritizing process. In the proposed methodology, distance-based optimization methodology that is, TOPSIS integrated with fuzzy system, identifies the features of general selection criteria. Since criteria are mostly based on the subjective judgment of 'technical requirement' and 'customer requirement', AHP and QFD (quality function deployment) methodology have been integrated into AHP–QFD methodology. Mehr housing mass building companies in Tehran Province are the population of this study, which belongs to the applied research category and has been conducted by the descriptive-survey method. Based on the results, the most crucial demands from the Mehr housing company are reasonable prices, constructor commitment to on-time rendering, and earthquake safety, respectively. Hence, mass construction companies should pay particular attention to these aspects. In this regard, 15 technical necessities have been investigated. Results indicate that the old established contractor company, the proximity of the city to the new settlement, and the consideration of the construction engineering organization are the most crucial. Because necessities cannot be ranked exclusively based on the results of the house quality, two other criteria of cost and applicability were also taken into account, and necessities were once again ranked. Therefore, the most crucial necessities are the cooperative nature of the company, the old established contractor company, and the cultural stratification of the complex. [ABSTRACT FROM AUTHOR]

Purpose: Accurate documentation is crucial in surgical patient care. Synoptic reports (SR) are structured checklist-based reports that offer a standardised alternative to traditional narrative reports (NR). This systematic review aims to assess the completeness of SR compared to NR in colorectal cancer (CRC) surgery. Secondary outcomes include the time to completion, surgeon satisfaction, educational value, research value, and barriers to implementation. Methods: Prospective or retrospective studies that assessed SR compared to NR in colorectal cancer surgery procedures were identified through a systematic search of Ovid MEDLINE, Embase (Ovid), CIHNAL Plus with Full Text (EBSCOhost), and Cochrane. One thousand two articles were screened, and eight studies met the inclusion criteria after full-text review of 17 papers. Results: Analysis included 1797 operative reports (NR, 729; SR, 1068). Across studies reporting this outcome, the completeness of documentation was significantly higher in SR (P < 0.001). Reporting of secondary outcomes was limited, with a predominant focus on research value. Several studies demonstrated significantly reduced data extraction times when utilising SR. Surgeon satisfaction with SR was high, and these reports were seen as valuable tools for research and education. Barriers to implementation included integrating SR into existing electronic medical records (EMR) and surgeon concerns regarding increased administrative burden. Conclusions: SR offer advantages in completeness, data extraction, and communication compared to NR. Surgeons perceive them as beneficial for research, quality improvement, and teaching. This review supports the necessity for development of user-friendly SR that seamlessly integrate into pre-existing EMRs, optimising patient care and enhancing the quality of CRC surgical documentation. [ABSTRACT FROM AUTHOR]

This paper studies a framework of Reliable Capacitated Facility Location Problem with Single source constraint, which allows us to capture the mental account management problem for a bank under uncertain environment. In the problem, each facility, corresponding to a financial product, has limited capacity and may fail randomly, which represents that the product fails to reach the threshold level of return. Each customer, corresponding to a mental account, is served by a single primary facility or product, and its demands, or the setting goals, can be split on several backup facilities or alternative investments with redundant capacity. With the operation, a portion of the satisfaction can still be met by the backup facilities when the primary service of a customer fails. We formulate a mixed integer programming model for the problem and design a Lagrangian relaxation based solution algorithm, which sophisticatedly exploits the structure of the model and transfers the complicated relaxation problems into 0–1 knapsack problems to reduce the complexity. A local search procedure is also incorporated into the algorithm to enhance the accuracy of small- and large-scale computation. Finally, a real-life case of mental accounting is investigated to illustrate the application of the decision model. [ABSTRACT FROM AUTHOR]

