Search Results (8,968)

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Burnout syndrome is a growing concern in higher education, affecting the psychological well-being and performance of university professors. This systematic review presents a narrative synthesis of findings from quantitative studies on sociodemographic and psychosocial determinants of academic burnout. Following PRISMA 2020 guidelines, sixty peer-reviewed articles published between Jan 2019 and May 2024 were selected from Scopus and Web of Science. Inclusion criteria required validated psychometric instruments and exclusive focus on university faculty. Methodological quality was assessed using the Newcastle-Ottawa Scale and CASP checklist. Data from approximately 43,639 academic staff were analyzed. Key risk factors identified include excessive workload, lack of institutional support, and workplace conflict. In contrast, collegial support, participative leadership, and job satisfaction functioned as protective elements. Variables such as age, gender, academic rank, and employment stability significantly influenced burnout vulnerability. While general patterns were observed across studies, differences in design and sampling require caution in generalization. The evidence supports the implementation of integrated strategies encompassing mental health programs, workload regulation, participatory governance, and culturally responsive approaches. These findings inform the development of institutional policies aimed at preventing burnout and fostering academic well-being. Future research should adopt longitudinal and cross-cultural designs to further explore burnout trajectories and support educational reform. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

This study employs a mixed method. In-depth interviews and observational studies are among the data collection approaches used in qualitative research. The quantitative method measures the weight of respondents’ answers to the distributed questionnaire. The questionnaire, containing 82 items, was distributed to 202 tourists to collect their perceptions based on the 4A tourist components. The results indicate that tourists’ perceptions of attractions, accessibility, and ancillary services are generally positive. In contrast, perceptions of amenity services are less favorable. Using the scores from IFAS, EFAS, and the I-E matrix, the total weighted scores for IFAS and EFAS are 2.68 and 2.83, respectively. The appropriate strategy for BTV is one of aggressive growth in a position of strengths and opportunities. The study highlights key techniques, including the application of information technology in service and promotion, the strengthening of community and government roles, the development of infrastructure and facilities, the utilization of external resources, sustainable innovation, and the encouragement of local governments to issue regulations for halal tourism villages. By identifying drivers and barriers from an economic, environmental, social, and cultural perspective, the SWOT analysis results help design strategies that can make positive contributions to the development of sustainable, community-based halal tourism and digital information in the future. Full article

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

Air pollution is linked to childhood mortality and morbidity in low- and middle-income countries globally. There is growing evidence linking air pollution to asthma and other respiratory diseases in children. Studies have shown that children are likely to experience asthma due to their narrow airways and their heightened sensitivity to environmental irritants. This study aims to investigate the relationship between ambient air pollution and respiratory diseases in children under the age of 5. The study will be conducted in the informal township of Alexandra, north of Johannesburg, South Africa. A quantitative approach will be used in this cross-sectional analytical study. Data will be collected using different tools that include a questionnaire to determine the prevalence of asthma and respiratory disease and potential risk factors. While environmental air pollution will be measured using Radiello passive samplers and Gillian pumps. Data will be analyzed using the latest version of the STATANow/MP 19.5 software. Furthermore, health risk assessment will be conducted for lifetime non-carcinogenic and carcinogenic risk estimation following the USEPA framework. The study will identify environmental triggers that exacerbate asthma and other respiratory conditions in other similar community settings and will contribute to the body of knowledge in public health. Ethical approval was obtained from the Research Ethics Committee, Faculty of Health Sciences at the University of Johannesburg. Full article

In this paper, we propose a compressionmethod for optical encryption using singular value decomposition (SVD). Double random phase encryption (DRPE), which employs two distinct random phase masks, is adopted as the optical encryption technique. Since the encrypted data in DRPE have the same size as the input data and consists of complex values, a compression technique is required to improve data efficiency. To address this issue, we introduce SVD as a compression method. SVD decomposes any matrix into simpler components, such as a unitary matrix, a rectangular diagonal matrix, and a complex unitary matrix. By leveraging this property, the encrypted data generated by DRPE can be effectively compressed. However, this compression may lead to some loss of information in the decrypted data. To mitigate this loss, we employ volumetric computational reconstruction based on integral imaging. As a result, the proposed method enhances the visual quality, compression ratio, and security of DRPE simultaneously. To validate the effectiveness of the proposed method, we conduct both computer simulations and optical experiments. The performance is evaluated quantitatively using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and peak sidelobe ratio (PSR) as evaluation metrics. Full article

