Search Results (17,365)

This study develops a Smart Mobility Index (SMI) tailored to the 17 Local Government Units (LGUs) of Metro Manila to evaluate their readiness to adopt integrated, efficient, and technology-enabled mobility systems. While global smart mobility indices are often ill-suited to the realities of developing countries, this research proposes a context-specific framework built around four thematically grounded dimensions: public transportation service, active mobility, unified cashless fare systems, and smart traffic management. The SMI was constructed through a mixed-method approach combining expert interviews with metropolitan transport specialists and co-occurrence network analysis. The results reveal substantial disparities across LGUs, with central jurisdictions such as Makati, Manila, and Pasay demonstrating significantly higher smart mobility readiness than peripheral LGUs. Clustering identifies three distinct mobility profiles, underscoring persistent structural inequalities in infrastructure, institutional capacity, and digital integration. Forecasts incorporating the completion of six major railway projects by 2035 indicate moderate improvements in overall SMI scores and limited changes in relative rankings, suggesting that infrastructural expansion alone will not reduce regional disparities. Expert insights further highlight both the potential and the constraints of leapfrogging, with interviewees expressing optimism regarding advanced ICT-enabled mobility solutions while acknowledging challenges related to governance fragmentation, limited funding, and uneven technical capabilities. Full article

Background: Responsive caregiving supports infant and toddler wellbeing. Yet, based on nursing observational data, a significant number of one institution’s inpatient infant and toddler patients with cancer—who are uniquely vulnerable due to the developmental risks associated with their illness and treatment—were not spoken to or held by their caregiver at any time when nursing was present over the course of day shifts. Objective: This clinical quality improvement project aimed to increase caregiver engagement in responsive interactions during inpatient stays. Methods: The Model for Improvement framework was used. Implementation, evaluation, and reporting followed the SQUIRE 2.0 framework. Root causes were analyzed with fishbone and key driver diagrams. Outcomes were tracked with control charts and percentage of nursing shifts during which responsive care was not observed. Statistical process control was used to study interventions. Results: Two intervention cycles were completed and resulted in significant and meaningful (>1 sigma) reductions in nursing shifts during which infants and toddlers were not spoken to or held. Conclusions: Caregiver psychoeducation interventions increased responsive care of infants and toddlers in our oncology inpatient setting. This low-cost intervention may be adaptable across inpatient settings. Full article

Visual object tracking from unmanned aerial vehicles (UAVs) demands both high accuracy and computational efficiency for real-time deployment on resource-constrained platforms. While state space models (SSMs) offer linear computational complexity, existing methods face critical deployment challenges. They rely on the HiPPO framework with complex discretization procedures and employ hardware-aware algorithms optimized for high-performance GPUs, which introduce deployment overhead and are difficult to transfer to edge platforms. Additionally, their fixed polynomial bases may cause information loss for tracking features with complex geometric structures. We propose LF-SSM, a lightweight HiPPO (High-order Polynomial Projection Operators)-free state space model that reformulates state evolution on Riemannian manifolds. The core contribution is the Geodesic State Module (GSM), which performs state updates through tangent space projection and exponential mapping on the unit sphere. This design eliminates complex discretization and specialized hardware kernels while providing adaptive local coordinate systems. Extensive experiments on UAV benchmarks demonstrate that LF-SSM achieves state-of-the-art performance while running at 69 frames per second (FPS) with only 18.5 M parameters, demonstrating superior efficiency for real-time edge deployment. Full article

