Search Results (1,242)

Yueqing Bay in China supports important marine resources that sustain local fisheries and food security, but increasing anthropogenic pressures and natural environmental changes threaten its biodiversity. This study used morphological identification, COI DNA barcoding, and 12S DNA metabarcoding to assess ichthyoplankton composition and fish spawning patterns. A total of 13,415 eggs and 17,291 larvae were collected using horizontal and vertical plankton nets. Morphological analysis identified 58 taxa, while molecular methods detected 21 species (COI) and 48 species (12S), with an overall total of 105 species from 78 genera and 42 families. Spawning activity showed clear seasonal and spatial patterns, with the highest abundance and diversity in spring and summer, particularly around Ximen Island and the Bay mouth. These areas function as key spawning and nursery grounds. 12S DNA metabarcoding contributed strongly to species detection, especially for cryptic and morphologically indistinguishable larvae, complementing traditional surveys. The findings provide essential baseline information for monitoring fish stocks, protecting critical habitats, and improving fishery management strategies under increasing anthropogenic and climate-related pressures in Yueqing Bay. Full article

The National Environmental Sustainability and Technology Tool (NESTT) is an online sustainability assessment and decision support tool developed for Canadian egg farmers in two phases—Lite NESTT and Full NESTT. To ensure that users (egg farmers) have a say in its design and development, and to foster a sense of ownership of the tool, a participatory design process was implemented in the development of NESTT. Specifically, a four-step participatory design process was adopted for this study with two discovery phases. The pre-launch discovery survey diagnosing use situations resulted in Lite NESTT being focused primarily on resource use efficiency and productivity, prioritization of benchmarking, and defining the focus areas for the prototyping phase. In the prototyping phase, farmers were interviewed with renderings and mock-ups, and improvements related to user-centeredness, data security, aesthetic appeal, accessibility, and simplicity were achieved. Finally, the post-launch discovery phase helped in defining the new features for Full NESTT such as the implementation of carbon footprint assessments, information on funding opportunities, and fixing data input issues. This last phase also helped in identifying several long-term strategic options to consider for NESTT such as integration with other on-farm programs, integrating economic assessments and financial incentives into NESTT, and adding more customized, farm-level decision support features. Full article

This study explores the digital transformation and public value created through the target city’s smart city Mobile Payment APP and digital city token system within the context of sustainable governance in Taiwan. Adopting a convergent mixed-methods research design, this research integrates quantitative Importance–Performance Analysis (IPA) surveys of 632 users with qualitative in-depth semi-structured interviews involving eight key stakeholders (namely, government officials, system developers, affiliated merchants, and citizen representatives). This methodology assesses service quality, user satisfaction, and cross-sector collaboration effects. The findings reveal that the mobile payment platform significantly enhances digital service delivery; fosters user engagement; and supports sustainable urban development goals, particularly net-zero carbon emissions. However, the IPA results highlight critical service gaps in the “Priority Improvement Zone,” specifically regarding the insufficient number of affiliated merchants and inconvenient information search functions. Qualitative findings attribute these gaps to cross-departmental administrative barriers and security-focused design trade-offs. This study contributes empirical evidence on the integration of financial technology and public service innovation as a means to advance smart governance and sustainable urban ecosystems. The results provide actionable insights for policymakers, city planners, and service designers focused on promoting digital public services that facilitate economic vitality, environmental sustainability, and collaborative governance. Full article

Geographic Information Systems (GIS) are increasingly applied to public health and urban security challenges, yet current evidence remains fragmented across methods, disciplines, and regions. This study integrates Systematic Mapping (SM) and Systematic Review (SR) within a unified PICOS–SPICE framework to consolidate existing GIS-based research. From an initial corpus of 7106 records, 65 studies met all methodological and reproducibility criteria. Scientific production shows consistent growth, peaking in 2023, with research concentrated in Asia and North America and limited representation from Africa and South America. Methodologically, the literature is dominated by accessibility assessments and spatial autocorrelation, while advanced analytical models—such as Bayesian inference and machine learning—remain scarce. GIS workflows rely mainly on ArcGIS and QGIS, complemented by open-source tools, including R, Python, and SaTScan. The fused SM + SR pipeline provides a transparent and replicable structure that highlights current strengths in spatial resolution and analytical versatility while revealing persistent gaps in data openness, reproducibility, and global equity. These findings offer a consolidated evidence base to guide future GIS research and support informed decision-making in public health and urban security. Full article