The success of a product in the market is largely defined by the quality of design decisions made during the early stages of development. The product design requires designers to balance multiple objectives such as functionality, cost, and user satisfaction, while addressing the challenges posed by increasing product variants and customization demands. To tackle these challenges, one approach is to structure a comprehensive model that incorporates design for assembly (DFA) guidelines during the formulation of product architecture in the conceptual phase of development. While numerous strategies have been proposed in the literature, information is often scattered, making it difficult for readers to gain a comprehensive understanding of the topic. This paper systematically reviews the role and impact of DFA in product development, consolidating and presenting the information coherently. The review provides an overview of the methods developed, along with their potential benefits and limitations. A common framework is identified that defines the structure of the models, helping designers integrate assembly consideration into their design processes, thus reducing assembly time, cost, and complexity. The framework describes the operational setting, including the domain and context in which models operate, and offers a classification of possible methods and desired outputs. Additionally, the review identifies the industry in which case studies have been most frequently presented, and the software used to facilitate the process. By connecting with such a framework, future models can be created following a structured approach, and existing models can be classified and upgraded accordingly. [ABSTRACT FROM AUTHOR]

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN. [ABSTRACT FROM AUTHOR]

In order to alleviate imbalances in radio spectrum usage, cognitive radio (CR) is designed as a dominant solution, which constantly senses the spectrum for free bands and opportunistically utilizes those bands to improve spectrum utilization. One of the essential functionalities of this emerging technology is to efficiently allocate licensed unused channels amongst Secondary Users (SUs). With this initiative, this paper deploys an auction theoretical model to provide transparent resource allocation. Auction offers a market-based mechanism where an auctioneer (primary owner) fairly leases its free channels to desirable buyers (SUs). In this paper, we propose a single-sided sealed-bid multi-unit auction mechanism for CR networks (CRNs) which sells temporarily available heterogeneous channels amongst SUs at suitable rates. Differences in channel availability time and dynamics in spectrum opportunities decide bid collection from SUs while reducing the possibility of disruption during SUs' transmission using the allocated channels. Multiple auction rounds with concurrent bidding allows the mechanism to make utmost use of the scarce radio resource. The proposed model derives a winner determination algorithm and a payment rule to select the winning bidders and their respective expenses. Additionally, we provide the proofs of truthfulness and individual rationality to obtain an economically robust auction. Simulation based results indicate performance improvements in the proposed model in terms of spectrum utilization, auctioneer's revenue, utility per buyer, user satisfaction compared to a similar approach from literature. [ABSTRACT FROM AUTHOR]

According to the existing literature on public management, many factors affect popular satisfaction with the government (referred to as "government satisfaction"), one of which is the use of e-government. Particularly, many e-government proponents regard increasing government transparency as an important way to improve government satisfaction. To test the validity of this assertion, this paper uses an Ordinary Least Squares (OLS) regression model to analyze survey data on individuals' e-government usage frequencies in China and their satisfaction with the local government during the COVID-19 pandemic. We find that people can rationally and consciously evaluate governments and make decisions by using the government performance information that they know about. This conscious and rational process is also mixed with the impact of some irrational and emotional factors, which is consistent with the concept of the "framing effect." This paper identifies local government transparency as the mediating variable in the relationship between the use of e-government and government satisfaction and public knowledge of local government performance in COVID-19 outbreak control as the moderating variable in the relationship between local government transparency and government satisfaction. Since the mediating effect of local government transparency is significantly different from those of overall and central government transparency, government transparency at different levels plays a role of the above-mentioned "frame." [ABSTRACT FROM AUTHOR]

Immigration restrictions close-off large portions of the earth to large proportions of the earth's population. For those who regard the earth and its natural resources as belonging to mankind equally and in common, this is a morally impermissible state of affairs. This is because, if the earth and its resources belong to all equally, then the exclusion of anyone from any portion of the earth will be a violation of their natural ownership rights. A commitment to Natural Resource Egalitarianism (NRE) is therefore held to entail a commitment to substantially more open, or even fully open borders. This paper does not seek to directly refute the claims of NRE, but rather seeks to demonstrate the failure of arguments from NRE, to establish the moral necessity of a general opening of borders. This paper addresses the two most prominent accounts of Open Borders NRE—Equal Division NRE and Egalitarian Ownership NRE—as well as a more recent attempt to advocate for open borders by combining these two positions into a so-called 'Combined Model'. This paper argues that none of these three positions can plausibly and coherently account for the moral necessity of open borders. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle charging and discharging energy exchange among electric vehicles (EVs) achieve economical and low-loss energy transactions. However, due to the openness of the border, EV power trading faces security issues. When an EV user communicates with a local aggregator, attackers may eavesdrop on the communication links to perform traffic analysis attacks. Moreover, unregistered illegal users may parade themselves as legitimate users and submit a bill query request to the local aggregator. In this paper, we develop a decentralized EV charging service architecture to defend against traffic analysis attacks and prevent users from tampering with transaction bills. Based on the architecture, we propose a blockchain-based secure transaction mechanism for electronic vehicles with multiple temporary identities. Specifically, we first propose a power transaction encryption protocol that utilizes multiple temporary identities to publish information streams, thereby preventing eavesdropping attacks and making the assignment between suppliers and demanders. We then propose a secure query transaction scheme that adds the accumulator value of the user's temporary identity into the Merkle tree, which identifies the query issued by legitimate users and verifies the query results. Extensive experimental results show that the proposed secure transaction mechanism promotes the user satisfaction and user utility by 5% and 10%, respectively, and promotes the security level by three times. [ABSTRACT FROM AUTHOR]

