Search Results (2,406)

Background: Dementia is one of the most common diseases of the elderly worldwide. Sharing experiences of caring for patients with dementia with other carers is essential to improve the quality of care, promote better outcomes, and learn from others. Aim: to explore nurses’ experiences of working with patients with dementia in Lithuanian supportive treatment and nursing hospitals. Methods: A qualitative descriptive design was employed in this study, with data collected through semi-structured interviews. Nurses with direct experience caring for patients with dementia in supportive treatment and nursing hospitals were recruited through purposive sampling. This sampling strategy was chosen to ensure that participants could provide rich, contextual, and experience-based insights into the phenomenon under investigation. Open-ended questions were divided into three themes: 1. Identifying nursing needs. 2. Care for people with dementia. 3. Patient behavior management and situation management. To ensure methodological rigor and transparency, the Consolidated Criteria for Reporting Qualitative Research (COREQ) were applied throughout the study’s planning, data collection, and analysis processes. Results: Nine nurses working in three different Lithuanian hospitals participated in the study. Theme 1: respondents reported that the needs of patients with dementia depend on their previous lifestyle and hobbies, as well as on essential physiological needs such as eating and drinking, bathing and personal hygiene, and the absence of pain. Theme 2: All participants emphasized that ensuring a safe environment is crucial for people with dementia. Theme 3: When faced with inappropriate patient behaviour, nurses attempt to calm the patient, speak gently, provide distraction, or, when necessary, temporarily separate the patient from others. Additional actions include administering medication and stabilizing the patient. Overall, these findings illustrate that dementia care requires continuous emotional presence, situational judgment, and adaptation to each patient’s individual needs. Conclusions: Patients with dementia require highly individualized care focused on nutrition, hygiene, pain control, and communication. Nurses’ daily activities centered on essential bodily care, medication management, and mobility support to maintain safety and prevent complications. Full article

Achieving a credible net-zero transition requires reliable corporate environmental information to support effective climate governance. When firms overstate environmental commitments without corresponding improvements in actual performance, regulatory signals become distorted, and decarbonization efforts are weakened. This study examines whether stringent command-and-control environmental regulation enhances the credibility of corporate climate commitments. Using the staggered implementation of China’s Air Pollution Prevention and Control Action Plan as a quasi-natural experiment, we construct a firm-level measure of corporate greenwashing that captures the divergence between environmental discourse and regulatory performance. Based on a multi-period difference-in-differences model, the results indicate that environmental regulation significantly reduces corporate greenwashing, with the probability of inconsistency between environmental claims and actual behavior declining by approximately 25 percent relative to the sample mean. Mechanism analysis shows that this effect operates through increased green technological innovation and heightened public environmental concern, which together strengthen substantive compliance and external monitoring. The moderating analysis shows heterogeneous responses across firms: board independence strengthens the policy’s inhibitory effect, while market share and institutional ownership attenuate it. Overall, the findings suggest that command-and-control regulation improves the credibility of disclosure and reinforces the informational foundations necessary for an effective net-zero transition. Full article

Humanoid manipulators with kinematic redundancy offer enhanced dexterity and adaptability to complex environments. Solving their inverse kinematics (IK) is fundamental to trajectory tracking, motion planning, and real-time control. Conventional Jacobian-based iterative methods are widely used, but they are often sensitive to the initial guess, computationally expensive, and less effective in handling strict constraints. Arm-angle-based analytical parameterization reduces redundancy resolution to a single parameter. However, joint limits may lead to multiple disconnected feasible arm-angle intervals. Many existing methods still depend on a numerical search or intelligent optimization to select the arm angle. This lowers computational efficiency and provides less explicit control over branch and configuration selection. To address these issues, this paper extends the arm-angle analytical IK framework. It introduces global configuration parameters to explicitly control the shoulder-elbow-wrist configuration. It also completes the analytical derivation of the rotational relationships of the first three joints in the reference plane. In addition, a feasibility determination and modeling scheme for the arm-angle domain is established, which covers disconnected feasible intervals. The IK problem is then reformulated as a one-dimensional optimization over the feasible domain. An efficient interval-based search is employed to determine the optimal arm angle. Experimental results demonstrate high accuracy and interference-free trajectory tracking. Comparative tests on randomly sampled target poses are also performed. The results show more concentrated error distributions, shorter average computation time, and higher success rates. These results confirm the advantages of the proposed method in accuracy, robustness, and real-time performance. Full article

MOS gas sensors have proven to be useful in electronic noses, which utilize these sensors to detect volatile organic compounds in human breath to detect various lung diseases. Unfortunately, the long settling time of MOS gas sensors is ill-suited to measuring human breath, where complete breathing cycles are often shorter than 5 s. Existing studies circumvent this limitation by collecting gas samples and injecting them into a sealed chamber to react with the sensors. However, it would be convenient if breath-by-breath analysis could be conducted without the need to store breath samples. To accomplish this, we present a novel forecasting methodology to predict the final value $t_{\infty }$ of a gas sensor’s response based on its initial transient behavior. To do this, we present and validate a second-order mathematical model of the sensors’ response characteristics, which we then use in our preliminary work using neural networks to predict the final sensor value. Although some challenges were encountered, the initial results are encouraging, and we plan to extend our study in the future to collect a more expansive dataset and explore the use of other types of machine learning algorithms for this application. Full article