Rapid land use and land cover (LULC) changes in densely populated watersheds pose serious challenges to the sustainability of ecosystem services (ES), yet their spatially explicit economic consequences remain insufficiently understood. This study analyzes the spatio-temporal dynamics of LULC and ecosystem service values (ESVs) in the Citarum Watershed, Indonesia, one of the country’s most critical and intensively transformed watersheds. Multi-temporal Landsat imagery from 2003, 2013, and 2023 was classified using a Random Forest algorithm, while future LULC conditions for 2043 were projected using a Multi-layer Perceptron–Markov Chain (MLP–MC) model under three scenarios: Business-as-Usual (BAU), Protecting Paddy Field (PPF), and Protecting Forest Area (PFA). ESVs were quantified at multiple spatial scales (county, 250 m grids, and 100 m grids) using both the Traditional Benefit Transfer (TBT) method and a Spatial Benefit Transfer (SBT) approach that integrates biophysical indicators with socio-economic variables. The contribution of LULC transitions to ESV dynamics was further assessed using the Ecosystem Service Change Intensity (ESCI) index. The results reveal substantial historical forest and shrubland losses, alongside rapid expansion of settlements and dryland agriculture, indicating intensifying anthropogenic pressure on watershed functions. Scenario analysis shows continued degradation under BAU, limited mitigation under PPF, and improved forest retention under PFA; although settlement expansion persists across all scenarios. Total ESV declined from USD 2641.33 million in 2003 to USD 1585.01 million in 2023, representing a cumulative loss of 46.13%. Projections indicate severe ESV losses under BAU and PPF by 2043, while PFA substantially reduces, but does not eliminate economic degradation. ESCI results identify forest and shrubland conversion to settlements and dryland agriculture as the dominant drivers of ESV decline. These findings demonstrate that integrating multi-scenario LULC modeling with spatially explicit ESV assessment provides a more robust basis for ecosystem-based spatial planning and supports sustainable watershed management under increasing development pressure. Full article

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) causes half a million deaths each year; therefore, it is essential to understand the factors that affect patient prognosis. Many studies fail to investigate the biological drivers behind survival disparities, especially sex-specific differences within racial groups. This study serves as a foundational project to begin elucidating biological differences in the tumor microenvironment between male and female African American HNSCC patients. Methods: A total of 111 patients who were diagnosed with HNSCC and identify as African American were grouped by sex. Analyses of socioeconomic status, co-morbidities, tumor characteristics, and treatment were conducted. Spatial transcriptomic analysis was performed on four randomly selected primary HNSCC tumor tissues. Results: No sex-based differences were observed in socioeconomic measures, treatments, tumor stage, follow-up, recurrence, or cause of death (all p > 0.15), though females had higher median income than males (p = 0.035). Comorbidity profiles were also largely comparable between males and females. Evaluating tumor microenvironments, we found that male tumors were dominated by malignant cells and fibroblasts, with limited adaptive immune infiltration. By contrast, female tumors displayed markedly higher proportions of immune cells, including T cells and B cells. Male tumors harbored sparse T cells, largely skewed toward exhausted phenotypes while female tumors displayed abundant T cell infiltration consistent with immunologically active tumor microenvironment. Conclusions: Clinical and demographic factors showed minimal sex-based differences among African American HNSCC patients, spatial transcriptomic profiling revealed strikingly distinct immune microenvironments by sex. These findings suggest that biological, rather than simply clinical, differences may drive survival disparities. This project serves as a novel and foundational study promoting the use of spatial transcriptomics to evaluate possible survival disparities within HNSCC populations to alleviate survival disparities. Full article

Traditional multi-criteria decision-making methods often operate on the assumption of symmetry, presupposing that the positive impact of a criterion’s presence is perfectly complementary to the negative impact of its absence. However, in real-world decision problems, this relationship is frequently asymmetric; some criteria act merely as “delighters,” while others represent “must-have” constraints. This study proposes a novel methodology, the Presence–Absence Synthesis (PAS) Method, which addresses this asymmetry by treating the “Presence Effect” and “Absence Effect” of criteria as two independent dimensions. The method is built upon intuitionistic fuzzy sets (IFSs) to effectively model the uncertainty and hesitation inherent in expert evaluations. The applicability of the proposed approach is demonstrated through a real-world workforce management problem aimed at assigning employees to the most suitable tasks based on their competencies in a retail store. In the study, the suitability scores derived from the PAS method are integrated into a mathematical optimization model for weekly employee scheduling, presenting a two-stage decision support framework. The results and comparisons with the Technique for Order Preference by Similarity to Ideal Solution method reveal that the PAS method more effectively distinguishes critical competency gaps (i.e., criteria with high absence effects), leading to more realistic task assignments and a measurable reduction in operational risks, such as skill mismatches and infeasible schedules. Furthermore, sensitivity analysis confirms that the proposed model yields consistent and robust results under varying conditions. Beyond the retail context, the proposed PAS framework is applicable to a wide range of decision-making problems, including healthcare staff allocation, project team formation, supplier selection, and other resource allocation settings where their presence cannot compensate for the absence of critical criteria. Full article