Women bear a disproportionate share of the unpaid labor within a household, which contributes to gender gaps in life and relationship satisfaction. This paper examines how an exogenous shock that increases the workload within the household impacts the burden of unpaid labor. By exploiting a rich longitudinal dataset from Australia, I estimate the gendered impacts to parental workload and stress, life and relationship satisfaction, and household division of labor when parents have a child with a significant health shock. I find evidence that women experience a decrease in their satisfaction with parenting and life satisfaction. These results are most pronounced for households where the mother is less active in the labor market or less educated. Point estimates indicate that men do not experience the same negative effects. [ABSTRACT FROM AUTHOR]

In view of the fact that people pay more and more attention to the principle of "getting the position according to the person" and "adapting the person to the position" in person-position matching, a dynamic person-position matching decision method based on hesitant fuzzy numbers is proposed. First, the dynamic person-position matching problem with hesitant fuzzy numbers is described. Then, according to hesitant fuzzy evaluation matrices of positions and candidates, expected score matrices of bilateral subjects are calculated. Furthermore, based on the idea of the generalized optimal order method and the dominant correlation and the missing correlation coefficients, satisfaction means of people and positions are calculated. According to satisfaction means, growth satisfactions at each period are obtained, and then the exponential decay formula is used to determine weights of growth satisfactions. Dynamic satisfactions of bilateral subjects are calculated by aggregating initial satisfaction means and growth satisfactions. On this basis, a stable person-position matching model considering dynamic satisfactions is established and then is solved to obtain the optimal stable person-position matching scheme. Finally, the feasibility and effectiveness of the proposed method are verified by an example analysis of person-position matching. Main contributions of this paper are as follows: an effective calculation method for the missing correlation coefficient is presented; a novel effective calculation method for dynamic satisfactions is proposed by introducing the correlation parameter to combine the missing correlation coefficient with the dominant correlation coefficient; an effective stable person-position matching model considering dynamic satisfactions is established. [ABSTRACT FROM AUTHOR]

This research aims to understand the link between perceived innovativeness and customer satisfaction in the fine-dining catering segment. By employing a mixed-method approach, this paper proposes a multidimensional framework for measuring the perceived innovativeness of restaurants throughout the entire customer journey. Customer satisfaction was measured by considering online customer-generated data from TripAdvisor. The study not only finds a strong correlation between perceived innovativeness and customer satisfaction but also presents how fine-dining restaurants can employ user-generated data to co-innovate entire customer journeys and restaurant experiences. The results highlight menu-, service-, and customer experience-related innovativeness as the three most important criteria for fine-dining restaurant customers. Additionally, the results of the qualitative study indicate that in the context of fine-dining catering, the quality of the dishes, the service, and the customers' experience with the service staff and chefs are the main elements of satisfaction that restaurants should consider in promoting innovation. [ABSTRACT FROM AUTHOR]

