Search Results (125)

Background: Goal setting has always been a crucial management factor for workforce motivation and is quite complex due to multiple goal characteristics. Considering that the emergence of Quiet Quitting (QQ) has inflicted harm on employees’ mental well-being in the healthcare field, urgent attention needs to be paid to the impact of goal setting on QQ. This study aimed to assess the current state of work goal setting and QQ among primary health professionals and to explore the effect of goal characteristics on QQ. Methods: A cross-sectional study was performed among 520 primary health professionals from 11 primary health centers. The Modified Goal Setting Scale and Quiet Quitting Scale were utilized to measure goal characteristics and QQ. Descriptive analysis, cluster analysis, and multiple regression were used for statistical analysis. Results: The mean score of QQ was 2.12. The eight goal characteristics were clustered into five categories. Among them, two categories demonstrated significant negative effects on QQ: Goal Specificity and Identity (Category 1; β = −0.096, p < 0.05) and Goal Fulfillment and Organizational Support (Category 2; β = −0.466, p < 0.001). Conversely, three categories showed significant positive effects: Goal Difficulty (Category 3; β = 0.112, p < 0.05), Goal Attainability (Category 4; β = 0.142, p < 0.01), and Goal Conflict (Category 5; β = 0.185, p < 0.001). Conclusions: The phenomenon of QQ requires attention among Chinese primary health professionals. Setting work goals scientifically may prove to be beneficial in curbing its spread. From a practical perspective, goal setting should be specific, moderately challenging, yet attainable, recognized and accepted by employees, and strongly supported by the organization. This approach is valuable for reducing QQ and fostering supportive work environments in primary healthcare. It should be noted, however, that while this study identifies significant associations, its cross-sectional design precludes causal inference, and the findings are context-specific to Chinese primary healthcare institutions. Full article

Accurate estimation of the forest canopy height is crucial in monitoring the global carbon cycle and evaluating progress toward carbon neutrality goals. The Global Ecosystem Dynamics Investigation (GEDI) mission provides an important data source for canopy height estimation at a global scale. However, the non-zero half-width of the transmitted laser pulses (NHWTLP) and the influence of terrain slope can cause waveform broadening and overlap between canopy returns and ground returns in GEDI waveforms, thereby reducing the estimation accuracy. To address these limitations, we propose a canopy height retrieval method that combines the deconvolution of GEDI’s transmitted waveforms with terrain slope constraints on the ground response function. The method consists of two main components. The first is performing deconvolution on GEDI’s effective return waveforms using their corresponding transmitted waveforms to obtain the true ground response function within each GEDI footprint, thereby mitigating waveform broadening and overlap induced by NHWTLP. This process includes constructing a convolution convergence function for GEDI waveforms, denoising GEDI waveform data, transforming one-dimensional ground response functions into two dimensions, and applying amplitude difference regularization between the convolved and observed waveforms. The second is incorporating terrain slope parameters derived from a digital terrain model (DTM) as constraints in the canopy height estimation model to alleviate waveform broadening and overlap in ground response functions caused by topographic effects. The proposed approach enhances the precision of forest canopy height estimation from GEDI data, particularly in areas with complex terrain. The results demonstrate that, under various conditions—including GEDI full-power beams and coverage beams, different terrain slopes, varying canopy closures, and multiple study areas—the retrieved height (rh) model constructed from ground response functions derived via the inverse deconvolution of the transmitted waveforms (IDTW) outperforms the RH (the official height from GEDI L2A) model constructed using RH parameters from GEDI L2A data files in forest canopy height estimation. Specifically, without incorporating terrain slope, the rh model for canopy height estimation using full-power beams achieved a coefficient of determination (R²) of 0.58 and a root mean square error (RMSE) of 5.23 m, compared to the RH model, which had an R² of 0.58 and an RMSE of 5.54 m. After incorporating terrain slope, the rh_g model for full-power beams in canopy height estimation yielded an R² of 0.61 and an RMSE of 5.21 m, while the RH_g model attained an R² of 0.60 and an RMSE of 5.45 m. These findings indicate that the proposed method effectively mitigates waveform broadening and overlap in GEDI waveforms, thereby enhancing the precision of forest canopy height estimation, particularly in areas with complex terrain. This approach provides robust technical support for global-scale forest resource assessment and contributes to the accurate monitoring of carbon dynamics. Full article

