Search Results (14,207)

This paper examines how organizations and regions integrate digital transformation with environmental sustainability (“twin transition”). Based on 43 semi-structured expert interviews across 27 countries, we identify five empirically grounded insights. First, adoption is propelled by competitive pressure, external shocks, and rising regulatory and stakeholder demands. Second, success depends on internal capabilities—clear leadership vision and workforce skills—together with supportive regional innovation ecosystems. Third, deliberate technological synergies—especially digital twins for lifecycle optimization, Artificial Intelligence (AI)/analytics and Internet of Things (IoT) for monitoring, and blockchain for traceability—enable measurable gains in resource efficiency and environmental performance. Fourth, integration strengthens eco-innovation capacity, resilience to disruption, competitive positioning, and regional innovation dynamics. Fifth, persistent barriers remain; organizational silos, key performance indicators (KPIs) misalignment, rebound effects from digital infrastructures, and uneven regional capabilities. By linking enabling conditions, integration mechanisms, and barriers, the study advances theory and offers actionable guidance for managers and policymakers on realizing the twin transition, using descriptive counts to indicate salience within a purposive expert sample rather than to draw statistical inferences. Full article

Tensegrity structures, by definition composed of compressed members suspended in a network of tensile cables, are characterised by a high strength-to-weight ratio and the ability to undergo reversible deformations. Their application as cores of sandwich panels represents an innovative approach to lightweight design, enabling the regulation of mechanical properties while reducing material consumption. This study presents a finite element modelling procedure that combines analytical determination of prestress using singular value decomposition with implementation in the ABAQUS™ 2019 software. Geometry generation and prestress definitions were automated with Python 3 scripts, while algebraic analysis of individual modules was performed in Wolfram Mathematica. Two models were investigated: M1, composed of four identical modules, and M2, composed of four modules arranged in two mirrored pairs. Model M1 exhibited a linear elastic response with a constant global stiffness of 13.9 kN/mm, stable regardless of the prestress level. Model M2 showed nonlinear hardening behaviour with variable stiffness ranging from 0.135 to 1.1 kN/mm and required prestress to ensure static stability. Eigenvalue analysis confirmed the full stability of M1 and the increase in stability of M2 upon the introduction of prestress. The proposed method enables precise control of prestress distribution, which is crucial for the stability and stiffness of tensegrity structures. The M2 configuration, due to its sensitivity to prestress and variable stiffness, is particularly promising as an adaptive sandwich panel core in morphing structures, adaptive building systems, and deployable constructions. Full article

The thermal behavior of 3D-printed polyvinylidene fluoride (PVDF)-based composites enhanced with carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and their hybrid formulations was investigated under Joule heating at applied voltages of 2, 3, and 4 V. The influence of filler type and weight fraction on both electrical and thermal conductivity was systematically assessed using a Design of Experiments (DoE) approach. Response Surface Methodology (RSM) was employed to derive an analytical relationship linking conductivity values to filler loading, revealing clear trends and interaction effects. Among all tested formulations, the composite containing 6 wt% of GNPs exhibited the highest performance in terms of thermal response and electrical conductivity, reaching a steady-state temperature of 88.1 °C under an applied voltage of just 4 V. This optimal formulation was further analyzed through multiphysics simulations, validated against experimental data and theoretical predictions, to evaluate its effectiveness for potential practical applications—particularly in de-icing systems leveraging Joule heating. The integrated experimental–theoretical–numerical workflow proposed herein offers a robust strategy for guiding the development and optimization of next-generation polymer nanocomposites for thermal management technologies. Full article

Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has traditionally relied on engineers’ expertise or simple univariate search methods. This paper proposes an automatic tuning method using an improved differential evolution algorithm. This algorithm introduces a ranking and subpopulation classification for individuals, enabling distinct mutation strategies. Validation on the CEC-2017 benchmark functions confirms the superiority of the improved algorithm. In automatic tuning experiments, it achieved a 25.3% performance gain over the univariate search method and also surpassed both the classical differential evolution algorithm and standard particle swarm optimization algorithm. This method proves to be an effective approach for enhancing the performance of quadrupole mass spectrometers. Full article