This paper presents a novel proof of the conservativity of the intuitionistic theory of strictly positive fixpoints, ID ^ 1 i , over Heyting arithmetic (HA ), originally proved in full generality by Arai (Ann Pure Appl Log 162:807–815, 2011. https://doi.org/10.1016/j.apal.2011.03.002). The proof embeds ID ^ 1 i into the corresponding theory over Beeson's logic of partial terms and then uses two consecutive interpretations, a realizability interpretation of this theory into the subtheory generated by almost negative fixpoints, and a direct interpretation into Heyting arithmetic with partial terms using a hierarchy of satisfaction predicates for almost negative formulae. It concludes by applying van den Berg and van Slooten's result (Indag Math 29:260–275, 2018. https://doi.org/10.1016/j.indag.2017.07.009) that Heyting arithmetic with partial terms plus the schema of self realizability for arithmetic formulae is conservative over HA . [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the effects of the topics, moods, and sentiments of the comments describing a phenomenon upon its overall rating. TOBIAS is built combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, and Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how they affect an overall rating that summarizes the performance of the analyzed phenomenon. We carried out TOBIAS on a real case study on the university courses' quality evaluated by the University of Cagliari (Italy) students. We found TOBIAS able to provide interpretable results on the impact of discussed topics by students with their expressed sentiments, emotions, and moods and with the overall rating. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

This paper reports on three Victorian early career Foundation to Year Two (F-2) teachers and the impact of leadership on their daily pedagogic decision-making about the teaching of reading. Data sets from each teacher drew on observations of three of their reading lessons and researcher-generated field notes, a semi-structured teacher interview, an online survey of reported teacher practice, teacher planning documents of the observed lessons and classroom artefacts related to reading. Selected from a wider qualitative case study undertaken with 16 F-2 teachers across six government primary sites in metropolitan Melbourne, the teachers in this paper reported on how mandated and non-negotiable principles and practices of reading affected their ability to make daily pedagogic decisions for their students. As a result of leadership directives, the three teachers expressed frustration at the lack of opportunity for teacher voice and autonomy in their craft of teaching. If the teaching profession wishes to continue to renew its numbers and promote the teaching profession as an ongoing career of choice, the focus on the professional development of early career teachers is timely and necessary. Therefore, this paper aims to shine a light on the importance of instructional leadership models which support the development of teacher knowledge, teacher efficacy and teacher satisfaction, ultimately resulting in more targeted outcomes for students and more teachers likely to remain in the profession [ABSTRACT FROM AUTHOR]

Machine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria (e.g., demographic parity, equality of opportunity, treatment equality) as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to the score, and adopting decisions based on the classification. Throughout our inquiry we use the COMPAS system, complemented by a radical simplification of it (our SAPMOC I and SAPMOC II models), as our running examples. Through these examples, we show how a system that is equally accurate for different groups may fail to comply with group-parity standards, owing to different base rates in the population. We discuss the general properties of the statistics determining the satisfaction of group-parity criteria and levels of accuracy. Using the distinction between scoring, classifying, and deciding, we argue that equalisation of classifications/decisions between groups can be achieved thorough group-dependent thresholding. We discuss contexts in which this approach may be meaningful and useful in pursuing policy objectives. We claim that the implementation of group-parity standards should be left to competent human decision-makers, under appropriate scrutiny, since it involves discretionary value-based political choices. Accordingly, predictive systems should be designed in such a way that relevant policy goals can be transparently implemented. Our paper presents three main contributions: (1) it addresses a complex predictive system through the lens of simplified toy models; (2) it argues for selective policy interventions on the different steps of automated decision-making; (3) it points to the limited significance of statistical notions of fairness to achieve social goals. [ABSTRACT FROM AUTHOR]

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. This paper considers the scenarios of AI-based IoT applications based on container technology in a cloud-edge collaborative environment, and proposes a container-based task scheduling algorithm. Using priority-aware greedy strategy, a new scheduling algorithm named PGT has been proposed which adopts the multi-criteria approach Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The containers in cloud servers and edge servers are managed in a uniform platform, and IoT application services are deployed in containers. The task with smaller deadline constraint is scheduled first due to its higher priority. Then, multiple indicators are considered comprehensively, such as task response time, energy consumption, task execution cost, to find the optimal container to execute task. Through varying the number of edge servers and the number of tasks, the simulation results in a cloud-edge collaborative environment indicate that the proposed scheduling approach outperforms the four baseline algorithms in improving QoS satisfaction rate, energy consumption, penalty cost and total violation time. [ABSTRACT FROM AUTHOR]