We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming solutions typically either assume near-perfect CSI or rely on greedy/black-box designs without an explicit mechanism to regularize the error-distorted singular modes, leaving a gap in unified, low-complexity, and theoretically grounded robustness. We unfold the Alternating Direction Method of Multipliers (ADMM) into a trainable Deep Learning (DL) network, termed DL-ADMM, to jointly optimize Radio-Frequency (RF) and baseband precoders and combiners. In DL-ADMM, the ADMM update mappings are learned (layer-wise parameters and projections) to amortize the joint RF/baseband optimization, whereas Regularized Singular Value Decomposition (RSVD) acts as an analytical regularizer that reshapes the observed channel’s singular values to suppress noise amplification under imperfect CSI. RSVD is integrated to stabilize singular modes and curb noise amplification, yielding a unified and scalable design. For ${\sigma }_e^2=0.1$, the proposed DL-ADMM-Reg achieves approximately 8–11 bits/s/Hz higher spectral efficiency than Orthogonal Matching Pursuit (OMP) at Signal-to-Noise Ratio (SNR) $=20$–40 dB, while remaining within <1 bit/s/Hz of the digital-optimal benchmark across both $(N_t,N_r)=(32,32)$ and $(64,64)$ settings. Simulations confirm higher spectral efficiency and robustness than OMP and Adaptive Phase Shifters (APSs). Full article

Background/Objective: Hand hygiene is a cornerstone of infection prevention; however, compliance is inconsistent in intensive care units (ICUs), particularly in resource-constrained settings. This study evaluated whether a low-cost, multimodal quality improvement intervention could improve process-level hand hygiene compliance using routine, episode-based audits embedded in the ICU practice. Methods: We conducted a single-cycle Plan-Do-Study-Act quality improvement project in a 12-bed mixed medical–surgical ICU in Pakistan (December 2023–January 2024). Hand hygiene performance was assessed using the unit’s routine weekly episode-based audit protocol, aligned with the WHO Five Moments framework. A targeted multimodal intervention comprising education, point-of-care visual reminders, audit feedback, and leadership engagement was implemented between the pre- and post-intervention phases (four weeks each). Non-applicable moments were scored as “compliant by default” according to the institutional protocol. A sensitivity analysis was performed excluding these moments to calculate pure adherence. Compliance proportions were summarized using exact 95% Clopper–Pearson confidence intervals without inferential testing. Results: A total of 942 audit episodes (471 per phase) generated 4710 moment-level assessments were generated. Composite hand hygiene compliance increased from 63.1% pre-intervention to 82.0% post-intervention [absolute increase: 18.9 percentage points (pp)]. Sensitivity analysis excluding non-applicable moments demonstrated pure adherence improvement from 54.2% to 82.5% (+28.3 pp), confirming a genuine behavioral change rather than a measurement artifact. Compliance improved across all five WHO moments, with the largest gains in awareness-dependent moments targeted by the intervention: before touching the patient (+27.0 pp) and after touching patient surroundings (+40.0 pp). Week-by-week compliance remained stable within both phases, without immediate post-intervention decay. Conclusions: A pragmatic, low-cost multimodal intervention embedded in routine ICU workflows was associated with substantial short-term improvements in hand hygiene compliance over a four-week observation period, particularly for awareness-dependent behaviors. Episode-based audit systems can support directional process monitoring in resource-limited critical care settings without the need for electronic surveillance. However, its long-term sustainability beyond one month and generalizability to other settings remain unknown. Sensitivity analyses are essential when using “compliant by default” scoring to distinguish adherence patterns from measurement artifacts. Full article