This study proposes a composite acoustic porous duct metamaterial muffler composed of a perforated tortuous channel and an externally wrapped porous layer, integrating both structural resonance and material damping effects. Theoretical models for the perforated plate, tortuous channel, and porous material were established, and analytical formulas for the total acoustic impedance and transmission loss of the composite structure were derived. Finite element simulations verified the accuracy of the models. A systematic parametric study was then performed on the effects of porous material type, thickness, and width on acoustic performance, showing that polyester fiber achieves the best results at a thickness of 30 mm and a width of 5 mm. Further analysis of periodic distribution modes revealed that axial periodic arrangement significantly enhances the peak noise attenuation, radial periodic arrangement broadens the effective bandwidth, and multi-frequency parallel configurations further expand the operating range. Considering practical duct conditions, a single-layer multi-cell array was constructed, and its modal excitation mechanism was clarified. By employing the Particle Swarm Optimization (PSO) algorithm for multi-parameter optimization, the average transmission loss was improved from 26.493 dB to 29.686 dB, corresponding to an increase of approximately 12.05%. Finally, physical samples were fabricated via 3D printing, and four-sensor impedance tube experiments confirmed good agreement among theoretical, numerical, and experimental results. The composite structure exhibited an average experimental transmission loss of 24.599 dB, outperforming the configuration without porous material. Overall, this work highlights substantial scientific and practical advances in sound energy dissipation mechanisms, structural optimization design, and engineering applicability, providing an effective approach for broadband and high-efficiency duct noise reduction. Full article

Background/Objectives: Patients with multimorbidity frequently rely on emergency services when continuity of care is weak. Strengthening communication between emergency and primary care can prevent unnecessary hospitalizations, yet this relationship remains underexplored. The aim of this study was to analyze the relationship between primary health care utilization in patients with multimorbidity and their demand for prehospital emergency services. Methods: An observational, longitudinal, analytical, and retrospective study was conducted in Málaga (Spain) between 2013 and 2017. Adults (>18 years) with multimorbidity who requested prehospital emergency care services at home were included; those with cancer, rare diseases, severe mental disorders, or incomplete electronic records were excluded. Variables encompassed sociodemographic, clinical, and behavioral characteristics, comorbidities, functional status, polypharmacy, resource type, and outcomes (on-site resolution or hospital referral). Primary health care visits before and after prehospital emergency use were extracted from electronic records. Descriptive, bivariate, and multivariate analyses were performed. Results: Among 532 patients, prior primary health care attendance predicted subsequent utilization (β = 0.57; p < 0.001), along with caregiver availability (β = 0.12; p = 0.001) and prehospital emergency services hyper-demand (β = 0.08; p = 0.022). Super-utilizers were younger, had ≥4 comorbidities, polypharmacy, prior family medicine visits, home oxygen therapy, and lower substance or alcohol use. Conclusions: In multimorbid adults, prehospital emergencies demand is influenced by factors beyond severity, including comorbidities, polypharmacy, the use of home medical devices, caregiver availability, and primary health care utilization patterns. Strengthening coordination between prehospital emergencies and primary health care, promoting patient–caregiver education, and implementing early notification pathways may improve care continuity and reduce avoidable emergencies. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) of antipsychotic medications plays an important role in optimizing treatment efficacy, reducing adverse effects, and supporting adherence. While Ultra-High Performance Liquid Chromatography–Tandem Mass Spectrometry (UHPLC–MS/MS) has long been the gold standard for antipsychotic quantification, recent advances in automated platforms and microsampling raise questions about its current clinical practicality. This systematic review evaluated the clinical applicability and analytical performance of UHPLC-based methods for monitoring antipsychotic drugs, focusing on precision, recovery, matrix effects, and suitability across various biological matrices. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted for studies published between 2013 and 2024 involving UHPLC-based quantification of antipsychotics in clinical samples from adult patients. Data on analytical parameters, sample matrices, and study characteristics were extracted. A custom quality checklist was used to assess methodological rigor. In addition to qualitative synthesis, non-traditional quantitative approaches were applied, including descriptive aggregation of recovery, matrix effects, and precision across studies, as well as correlation analyses to explore relationships among performance parameters. Results: Twelve studies were included, spanning a range of typical and atypical antipsychotics and metabolites. Plasma and serum demonstrated the highest analytical reliability (recovery >90%, minimal matrix effects), while dried blood spots (DBSs), whole blood, and oral fluid showed greater variability. Clinically, UHPLC–MS/MS enabled more accurate dose adjustments and identification of non-adherence, outperforming immunoassays in sensitivity, specificity, and metabolite detection. Microsampling methods showed promise for outpatient and decentralized care but require further clinical validation. Conclusions: UHPLC–MS/MS remains the most robust and reliable method for TDM of antipsychotics, especially when quantification of active metabolites is required. While logistical barriers remain, technological advances may enhance feasibility and support broader integration into routine psychiatric care. Full article

Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R² = 0.98, error = 11.48%) and canopy area (R² = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R² of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting. Full article

Metal-organic frameworks (MOFs), a class of crystalline porous materials featuring a high specific surface area, tunable pore structures, and functional surfaces, exhibit remarkable potential in fluorescent sensing. This review systematically summarizes recent advances in the construction strategies, sensing mechanisms, and applications of MOF-based fluorescent sensors. It begins by highlighting the diverse degradation pathways that MOFs encounter in practical applications, including hydrolysis, acid/base attack, ligand displacement by coordinating anions, photodegradation, redox processes, and biofouling, followed by a detailed discussion of corresponding stabilization strategies. Subsequently, the review elaborates on the construction of sensors based on individual MOFs and their composites with metal nanomaterials, MOF-on-MOF heterostructures, covalent organic frameworks (COFs), quantum dots (QDs), and fluorescent dyes, emphasizing the synergistic effects of composite structures in enhancing sensor performance. Furthermore, key sensing mechanisms such as fluorescence quenching, fluorescence enhancement, Stokes shift, and multi-mechanism coupling are thoroughly examined, with examples provided of their application in detecting biological analytes, environmental pollutants, and food contaminants. Finally, future directions for MOF-based fluorescent sensors in food safety, environmental monitoring, and clinical diagnostics are outlined, pointing to the development of high-performance, low-cost MOFs; the integration of multi-technology platforms; and the construction of intelligent sensing systems as key to enabling their practical deployment and commercialization. Full article

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

This paper presents a serious game designed to support the teaching of PID controllers. The game couples a visually clear Unity scene with a physics-accurate digital model of a drone with a single degree of freedom (called a one-axis drone) and helps prepare students to meet the demands of Industry 4.0 and 5.0. An analytics back-end logs system error at 10 Hz and interaction metrics, enabling instructors to diagnose common tuning issues from a plot and to provide actionable hints to students. The design process that led to choosing the one-axis drone and turbulence application via “turbulence balls” is explained, after which the implementation is described. The proposed solution is evaluated in a within-subjects study performed with 21 students from mixed technical backgrounds across two short, unsupervised tinkering sessions of up to 10 min framed by four quizzes of both general and theoretical content. Three questions shaped the analysis: (i) whether error traces can be visualized by instructors to generate actionable hints for students; (ii) whether brief, unsupervised play sessions yield measurable gains in knowledge or stability; and (iii) whether efficiency of tuning improves without measurable changes in tune performance. Results show that analysis of plotted error values exposes recognizable issues with PID tunes that map to concrete hints provided by the instructor. When it comes to unsupervised play sessions, no systematic pre/post improvement in quiz scores or normalized area under absolute error was observed. However, it required significantly less effort from students in the second session to reach the same tune performance, indicating improved tuning efficiency. Overall, the proposed serious game with the digital twin-inspired one-axis drone and custom analytics back-end has emerged as a practical, safe, and low-cost auxiliary tool for teaching PID controllers, helping bridge the gap between theory and practice. Full article