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

Accurate fluid characterization is critical for reservoir development planning and typically relies on pressure-volume-temperature (PVT) experiments. However, in structurally complex reservoirs, fluid classification based solely on laboratory measurements can lead to misinterpretations. In the Gongshanmiao block of the Sichuan Basin, initial PVT analysis suggested that the reservoir was a condensate gas system. Subsequent field development revealed inconsistencies with this interpretation, including abnormal gas–oil ratios and atypical pressure build-up behavior that deviated from expected condensate gas reservoir performance. To resolve this discrepancy, this study proposes a diagnostic framework that integrates geoscience and engineering data, including fluid sampling, 3D structural modeling, production performance analysis, pressure build-up testing, and hydraulic fracturing data. The integrated analysis revised the initial PVT-based interpretation, and the results indicated that the reservoir is more accurately characterized as a saturated oil system with an overlying gas cap, rather than a condensate gas reservoir. Furthermore, the integrated interpretation clarifies the structural trapping mechanism and delineates the spatial extent of the gas cap. Overall, the proposed approach provides an integrated geoscience-engineering workflow for fluid reclassification in structurally complex reservoirs, which reconciles laboratory fluid analysis with field production behavior, offering a systematic framework for fluid interpretation in similar geological settings. Full article

Background: Engaging diverse populations, including Muslims, in research activities is important to support patient-centered research and improve health equity. Objectives: The research aimed to describe the community engagement steps that informed conducting research with five distinctively diverse U.S. Muslim communities. Methods: This work provides methodological reflections on engaging diverse Muslim communities in the U.S. Researchers built trust-based partnerships with community healthcare organizations and engaged with administrative leaders, advisory members, and people from five diverse communities. Strategies to support sampling, recruitment, multi-language interpretation methods, and how to engage communities and address their concerns are discussed. Results: A total of 22 participants were included in the original study. The research team successfully engaged five of the six planned communities, utilizing multiple interpretation methods and participating in community events to support recruitment and relationship-building. Direct-to-participant recruitment efforts were strengthened by personal connections with trusted community members. Conclusions: Flexibility and adaptability are integral in recruitment and data collection, as diverse communities may respond differently to methods successfully used elsewhere. Attention to gender-related cultural norms, the inclusion of language-concordant researchers, and respect for communities’ autonomy in deciding whether and how to participate collectively contributed to more effective and culturally grounded engagement with Muslim communities. Full article

Digital tax reforms are implemented through staged, publicly announced milestones, yet policymakers rarely have timely indicators of whether these signals mobilize information-seeking and whether such demand can be anticipated for operational planning. We analyze monthly Google Trends series for Greece’s myDATA/e-invoicing rollout (2016–present) using preregistered event study models that separate step changes from post-event trend shifts with HAC-robust inference, and we evaluate 1–3-month predictive performance via rolling-origin cross-validation against a seasonal-naïve benchmark. Search-based attention shifts appeared most clearly in application-related queries: invoicing app terms spike around visible rollout phases (≈+34 to +38 index points over six months) and decline around VAT–myDATA alignment (≈−34 to −43). Ecosystem attention (the “Electronic invoicing” topic) exhibits large, opposite-signed movements (≈−53 around public-sector expansion; ≈+46 around VAT alignment), whereas platform terms show smaller and less regular responses; a back-office milestone produces no detectable change. In out-of-sample tests, event-aware regressions improve short-horizon accuracy for platform terms (≈40–50% MAE reduction at one month; ≈18–32% at two to three months), with series- and horizon-dependent results elsewhere. Overall, the evidence supports using search activity as an intermediate planning signal—informative about when and where guidance demand concentrates but not evidence of compliance. Full article

Currently, most mobile manipulators employ a “Stop-and-Grasp” strategy, where the base of the manipulator stops before the arm executes the grasp. However, achieving “Grasping-on-the-Move” actions—where the robot grasps a target while the base is in motion—remains a significant challenge due to the coupling of base and arm dynamics. To address this, we propose a two-phase collaborative motion planning framework. In the first phase (long-range approach), we introduce a spatially constrained visual servoing (SC-VS) method. By establishing a dynamic safety corridor based on the chassis path, this method ensures robust target tracking and obstacle avoidance for the arm during base motion. In the second phase (close-range grasping), to seize the brief grasping opportunity, we propose a Constrained-Sampling RRT-Connect (CSR-RRT-Connect) algorithm. By restricting the sampling region based on target prediction, this algorithm significantly reduces planning time. Comparative experiments demonstrate that our method achieves a 92% success rate at a base speed of 0.3 m/s, significantly outperforming the 46% success rate of baseline methods, while exhibiting superior robustness against dynamic operational disturbances and perception noise. Full article