Objectives: Different approaches to prosthodontic consultation, all involving a strong focus on shared decision-making, were analyzed from the perspective of patients by inter-group comparisons. No patient decision aid (PDA) was used in the control group, a paper-based PDA in test group 1, and a software-based PDA in test group 2. Materials and methods: Seventy-five patients were prospectively randomized to the control group or a test group. All patients then rated the consultation on a questionnaire, six key items of which were analyzed, along with the time spent on each consultation. Results: Overall satisfaction was highest in test group 2, with a significant difference from the control group (p = 0.015). Test group 2 showed the most favorable ratings for all six questionnaire items, which invariably was significant compared to the control group (p = 0.032). Test group 1 significantly differed from test group 2 based on two items (consultation was adequately intelligible: p = 0.011; consultation was adequately comprehensive: p = 0.034) but not from the control group based on any item (p = 0.070). Conclusions: Within the limitations of this study, the use of a software-based PDA, in particular, can be recommended based on patient satisfaction and was associated with the shortest sessions for consultation. Clinical relevance: Patients are routinely faced with a wealth of information in dental offices and may be overwhelmed especially by prosthetic treatment options and decision requirements. Our findings shed some light on the nature of aids that may truly be helpful in the process of shared decision-making. Trial registration: ClinicalTrials.gov.Identifier: ISRCTN11472465. [ABSTRACT FROM AUTHOR]

Background: Nerve conduction studies and electromyography (NCS/EMG) play a key role in the evaluation of patients with neuromuscular diseases. It serves as an extension of a clinical and neurological examination. Patients should be referred to electrodiagnosis (EDX) examination with an appropriate request, including history and neurological findings. When the referring diagnosis is appropriate, the examination is quicker, less invasive and limited to the verification of the provisional diagnosis. The referral system at the hospital involves only one paper asks for (NCS/EMG) with no specific information, the electromyographer is responsible for taking the patient history and performing clinical examination. Consequently, the current referral system had many flaws that needed to be corrected to enhance it. Aim: To evaluate the current referral system to the electrodiagnosis unit and to assess the electromyographers' satisfaction with the new referral system. Methods: A simple questionnaire was filled by all working electromyographers to assess their satisfaction about the referral system, then a new approved referral sheet was applied by a specialized physician to all patients attending the EDX unit for two months, then the same questionnaire to evaluate the new referral system was filled again by the same electromyographers. Results: There was a statistically significant difference regarding the electromyographers' satisfaction with the new referral system. Conclusion: The electrodiagnosis examination could be easier, quicker, and more organized if preceded by a proper referral system and electrodiagnosis sheet. [ABSTRACT FROM AUTHOR]

In today's rapidly changing economy, efficient lifestyle has become the current situation of most mobile product users. With the development of performance tools and technologies, a fast lifestyle has brought more wealth and opportunities to users. The slow pace and fluctuating time are indirect income losses, which cause user anxiety to some extent. When the waiting time exceeds the user's waiting threshold, users would experience negative emotions, such as boredom, anxiety and anger, and product satisfaction would drop significantly. Therefore, by analyzing the uniqueness of mobile Internet products and the characteristics of users, this paper studied the reasons and influencing factors of product interactive waiting, and then used machine learning algorithm to analyze the cost function of interactive waiting experience. Finally, the corresponding interactive waiting experience design strategy was proposed. By comparison, the user experience after product interaction optimization design was 8.4% higher than that before product interaction optimization design, and the user frequency was also 14.7% higher after optimization design. In short, user experience plays an important role in product interaction design. [ABSTRACT FROM AUTHOR]

Partner selection and matching is a key part of virtual enterprise formation, which directly affects the growth and development of virtual enterprises. The selection and matching of virtual enterprise partners can be regarded as multi-attribute two-sided matching problem. This paper considers the current situation of attribute preferences in the mutual evaluation process of virtual partner selection, introduces a heterogeneous multi-attribute preference evaluation language system, analyzes the psychological behavior characteristics of both subjects in the decision-making process of selection and matching, gives the strategies and conditions of bilateral matching of virtual enterprise partners, and realizes an effective matching with balanced satisfaction of both virtual enterprise partners. [ABSTRACT FROM AUTHOR]