This quantitative study examines the determinants of financial reporting quality in Indonesian local governments, focusing on good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization, with HR competencies as a moderator. Data were collected via surveys from 170 Local Government Work Units (SKPDs) across South Sulawesi Province, Indonesia. Employing Structural Equation Modeling (SEM), the findings indicate that good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization all positively influence financial reporting quality. Crucially, human resource competencies were found to significantly moderate the relationship between the internal control system and organizational commitment with financial reporting quality. However, this moderating effect was not significant for the relationships involving good governance, regional financial accounting systems, and information technology utilization. These results highlight the essential role of human resource development and systemic enhancements in fostering greater financial accountability and transparency within the public sector. Therefore, policy recommendations should focus not only on enhancing individual competencies but also on synergistically strengthening systems and governance frameworks to achieve transparent and reliable public financial reporting. Full article

The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked these differences. We utilized species data from field surveys in Inner Mongolia and drone-derived multispectral imagery to establish a quantitative relationship between SD and biodiversity. A geographically weighted regression (GWR) model was used to describe the SD–biodiversity relationship and map the biodiversity indices in different experimental areas in Inner Mongolia, China. Spatial autocorrelation analysis revealed that both SD and biodiversity indices exhibited strong and statistically significant spatial autocorrelation in their distribution patterns. Among all spectral diversity indices, the convex hull area exhibited the best model fit with the Margalef richness index (Margalef), the coefficient of variation showed the strongest predictive performance for species richness (Richness), and the convex hull volume provided the highest explanatory power for Shannon diversity (Shannon). Predictions for Shannon achieved the lowest relative root mean square error (RRMSE = 0.17), indicating the highest predictive accuracy, whereas Richness exhibited systematic underestimation with a higher RRMSE (0.23). Compared to the commonly used linear regression model in SVH studies, the GWR model exhibited a 4.7- to 26.5-fold improvement in goodness-of-fit. Despite the relatively low R² value (≤0.59), the model yields biodiversity predictions that are broadly aligned with field observations. Our approach explicitly considers the spatial heterogeneity of the SD–biodiversity relationship. The GWR model had significantly higher fitting accuracy than the linear regression model, indicating its potential for remote sensing-based biodiversity assessments. Full article

The right to development, an inherent human right for all, emphasizes that all individuals and communities have the right to participate in, contribute to, and benefit from development that ensures the full realization of human rights. In Kenya, where a significant portion of the population faces poverty and vulnerability to climate change, access to rights-based needs such as clean water, healthcare, and education still remains a critical challenge. This study explored the implementation of a Human Rights-Based approach to community development through a Public–Private Development Partnership model (PPDP), with a focus on alleviating poverty and improving access to rights-based services at the community level in Narok and Nakuru counties. The research aimed to identify critical success factors for scaling the PPDP model and explore its effects on socio-economic empowerment. The study employed a mixed-methods approach for data collection, using questionnaires to obtain quantitative data, focus group discussions, and key informant interviews with community members, local leaders, and stakeholders to gather qualitative data. We cleaned and analyzed all our data in R (version 4.4.3) and used the chi-square to establish the significance of differences between areas where the PPDP model was implemented and control areas where it was not. Results reveal that communities with the PPDP model experienced statistically significant improvements in employment, income levels, and access to rights-based services compared to control areas. The outcomes underscore the potential of the PPDP model to address inclusive and sustainable development. This study therefore proposes a scalable pathway beginning with access to rights-based needs, followed by improved service delivery, and culminating in economic empowerment. These findings offer valuable insights for governments, development practitioners, investment agencies, and researchers seeking community-driven developments in similar socio-economic contexts across Africa. For the first time, it can be adopted in the design and implementation of development projects in rural and local communities across Africa bringing into focus the need to integrate rights-based needs at the core of the project. Full article

