Search Results (51,570)

We develop a digitization-based analysis workflow for characterizing the entropy and correlation structure of truncated bosonic quantum fields after embedding them into small qubit registers, and illustrate it on the steady state of a coherently pumped micromaser. The cavity field is truncated to 32 Fock levels and embedded into a five-qubit register via a Gray-code mapping of photon number to computational basis states, with binary encoding used as a benchmark. On this register we compute reduced entropies, mutual informations, bipartite negativities and Coffman–Kundu–Wootters three-tangles for all qubit pairs and triplets, and use the resulting patterns to define information graphs. The micromaser Liouvillian naturally supports trapping manifolds in Fock space, whose structure depends on the choice of interaction angle and on thermal coupling to the reservoir. We show that these manifolds leave a clear imprint on the digitized information graph: multi-block trapping configurations induce sparse, banded patterns dominated by a few two-qubit links, while trapping on a single 32-dimensional manifold or coupling to a thermally populated cavity leads to more delocalized and collectively shared correlations. The entropy and mutual-information profiles of the register provide a complementary view on how energy and information are distributed across qubits in different parameter regimes. Although the full micromaser dynamics can in principle generate higher-order entanglement, we focus here on well-defined measures of two- and three-party correlations and treat the emerging information graph as a structural probe of digitized field states. We expect the workflow to transfer to other bosonic fields encoded in small qubit registers, and outline how the resulting information-graph view can serve as a practical diagnostic in studies of driven-dissipative correlation structure. Full article

Machine tools are the major consumers of industrial energy, but their energy efficiency remains low, posing a serious challenge to sustainable manufacturing. The current literature predominantly focuses on isolated subsystems or specific operational phases (e.g., cutting parameters), lacking systematic evaluations of how different methodologies interact within the Life Cycle Assessment (LCA) framework. This paper provides a critical synthesis of three core methodologies—modeling methods, system parameter optimization, and machine learning (ML)—across the design/production, usage, and recycling stages. Unlike descriptive reviews, this study highlights the scientific contribution by defining the applicability boundaries and complementary mechanisms of these approaches. The analysis reveals that while modeling lays the theoretical basis for eco-design and remanufacturing assessments, and optimization effectively resolves multi-objective trade-offs, these static methods struggle with the dynamic complexity of real-time operations where ML excels. However, ML is identified to be constrained by high data dependency and poor generalization in heterogeneous environments. Consequently, this review shows that the ‘cross-application’ of modeling methods and machine learning to construct hybrid models is essential for addressing complex nonlinear relationships and achieving accurate energy prediction throughout the entire life cycle. Finally, future directions such as transfer learning and digital twins are proposed to overcome current generalization bottlenecks, providing a theoretical foundation for the industry’s transition from passive energy assessment to active, intelligent energy management. Full article

Computer systems research faces significant challenges in reproducibility because of toolchain fragmentation and the rapid evolution of the RISC-V ecosystem. Many research artifacts stay as `digital tombstones’ because they lack stable build environments and suffer from undocumented dependencies. This work presents the AIPR (Automated Instruction-level Patching and Rewriting) framework to address the gap between unstable hardware specifications and reproducible research. The methodology shifts the focus from complex source-level recompilation to direct executable-level modification. A three-stage pipeline automates instruction-level analysis, immediate reconstruction, and binary patching in ELF binaries. Experimental evaluations with the V-FRONT RISC-V processor include 2000 independent trials. These trials verify the functional robustness of the framework under complex architectural constraints. Furthermore, the AIPR framework achieves a 29.57× speedup in artifact generation compared to traditional GCC-based flows. Full article

Multiband imaging (MBI) is a non-invasive, portable digital technique that has become increasingly widespread in the technical study and condition assessment of paintings, owing to its affordability and ease of use. This paper presents an experimental study aimed at optimising MBI at the microscopic scale—referred to as micro-multiband imaging (µMBI)—with the particular aim of expanding its diagnostic capabilities. A range of µMBI techniques was used on custom-made mock-ups made up of pigments selected for their spectral responses, and representative of traditional artistic materials. The techniques used included microphotography of polarised and unpolarised visible light (µVIS), raking light microphotography (µRL), transmitted light microphotography (µTL), ultraviolet-induced visible luminescence microphotography (µUVL), near-infrared microphotography (µIR), near-infrared micro-trans-irradiation (µIRT), and near-infrared false-colour microphotography (µIRFC). The results obtained through µMBI were compared with those from standard MBI methods, allowing for a critical discussion of the strengths and limitations of this emerging approach. Results evidence that µMBI provides high-resolution, spatially specific insights into materials and painting techniques, offering a more detailed understanding at the microscale of how a painting was executed. It also enables the assessment of deterioration processes (e.g., cracking, delamination, and metal soap formation), contributing to a deeper comprehension of the origin and progression of failure phenomena and supporting the development of more informed, preventive conservation strategies. Full article