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning. Full article

Electricity access deficits remain acute in Sub-Saharan Africa (SSA), where more than 600 million people lack reliable supply. Green hydrogen, produced through renewable-powered electrolysis, is increasingly recognized as a transformative energy carrier for decentralized systems due to its capacity for long-duration storage, sector coupling, and near-zero carbon emissions. This review adheres strictly to the PRISMA 2020 methodology, examining 190 records and synthesizing 80 peer-reviewed articles and industry reports released from 2010 to 2025. The review covers hydrogen production processes, hybrid renewable integration, techno-economic analysis, environmental compromises, global feasibility, and enabling policy incentives. The findings show that Alkaline (AEL) and PEM electrolyzers are immediately suitable for off-grid scenarios, whereas Solid Oxide (SOEC) and Anion Exchange Membrane (AEM) electrolyzers present high potential for future deployment. For Sub-Saharan Africa (SSA), the levelized costs of hydrogen (LCOH) are in the range of EUR5.0–7.7/kg. Nonetheless, estimates from the learning curve indicate that these costs could fall to between EUR1.0 and EUR1.5 per kg by 2050, assuming there is (i) continued public support for the technology innovation, (ii) appropriate, flexible, and predictable regulation, (iii) increased demand for hydrogen, and (iv) a stable and long-term policy framework. Environmental life-cycle assessments indicate that emissions are nearly zero, but they also highlight serious concerns regarding freshwater usage, land occupation, and dependence on platinum group metals. Namibia, South Africa, and Kenya exhibit considerable promise in the early stages of development, while Niger demonstrates the feasibility of deploying modular, community-scale systems in challenging conditions. The study concludes that green hydrogen cannot be treated as an integrated solution but needs to be regarded as part of blended off-grid systems. To improve its role, targeted material innovation, blended finance, and policies bridging export-oriented applications to community-scale access must be established. It will then be feasible to ensure that hydrogen contributes meaningfully to the attainment of Sustainable Development Goal 7 in SSA. Full article

Background/Objectives: People with primary progressive aphasia (PPA) and their communication partners report that having conversations becomes more difficult over time. They want speech and language therapy to help them have better conversations. Communication partner training has shown promise as an approach for people with PPA and their communication partners. However, there are currently no communication partner training programs available in Norwegian for people with PPA. The Better Conversations with Primary Progressive Aphasia (BCPPA) is a communication partner training program developed in the UK. In this study, we aimed to culturally adapt the BCPPA to meet the needs of Norwegian people with PPA. Methods: Guided by adaptation elements identified in a systematic review of frameworks for cultural adaptation, we translated the BCPPA into Norwegian before piloting it with four participant dyads, comprising people with PPA and their communication partners. The translated BCPPA was compared to the original BCPPA to identify adherence with core intervention components. Semi-structured interviews were used to explore the acceptability of the intervention to participant dyads. Outcome data, including Goal Attainment Scaling, coding of conversation behaviours from video samples, the Aphasia Impact Questionnaire, and the Communicative Effectiveness Index, were recorded pre-, post-, and three months after intervention delivery to explore outcomes for Norwegian participant dyads. We used the Framework for Reporting Adaptations and Modifications-Enhanced to document the modifications. Results: The results indicate high adherence to the core components in the original BCPPA. The pilot demonstrated that the participant dyads found the BCPPA acceptable, but they made some additional suggestions to complete the cultural adaptation further. Despite the progressive nature of PPA, the participant dyads achieved their goals on the Goal Attainment Scaling, and group analysis demonstrated maintenance on the Aphasia Impact Questionnaire and the Communicative Effectiveness Index over the three time points. Conclusions: This study demonstrates that the Norwegian version of the BCPPA was acceptable to the participants with PPA and their communication partner in this study. As the first communication partner training program for people with PPA and their communication partners in Norwegian, the BCPPA has the potential to be a valuable treatment tool to support people affected by PPA to have better conversations. Full article

This case report describes the implementation of participation-based occupational therapy for children with physical disabilities and their siblings in two families. Case 1 was a girl with myelomeningocele spina bifida and her brother, and case 2 was a boy with cerebral palsy and his sister. Goals targeted joint participation in play. The Sibling Participation in Occupational Therapy (SPOT) approach adapted the Collaborative Process for Action Plans to Achieve Children’s Participation Goals in order to assess goal-related factors and develop actionable steps to achieve the goal. Corresponding interventions addressing performance skills differed across cases and related to all children’s ages, interests, and functional abilities. The Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS) measured sibling dyad’s achievement of their family’s goal, and participants completed an experiential questionnaire. Parent COPM ratings demonstrated a meaningful increase in performance and satisfaction, and the therapist and parent ratings of the GAS met or exceeded expected achievement. Parents and sibling dyads reported positive experiences in SPOT. The outcomes support the use of a participation-based approach inclusive of siblings that is consistent with family-centered practice to facilitate participation in meaningful joint sibling activities with families who have a child with a physical disability. Full article