Based on molecular dynamics simulation, we conducted a comprehensive study on the tensile behaviors and properties of the $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy with varying ${\gamma }^{\prime }$(Ni₃Al) phase volume fractions ($V\left({\gamma }^{\prime }\right))$ under high-temperature, high-strain-rate service environments. Our investigation revealed that the tensile behavior of the superalloy depends critically on the $V\left({\gamma }^{\prime }\right)$. When the $V\left({\gamma }^{\prime }\right)$ increased from 13.5 to 67%, the system’s tensile strength exhibited a non-monotonic response, peaking at $\mathrm{V}\left({\gamma }^{\prime }\right)$ = 40.3% before progressively decreasing. Conversely, the maximum uniform plastic strain decreased linearly and significantly when $V\left({\gamma }^{\prime }\right)$ increased. These results establish an atomistically informed framework that elucidates the composition–microstructure–property relationships in $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloys, specifically addressing how $V\left({\gamma }^{\prime }\right)$ governs variations in deformation mechanisms and mechanical performance. Furthermore, this work provides quantitative design paradigm for optimizing ${\gamma }^{\prime }$(Ni₃Al) precipitate architecture and compositional tuning in the Ni-based $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy. Full article

The Kekeya oilfield is located at the southwestern edge of the Tarim Basin, in the southern margin of the Yecheng depression, at the western end of the second structural belt of the northern foothills of the Kunlun Mountains. It is one of the important oil and gas fields in western China, with significant oil and gas resource potential in the X4-X5 section of the Xihepu Formation. This study focuses on the edge of the alluvial fan depositional system, employing various techniques, including core data and well logging data, to precisely characterize the sand body architecture and comprehensively analyze the reservoir architecture in the study area. First, the regional geological background of the area is analyzed, clarifying the sedimentary environment and evolutionary process of the Xihepu Formation. Based on the sedimentary environment and microfacies classification, the sedimentary features of the region are revealed. On this basis, using reservoir architecture element analysis, the interfaces of the reservoir architecture are finely subdivided. The spatial distribution characteristics of the planar architecture are discussed, and the spatial distribution and internal architecture of individual sand body units are analyzed. The study focuses on the spatial combination of microfacies units along the profile and their internal distribution patterns. Additionally, a quantitative analysis of the sizes of various types of sand bodies is conducted, constructing the sedimentary model for the region and revealing the control mechanisms of different sedimentary architectures on reservoir properties and oil and gas accumulation patterns. This study pioneers a quantitative model for alluvial fan fringe in gentle-slope basins, featuring the following: (1) lobe width-thickness ratios (avg. 128), (2) four base-level-sensitive boundary markers, and (3) a retrogradational stacking mechanism. The findings directly inform reservoir development in analogous arid-climate systems. This research not only provides a scientific basis for the exploration and development of the Kekeya oilfield but also serves as an important reference for reservoir architecture studies in similar geological contexts. Full article

The imaging modalities of correlation-assisted techniques utilize the inherent information present in the spatial correlation of random intensity patterns for the successful reconstruction of object information. However, most correlation approaches focus only on the reconstruction of amplitude information, as it is a direct byproduct of the correlation, disregarding the phase information. Complex-field reconstruction requires additional experimental or computational schemes, alongside conventional correlation geometry. The resurgence of holography in recent times, with advanced digital techniques and the adoption of the full-field imaging potential of holography in correlation with imaging techniques, has paved the way for the development of various state-of-the-art approaches to correlation optics. This review article provides an in-depth discussion of the recent developments in speckle-correlation-assisted techniques by focusing on various quantitative imaging scenarios. Furthermore, the recent progress and application of correlation-assisted holographic imaging techniques are reviewed, along with its potential challenges. Full article