For optimizing the operational efficiency and productivity within blast furnace processes, a profound understanding of the viscous flow characteristics of CaO–SiO₂–MgO–Al₂O₃–B₂O₃ slag systems is of paramount importance. In this study, we conducted a comprehensive investigation into the influence of the CaO/SiO₂ and MgO/Al₂O₃ ratios on the viscosity, break point temperature (T_Br), and activation energy (E_η) of low boron-bearing high-alumina slag. Concurrently, we elucidated the underlying mechanisms through which these ratios affect the viscous behavior of the slag by employing a combination of analytical techniques, including X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and thermodynamic modeling using the Factsage software. The experimental findings reveal that, as the CaO/SiO₂ ratio increases from 1.10 to 1.30, the slag viscosity at 1773 K decreases from 0.316 Pa·s to 0.227 Pa·s, while both the T_Br and E_η exhibit an upward trend, rising from 1534 K and 117.01 kJ·mol⁻¹ to 1583 K and 182.86 kJ·mol⁻¹, respectively. Conversely, an elevation in the MgO/Al₂O₃ ratio from 0.40 to 0.65 results in a reduction in slag viscosity at 1773 K from 0.290 Pa·s to 0.208 Pa·s, accompanied by a decrease in T_Br from 1567 K to 1542 K. The observed deterioration in slag flow properties can be attributed to an enhanced polymerization degree of complex viscous structural units within the slag matrix. Ultimately, our study identifies that an optimal viscous performance of the slag is achieved when the CaO/SiO₂ ratio is maintained at 1.25 and the MgO/Al₂O₃ ratio is maintained at 0.55, providing valuable insights for the rational design and control of blast furnace slag systems. Full article

Background: Supplier evaluation and selection are pivotal processes in supply chain management, profoundly influencing organisational efficiency and sustainability. This study addresses the limitations of traditional multi-criteria decision-making approaches, particularly the Technique for Order Preference by Similarity to an Ideal Solution, which often lacks dimensional reduction capability and assumes uniform weight distribution across criteria. Methods: To overcome these challenges, a hybrid model integrating non-negative matrix factorisation, random forest, and the Technique for Order Preference by Similarity to an Ideal Solution is developed for supplier evaluation in the pharmaceutical sector. The method first applies non-negative matrix factorisation to condense twenty-four evaluation criteria into eight core dimensions, enhancing analytical efficiency and reducing complexity. Random forest is then employed to derive data-driven weights for each criterion, ensuring accurate prioritisation. Finally, the Technique for Order Preference by Similarity to an Ideal Solution ranks suppliers and provides actionable insights for decision-makers. Results: Results from real-world pharmaceutical data validate the model’s effectiveness and demonstrate superior performance over conventional evaluation methods. Conclusions: The findings confirm that integrating machine learning techniques with established decision-making frameworks enhances precision, interpretability, and sustainability in supplier selection while requiring adequate data quality and computational resources for implementation. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

The management of waste has become increasingly complex due to the growing volume and diversity of waste generated by modern societies. Effective collection systems are essential for mitigating environmental impacts and promoting sustainability. However, the increasing complexity of waste management requires a comprehensive approach that considers multiple criteria in order to evaluate the performance of these systems. This study evaluates the environmental performance of waste collection systems by comparing various methods using the Analytic Hierarchy Process (AHP). The research involves identifying key performance indicators (KPIs) that could be relevant for all the stakeholders involved and important for environmental sustainability. These KPIs are then used as criteria for the AHP model, allowing for a detailed comparison of each collection method. Data is collected from a case study in the Friuli-Venezia Giulia region in Italy. The preliminary results indicate significant variations in environmental performance and user fruitfulness across different collection methods. Door-to-door collection was found to be the preferred methodology with an absolute weight of 0.527. The AHP framework proves to be a robust tool for integrating diverse criteria and stakeholder preferences, facilitating informed decision-making in waste management. Moreover, it underscores the importance of adopting a holistic approach to evaluate and improve recycling systems. By leveraging AHP, policymakers and waste management professionals can identify optimal strategies that align with environmental sustainability goals. Full article