In today's rapidly changing economy, efficient lifestyle has become the current situation of most mobile product users. With the development of performance tools and technologies, a fast lifestyle has brought more wealth and opportunities to users. The slow pace and fluctuating time are indirect income losses, which cause user anxiety to some extent. When the waiting time exceeds the user's waiting threshold, users would experience negative emotions, such as boredom, anxiety and anger, and product satisfaction would drop significantly. Therefore, by analyzing the uniqueness of mobile Internet products and the characteristics of users, this paper studied the reasons and influencing factors of product interactive waiting, and then used machine learning algorithm to analyze the cost function of interactive waiting experience. Finally, the corresponding interactive waiting experience design strategy was proposed. By comparison, the user experience after product interaction optimization design was 8.4% higher than that before product interaction optimization design, and the user frequency was also 14.7% higher after optimization design. In short, user experience plays an important role in product interaction design. [ABSTRACT FROM AUTHOR]

The vehicular named data networking (VNDN) is gaining more attention as a future vehicular networking (VN) model. The VNDN's fundamental principles of data naming, data-centric forwarding, and in-network caching make it inherently capable of mitigating the challenges in traditional VN. However, broadcast communication for content discovery and delivery causes VNDN to face challenges such as overhead due to packets redundancy, collision, and retransmission. In this paper, we introduce an Interest-Data-flow tracking-based forwarding scheme (IDTracS) for VNDN. IDTracS aims to improve VNDN performance by prioritizing the potential best forwarders and addressing Interest and Data packet broadcast issues. IDTracS is a receiver- and time-contention-based forwarding scheme that controls both Interest and Data packets forwarding. IDTracS uses the node's degree of centrality to the Interest/Data flow and the node's geo-distance closeness from the last Interest/Data forwarder for the requested content name-prefix. Accordingly, it computes an Interest/Data forwarding delay timer for prioritizing Interest/Data among nodes. The simulation findings reveal that the proposed IDTracS achieves an enhanced Interest satisfaction ratio, delivery delay and network transmission overhead compared to related geo-position- and distance-based protocols. [ABSTRACT FROM AUTHOR]

Widespread concern has been raised about the possibility of potential biasing factors influencing the measurement of organizational variables and distorting inferences and conclusions reached about them. Recent research calls for a measure-centric approach in which every measure is independently evaluated to assess what factor(s) may uniquely bias it. This paper examines three popular stressor measures from this perspective. Across three studies, we examine factors that may bias three popular measures of job stressors: The Interpersonal Conflict at Work Scale (ICAWS), the Organizational Constraints Scale (OCS), and the Quantitative Workload Inventory (QWI). The first study used a two-wave design to survey 276 MTurk workers to assess the three stressor scales, four strains, and five measures of potential bias sources: hostile attribution bias, negative affectivity, mood, neutral objects satisfaction, and social desirability. The second study used an experimental design with 439 MTurk workers who were randomly assigned to a positive, negative, or no mood induction condition to assess effects on means of the three stressor measures and their correlations with strains. The third study surveyed 161 employee-supervisor dyads to explore the convergence of results involving the three stressor measures across sources. Based on several forms of evidence we conclude that potential biasing factors affect the three stressor measures differently, supporting the merits of a measure centric approach, even among measures in the same domain. [ABSTRACT FROM AUTHOR]

A cornerstone of well-being research is that the resource-rich are happier with their lives than the resource-poor and better positioned to cope with stressful life events. This paper addresses the role of various resources—human, economic, social, and psychological capital—in life satisfaction during the coronavirus pandemic, using panel data from Germany and the United Kingdom for 2020 and 2021. Cross-sectionally, we find life satisfaction to be clearly related to all these forms of capital, with psychological capital being the strongest predictor of life satisfaction. Longitudinally, the capital endowments in 2020 did not predict changes in life satisfaction within individuals from 2020 to 2021, except for psychological capital. Our results suggest two things: first, the unfolding pandemic did not heighten well-being inequalities; and second, weathering the pandemic required psychological resources in the first place. [ABSTRACT FROM AUTHOR]