High-sensitivity, high-resolution electric field sensors (EFS) find extensive applications across multiple domains, including atmospheric monitoring, aerospace, power grid management, and industrial automation. While conventional electric field measurement techniques suffer from integration challenges and high-power consumption, micro-electromechanical systems (MEMS)-based EFS offer distinct advantages through miniaturization, integration capability, and functional intelligence. This research incorporates frequency modulation technology into MEMS EFS, leveraging its inherent noise immunity, long-range transmission capacity, and compatibility with digital systems to enhance measurement precision. The sensor’s lateral and axial symmetry configurations are systematically investigated to reveal how asymmetric stiffness perturbations (negatives vs. positives) optimize performance, aligning with symmetry principles in MEMS design. Experimental results demonstrate that the lateral configuration achieves optimal performance with a sensitivity of 0.091√Hz/(kV/m) and a resolution of 1.01 kV/m, whereas the axial configuration yields an average sensitivity of 0.038 √Hz/(kV/m) with a corresponding resolution of 2.37 kV/m. The measurement range of the sensor is from −193.4 kV/m to 193.4 kV/m. Full article

Background: In recent years, growing attention has been paid to the lifestyle factors that influence young adults’ well-being. University students represent young adults at risk of Sedentary Behavior (SB) and mental distress. Sport Science students represent a health-conscious population, less prone to mental distress. This study aims to investigate the associations between physical activity (PA) levels, different determinants of quality of life (QoL), orthorexia nervosa (ON) symptoms, and internet use among Sport Science students living in Naples. Methods: An online survey comprising General Data (GD) and eight validated questionnaires was used to assess PA levels, mood, sleep quality, eating habits, and digital behavior in a population of university students enrolled in Sport Science courses at Parthenope University, Naples. The statistical analyses included descriptive statistics, Student’s t-test, a Mann–Whitney U Test, frequencies, chi-square tests, and a Spearman’s rank correlation. All the analyses were performed using JASP and Jamovi software. Results: We surveyed 775 students (472 M; 303 F; 22.85 ± 3.85 y; BMI 23.74 ± 3.63 kg/m²). Regarding the MET-min/week, 65% of participants reported being highly active, 28% moderately active, and 7% inactive. Poor sleep quality was reported by 20% of those surveyed. Additionally, 84% of participants declared average internet use, which positively correlated with their emotional profile and sleep quality. High PA levels were directly associated with the presence of ON symptoms in 27% of the participants, most of whom exercised in gyms. Conclusions: To our knowledge, this is the first study conducted on a study population of Sport Science University students addressing the complex and interconnected relationships between PA levels, QoL, ON symptoms, and internet use. Full article

The intricate and dynamic nature of agricultural supply chains imposes stringent demands on optimization methodologies, necessitating multi-objective considerations, privacy safeguards, and decision transparency to address pivotal challenges in ensuring food security and sustainable development. This study introduces a Dynamic Heterogeneous Multi-Objective Graph Neural Network (DHMO-GNN) model, meticulously tailored for optimizing agricultural supply chains. It integrates five core modules: data preprocessing and heterogeneous graph construction, dynamic graph neural networks, multi-objective optimization, interpretability enhancement, and federated learning collaboration. The model adeptly captures temporal dynamics through sequential attention mechanisms and incremental updates, harmonizes cost, delivery time, and carbon emissions via multi-task learning and Pareto optimization, augments decision transparency with GNNExplainer and SHAP, and surmounts data silos by leveraging federated learning alongside differential privacy. Empirical evaluations on the Chengdu Hongguang Town Farmers’ Market dataset demonstrate that the centralized DHMO-GNN variant achieves a hypervolume indicator (HV) of 0.849, surpassing baseline models; the federated variant exhibits only a 2.6% decline under privacy constraints, underscoring its robustness. Ablation studies further corroborate the synergistic contributions of each module. This research furnishes an efficacious and trustworthy framework for the intelligent management of agricultural supply chains, holding profound implications for advancing digital transformation and green development. Full article

