Search Results (1,896)

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

In the context of a rapidly changing global energy landscape and mounting pressures on energy security, enhancing the resilience of the oil and gas industry (OGI) has become a critical task for safeguarding China’s energy security. This study develops a multidimensional resilience indicator system—comprising recovery, adaptability, responsiveness, and innovation—and, based on OGI data for 2001–2022, employs the entropy method to quantitatively assess resilience by sub-dimension and development stage. Leveraging a backpropagation (BP) neural network, we construct a dynamic simulation model to produce long-term, multi-scenario forecasts of China’s OGI resilience for 2023–2032, enabling comparison of development potential across scenarios. The results indicate that overall resilience exhibited a fluctuating upward trend and reached a medium-strength resilience level by 2022, with innovation and recovery gradually emerging as the dominant drivers. Forecasts show that under the green-transition scenario, resilience will improve the most, increasing by 5.49% by 2032 and reaching the threshold for strong resilience earlier than under other scenarios. These findings offer actionable insights for enhancing the reliability and sustainability of energy supply chains in the face of climatic and geopolitical challenges. Full article

Background/Objectives: Frailty is a multidimensional syndrome characterized by diminished physiological reserves, reduced mobility, and increased fall risk. While clinical assessments are commonly used to screen for frailty, they may not capture minor deficits in postural control. Center-of-pressure (CoP) metrics from force plates provide objective markers of postural control, yet their role in frailty screening remains underexplored. This study aimed to investigate the associations between functional performance measures and CoP-based metrics to identify predictors of frailty among older adults. Methods: Eighty-three adults aged ≥ 55 years with a history of falls were classified as frail or pre-frail based on modified Fried criteria. Functional assessments (Timed Up and Go (TUG), grip strength, Berg Balance Scale [BBS], Falls Efficacy Scale [FES]) and CoP metrics (mean velocity, sway path; eyes open/closed) were evaluated. Both unadjusted and age-adjusted logistic regression models were used to identify independent predictors of frailty. Results: Increased TUG time and number of falls were the strongest risk factors for frailty, while increased sway path and CoP velocity were protective. In particular, sway path under eyes-closed conditions showed the strongest protective association (OR = 0.323, p < 0.001). Additionally, fear of falling (OR = 1.078, p = 0.013) emerged as a significant psychological factor, consistently associated with increased frailty risk regardless of physical performance. Correlation analysis supported these findings, showing that better functional performance was linked to lower frailty risk. Conclusions: CoP sway path and mean velocity independently predict frailty status and offer added value beyond traditional clinical tools. These findings highlight the importance of incorporating instrumented balance assessments into frailty screening to capture nuanced postural control deficits and guide early intervention strategies. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

This study develops and operationalizes a multi-dimensional framework for sustainable building retrofit that aligns with national 2050 net-zero objectives. First, we conduct a scoping review of international standards (e.g., ISO), sustainability reporting guidelines (GRI G4), and peer-reviewed studies to define an indicator system spanning three pillars—environmental (carbon neutrality, resource circulation, pollution management), social (habitability, durability/safety, regional impact), and economic (direct support, deregulation). Building on this structure, we propose a transparent 0–3 rubric at the sub-indicator level and introduce the Sustainable Building Retrofit Index (SRI) to enable cross-case comparability and over-time monitoring. We then apply the framework to seven countries (United States, Canada, United Kingdom, France, Germany, Japan, and South Korea), score their retrofit systems/policies, and synthesize results through radar plots and a composite SRI. The analysis shows broad emphasis on carbon neutrality and habitability but persistent gaps in resource circulation, pollution management, regional impacts, and deregulatory mechanisms. For South Korea, policies remain energy-centric, with relatively limited treatment of resource/pollution issues and place-based social outcomes; economic instruments predominantly favor direct financial support. To address these gaps, we propose (i) life-cycle assessment (LCA)–based reporting that covers greenhouse gas and six additional impact categories for retrofit projects; (ii) a support program requiring community and ecosystem-impact reporting with performance-linked incentives; and (iii) targeted deregulation to reduce uptake barriers. The paper’s novelty lies in translating diffuse sustainability principles into a replicable, quantitative index (SRI) that supports benchmarking, policy revision, and longitudinal tracking across jurisdictions. The framework offers actionable guidance for policymakers and a foundation for future extensions (e.g., additional countries, legal/municipal instruments, refined weights). Full article