Industrial symbiosis and carbon capture are increasingly recognized as critical strategies for reducing emissions and resource consumption in industrial parks. However, existing research remains fragmented across tools, methods, and case-specific applications, providing limited guidance for effective real-world deployment of data-driven approaches. This study addresses this gap through a PRISMA-guided scoping review of 116 publications, complemented by a targeted practitioner survey conducted within the IEA IETS Task 21 initiative to assess practical relevance and adoption challenges. The review identifies a broad landscape of data-driven tools, ranging from high-technology-readiness simulation and optimization platforms to emerging visualization and matchmaking solutions. While the literature demonstrates substantial methodological maturity, the combined evidence reveals a persistent gap between tool availability and effective implementation. Key barriers include fragmented and non-standardized data infrastructures, confidentiality constraints, limited stakeholder coordination, and weak policy and market incentives. Based on the integrated analysis of literature and practitioner insights, the paper proposes a conceptual framework that links tools and methods with data infrastructure, stakeholder governance, policy, and market enablers, and implementation contexts. The findings highlight that improving data governance, interoperability, and collaborative implementation pathways is as critical as advancing analytical capabilities. The study concludes by outlining focused directions for future research, including AI-enabled optimization, standardized data-sharing frameworks, and coordinated pilot projects to support scalable low-carbon industrial transformation. Full article

Older LGBTQ+ adults face persistent inequalities in health and social care, often shaped by historical trauma and systemic exclusion, raising critical questions about how ageing can be supported equitably. This public involvement project explored the narratives and expectations of older LGBTQ+ individuals regarding ageing, health, and social care. Public involvement events were organised and facilitated by an interdisciplinary group of academics between January and April 2025 in Cardiff, Exeter, and Bristol, locations chosen for their rural, coastal, and urban contexts. Creative participatory methods were used to engage LGBTQ+ individuals, aged 50 years old and over, in discussion and sharing narratives. The work found attendees wanting to talk about themes of isolation and invisibility in society, and within LGBTQ+ spaces, the need for inclusive spaces, as well as concerns about discrimination in healthcare settings. Throughout the three events, there was a clear desire among attendees for future research and advocacy, alongside the emergence of a strong community network committed to inclusive and affirming care. These insights can help guide future research projects and initiatives aimed at improving support for LGBTQ+ ageing. Full article

Laser beams are excellent projection sources due to their high brightness and color purity; however, their high coherence produces speckle noise, which reduces the clarity of images cast by compact projection systems. Existing suppression methods often require complex designs. Here, we propose a simple miniaturized speckle suppression structure (SSS) that consists of a low-absorption particle surface and a micro-vibrating unit. By generating and superimposing different speckle patterns over time, the structure simultaneously reduces both temporal and spatial coherence. A time-varying functional model was developed using a simulation to optimize its dynamic operation. The results of the experimental validation show that at 50 Hz vibration, the speckle contrast decreases from 30.23% to 6.98%, closely matching the simulated prediction of 7.12% and outperforming static configurations by 24%. The results indicate that the SSS is a straightforward, effective solution for enhancing the image quality of compact laser projection displays. Full article

Remote sensing image segmentation requires balancing global context and local detail across multi-scale objects. However, convolutional neural network (CNN)-based methods struggle to model long-range dependencies, while transformer-based approaches suffer from quadratic complexity and become inefficient for high-resolution remote sensing scenarios. In addition, the semantic gap between deep and shallow features can cause misalignment during cross-layer aggregation, and information loss in upsampling tends to break thin continuous structures, such as roads and roof edges, introducing pronounced structural noise. To address these issues, we propose lightweight Lite-BSSNet (Blueprint-Guided State Space Network). First, a Structural Blueprint Generator (SBG) converts high-level semantics into an edge-enhanced structural blueprint that provides a topological prior. Then, a Visual State Space Bridge (VSS-Bridge) aligns multi-level features and projects axially aggregated features into a linear-complexity visual state space, smoothing high-gradient edge signals for sequential scanning. Finally, a Structural Repair Block (SRB) enlarges the effective receptive field via dilated convolutions and uses spatial/channel gating to suppress upsampling artifacts and reconnect thin structures. Experiments on the ISPRS Vaihingen and Potsdam datasets show that Lite-BSSNet achieves the highest segmentation accuracy among the compared lightweight models, with mIoU of 83.9% and 86.7%, respectively, while requiring only 45.4 GFLOPs, thus achieving a favorable trade-off between accuracy and efficiency. Full article

Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using computational intelligence. We develop a novel framework that models expressways as L-space networks and evaluates how construction sequences create path-dependent evolutionary trajectories, introducing network science principles into infrastructure planning decisions. Our decision support framework quantifies project impacts on accessibility, connectivity, and reliability using nine topological metrics and a hybrid weighting mechanism that combines domain expertise with entropy-based uncertainty quantification. The system employs a hybrid TOPSIS algorithm that relies on geometric symmetry to simulate network evolution, capturing emergent properties in which each decision restructures possibilities for subsequent choices—a computational challenge that conventional planning approaches have not addressed. The system was validated with real-world Chongqing expressway planning data, demonstrating its ability to identify sequences that maximize synergistic network effects. Results reveal how topologically equivalent projects produce dramatically different system-wide outcomes depending on implementation order. Analysis shows that network science-informed sequencing substantially enhances system performance by exploiting structural synergies. This research advances decision support frameworks by bridging complex network theory with computational decision-making, creating a novel analytical tool that enables transportation authorities to implement evidence-based infrastructure sequencing strategies beyond the reach of conventional planning methods. Full article

The international literature shows significant growth in relation to sustainable rural development in response to ongoing problems. The Principles for Responsible Investment in Agriculture and Food Systems (CFS-RAI) enable projects to be aligned with the Sustainable Development Goals (SDGs). In this article, we present an empirically grounded analysis of these RAI principles based on in-depth case studies in seven countries (Spain, Ecuador, Peru, Dominican Republic, Bolivia, Colombia, and Mexico). This experience comes from an international project coordinated by the GESPLAN Research Group at the Polytechnic University of Madrid. The Working with People model is incorporated into the methodological process to analyze rural actors’ understanding of the CFS-RAI principles in different countries and in university–business relation contexts. The results show the effectiveness of the WWP model based on the integration of three dimensions—ethical–social, technical–business, and political–contextual—as an effective method for planning sustainable rural development projects in various contexts. The empirical evidence presented indicates that combining the WWP model with the principles of CFS-RAI in rural contexts allows progress toward sustainable development, balancing economic aspects with human, social, and environmental well-being. Full article

Background/Objectives: This article presents the feasibility and preliminary outcomes of an international cooperation project between the University of Valencia (Spain) and the University and health authorities of Pinar del Río (Cuba), designed to implement and evaluate an innovative rehabilitation protocol. Aligned with the United Nations Sustainable Development Goals (SDGs 3, 4, and 10), the initiative aims to implement a low-cost, evidence-based rehabilitation program combining mirror-neuron stimulation via Virtual Walking and therapeutic exercise. Methods: The program included multidisciplinary meetings and both digital and on-site training for healthcare professionals, caregivers, and educators, aimed at strengthening local capacities in evidence-based practice. The transferred protocol consisted of Virtual Walking (10 min) and therapeutic exercise (30 min), implemented three times per week, for eight weeks. Outcomes assessed included gait speed and endurance (10-Minute Walking Test, 6-Minute Walking Test), lower limb function (Timed Up and Go Test), frailty status (Fried criteria), pain (Visual Analog Scale), and satisfaction with the training program. Pre-post comparisons were conducted using the Wilcoxon signed-rank test for continuous data. Results: The program was successfully implemented in two polyclinics with high levels of participant satisfaction. Eleven patients completed the program, showing significant improvements in gait endurance (p < 0.05), while lower limb function and pain did not change significantly. Noteworthily, severe infrastructural and connectivity limitations were found. Overall, results demonstrate the feasibility, adaptability, and acceptability of the proposed protocol, which integrates technological innovation, clinical training, and community engagement to promote health quality and equity. Conclusions: This project provides a replicable framework for rehabilitation initiatives in low-resource settings and demonstrates the potential to achieve meaningful clinical results. Full article