Whether perceived importance can be used as a weighting factor for the aggregation of domain satisfaction is an important issue in the literature on subjective well-being (SWB) measures with multidimensional domains. This paper extends the weighting approach proposed by Benjamin et al. (Am Econ Rev 104:2698–2735, 2014) and uses it to weight domain satisfaction. The weights estimated by this approach can be interpreted as marginal utility, and the weighted average of the domain satisfaction of each individual can be interpreted as the individual's utility. Using a data set collected from a 2015 survey that we conducted with residents of all Japanese prefectures, we show that this weighted average improves the goodness of fit for overall SWB measures compared with the unweighted averages of domain satisfaction. This finding supports incorporating perceived importance measures into SWB measures. Moreover, we show that, although there are differences in perceived importance among some domains that are less relevant to specific sub-populations, such as work for people above 65 years or relations with family for those who live alone, the relative importance of most domains is similar across the different categories of gender, household income, age, and household composition. Allowing for heterogeneity in the importance weights across these demographic groups provides only negligible improvements to the validity of the domain importance. The approach suggested in this paper can be considered a resident-oriented approach in the sense that the weights are derived from the perceived importance of residents themselves and are thus expected to contribute to resident-oriented policymaking. [ABSTRACT FROM AUTHOR]

Background: This paper presents the baseline characteristics and their moderators in the Healthy Learning Mind (HLM)– school-based cluster-randomized controlled trial. Objectives: The paper evaluates the state of various measures of well-being, their moderators and how these results compare to national and global norms/population studies. Methods: Data were collected from all participants prior to the intervention and further analyzed by gender, grade and perceived socioeconomic status, including standardized measures for resilience, depressive symptoms and socioemotional functioning; health-related quality of life, dispositional mindfulness, satisfaction with life, compassion/self-kindness, self-rated health and morning tiredness. Results: Participating 2793 students (1425 girls, 1368 boys), ages 12 to 15 years, filled in the questionnaires. The outcomes were in line with previous research, demonstrating gender differentiation and lower wellbeing among older children and adolescents. Conclusions: All outcomes were associated with perceived socioeconomic status, suggesting that perceived low socioeconomic status should be addressed as a serious risk factor and included as a moderator in similar trials. [ABSTRACT FROM AUTHOR]

To develop a new model of case scenarios for medical students attending practical exams, in a more feasible and effective method for practical assessment exams, so as to improve the quality of assessment both subjectively and objectively, and bridge the gap produced by paper cases and clinical scenarios. We involved real patients as well as mock subjects who voluntarily co-operated for the video recordings, and used typical formats for 'history taking' which were carefully curated, edited and drafted into their final form and shape before presenting to the students. This was combined with clinical and local examination findings of the respective case history. Combining both, the students were asked to write case sheets and narrate them. A greater number of students reported increased satisfaction and level of confidence after exposure to the video case scenarios, compared to discussing paper cases in either online or offline mode, and this difference was statistically significant. A chi-square test was used to measure the satisfaction score with paper cases and video case scenarios, using the number of students who provided responses on a Likert type scale presented through a Google survey. Video case scenarios are an effective method for teaching clinical case presentation, and are also a more efficient way for assessment, fostering active listening skills and critical thinking. This exam model can bring about a uniform, objective and standardized way of evaluating the students. [ABSTRACT FROM AUTHOR]

Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS. [ABSTRACT FROM AUTHOR]

Background: Nerve conduction studies and electromyography (NCS/EMG) play a key role in the evaluation of patients with neuromuscular diseases. It serves as an extension of a clinical and neurological examination. Patients should be referred to electrodiagnosis (EDX) examination with an appropriate request, including history and neurological findings. When the referring diagnosis is appropriate, the examination is quicker, less invasive and limited to the verification of the provisional diagnosis. The referral system at the hospital involves only one paper asks for (NCS/EMG) with no specific information, the electromyographer is responsible for taking the patient history and performing clinical examination. Consequently, the current referral system had many flaws that needed to be corrected to enhance it. Aim: To evaluate the current referral system to the electrodiagnosis unit and to assess the electromyographers' satisfaction with the new referral system. Methods: A simple questionnaire was filled by all working electromyographers to assess their satisfaction about the referral system, then a new approved referral sheet was applied by a specialized physician to all patients attending the EDX unit for two months, then the same questionnaire to evaluate the new referral system was filled again by the same electromyographers. Results: There was a statistically significant difference regarding the electromyographers' satisfaction with the new referral system. Conclusion: The electrodiagnosis examination could be easier, quicker, and more organized if preceded by a proper referral system and electrodiagnosis sheet. [ABSTRACT FROM AUTHOR]