Assessing crop nitrogen status is crucial for optimizing fertilization strategies and promoting sustainable production. Although hyperspectral data offer significant advantages for monitoring subtle physiological changes in crops, accurately determining nitrogen status based on spectral information remains challenging. In this study, field experiments were conducted during the jointing stage of winter wheat on the Loess Plateau from 2018 to 2020. Concurrent measurements of leaf nitrogen concentration (LNC) and hyperspectral reflectance were collected to derive three types of spectral parameters: traditional vegetation indices, two-dimensional optimal spectral indices, and three-dimensional optimal spectral indices. Spectral parameters exhibiting a significant correlation with LNC (p < 0.05) were selected and combined as inputs for three machine learning models—extreme learning machine (ELM), back-propagation neural network (BPNN), and random forest (RF)—to develop LNC estimation models. The results demonstrated that, among the traditional indices, the Double Difference Index (DDn) showed the strongest correlation with LNC (r = 0.674). Within the multidimensional optimal indices, the differential three-dimensional scattering index (DTSI) exhibited the highest sensitivity to LNC (r = 0.721) at wavelength combinations of 833 nm, 755 nm, and 802 nm. Moreover, Model Input Combination 5 (comprising empirical indices plus three-dimensional optimal indices) further enhanced estimation accuracy. The RF model using Combination 5 achieved the best performance on the validation set (R² = 0.827, RMSE = 2.803 mg g⁻¹, MRE = 7.664%), significantly outperforming other model–input combinations. This study confirms the feasibility and high accuracy of winter wheat LNC inversion using novel multidimensional spectral indices and provides a new approach for real-time, non-destructive monitoring of nitrogen status in winter wheat. Full article

As society ages, there is a growing concern about the comfort and health of elderly people. Although populations around the world, including Slovenia, are rapidly aging, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. An increasing number of older adults live with chronic diseases, functional limitations, or frailty. In 2025, Slovenia launched the project Systemic Approach to Frailty with a Focus on Pre-Frailty for Healthy and Hight-Quality Ageing, within the European Cohesion Policy Programme 2021–2027, aiming to address frailty through multidimensional and community-based interventions. In addition to presenting the project framework, this paper provides an analytical preliminary review of existing literature, critically reflecting on research gaps in the field. The main aim of this paper is to explore the possibilities for creating healthy living environments that support the prevention and management of frailty. The project’s core innovation lies in the integration of public health principles into urban planning and design through a structured, community-based approach and the use of the Living Environmental Assessment (OBO) Tool. This tool enables urban planners, municipalities, and local communities to collaboratively evaluate and co-design living environments (e.g., optimizing walkability, green space access, barrier-free design, and social amenities) to build resilience and independence among older adults. Designing inclusive, accessible, and health-promoting environments can help to prevent frailty and improve well-being across all age groups. Full article

Rising household carbon emissions (HCEs) substantially increase residential energy consumption. This review evaluates the four principal quantification methods: Emission Coefficient Method (ECM), Input–Output Analysis (IOA), Consumer Lifestyle Approach (CLA), and Life Cycle Assessment (LCA). The methods are compared according to data requirements, uncertainty levels, and scale suitability. The study synthesizes multidimensional determinants—including household income, household size, urbanization, energy intensity and composition, population aging, and household location—and translates these insights into behavior-informed mitigation pathways grounded in behavioral economics principles. Combining compact-city planning, targeted energy-efficiency incentives, and behavior-nudging measures can reduce HCEs without compromising living standards, providing local governments with an actionable roadmap to carbon neutrality. Full article