High cybersecurity risks and attacks cause information theft, unauthorized access to data and information, reputational damage, and financial loss in small and medium enterprises (SMEs). This creates a need to adopt information security systems of SMEs through innovation and compliance with information security policies. This study seeks to develop an integrated research model assessing the adoption of InfoSec systems in SMEs based on three existing theories, namely the technology acceptance model (TAM), theory of reasoned action (TRA), and unified theory of acceptance and use of technology (UTAUT). A thorough review of literature identified prior experience, enjoyment of new InfoSec technology, top management support, IT infrastructure, security training, legal-governmental regulations, and attitude as potential determinants of adoption of InfoSec systems. A self-developed and self-administered questionnaire was distributed to 418 employees, mid-level managers, and top-level managers working in SMEs operating in Riyadh, Saudi Arabia. The study found that prior experience, top management support, IT infrastructure, security training, and legal-governmental regulations have a positive impact on attitude toward InfoSec systems, which in turn positively influences the adoption of InfoSec systems. Gender, education, and occupation significantly moderated the impact of some determinants on attitude and, consequently, adoption of InfoSec systems. Such an integrated framework offers actionable insights and recommendations, including enhancing information security awareness and compliance with information security policies, as well as increasing profitability within SMEs. The study findings make considerable theoretical contributions to the development of knowledge and deliver practical contributions towards the status of SMEs in Saudi Arabia. Full article

The exponential growth of Internet infrastructure and the widespread adoption of smart sensing devices have empowered industrial personnel to conduct remote, real-time data analysis within the Industrial Internet of Things (IIoT) framework. However, transmitting this real-time data over public channels raises significant security and privacy concerns. To prevent unauthorized access, user authentication mechanisms are crucial in the IIoT environment. To mitigate security vulnerabilities within IIoT environments, a novel user authentication and key agreement protocol is proposed. The protocol is designed to restrict service access exclusively to authorized users of designated smart sensing devices. By incorporating cryptographic hash functions, chaotic maps, Physical Unclonable Functions (PUFs), and fuzzy extractors, the protocol enhances security and functional integrity. PUFs provide robust protection against tampering and cloning, while fuzzy extractors facilitate secure biometric verification through the integration of smart cards, passwords, and personal biometrics. Moreover, the protocol accommodates dynamic device enrollment, password and biometric updates, and smart card revocation. A rigorous formal security analysis employing the Real-or-Random (ROR) model was conducted to validate session key security. Complementary informal security analysis was performed to assess resistance to a broad spectrum of attacks. Comparative performance evaluations unequivocally demonstrate the protocol’s superior efficiency and security in comparison to existing benchmarks. Full article

Digital transformation in government services represents a strategic shift that leverages digital technologies to enhance efficiency, accessibility, convenience, and user-centricity. In the wake of the COVID-19 pandemic, many governments accelerated the digitisation of services to support remote access and social distancing. Governments typically progress from digitisation (converting physical processes into digital formats) to digitalisation (automating service delivery and improving process efficiency), and ultimately to full digital transformation, where services are completed instantly and entirely online. However, varying levels of maturity across countries influence service outcomes differently, and indicators related to service quality, convenience, and security remain underexamined, particularly in developing contexts. This study addresses these gaps by examining Kuwait’s progress along the digitalisation–digital transformation continuum. It investigates current trends and user preferences in the use of digital government services based on empirical quantitative data collected from users in Kuwait. Specifically, the research objectives are fourfold: (i) to identify crucial outcome metrics for the success of digital government services, (ii) to assess user evaluations of these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and empirically test a model for evaluating digital transformation success. Drawing on established Information Systems’ (ISs’) success perspectives, a customised conceptual model incorporating six outcome metrics in three domains—service-related (user satisfaction, service quality), convenience-related (accessibility, ease of use), and security-related (perceived security, perceived trust)—was developed. A survey of 378 users of digital government services in Kuwait was conducted to compare perceptions across service types using independent-samples t-tests and linear regression analyses. The study found that users primarily accessed government services through smartphones and dedicated applications, highlighting the importance of mobile optimisation, and showed a clear preference for real-time, fully automated services over those requiring extended approval processes. The results indicate that digital transformation services significantly outperform digitalisation services across five outcome metrics—satisfaction, service quality, accessibility, ease of use, and perceived security—while trust remains consistent across both. These findings underscore the importance of advancing comprehensive digital transformation to enhance public service delivery. Practical recommendations are provided to support Kuwait’s digital government strategy. Given the purposive sampling and cross-sectional, comparative design, the findings should be interpreted with caution, and future studies are encouraged to apply probability-based sampling and more advanced multivariate techniques (e.g., structural equation modelling) to validate and extend the proposed model. Full article

