Search Results (42)

As modern life accelerates, demand for housekeeping services is rising. However, safety concerns deter potential users, causing a persistent imbalance that poses significant challenges to social sustainability. This research aims to design a home service system that is not only operationally efficient but also socially and economically sustainable. Using a user behavior analysis method, this study investigated the safety and hygiene needs of potential users. From a user-centered design perspective, an innovative housekeeping service system, along with its key service touchpoints: a mobile application and smart products. The system’s design is underpinned by the “Internet+” and Online-to-Offline (O2O) business models, integrating service design and sustainability principles. We present key system architecture and technologies, along with an analysis of implementation challenges. The findings suggest that the proposed system can enhance resource efficiency and economic viability (e.g., reduce operational costs by an estimated 15–25% compared with traditional models); improve user well-being and social equity (e.g., increase user trust by 15–20% through integrated credibility mechanisms and reduce household labor hours by approximately 4–6 h per week through optimized scheduling); and offer a replicable design framework for promoting sustainable service-sector development. Provide a scalable platform for the housekeeping industry’s sustainable transition. This research contributes a design paradigm for service systems that aligns business viability with Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 5 (Gender Equality), and SDG 8 (Decent Work and Economic Growth), by fostering trust, efficiency, and responsible consumption. Full article

Digital transformation has profoundly reshaped caregiving practices, yet its influence on family cohesion within disability contexts remains underexplored, particularly in Arab societies. This qualitative phenomenological study examines how digital technologies shape family cohesion among Jordanian caregivers of children with disabilities. In-depth, semi-structured interviews were conducted with 22 primary caregivers, and data were analyzed using reflexive thematic analysis. The findings reveal a central tension of being “between connectivity and care,” articulated through four interrelated themes: (1) a digital double-bind in which online support networks function as a vital “virtual village” while simultaneously contributing to intra-familial fragmentation; (2) the reconfiguration of care labor, whereby digital management emerges as an invisible and gendered form of caregiving work, often positioning mothers as primary digital coordinators; (3) the translation of traditional social capital (wasta) into digital spaces to navigate systemic resource constraints, producing new moral and emotional burdens; and (4) the strategic use of digital platforms to preserve cultural, religious, and familial identity in the face of stigma, thereby reinforcing internal cohesion. These findings suggest that digital technologies do not merely facilitate connection but actively reconfigure family dynamics through ongoing negotiation between support and strain. The study underscores the need for family-centered digital inclusion policies and support interventions that mitigate digital burdens while harnessing technology’s potential to strengthen culturally grounded resilience among families of children with disabilities. Full article

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S²SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported. Full article

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making. Full article

Bactrocera dorsalis (Hendel, 1912) is a highly destructive pest affecting fruits and vegetables, making population monitoring essential for farmers to implement timely control measures. In recent years, although automatic monitoring systems for B. dorsalis have been introduced, challenges such as limited accuracy, difficulty in accurately identifying the target pest using infrared interruption sensors alone, and high labor requirements persist. This study presents an automatic monitoring system consisting of intelligent bait equipment (IBE), an advanced detection model based on YOLOv8, and an online monitoring platform. The developed IBE is equipped with cameras, attractant-based lures, and an automatic removal mechanism for B. dorsalis. Field tests demonstrated the IBE exhibited an attractiveness to B. dorsalis comparable to conventional traps, achieved a near-perfect cleaning efficiency (~100%), and maintained a reliable wireless transmission system. The YOLOv8l-based automatic pest detection model outperformed other YOLOv8 variants (n, s, m, x), achieving the highest precision (95.17%), recall (94.15%) and F1 score (94.66%), underscoring its effectiveness in pest detection. Further analysis of the impact of B. dorsalis density on YOLOv8l’s detection performance revealed a decline in accuracy as density increased; however, even at high densities, the model maintained a strong F1 score of 93.36%, demonstrating robustness. Finally, the automatic pest detection model was integrated into ‘YunShanPu’, an online platform for real-time pest monitoring. The proposed method has demonstrated promising performance in the automatic identification and counting of B. dorsalis and has potential for monitoring B. dorsalis populations continuously, providing early warning and forecasting for integrated pest management. Full article

Identifying the botanical origin of honey is essential for ensuring its quality, preventing adulteration, and protecting consumers. Traditional techniques, such as melissopalynology, physicochemical analysis, and PCR, are often labor-intensive, time-consuming, or limited to the detection of only known species, while advanced DNA sequencing remains prohibitively costly. In this study, we aim to develop a deep learning-based approach for identifying pollen grains extracted from honey and captured through microscopic imaging. To achieve this, we first constructed a dataset named VNUA-Pollen52, which consists of microscopic images of pollen grains collected from flowers of plant species cultivated in the surveyed area in Hanoi, Vietnam. Second, we evaluated the classification performance of advanced deep learning models, including MobileNet, YOLOv11, and Vision Transformer, on pollen grain images. To improve performances of these model, we proposed data augmentation and hybrid fusion strategies to improve the identification accuracy of pollen grains extracted from honey. Third, we developed an online platform to support experts in identifying these pollen grains and to gather expert consensus, ensuring accurate determination of the plant species and providing a basis for evaluating the proposed identification strategy. Experimental results on 93 images of pollen grains extracted from honey samples demonstrated the effectiveness of the proposed hybrid fusion strategy, achieving 70.21% accuracy at rank 1 and 92.47% at rank 5. This study demonstrates the capability of recent advances in computer vision to identify pollen grains using their microscopic images, thereby opening up opportunities for the development of automated systems that support plant traceability and quality control of honey. Full article