Healthcare systems worldwide face growing challenges related to staff shortages, excessive workload, and deteriorating working conditions, which compromise both staff well-being and care quality. Despite these issues, there is a lack of validated tools that capture healthcare professionals’ subjective perceptions of resource adequacy. This study presents the development and initial validation of the Staff Resource Adequacy Questionnaire for Healthcare Professionals (SRAQ-HP), a multidimensional tool designed to assess staffing adequacy and workload, quality of care, and working conditions and support. The development process followed a mixed-methods design, incorporating theoretical foundations from Kanter’s empowerment theory, role enactment models, and professional competence frameworks. The initial item pool of 32 statements was reduced to 26 through expert reviews, focus groups, and pilot testing (n = 35). Content validity index (CVI = 0.931) and face validity index (FVI = 0.976) demonstrated high content relevance and clarity. Cronbach’s alpha for the full scale was 0.841, confirming internal consistency. Expert re-review confirmed strong content (S-CVI/Ave = 0.931) and face validity (FVI = 0.976) for the final 26-item version. Three core dimensions were retained: Staffing Adequacy and Workload, Quality of Care, and Working Conditions and Support. The SRAQ-HP provides a novel, evidence-based approach to systematically assess workforce sufficiency and support structures in clinical settings. It can guide decision-making in healthcare institutions and inform national workforce policies. Further research with larger and more diverse samples is needed to confirm its factorial validity and practical applicability. Full article

Backgorund. Multiple sclerosis (MS) profoundly affects the lives of patients and their families. The experience of the disease is shaped not only by its progression and specific characteristics but also by the quality of medical and caregiving support received. The diagnosis of MS represents a transformative event that may lead to job loss, the need for continuous care, and a significant reorganization of family roles. In Italy, more than 140,000 people are affected by MS (AISM data, 2024). The impact of the disease is multifaceted and complex, involving various aspects of the patient’s life. Dependence on external assistance often becomes an unavoidable necessity, highlighting the importance of exploring the quality of life of people with MS in the Italian context. The main objective is to assess the quality of life of individuals affected by MS, both before diagnosis and during the course of the disease. A secondary aim is to identify related psycho-physical consequences, including care-related needs. Methods: An online survey was conducted through various associations operating across Italy, involving a sample of 99 individuals diagnosed with MS. Results: The results show a predominance of female participants, with a mean age of 41 years. The disease was reported to be at an early stage in 66.7% of cases and advanced in 33.3%, with none of the respondents being in a terminal phase. The most frequent clinical form was relapsing–remitting MS (RRMS), which accounted for 78.8% of the cases. In terms of employment and daily activities, more than half of the participants reported underperforming (59.6%) or limiting specific tasks (51.5%) due to disability caused by the disease. Emotional distress had even more pronounced effects, with 63.6% reporting a decline in performance and 62.6% experiencing concentration difficulties. Quality of life was significantly affected, particularly in the physical and emotional domains. Vitality, physical pain, perceived health, and psychological well-being emerged as compromised dimensions, pointing to the need for a multidimensional care model that integrates therapeutic, rehabilitative, and psychosocial interventions. Individuals in the early stages of MS tended to maintain better work relationships and demonstrated higher levels of professional engagement. Conclusions: The findings underscore the importance of a continuous and personalized care approach, addressing not only clinical treatment but also psychological and social support. These aspects are crucial for monitoring patients’ needs, promoting quality of life, facilitating disease acceptance, and mitigating psychological distress. Full article

Blood lactate (BLa) concentration is a pivotal biomarker of exercise intensity and physiological stress, which provides insights into athletic performance and recovery. However, traditional lactate measurement requires invasive blood sampling, which presents significant limitations, including procedural discomfort, infection risks, and impracticality for continuous monitoring. Though non-invasive measurements of BLa concentration have emerged, most rely on a single physiological indicator like heart rate and sweat rate, and their accuracy and reliability remain limited. To address these limitations, this study proposes an innovative multi-sensor fusion framework for non-invasive estimation of BLa. By leveraging the inherent multisystem and multidimensional coordination of human physiology during exercise, the framework integrates a range of physiological signals (e.g., heart rate variability and respiratory entropy) and biomechanical signals (e.g., motion data). We proposed a stacking ensemble model that leverages the complementary strengths of these signals and achieved exceptional predictive performance with near-perfect correlation (R² = 0.9661) while maintaining high precision (MAE = 0.1816 mmol/L) and robustness (RMSE = 0.5891 mmol/L). Furthermore, the model’s exceptional capability extends to blood lactate threshold detection with 98.15% classification accuracy, which is a critical metric for training intensity optimization. This approach provides a robust, non-invasive solution for continuous exercise intensity monitoring, demonstrating significant potential for optimizing athletic performance through real-time physiological assessment and data-driven training modulation. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

