Search Results (20)

The research examines how digital inclusive finance reshapes the rural labor market using an auditable index system and an interpretable learning pipeline. We construct a four-pillar framework for the rural labor market covering labor behavior, labor structure, security and fairness, and sustainability, and compute county-level scores with an Attribute Hierarchy Model plus Fuzzy Comprehensive Evaluation (AHM–FCE). Using data for 58 counties in Jiangsu from 2014 to 2023, we estimate nonlinear links from overall and sub-dimensional digital finance to labor market outcomes with a random forest optimized by Particle Swarm Optimization plus Genetic Algorithm (PSO-GA-RF). Theoretical contribution: we provide a measurement-based bridge from digital inclusive finance to rural labor markets by aligning access, usage, and service quality with the four pillars of the rural labor market index, which yields testable county level predictions on participation, job quality, equity, and persistence of gains. Maps show heterogeneity, with higher behavior scores, lagging sustainability, and a north–south gradient. Empirically, stronger digital finance is associated with higher non-agricultural employment, better job quality, narrower urban–rural gaps, and stronger protection mechanisms, with larger effects where rural population shares and policy support are higher. Findings are robust to variable transforms, bandwidth choices, and tuning. Full article

This study aims to analyze online job postings using machine learning-based, semantic approaches and to identify the expertise roles and competencies required for big data professions. The methodology of this study employs latent Dirichlet allocation (LDA), a probabilistic topic modeling technique, to reveal hidden semantic structures within a corpus of big data job postings. As a result of our analysis, we have identified seven expertise roles, six proficiency areas, and 32 competencies (knowledge, skills, and abilities) necessary for big data professions. These positions include “developer”, “engineer”, “architect”, “analyst”, “manager”, “administrator”, and “consultant”. The six essential proficiency areas for big data are “big data knowledge”, “developer skills”, “big data analytics”, “cloud services”, “soft skills”, and “technical background”. Furthermore, the top five skills emerged as “big data processing”, “big data tools”, “communication skills”, “remote development”, and “big data architecture”. The findings of our study indicated that the competencies required for big data careers cover a broad spectrum, including technical, analytical, developer, and soft skills. Our findings provide a competency map for big data professions, detailing the roles and skills required. It is anticipated that the findings will assist big data professionals in assessing and enhancing their competencies, businesses in meeting their big data labor force needs, and academies in customizing their big data training programs to meet industry requirements. Full article

Digital disparities remain a significant challenge in Indonesia, particularly across its diverse regions, with uneven access to digital infrastructure, skills, and economic opportunities. This study aims to map these digital disparities at the district level, analyze the spatial distribution and clustering of digital transformation using the Digital Society Index of Indonesia (IMDI), and investigate the key drivers of digital inequality across four core pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Jobs. To measure the IMDI, primary data were collected from the industrial sector and the general population over three years (2022–2024), combined with secondary data on internet usage and service standards. A multistage random sampling approach ensured representativeness, considering demographic variations and industrial segments. The analysis employed spatiotemporal methods to capture temporal trends and spatial clustering. The results revealed a significant IMDI increase from 37.80 in 2022 to 43.18 in 2023, followed by stability at 43.34 in 2024. The hotspots of digital transformation remain concentrated on Java Island, while low spots persist in eastern Indonesia. This study provides critical insights into Indonesia’s digital readiness, identifying priority areas for targeted interventions to bridge the digital divide and foster equitable digital development. Full article

Background/Objectives: Evidence-based information is crucial for policymakers and providers of mental health and psychosocial services (MHPSS) for unaccompanied asylum-seeking children (UASC). However, there is a scarcity of national-level studies investigating the MHPSS needs of UASC and how these are addressed in Greece. The research objectives of this study were to explore: (a) the psychosocial and mental health needs of UASC living in Greek long-term accommodation facilities as perceived by MHPSS providers, and (b) the range of services across the country, highlighting gaps and best practices in service delivery. Method: An exploratory, predominantly quantitative design was adopted to map UASC’s psychosocial difficulties, mental health problems, and MHPSS delivery. Purposive sampling was implemented, with 16 of 17 NGOs operating long-term accommodation facilities for UASC and 16 child and adolescent mental health services (CAMHS) participating. The sample included 79 participants (34 facility coordinators, 28 field psychologists, and 16 CAMHS directors). A 5-W mapping tool (Who, Where, What, When, and Which) was used for data collection, through an online survey. Data analysis involved quantitative and qualitative methods (content analysis). Results: Of 798 minors, almost 59% showed signs of behavioral or emotional disturbance, with over half referred for psychiatric assessment and 27.7% needing inpatient care. Aggression, disruptive behaviors, self-harm, and suicidal ideation were the most challenging issues. CAMHS directors reported a high rate of crisis-driven responses, with 42.1% of UASC needing emergency psychiatric evaluation. Psychosocial support was hindered by communication difficulties, lack of a shared care philosophy, understaffing, job insecurity, and limited resources. Conclusions: Our findings highlight the mental health needs of UASC, and the challenges faced by facility coordinators, psychologists, and community mental health specialists. Future research should focus on the institutional and organizational factors influencing service delivery to improve support for UASC. Full article