Real-time motion planning in cluttered and dynamically evolving environments remains challenging due to the need to ensure rapid convergence, collision avoidance, computational efficiency, and robustness against local minima under frequent changes. Although sampling-based planners such as RRTX* and ABIT* provide strong theoretical guarantees, their practical deployment in dense dynamic scenarios is often limited by high sampling overhead and computational latency. This paper proposes a Fast Converging Adaptive Algorithm (FCAA), a deterministic sampling-based framework integrating adaptive sampling density, temperature-controlled exploration, and dynamic step-size regulation within a unified heating and annealing mechanism. The temperature parameter governs both the spatial sampling band and incremental expansion radius, enabling controlled transitions between goal-directed expansion and stochastic exploration when stagnation occurs. The algorithm is evaluated using a two-stage protocol comprising intrinsic validation and benchmarking. Across 36 environments with obstacle densities ranging from 3% to 20% and velocities between −30 and +30 m/s, FCAA achieved a 100% success rate within the defined experimental design while maintaining path quality comparable to or better than RRTX* and ABIT*. Unlike the reference planners, which typically required tens of thousands of samples and seconds of computation, FCAA operated with substantially reduced sampling effort, typically tens of nodes, and planning times from 0.1 to 320 ms depending on scenario complexity. Within the simulation framework, the results indicate that the proposed temperature-regulated strategy enables fast and computationally efficient motion planning under dynamic constraints, making FCAA suitable for time-critical robotic navigation scenarios. Full article

In recent years, emerging economies have intensified their efforts to promote sustainable mobility as part of global decarbonization strategies. Although Morocco has made substantial investments in renewable energy and electric vehicle (EV) manufacturing, domestic EV adoption remains modest, revealing a structural gap between industrial capacity and consumer uptake. Identifying the behavioral and value-based determinants of EV adoption is therefore essential for accelerating sustainable transport transitions. Building on the Theory of Planned Behavior (TPB) and the Value–Belief–Norm (VBN) theory, this study integrates sustainability-related values, moral responsibility, perceived behavioral control, and institutional trust within a unified behavioral framework to explain EV adoption intention in Morocco. A quantitative cross-sectional survey was conducted among 223 Moroccan consumers aged 18–55. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived environmental benefits, moral responsibility, perceived behavioral control, and institutional trust significantly influence EV adoption intention. Environmental awareness was not statistically significant in this sample and model, whereas subjective norms and awareness of government incentives did not exert significant effects. The proposed model explains 66.4% of the variance in adoption intention. Full article

This study examined the relationships between norm perceptions about innovation, innovation source and customer satisfaction with sample data from small-scale hospitality businesses in Ghana. We adopted the quantitative approach and correlational survey design using sample data from 465 small-scale hospitality firms. Partial Least Squares Structural Equation Modelling was used to analyse the data. Measurement model classification and validation procedures comprised construct specification, indicator reliability assessment, internal consistency reliability, convergent validity (AVE), discriminant validity (HTMT and Fornell–Larcker), and collinearity diagnostics within the PLS-SEM framework. Results showed that a significant negative relationship exists between subjective norms about innovation adoption and customer satisfaction. This finding diverges from the Theory of Planned Behaviour because, contrary to its assumption that subjective norms foster positive behavioural outcomes, socially driven innovation in small-scale hospitality settings may encourage conformity-based decisions that undermine customer-oriented value creation. However, a significant positive relationship was found to exist between subjective norm perceptions about innovation adoption and innovation source. A significant positive relationship was also found to exist between innovation source and customer satisfaction. Innovation source positively mediated the relationship between subjective norm perceptions about innovation adoption and customer satisfaction. The study’s findings are relevant for owners and managers of small-scale hospitality firms seeking to align innovation decisions with customer needs, as well as for policymakers aiming to strengthen industry support systems. It offers insights into how social influences and innovation sources can be leveraged to enhance service quality and customer satisfaction in small hospitality businesses. Full article

The rapid growth of avocado production in Mexico has intensified concerns about balancing economic competitiveness with environmental sustainability, especially regarding water use and production intensification. This study introduces the Integrated Competitiveness and Sustainability Composite Index for Avocado (ICSCA), a multidimensional indicator that evaluates the structural performance of avocado production systems across Mexican states. The index combines environmental efficiency, economic productivity, and socio-productive stability, using state level data from 2014–2024. The ICSCA was calculated and examined through principal component analysis, regression models, and spatial visualization. Results show strong heterogeneity among producing regions. States like Michoacan and Jalisco achieve high productivity and economic output but exert significant pressure on water resources, while others maintain more balanced sustainability profiles with moderate productivity; furthermore, spatial patterns reveal clear regional gradients in sustainability performance. Robust tests, including out of sample temporal validation, expanding window validation, and sliding window analysis, demonstrate high predictive consistency across analysis periods. Overall, the ICSCA provides a robust tool for assessing the interactions between productivity, environmental efficiency, and social structural stability, supporting evidence-based policy design and regional planning for more sustainable avocado production. Full article

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R² = 0.83, RMSE = 32.45%) and moderate performance for forests (R² = 0.43, RMSE = 20.84%) and shrublands (R² = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R² = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R² = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments. Full article