Limited evidence exists on how teachers contribute to student learning gains in early childhood education. This study draws on the Dynamic Model of Educational Effectiveness (DMEE) and investigates the impact of teacher factors on pre-primary students’ mathematics achievement. It also examines whether the five proposed dimensions—frequency, quality, focus, stage, and differentiation—can clarify the conditions under which these factors influence learning. Using a stage sampling procedure, 463 students and 27 teachers from Greek pre-primary schools were selected. Mathematics achievement was assessed at the beginning and end of the school year, while external observations measured the DMEE factors. Analysis of observation data using multi-trait multilevel models provided support for the construct validity of the measurement framework. Teacher factors explained variation in student achievement gains in mathematics. The added value of using a multidimensional approach to measure the functioning of the teacher factor was identified. Implications of the findings are drawn. Full article

The vocal repertoire of the domestic pig (Sus scrofa domesticus) was examined in this study under conventional housing conditions. Therefore, direct behavior-associated vocalizations of fattening pigs were recorded and assigned to behavioral categories. Subsequently, a mathematical analysis of the recorded vocalizations was conducted using the frequency-based parameters of 25%, 50% and 75% quantiles of the frequency spectrum and the time-based parameters of variance of the time signal, mean level of the individual amplitude modulation and cumulative amplitude modulation. Most vocalizations were positively/neutrally assessed vocalizations constituting 59.7%, of which grunting was by far the most frequent vocalization. Negatively assessed vocalizations accounted for 37.8% of all vocalizations. Data analysis based on the six parameters resulted in a distinguishability of vocalizations related to negatively valenced behavior from those related to positively/neutrally valenced behavior. The study illustrates the relationship between auditory sensory perception and the underlying mathematical signals. It shows how pig vocalizations assessed by observations, for example, as positive or negative, are distinguishable using mathematical parameters but also which ambiguities arise when objective mathematical features widely overlap. In this way, the study encourages the use of more complex algorithms in the future to solve this challenging, multidimensional problem, forming the basis for future automatic detection of negative pig vocalizations. Full article

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality worldwide, with a high proportion of cases diagnosed at advanced stages. Accurate mediastinal staging is essential to guide optimal therapeutic decisions. This study aimed to evaluate the diagnostic performance of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and to develop a composite clinical–molecular score (EXPoSURE) for risk stratification. Methods: A retrospective study was performed that included 131 patients diagnosed with lung cancer between December 2023 and December 2024 at a regional oncology center in Oltenia, Romania. All patients underwent bronchoscopy and EBUS-TBNA using a standardized protocol. Clinical, pathological, and molecular data were collected to assess diagnostic yield, staging performance, and the association with molecular markers. The EXPoSURE score integrated PD-L1, p63, EGFR status, comorbidities, histological type, and TNM stage. Results: EBUS-TBNA provided a conclusive diagnosis in 91.6% of cases, with a low rebiopsy rate of 8.4% and no requirement for mediastinoscopy. Most patients (68%) were diagnosed at stage IV. PD-L1, p63, and EGFR expression showed no significant correlation with TNM stage, while the EXPoSURE score demonstrated promising stratification capability. Occupational exposure appeared to influence disease severity in some subgroups, although further validation is needed. Conclusions: EBUS-TBNA is a valuable, safe, and effective approach for minimally invasive diagnosis and mediastinal staging of lung cancer. The proposed EXPoSURE composite score may contribute to a multidimensional risk assessment, supporting more tailored management strategies and warranting prospective validation. Full article