Shared mobility services furnish convenient transportation alternatives for individuals without vehicle ownership or a preference against driving. Shared mobility could benefit developing countries by providing a cost-effective alternative, enhancing accessibility, reducing congestion, and creating multiple job opportunities. In this study, a comprehensive analysis to assess shared mobility options as an avenue to sustainable transportation in Amman, Jordan, is presented. The study employs a multifaceted methodology, including a survey questionnaire, preliminary analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis, and Structural Equation Model (SEM). The data were collected from a diverse group of Amman residents using a survey composed of 29 questions. The survey included demographic information, travel behavior, willingness to adopt shared mobility, perceived benefits, and possible barriers. These data were analyzed using Structural Equation Modeling (SEM), providing an in-depth understanding of the interrelationships among the variables studied. This study concludes by contributing to the ongoing discussion on sustainable urban transportation in Jordan and providing a road map for policymakers, urban planners, and transportation service providers. The presented findings provide an empirical basis for guiding future strategies and interventions toward sustainable urban development in Amman and potentially other urban contexts with comparable characteristics. Key findings reveal a significant potential for shared mobility to enhance urban transportation sustainability. Specifically, a notable positive perception among Amman residents was observed, with an average willingness to switch to shared mobility for daily commuting scoring 4.68 on a 7-point Likert scale. Moreover, a statistical analysis indicates that factors such as reduced costs, improved service reliability, and better environmental sustainability, notably influence the adoption of shared mobility services. Full article

Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method. Full article

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements. Full article

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions. Full article

Background: Community-based case managers in health have been compared to glue which holds the dynamic needs of clients to a disjointed range of health and social services. However, case manager roles are difficult to understand due to poorly defined roles, confusing terminology, and low visibility in New Zealand. Aim: This review aims to map the landscape of case management work to advance workforce planning by clarifying the jobs, roles, and relationships of case managers in Aotearoa New Zealand (NZ). Methods: Our scoping and mapping review includes peer-reviewed articles, grey literature sources, and interview data from 15 case managers. Data was charted iteratively until convergent patterns emerged and distinctive roles identified. Results: A rich and diverse body of literature describing and evaluating case management work in NZ (n = 148) is uncovered with at least 38 different job titles recorded. 18 distinctive roles are further analyzed with sufficient data to explore the research question. Social ecology maps highlight diverse interprofessional and intersectoral relationships. Conclusions: Significant innovation and adaptations are evident in this field, particularly in the last five years. Case managers also known as health navigators, play a pivotal but often undervalued role in NZ health care, through their interprofessional and intersectoral relationships. Their work is often unrecognised which impedes workforce development and the promotion of person-centered and integrated health care. Full article

Background: No systematic review in the literature has analyzed the intensity and frequency of moral distress among ICU nurses. No study seems to have mapped the leading personal and professional characteristics associated with high levels of moral distress. This systematic review aimed to describe the intensity and frequency of moral distress experienced by nurses in ICUs, as assessed by Corley’s instruments on moral distress (the Moral Distress Scale and the Moral Distress Scale–Revised). Additionally, this systematic review aimed to summarize the correlates of moral distress. Methods: A systematic search and review were performed using the following databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), the National Library of Medicine (MEDLINE/PubMed), and Psychological Abstracts Information Services (PsycINFO). The review methodology followed PRISMA guidelines. The quality assessment of the included studies was conducted using the Newcastle–Ottawa Scale. Results: Findings showed a moderate level of moral distress among nurses working in ICUs. The findings of this systematic review confirm that there are a lot of triggers of moral distress related to patient-level factors, unit/team-level factors, or system-level causes. Beyond the triggers of moral distress, this systematic review showed some correlates of moral distress: those nurses working in ICUs with less work experience and those who are younger, female, and intend to leave their jobs have higher levels of moral distress. This systematic review’s findings show a positive correlation between professional autonomy, empowerment, and moral distress scores. Additionally, nurses who feel supported by head nurses report lower moral distress scores. Conclusions: This review could help better identify which professionals are at a higher risk of experiencing moral distress, allowing the early detection of those at risk of moral distress, and giving the organization some tools to implement preventive strategies. Full article

Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. Existing job scheduling strategies are single-focused and have limited support for large-scale end-device scenarios. In this paper, we present ADDP, an adaptive distributed DNN partition method that supports video analysis on large-scale smart cameras. ADDP applies to the commonly used DNN models for computer vision and contains a feature-map layer partition module (FLP) supporting edge-to-end collaborative model partition and a feature-map size partition (FSP) module supporting multidevice parallel inference. Based on the inference delay minimization objective, FLP and FSP achieve a tradeoff between the arithmetic and communication resources of different devices. We validate ADDP on heterogeneous devices and show that both the FLP module and the FSP module outperform existing approaches and reduce single-frame response latency by 10–25% compared to the pure on-device processing. Full article