Research Problem: Despite growing recognition that digital transformation strategies affect economic coordination, no study has empirically tested whether different national digital development models create systematically different transaction cost environments, particularly in emerging economies pursuing Sustainable Development Goals (SDGs). Research Gap and Novelty: This study addresses a critical gap by providing the first comprehensive empirical validation of how equity-focused versus scale-intensive digital development strategies influence coordination efficiency outcomes. Unlike previous studies that focus on aggregate digital infrastructure investment or single-country analyses, we develop a novel multi-dimensional Digital Coordination Efficiency Index and systematic development model classification framework to test transaction cost economics (TCE) predictions across diverse emerging economy contexts. Methodology: Using panel data from 16 strategically selected emerging economies (2017–2022) representing distinct development pathways, we apply advanced econometric techniques including comprehensive diagnostic testing, jackknife analysis, and bootstrap procedures to ensure robust causal inference. Key Findings: Development model choice explains 63.4% of variation in digital coordination efficiency compared to only 8.9% explained by GDP per capita—a 7.1-fold improvement in explanatory power—though this finding is based on a limited sample of 16 countries. Countries pursuing equity-focused strategies achieve 15.42 points higher coordination efficiency (p < 0.05) and demonstrate 49.4% superior mobile infrastructure penetration in our sample. The Vietnam–India comparison illustrates how an equity-focused model can systematically outperform a scale-intensive approach, with Vietnam achieving 68.4% higher GDP per capita, though we acknowledge this represents one specific case rather than a universal pattern. Practical Implications: Emerging economies can achieve superior economic outcomes by prioritizing digital inclusion over concentrated innovation, with equity-focused approaches providing measurable coordination advantages that translate into higher GDP growth and better SDG attainment. Multinational corporations should consider coordination capabilities when making location decisions, as equity-focused countries offer superior environments for distributed operations. Full article

Background/Objectives: Fatigue is the most common systemic manifestation of chronic liver diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD). Fatigue not only adversely affects quality of life in MASLD patients but also complicates the attainment of therapeutic goals and contributes to a worse prognosis. This study aimed to analyze the relationship between clinically significant fatigue and laboratory parameters reflecting systemic inflammation, liver function, body composition, muscle strength, and blood pressure in patients with MASLD. Methods: A total of 154 patients with a confirmed diagnosis of MASLD were enrolled in this study. All participants underwent anthropometric assessment, laboratory testing, abdominal ultrasonography, and point shear-wave elastography. Muscle strength was evaluated using handgrip strength (GS) measurement and the Five Times Sit-to-Stand Test (5TSTS). Skeletal muscle mass (SMM) was quantified using dual-energy X-ray absorptiometry (DXA). Fatigue was evaluated using the Fatigue Assessment Scale (FAS), with scores ≥ 22 indicating clinically significant fatigue. Results: Patients with FAS scores ≥ 22 exhibited significantly lower hemoglobin levels (p = 0.004) and erythrocyte counts (p = 0.011), along with a significantly elevated erythrocyte sedimentation rate (ESR; p = 0.002) and C-reactive protein level (CRP; p = 0.007). Furthermore, MASLD patients with FAS scores ≥ 22 demonstrated significantly reduced relative grip strength (p = 0.012) and took longer to complete the 5TSTS (p = 0.011). Additionally, these patients had higher maximum systolic and diastolic blood pressure values compared to those with FAS scores < 22 (p = 0.028 and p = 0.019, respectively). Conclusions: These findings underscore the multifactorial nature of fatigue in MASLD and highlight the need for a comprehensive management strategy. Such a strategy should include dietary modification, increased physical activity, targeted treatment of systemic manifestations of MASLD, and appropriate management of comorbidities. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images often feature substantial overlap, obstructing the precise differentiation of individual leaf edges. Moreover, existing segmentation methods fail to preserve fine edge details, a deficiency that compromises precise morphological analysis. To overcome these challenges, we introduce MSFUnet, an innovative network for semantic segmentation. MSFUnet integrates a multi-path feature fusion (MFF) mechanism and an edge-detail focus (EDF) module. The MFF module integrates multi-scale features to improve the model’s capacity for distinguishing overlapping leaf areas, while the EDF module employs extended convolution to accurately capture fine edge details. Collectively, these modules enable MSFUnet to achieve high-precision individual leaf segmentation. In addition, standard image augmentations (e.g., contrast/brightness adjustments) were applied to mitigate the impact of variable lighting conditions on leaf appearance in the input images, thereby improving model robustness. Experimental results indicate that MSFUnet attains an MIoU of 93.35%, outperforming conventional segmentation methods and highlighting its effectiveness in crop leaf growth monitoring. Full article