A meshless numerical model is developed for the simulation of two-dimensional incompressible viscous flows. We directly deal with the pressure–velocity coupling system of the Navier–Stokes equations. With an efficient time marching scheme, the flow problem is separated into a series of time-independent boundary value problems (BVPs) in which we seek the pressure distribution at discretized time instants. Unlike in conventional works that need iterative time marching processes, numerical results of the present model are obtained straightforwardly. Iteration is implemented only when dealing with the linear simultaneous equations while solving the BVPs. The method for solving these BVPs is a strong form meshless method which employs the local polynomial collocation with the weighted-least-squares (WLS) approach. By embedding all the constraints into the local approximation, i.e. ensuring the satisfaction of governing equation at both the internal and boundary nodes and the satisfaction of the boundary conditions (BCs) at boundary points, this strong form method is more stable and robust than those just collocate one boundary condition at one boundary node. We innovatively use this concept to embed the satisfaction of the continuity equation into the local approximation of the velocity components. Consequently, their spatial derivatives can be accurately calculated. The nodal arrangement is quite flexible in this method. One can set the nodal resolution finer in areas where the flow pattern is complicated and coarser in other regions. Three benchmark problems are chosen to test the performance of the present novel model. Numerical results are well compared with data found in reference papers. [ABSTRACT FROM AUTHOR]

The links between worker well-being and quit intentions have been well researched. However, the vast majority of extant studies use just one measure, job satisfaction, to proxy for worker well-being as a whole, thus ignoring its documented multidimensionality. This paper examines whether this approach is justified. Using novel survey data, I compare the extent to which alternative well-being indicators (job satisfaction, affect, engagement and the satisfaction of basic psychological needs) individually, and jointly, explain variation in the quit intentions of 994 full-time workers. I find systematic differences in the personal and well-being profiles of workers who intend quitting and those who do not. Furthermore, well-being indicators explain four to nine times more variation in quit intentions than wages and hours combined. The engagement measure performs best, explaining 22.5% of variation in quit intentions. Employing a composite model (job satisfaction + affect + engagement) significantly increases explanatory power. My results suggest that the standard single-item job satisfaction measure may be good enough for organisations who merely wish to identify categories of workers who may be most at risk of quitting. For organisations seeking to develop proactive quit prevention strategies however, supplementing job satisfaction with other indicators such as engagement should increase explanatory power and yield valuable, potentially actionable, insights. [ABSTRACT FROM AUTHOR]

This paper empirically assesses the impact of media freedom on citizen's trust in politicians and satisfaction with national governments. Restrictions potentially allow governments to provide citizens with biased information, which may then increase trust in, and satisfaction with, ruling elites. Yet, these restrictions may also be perceived as a signal that the latter are not trustworthy. Employing data from the European Social Survey to compare respondents with different levels of media consumption, we show that unfree media are partially effective in manipulating perceptions. Using age as an instrument for the time dedicated to media consumption, we find that higher levels of media freedom reduce citizen's trust in government but are unrelated to satisfaction with national governments. [ABSTRACT FROM AUTHOR]

Requirements prioritization is essential for development of quality software products. Requirements prioritization helps focus on the most important requirements between the available software requirements. This process is also called preserving core vision according to customer viewpoint or managing the quality requirements. Many requirements prioritization techniques have been proposed in the Software Engineering field. However, one requirement prioritization technique can only be generalized for some software projects. This research effort aims to develop a sustainable hybrid model for requirements prioritization, utilizing critical software project factors. The prioritization technique is shortlisted from a pool of software prioritization techniques, based on these project factors and their quantized scores. The proposed model is equipped with state-of-the-art literature to provide a customized prioritization technique improving the requirements prioritization process for a specific software project. The paper also presents popular software prioritization techniques used in the software industry and an extensive comparison using a thorough empirical analysis. Finally, the quantization of the software project factors for each technique has been assessed for better understanding, and a sustainable hybrid model has been constructed. With the proposed hybrid model, requirements prioritization in software development has been controlled effectively, reducing the failure risks and increasing the overall benefit and success rate. The success of the proposed hybrid approach is verified with two software case studies attaining satisfactory results and improving the organization's productivity by incorporating pivotal software requirements on which all the stakeholders agree. Furthermore, an automated approach will be developed by implementing the proposed approach to benefit from human–machine collaboration, improving user satisfaction by incorporating end-user opinions effectively and timely. [ABSTRACT FROM AUTHOR]