With the development of global value chains, more and more countries are involved in global trade, which has brought an extensive social impact. Past studies on the employment impact of trade have pointed out that free trade has significantly boosted employment in developing economies, with large populations working in export-related jobs along the value chains. Recently, the COVID-19 pandemic has caused global trade protectionism to become more rampant. This study aims to establish a trade employment effect accounting model, based on the comparison of multiple scenarios, to discuss the employment impact of trade lockdown on major developing and developed countries. Specifically, based on a multi-regional input–output model, we map the flow network of trade-induced employment in 15 major global economies, and the scenarios of free trade and restricted trade are simulated to determine the employment impact of protectionism across multiple trade patterns. The results show that the current labor flow induced by global trade mainly flows from developing countries such as China and India to developed countries such as the EU and the United States. In the total employment induced by trade, the proportion of final products trade reached 42.82%. Trade protection would cut 19.86 million jobs worldwide. Under the trade restriction scenario, employment in developing countries would be reduced, with China and India losing 45.24 million and 10.10 million jobs, respectively. People working in the final product processing trade face the greatest risk of unemployment, especially in manufacturing and services. Among developed countries, the EU and the US would add 5.52 and 2.23 million jobs due to industrial repatriation. Full article

Advancements in IoT technology have been instrumental in the design and implementation of various ubiquitous services. One such design activity was carried out by the authors of this paper, who proposed a novel cloud-centric IoT-based disaster management framework and developed a multimedia-based prototype that employed real-time geographical maps. The multimedia-based system can provide vital information on maps that can improve the planning and execution of evacuation tasks. This study was intended to explore the acceptance of the proposed technology by the specific set of users that could potentially lead to its adoption by rescue agencies for carrying out indoor rescue and evacuation operations. The novelty of this study lies in the concept that the acceptability of the proposed system was ascertained before the complete implementation of the system, which prevented potential losses of time and other resources. Based on the extended Technology Acceptance Model (TAM), we proposed a model included factors such as perceived usefulness, perceived ease of use, attitude, and behavioural intention. Other factors include trust in the proposed system, job relevance, and information requirement characteristics. Online survey data collected from the respondents were analyzed using structural equation modelling (SEM) revealed that although perceived ease of use and job relevance had significant impacts on perceived usefulness, trust had a somewhat milder impact on the same. The model also demonstrated a statistically moderate impact of trust and perceived ease of use on behavioural intention. All other relationships were statistically strong. Overall, all proposed relationships were supported, with the research model providing a better understanding of the perceptions of users towards the adoption of the proposed technology. This would be particularly useful while making decisions regarding the inclusion of various features during the industrial production of the proposed system. Full article

Expanding the scale of GPU-based deep learning (DL) clusters would bring not only accelerated AI services but also significant energy consumption costs. In this paper, we propose a cost efficient deep learning job allocation (CE-DLA) approach minimizing the energy consumption cost for the DL cluster operation while guaranteeing the performance requirements of user requests. To do this, we first categorize the DL jobs into two classes: training jobs and inference jobs. Through the architecture-agnostic modeling, our CE-DLA approach is able to conduct the delicate mapping of heterogeneous DL jobs to GPU computing nodes. Second, we design the electricity price-aware DL job allocation so as to minimize the energy consumption cost of the cluster. We show that our approach efficiently avoids the peak-rate time slots of the GPU computing nodes by using the sophisticated mixed-integer nonlinear problem (MINLP) formulation. We additionally integrate the dynamic right-sizing (DRS) method with our CE-DLA approach, so as to minimize the energy consumption of idle nodes having no running job. In order to investigate the realistic behavior of our approach, we measure the actual output from the NVIDIA-based GPU devices with well-known deep neural network (DNN) models. Given the real trace data of the electricity price, we show that the CE-DLA approach outperforms the competitors in views of both the energy consumption cost and the performance for DL job processing. Full article

The EU Commission has introduced the instrument of National Energy and Climate Plans (NECP) to better achieve energy and climate policy targets. In Germany, a comprehensive study was commissioned for this purpose. Its methods and main results are presented here. It starts with a set of energy system models that maps the necessary changes in the energy system, together with corresponding measures bottom-up. The results then enter the PANTA RHEI macroeconometric top-down model as scenario inputs to determine the socioeconomic effects. According to the bottom-up models, achieving the target of 55% GHG reduction by 2030 will not be easy. The macroeconomic effects are mostly positive. Driven by additional investment, GDP and the number of jobs will be higher than in the baseline. The construction and service sectors can benefit from energy and climate policy measures. The share of final consumer expenditures on energy in GDP declines by 2030 compared to today. However, the direction and magnitude of the effects are not undisputed in the literature. The results show that ambitious climate policies are possible in Germany, which can also improve the achievement of economic and social goals. Full article