As global food demand grows, the limited availability of natural resources exacerbates environmental and food security challenges. Household food waste is a major yet underexplored issue, contributing to inefficiencies, economic losses, and environmental harm. This study applies the Environmental-Economic Footprint (EN-EC) index to assess household food waste in Ireland. By integrating environmental and economic data, this index facilitates a comprehensive dual-perspective evaluation of food waste impacts. Data were collected from 1000 Irish households, analyzing waste patterns across 12 food categories. Environmental impacts were quantified using global warming potential (GWP) and water footprint (WF), while economic costs were based on waste generation and disposal. The EN-EC index synthesizes these parameters to facilitate informed decision-making. On average, Irish households reported approximately 966 g (0.97 kg) of edible food waste per week, equivalent to around 50 kg annually per household. This amount results in substantial associated impacts, including greenhouse gas emissions and water consumption, quantified through literature-based footprint coefficients. Red meat, particularly beef, contributes disproportionately to environmental and economic burdens despite its relatively lower waste volume. A 50% reduction in meat waste could cut CO₂ emissions by 2.5 kg, water use by 563.50 L, and costs by €3623.48. These insights equip policymakers with targeted strategies to mitigate food waste, aligning with global sustainability goals. Full article

In the current digital landscape, where platforms process AI-generated content and intelligent network traffic on a large scale, it is the duty of such platforms to continuously measure the reliability, trustworthiness, and security of various data streams. Driven by this practical challenge, this research develops an effective decision-support mechanism in intelligent decision-making in big-data AI-generated content and network systems. The decision problem has considered several uncertainties, including content authenticity, processing efficiency, user trust, cybersecurity, system scalability, privacy protection, and cost of computing. The multidimensional uncertainty of AI-generated information and trends in network behavior are challenging to capture in traditional crisp and fuzzy decision-making models. To fill that gap, a new Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) methodology is proposed, based on multi-perspective expert assessment and better computational aggregation to improve the accuracy of rankings, symmetry, and uncertainty treatment. A case scenario comprising fifteen different alternative intelligent decision strategies and seven evaluation criteria are examined under the evaluation of four decision-makers. The PF-FUCA model successfully prioritizes the best strategies to control AI-based content and network activities to generate a stable and realistic ranking. The comparative and sensitivity analysis show higher robustness, accuracy, and flexibility levels than the existing MCDM techniques. The results indicate that PF-FUCA is specifically beneficial in settings where a large amount of data has to flow, a high uncertainty rate exists, and the variables of decision are dynamic. The research introduces a scalable and credible methodological conception that can be used to facilitate high levels of intelligent computing applications to content governance and network optimization. Full article