The determination of RNA secondary structure (RSS) could help understand RNA’s functional mechanisms, guiding the design of RNA-based therapeutics, and advancing synthetic biology applications. However, traditional methods such as NMR for determining RSS are typically time-consuming and labor-intensive. As a result, the accurate prediction of RSS remains a fundamental yet unmet need in RNA research. Various deep learning (DL)-based methods achieved improved accuracy over thermodynamic-based methods. However, the over-parameterization nature of DL makes these methods prone to overfitting and thus limits their generalizability. Meanwhile, the inconsistency of RSS predictions between these methods further aggravated the crisis of generalizability. Here, we propose TrioFold to achieve enhanced generalizability of RSS prediction by integrating base-pairing clues learned from both thermodynamic- and DL-based methods by ensemble learning and convolutional block attention mechanism. TrioFold achieves higher accuracy in intra-family predictions and enhanced generalizability in inter-family and cross-RNA-types predictions. Additionally, we have developed an online webserver equipped with widely used RSS prediction algorithms and analysis tools, providing an accessible platform for the RNA research community. This study demonstrated new opportunities to improve generalizability for RSS predictions by efficient ensemble learning of base-pairing clues learned from both thermodynamic- and DL-based algorithms. Full article

This entry explores the multifaceted intersections of gender and digital technologies, offering a comprehensive analysis of how structural inequalities are reproduced, contested, and transformed in digital contexts. It is structured into six interrelated sections that collectively address key dimensions of gendered digital contexts. It begins by addressing the gender digital divide, particularly in the Global South, emphasizing disparities in access, literacy, and sociocultural constraints. The second section examines gendered labor in the tech industry, highlighting persistent inequalities in Science, Technology, Engineering, and Mathematics (STEM) education, employment, and platform-based work. The third part focuses on gender representation in digital spaces, revealing how algorithmic and platform design perpetuate biases. The fourth section discusses gender bias in AI and disinformation, underscoring the systemic nature of digital inequalities. This is followed by an analysis of online gender-based violence, particularly its impact on marginalized communities and participation in digital life. The final section considers the potentials and limitations of digital activism in advancing gender justice. These sections collectively argue for an intersectional, inclusive, and justice-oriented approach to technology policy and design, calling for coordinated global efforts to create equitable digital futures. Full article

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

In Thailand, nipa palm (Nypa fruticans Wurmb) is considered a plant of great value to communities due to its social, environmental, and economic benefits. Effective management of the supply chain of products derived from nipa palm resources is essential to providing communities with suitable channels for selling each type of product and deriving maximum benefits. This study sought to map the supply chain structure of six different nipa palm products. Data were collected through semi-structured interviews with 30 farmers and 20 middlemen engaged in nipa palm cultivation and marketing. The analysis focused on the characteristics of the products, challenges associated with production and marketing, and the current supply chain dynamics. The findings revealed that among six products, palm sugar had the highest production volume. The most significant challenges identified were related to labor shortages and limited marketing channels. To achieve sustainable supply chain management, it is crucial to maintain a balance between the supply and demand of nipa palm products. This study recommends exploring appropriate marketing channels for each product, with particular emphasis on online platforms that allow communities to sell directly to consumers without intermediaries. Moreover, the findings underscore the importance of sustainable supply chain management at the community level, particularly in enhancing operational efficiency and income generation. They also highlight the need to expand knowledge and propose policy support for existing community enterprises through targeted investment in infrastructure, skill development, and the promotion of inclusive, community-based marketing strategies. Full article

Through the lens of the theory of mediamorphosis, the authors intend to demonstrate scientific contributions in the native languages of Croatian and Slovenian scholars to the study of digital journalism. This paper provides an insight into the common points but also similarities and differences in the evolution of interests and approaches of these scientific communities, which are special in that they are the only two members of the European Union that also share the legacy of the socio-political system of the former Yugoslavia. The central goal of this study was to identify, evaluate, and synthesize all the relevant empirical research, categorized only as original scientific papers that had been published from 1994 to 2024, together with scientific monographs on digital media and journalism, in order to contribute to filling the gap in regard to the review of local literature and internationalized local research. The primary search on the following terms and keywords: digital media, digital journalism, electronic publications, Internet, Internet journalism, Internet portals, new media, online media, online journalism, online portals, and web portals was conducted via several local databases (HRČAK, CEEOL, SCIndex, and COBISS). Using a systematic literature review strategy and content analysis methods, this study deeply explored several aspects of the empirical studies (N = 24) it found: metadata, theoretical background, research objects, methodology, and results. The articles were, in principle, published in local journals with high international impact and included in major databases WOS and SCOPUS. The findings show the first articles in Croatia date only from 2007 and from 2010 in Slovenia. With some interruptions, they are published relatively regularly. In the corpus analyzed, two scientific monographs from Croatia and one from Slovenia were included. The research confirms that the topics studied in this region and the methodologies used are characteristic of the contemporary trends in digital journalism studies. Although the thematic focuses are somewhat similar—journalistic texts and writing; editorial protocols; practices; policies; division of labor; organization; platform regulations and preferences; and audience behavior—there is a significant difference in the methodological approach; Croatian scholars rely on output manifestation and the analysis of media content; while Slovenian colleagues mainly research media organizations from the inside; through interviews. Full article