Rock glaciers are key indicators of mountain permafrost and act as climatically resilient water reservoirs in arid mountains. This study presents the first inventory and kinematic classification of rock glaciers in Western Tien Shan (Kazakhstan and Kyrgyzstan), combining geomorphological mapping with InSAR time-series analysis. Using high-resolution optical imagery (Google Earth Pro (version 7.3.6.10441), Bing Maps, SAS Planet (version 200606.10075), digital elevation models, and Small Baseline Subset InSAR processing, 741 rock glaciers covering more than 70.5 km² were identified. Activity classification revealed 232 transitional and 509 active forms, with mean seasonal displacement rates of ~15 cm yr⁻¹ calculated based on August and September observations. Spatial analysis showed a strong rock glacier concentration on north-facing slopes (>66% of total area) with reduced potential incoming solar radiation. Rock glaciers mainly occur between 2800 and 3800 m a.s.l., with a mean elevation of 3340 m a.s.l. However, their kinematic activity varies across mid-altitudinal ranges, underscoring the influence of slope, aspect, shading, and local topography. Integration with the Global Permafrost Zonation Index (PZI) indicated a lower permafrost boundary at ~1922 m a.s.l., with the largest and most active glaciers occurring at intermediate PZI values (0.5–0.7). This first rock glacier inventory for the Western Tien Shan establishes a benchmark dataset that supports the validation and refinement of global models at a regional scale, guides priorities for permafrost monitoring, and provides a replicable framework for inventory development in other data-scarce mountain regions. Full article

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by complex interactions across molecular, cellular, circuit, and behavioral scales. While experimental and clinical studies have provided critical insights into PD pathology, integrating these heterogeneous data into coherent mechanistic frameworks and translational strategies remains a major challenge. Computational modeling offers a powerful approach to bridge these scales, enabling the systematic investigation of disease mechanisms, candidate biomarkers, and therapeutic strategies. In this review, we survey state-of-the-art computational approaches applied to PD, spanning molecular dynamics and biophysical models, cellular- and circuit-level network models, systems and abstract-level simulations of basal ganglia function, and whole-brain and data-driven models linked to clinical phenotypes. We highlight how multiscale and hybrid modeling strategies connect α-synuclein pathology, mitochondrial dysfunction, oxidative stress, and dopaminergic degeneration to alterations in neural dynamics and motor and non-motor symptoms. We further discuss the role of computational models in biomarker discovery, including imaging, electrophysiological, and digital biomarkers. In particular, eye-movement-based measures are highlighted as quantitative, reproducible behavioral signals that provide principled constraints for individualized computational modeling. We also review the emerging impact of computational approaches on drug discovery, target prioritization, and in silico clinical trials. Finally, we examine future directions toward personalized and precision medicine in PD, emphasizing digital twin frameworks, longitudinal validation, and the integration of patient-specific data with mechanistic and data-driven models. Together, these advances underscore the growing role of computational modeling as an integrative and hypothesis-generating framework, with the long-term goal of supporting data-constrained predictive approaches for biomarker development and translational applications. Full article

Cultural heritage is increasingly recognized as a pivotal driver of sustainable rural tourism, helping destinations diversify their offerings, reduce seasonality, strengthen local identity, and bring socio-economic benefits to depopulating communities. This study investigates its role in Šibenik-Knin County (Croatia), a Mediterranean region characterized by abundant tangible heritage (archaeological sites, medieval fortresses, sacral monuments, dry-stone architecture) and rich intangible traditions (gastronomic practices, klapa and ojkanje singing, local customs), yet still affected by a pronounced coastal–hinterland tourism imbalance. Through semi-structured interviews with ten key stakeholders from museums, tourist boards, academia, cultural institutions, and rural entrepreneurship organizations, complemented by literature review and analysis of policy and statistical data, the research reveals unanimous agreement that cultural heritage constitutes the county’s strongest competitive advantage and the most authentic foundation for year-round rural tourism products. However, systematic under-valorization persists due to chronic underfunding, weak cross-sectoral cooperation, limited professional capacity, and the absence of dedicated hinterland destination-management structures. The findings indicate that targeted investment, high-quality interpretation, and genuine community engagement can rapidly transform heritage resources into viable tourism assets, as demonstrated by existing successful cases. Realizing this potential requires coordinated governance, improved interpretive and digital infrastructure, and active resident involvement. Full article

This paper investigates whether intelligent workplace technologies improve firm-level productivity and, if so, under what conditions, with particular attention to their implications for the economic and social sustainability of firms. This investigation occurs in a context where firms increasingly combine automation, artificial intelligence (AI), and work-from-home (WFH) practices to sustain performance under structural shocks such as the COVID-19 pandemic. Despite evidence that firms adopt these technologies jointly and reorganize work accordingly, existing research typically examines them in isolation. We develop a micro-founded, task-based production model in which firms allocate tasks between on-site and remote labor and automated capital in an optimal manner. This model allows both automation technologies and remote work collaboration tools to affect productivity and coordination costs that are central to long-term organizational sustainability. Using firm-level survey data from nearly 4000 large firms across industries and countries (2018–2021), we show that working from home (WFH) exhibits diminishing productivity returns when scaled in isolation, reflecting rising coordination frictions. In contrast, firms that combine WFH with automation and digital collaboration tools experience significantly higher labor productivity growth. These integrated technology systems support sustainable productivity by enabling capital deepening, resilient task reallocation, and more efficient use of labor resources over time. Overall, the findings suggest that productivity gains—and by extension sustainable firm performance—stem from integrated workplace technology systems rather than isolated investments, highlighting the importance of coherent technology strategies for organizing work in the post-pandemic economy. Full article