Background/Objectives: Spasticity in children with cerebral palsy (CP) can impair motor-related functions. The objective of this exploratory, prospective study was to examine if transcutaneous electrical nerve stimulation (TENS) in a whole-body suit leads to changes in spasticity and other related effects. Methods: Thirty-one children with CP GMFCS III–V, with a median age of 11.0 years (age range of 7–17 years), were consecutively included, and they used the suit with TENS for 24 weeks. The primary outcome was spasticity measured using the Modified Ashworth Scale (MAS). Functional motor-related tasks were evaluated by the Goal Attainment Scale (SMART GAS). The Modified Tardieu Scale (MTS), passive Range of Motion (pROM), GMFM-66, and Posture and Postural Ability Scale (PPAS) assessments were performed. Results: Seventeen subjects (17/31) completed the 24 weeks. Dropout was due to difficulty in donning the suit. The level of overall spasticity, most pronounced in the proximal arms and legs, was reduced according to the MAS, but not the MTS or pROM. Subject-relevant motor-related goals improved significantly in standing/walking and hand/arm function. Changes in the GMFM-66 and PPAS were not significant. Conclusions: Although there were statistically significant but underpowered changes in the MAS after 24 weeks, there were no clinically relevant effects. Exploratorily, we found observer-reliant motor-related functional improvements, which, however, we were unable to detect when trying to quantify them. Donning the suit led to dropout throughout the study. Caregivers need to allocate time, mental capacity and have the physical skill set for donning the suit for long-term use. Full article

This article summarizes the scope and content of the Special Issue (SI) entitled “Geographic Information Systems (GIS) and Cartography for a Sustainable World” and its contribution to the global discourse regarding sustainability concerns. At the heart of the discussion in this SI lies: (i) GIS, a valuable tool and a means for modeling, designing, and analyzing (spatial) data and processes related to the pursuance of sustainability objectives at both local and global scales; and (ii) Cartography as a discipline, which through maps and visualizations can convey the present state. The latter can play a vital role in educating, empowering, and raising public awareness with regard to sustainability concerns on the one hand, and can form a basis for policy-makers, scientists, and citizens for articulating effective sustainability strategies on the other. The fulfillment of the SI goals is attained through a collection of 26 papers that delve into and attempt to visualize sustainability achievements or concerns on a variety of themes in different parts of the world. More specifically, the content of this collection of papers can be categorized into the following sustainability-related themes: Urbanization, Transportation, Carbon Emissions Management, Infrastructure, Rural Development, and Climate Change. The main conclusion is that planning and implementing sustainability policies is a challenging and multi-level task, and must be carried out within a fully dynamic decision environment. Although some progress has already been made, more intensive and collective efforts from scientists, governments, the entrepreneurial community, and citizens are needed in order for the ambitious goals of Agenda 2030 to be reached. Full article

Robust indoor robot navigation typically demands either costly sensors or extensive training data. We propose a cost-effective RGB-D navigation pipeline that couples feature-based relative pose estimation with a lightweight multi-layer-perceptron (MLP) policy. RGB-D keyframes extracted from human-driven traversals form nodes of a topological map; edges are added when visual similarity and geometric–kinematic constraints are jointly satisfied. During autonomy, LightGlue features and SVD give six-DoF relative pose to the active keyframe, and the MLP predicts one of four discrete actions. Low visual similarity or detected obstacles trigger graph editing and Dijkstra replanning in real time. Across eight tasks in four Habitat-Sim environments, the agent covered 190.44 m, replanning when required, and consistently stopped within 0.1 m of the goal while running on commodity hardware. An information-theoretic analysis over the Multi-Illumination dataset shows that LightGlue maximizes per-second information gain under lighting changes, motivating its selection. The modular design attains reliable navigation without metric SLAM or large-scale learning, and seamlessly accommodates future perception or policy upgrades. Full article

This study evaluates the progress of Korea’s National Strategic Smart City Program (NSSCP), a flagship R&D initiative, in advancing sustainable and intelligent urban development on a global scale. Utilizing the United Nations’ United for Smart Sustainable Cities (U4SSC) framework, which integrates both sustainability and smartness in city development, this research examines the program’s alignment with global standards. The findings reveal that the NSSCP contributes to the attainment of the Sustainable Development Goals (SDGs) in areas such as health, energy, innovation, and sustainable communities. It also effectively addresses key dimensions of smart cities, including smart living, environmental stewardship, mobility, and economic vitality. Despite these achievements, this study identifies critical challenges, such as the absence of robust evaluation tools and an overemphasis on quantitative targets. This research is important in advancing the discourse on smart city development, offering insights into the efficacy of smart services and systems through the lens of the NSSCP’s cloud-based open data hub model. Full article