Labor force mobility plays a crucial role in achieving balanced regional development in China. This study investigates whether urban internet development can effectively attract labor force inflow using data from the China Labor-force Dynamic Survey (CLDS) and constructing a comprehensive urban internet development index through factor analysis. Employing a conditional logit model and addressing potential endogeneity through instrumental variables, we find that (1) urban internet development significantly attracts labor force inflow, with a one-unit increase in the urban internet development index significantly raising the log odds of individual city choice by 0.2, and this effect remains robust across multiple specifications and estimation methods; (2) the attraction effect shows significant heterogeneity—it is stronger among highly educated, younger, foreign language proficient, and unmarried individuals, and more pronounced in regions with lower housing costs, lower income levels, and inland locations. These findings suggest that less-developed cities should prioritize internet infrastructure development to accumulate high-quality labor resources and achieve high-quality economic development, while also enhancing support for bottom-tier workers through public-benefit online platforms. Full article

This research identifies the maritime industry’s key manpower recruitment criteria and uses a specialized online platform to assess the impact of recruitment digital transformation. Through the analysis of 183 validated surveys of Romanian shipbuilding and ship-repair technicians using IBM SPSS Statistics, this study examines digital recruitment trends and potential outcomes. Additionally, it highlights a notable gap in recent literature on digital recruitment optimization within the EU shipbuilding and ship-repair sectors. The findings demonstrate that digitally integrating recruitment tools—such as technical interviews, verified performance evaluations, and machine-learning algorithms for candidate prioritization—could significantly enhance recruitment accuracy, transparency, and efficiency. The key potential outcomes include improved efficiency, reduced bias, scalability, and cost savings overall for the recruitment process—valuable insights for European maritime stakeholders seeking to remain competitive, while addressing increasing labor demands. Full article

The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims to identify and develop innovative financial instruments that foster closer collaboration within communities and promote a more equitable distribution of wealth and resources, directly impacting financial inclusion and well-being. The methodology used in our study is based on existing empirical research in the specialized scientific literature, as well as on identifying variables within existing models. Additionally, the use of bibliometric analyses and research tools based on artificial intelligence allows us to structure the innovative financial instruments found in the scientific databases. Building on the existence of innovative financial instruments, our paper specifically explores the concept of time banking as an innovative financial instrument, offering a new approach to economic exchange and the construction of financial mechanisms at the local community level. By using technology, especially in digital and ecological eras, time banks can be efficiently managed through online platforms where individuals can register their contributed hours and access the services they need. This study’s conclusions emphasize that time banks have the potential to serve as innovative financial instruments. Furthermore, through the analysis conducted in this study and the identified models, this study contributes to redefining the concept of time banking as an innovative financial instrument. Time banks focus on the productivity and efficiency of local community activities, with direct implications for reducing dependence on traditional currency and promoting an equitable distribution of labor. This innovative approach is promising, especially in an increasingly digitized financial landscape. Our paper seeks to capture this transformative potential and highlight our personal contributions to redefining the time bank as an innovative financial instrument. Full article

Algorithmic technological progress presents both opportunities and challenges for organizational management. The success of online labor platforms hinges on algorithmic control, making it imperative to explore how this control affects gig workers’ prosocial service behaviors. Drawing from affective event theory, our study delves into the factors influencing gig workers’ prosocial service behaviors in the online labor platform setting. We utilize the challenge–hindrance appraisal framework to highlight the pivotal role of algorithmic control. To rigorously test our hypotheses, we gathered empirical data from an online questionnaire survey of 660 gig workers. Our results indicate that challenge appraisals and hindrance appraisals in regard to platform algorithm control have a nuanced dual impact on gig workers’ prosocial service behaviors. This relationship is clarified by the mediating function of work engagement. A challenge appraisal of platform algorithmic control can positively influence gig workers’ prosocial service behaviors. However, hindrance appraisal of platform algorithmic control can negatively influence gig workers’ prosocial service behaviors. Interestingly, workplace interpersonal capitalization boosts the effect of challenge appraisal on employees’ prosocial service behaviors. However, it does not mitigate the adverse effects of hindrance appraisal on such behaviors. This study has multiple theoretical implications, and it also provides valuable practical insights into organizational management. Full article