Financial literacy plays a crucial role in promoting social and economic resilience, particularly in vulnerable contexts where access to education and financial services is limited. This study provides the first empirical analysis of the determinants of financial literacy among women horticulturists in Guinea Bissau in West Africa, a group that sustains household income and local markets through informal work. A survey with face-to-face data collection was employed, using a structured questionnaire to assess financial literacy across three dimensions: financial attitude, financial behavior, and financial knowledge. All 978 women horticulturists at the Pessubé Farm were invited to participate in the survey, and 200 valid questionnaires were returned and used as the final sample. Data were analyzed using descriptive statistics and multiple linear regression. Results revealed prudent and consistent financial behaviors, mid to low financial attitudes marked by concern about expenses and short-term planning, and limited conceptual financial knowledge, with frequent uncertainty on basic topics such as inflation, interest, and diversification. Regression analysis showed that financial satisfaction and food sufficiency are positively associated with higher levels of financial literacy, while overdue debts exert a negative effect. These findings highlight that strengthening financial literacy in low income and informal settings requires context sensitive strategies integrating financial education, debt management, and food security initiatives, emphasizing the multidimensional nature of financial literacy and its role in inclusive and sustainable development. Full article

This study presents SpineCheck, a fully integrated deep-learning-based clinical decision support platform for automatic vertebra segmentation and Cobb angle (CA) measurement from scoliosis X-ray images. The system unifies end-to-end preprocessing, U-Net-based segmentation, geometry-driven angle computation, and a web-based clinical interface within a single deployable architecture. For secure clinical use, SpineCheck adopts a stateless “process-and-delete” design, ensuring that no radiographic data or Protected Health Information (PHI) are permanently stored. Five U-Net family models (U-Net, optimized U-Net-2, Attention U-Net, nnU-Net, and UNet3++) are systematically evaluated under identical conditions using Dice similarity, inference speed, GPU memory usage, and deployment stability, enabling deployment-oriented model selection. A robust CA estimation pipeline is developed by combining minimum-area rectangle analysis with Theil–Sen regression and spline-based anatomical modeling to suppress outliers and improve numerical stability. The system is validated on a large-scale dataset of 20,000 scoliosis X-ray images, demonstrating strong agreement with expert measurements based on Mean Absolute Error, Pearson correlation, and Intraclass Correlation Coefficient metrics. These findings confirm the reliability and clinical robustness of SpineCheck. By integrating large-scale validation, robust geometric modeling, secure stateless processing, and real-time deployment capabilities, SpineCheck provides a scalable and clinically reliable framework for automated scoliosis assessment. Full article

Background: To evaluate women’s satisfaction with gynecological services at a public referral facility, recognizing the importance of patient satisfaction in assessing healthcare quality. Methods: Patient satisfaction was assessed with a 52-item questionnaire, targeting women undergoing laparoscopy/laparotomy, operative and office hysteroscopy, hormonal diagnostics, and others. The impact of patient-specific and hospitalization-related variables on satisfaction indicators and the relationships between provider-specific factors, expectation fulfillment, and readmission willingness were analyzed. Additionally, the institution’s organizational culture was evaluated. Results: Analysis of 790 questionnaires revealed a median satisfaction score of 88.48% and scores of 92.31%, 89.42%, 88.18%, 88.33%, and 85% across groups, with the highest satisfaction for laparoscopy/laparotomy (p = 0.003). Expectation fulfillment and total satisfaction relied on devoted time and alternatives discussion (both p = 0.001 and <0.001, respectively), positively correlating with comprehension, sense of security, and information quality (all p < 0.001). Readmission willingness was affected by devoted time (p = 0.016) and alternatives discussion (p = 0.028), and positively correlated with sense of security (p = 0.01). Expectation fulfillment and total satisfaction for office hysteroscopy depended on ongoing information (both p < 0.001) and pain aspects, positively correlating with comprehension, communication, and security, but negatively with pain (all p < 0.001), without affecting readmission willingness. Significant correlations existed between patient-specific and hospitalization-related variables and satisfaction, expectation fulfillment, and readmission willingness. A hierarchical culture with clan orientation was identified. Conclusions: Women’s high satisfaction was primarily linked to information quality. Readmission willingness correlated with a sense of security. Communication was crucial during office hysteroscopy. Public healthcare setting did not affect staff attitudes. Clan-oriented hierarchical culture favored a sense of security. Full article