Simulating entangled, many-body quantum systems is notoriously hard, especially in the case of the high-dimensional nature of the underlying physical objects. In this work, we propose an approach for simulating the Potts model based on the Suzuki–Trotter decomposition that we construct for qudit systems. Specifically, we introduce two qudit-native decomposition schemes: (i) the first utilizes the Mølmer–Sørensen gate and additional local levels to encode the Potts interactions, while (ii) the second employs a light-shift gate that naturally fits qudit architectures. These decompositions enable a direct and efficient mapping of the Potts model dynamics into hardware-efficient qudit gate sequences for a trapped-ion platform. Furthermore, we demonstrate the use of a Suzuki–Trotter approximation with our evolution-into-gates framework for detecting the dynamical quantum phase transition. Our results establish a pathway toward qudit-based digital quantum simulation of many-body models and provide a new perspective on probing nonanalytic behavior in high-dimensional quantum many-body models. Full article

Bridges are a crucial part of infrastructure, but many are in urgent need of maintenance. Digital methods like bim and Digital Twinning can support this process but depend on digital models that are often missing for existing structures. Automating the reconstruction of these models from existing documentation, such as construction drawings, is essential to accelerate digital adoption. Addressing a key step in the reconstruction process, this paper presents an end-to-end pipeline for extracting bridge cross-sections from drawings. First, the YOLOv8 network locates and classifies the cross-sections within the drawing. The results are then processed by the segmentation model sam, which generates pixel-wise masks without requiring task-specific training data. This eliminates the need for manual mask annotation and enables straightforward adaptation to different cross-section types, making the approach broadly applicable in practice. Finally, a global optimization algorithm fits parametric templates to the masks, minimizing a custom loss function to extract geometric parameters. The pipeline is evaluated on 33 real-world drawings and achieves a median parameter deviation of −2.2 cm and 2.4 cm, with an average standard deviation of 35.4 cm. Full article

Digital mental health resources have expanded rapidly in the wake of the COVID-19 pandemic, offering new opportunities to improve access to mental healthcare through technologies such as AI chatbots, mobile apps, and online platforms. Despite this growth, significant challenges persist, including low user retention, limited digital literacy, unclear privacy regulations, and insufficient evidence of clinical effectiveness and safety. AI chatbots, which act as virtual therapists or companions, provide counseling and personalized support, but raise concerns about user dependence, emotional outcomes, privacy, ethical risks, and bias. User experiences are mixed: while some report enhanced social health and reduced loneliness, others question the safety, crisis response, and overall reliability of these tools, particularly in unregulated settings. Vulnerable and underserved populations may face heightened risks, highlighting the need for engagement with individuals with lived experience to define safe and supportive interactions. This review critically examines the empirical and grey literature on AI chatbot use in mental healthcare, evaluating their benefits and limitations in terms of access, user engagement, risk management, and clinical integration. Key findings indicate that AI chatbots can complement traditional care and bridge service gaps. However, current evidence is constrained by short-term studies and a lack of diverse, long-term outcome data. The review underscores the importance of transparent operations, ethical governance, and hybrid care models combining technological and human oversight. Recommendations include stakeholder-driven deployment approaches, rigorous evaluation standards, and ongoing real-world validation to ensure equitable, safe, and effective use of AI chatbots in mental healthcare. Full article

Geomorphological change is a fundamental consequence of high-magnitude flood events, as extreme hydraulic forcing can rapidly reshape river channels, redistribute sediment, and alter floodplain connectivity. This study applies multi-temporal UAS-based Structure-from-Motion (SfM) photogrammetry to quantify flood-induced geomorphological changes along two representative reaches of the Lilas River (Evia Island, Central Greece) affected by the extreme August 2020 flash flood. High-resolution aerial surveys were conducted prior to the event (June 2018) and shortly after the flood (September 2020), producing Digital Surface Models (DSMs) and orthomosaics with a ground sampling distance of ~2.5 cm. Differential DSM analysis reveals pronounced spatial heterogeneity in erosion and deposition, with net erosional lowering locally exceeding 7 m and depositional aggradation reaching up to ~5 m after accounting for vegetation effects. Channel widening was the dominant response, with cross-sectional widths increasing by a factor of three to nine at selected locations, driven primarily by lateral bank erosion. The results highlight the strong interaction between extreme hydrological forcing, loose alluvial sediments, vegetation removal, and human interventions such as roads and engineered terraces. The study demonstrates how repeatable UAS–SfM workflows can provide quantitative evidence to support post-flood assessment, guide infrastructure adaptation, and inform river restoration and flood risk management in Mediterranean catchments prone to extreme events. Full article