This study evaluates regional differences in ecosystem service values (ESVs) between the integrated rice–crayfish systems of Huoqiu County (HQ) and Chongming District (CM) in China. The assessment was based on the Common International Classification of Ecosystem Services (CICES) V5.1, which categorizes ecosystem services into provisioning, regulation and maintenance, and cultural services. In this framework, each service category was quantified using region-specific biophysical indicators combined with monetary valuation methods. The results showed that the ESVs in HQ and CM were 346,113.59 CNY/ha and 467,334.89 CNY/ha, respectively, with greenhouse gas (GHG) emissions accounted for as a negative value. Regulation and maintenance services dominated both regions (59% in HQ and 52% in CM), followed by provisioning services (22%) in HQ and cultural services (19%) in CM. Among these, temperature regulation, water storage and flood control, soil nutrient retention, social security functions, and greenhouse gas emissions were higher in HQ than in CM, with the key difference lying in social security value in HQ and greater tourism development value in CM. A SWOT-AHP analysis recommends a pioneering strategy leveraging strengths and opportunities for sustainable development. These findings inform region-specific policies to balance economic growth and environmental sustainability, contributing to global discourse on integrated agriculture–aquaculture (IAA) systems. Future research incorporating primary data and refining model parameters would further enhance the precision and practical application of these assessments. Full article

Background: The 2022 Pakistan floods devastated healthcare access for pregnant women in already impoverished areas in Sindh province. This study examines how Cash for Health assistance (CH) of USD 112 alleviated financial burdens and improved maternal health outcomes and resilience, bridging a critical literature gap on cash effectiveness in humanitarian crises. Methodology: This study used a mixed-methods approach to assess the CH assistance intervention for families of pregnant/lactating women in flood-affected rural Sindh, Pakistan. A pre-post quantitative analysis of baseline (May–June 2024) and endline (August–November 2024) survey data in ~100 villages (Jamshoro/Sehwan) examined changes in healthcare access, expenditure, and preferences using t-tests, proportion tests, and multivariable regression. Concurrently, five qualitative case studies from key informant interviews provided thematic content analysis, triangulating findings on economic, health, and social impacts. Results: Respondents predominantly had low literacy rates and were from households of daily wage laborers in vulnerable, flood-affected areas. While income and education remained low, instances of forgone care due to financial barriers increased (68% to 97%, p < 0.001). CH significantly improved healthcare access (58% to 98%, p < 0.001). Access to regular physicians (20% to 69%) and private facilities (10% to 41%) notably expanded. Healthcare expenditure significantly increased from USD 9.3 to USD 25, with a shift in spending preference towards medication, consultations, and diagnostics. CH also significantly improved food security (21% to 97%), meal frequency, and overall household stability, including reducing domestic violence. Qualitative data emphasized pre-existing vulnerabilities and CH’s role in addressing health, nutrition, and psychosocial needs. Conclusions: CH significantly improved healthcare access and reduced financial burdens for vulnerable pregnant women post-disaster. However, a sustainable impact requires integrated “cash plus” models, combining financial aid with stronger health systems, psychosocial support, and literacy for long-term resilience. Full article

Accurate identification and monitoring of rice cultivation areas are essential for food security and sustainable agricultural development. However, regions with frequent cloud cover, high rainfall, and fragmented fields often face challenges due to the absence of temporal features caused by cloud and rain interference, as well as spectral confusion from scattered plots, which hampers rice recognition accuracy. To address these issues, this study employs a Satellite Image Time Series Transformer (SITS-Former) model, enhanced with the integration of diverse phenological features to improve rice phenology representation and enable precise rice identification. The methodology constructs a rice phenological feature set that combines temporal, spatial, and spectral information. Through its self-attention mechanism, the model effectively captures growth dynamics, while multi-scale convolutional modules help suppress interference from non-rice land covers. The study utilized Sentinel-2 satellite data to analyze rice distribution in Wuxi City. The results demonstrated an overall classification accuracy of 0.967, with the estimated planting area matching 91.74% of official statistics. Compared to traditional rice distribution analysis methods, such as Random Forest, this approach outperforms in both accuracy and detailed presentation. It effectively addresses the challenge of identifying fragmented rice fields in regions with persistent cloud cover and heavy rainfall, providing accurate mapping of cultivated areas in difficult climatic conditions while offering valuable baseline data for yield assessments. Full article