Search Results (108)

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined alongside communication protocols such as SPI, I²C, Bluetooth Low Energy, Wi-Fi, and NFC. Critical challenges related to miniaturization, noise, power constraints, and reproducibility are analyzed using representative platforms. This review highlights the transition of potentiostats into integrated, intelligent, and connected sensing systems, and outlines design considerations for scalable electrochemical applications in clinical, environmental, and industrial domains. Full article

The design of differentiated fiscal instruments for industrial sustainability requires robust, data-driven tools capable of capturing the heterogeneity of environmental performance across manufacturing units—a challenge that conventional econometric approaches address only partially, given the non-linear nature of operational–environmental interactions in reconfigurable production systems. This study introduces a two-phase computational framework that integrates unsupervised machine learning and supervised classification to generate evidence-based sustainability profiles for fiscal policy targeting. Its principal contribution is the combination of K-Means clustering with a binary artificial neural network (ANN) classifier, operationalized through an accessible decision-support interface that enables differentiated incentive allocation without requiring programming expertise from policymakers. A dataset of 1000 manufacturing records comprising seven operational and technological input variables—material usage, production capacity, reconfiguration time, downtime, AI optimization, IoT connectivity, and predictive maintenance—and three environmental output indicators—energy consumption, carbon emissions, and waste generation—was analyzed. In Phase One, K-Means segmentation with k = 6, selected through multi-criteria convergence (Silhouette = 0.102; Elbow, Davies–Bouldin, and Calinski–Harabasz indices), identified six distinct sustainability profiles with marked environmental differentiation. In Phase Two, a binary ANN classifier (architecture: 7 → 64 → 32 → 1 neurons; ReLU and sigmoid activations) was trained to distinguish the reference cluster C0 (low environmental impact: energy 145.1 kWh, emissions 45.2 CO₂-eq) from the high-impact cluster C1 (emissions 67.8 CO₂-eq, waste 41.5 kg). The trained classifier achieved an overall accuracy of 75.4% and an AUC-ROC of 0.774 on the held-out test set, with a macro-averaged F1-score of 0.753 and a Cohen’s kappa coefficient of 0.508, indicating moderate-to-substantial agreement beyond chance. Class C1 (high-impact establishments) achieved a precision of 0.794 and a recall of 0.730, supporting reliable identification of manufacturing units that would most benefit from targeted fiscal support. The framework is deployed through a Gradio-based graphical interface incorporating a traffic-light sustainability classification (green/yellow/red), enabling direct and interactive application by tax authorities and industrial policymakers. The modular architecture supports adaptation to larger or sector-specific datasets, making it transferable across industrial policy contexts. Full article

Supply chain risk management has become a core element of corporate strategy, yet systematic evidence on how innovation information disclosure affects supply chain risk remains scarce. We study how innovation information disclosure in firms’ MD&A sections affects supply chain risk. Using data on Chinese A-share listed firms from 2012 to 2023, we find that firms disclosing more innovation-related content face significantly a lower supply chain risk. This result remains true following instrumental variable estimation, propensity score matching, entropy balancing, and controlling for province- and industry-specific time trends. We provide supportive evidence for three circumstances: firms that disclose more have a broader and more diverse set of supply chain partners; they engage in more joint patenting with partners, consistent with higher switching costs and more stable relationships; and they exhibit stronger reputations and commercial credit capacity, consistent with partnerships reinforced through both trust and financial ties. The effect is concentrated among non-SOEs, high-tech firms, firms in competitive industries, and firms outside the digital economy, all settings in which information asymmetry is more severe and alternative channels for conveying innovation capabilities are limited. We also document asymmetric vertical spillovers: downstream customers’ innovation disclosure prompts upstream suppliers to become more transparent, but the reverse does not hold. Supply chain risk, by contrast, affects connected firms in both directions. These findings extend the literature on the economic consequences of innovation disclosure from capital markets to supply chain management. Full article

This paper examines how digital infrastructure affects agricultural upgrading in China’s edible fungi industry, focusing on the divergence between output expansion and unit value enhancement. Using a balanced panel of 28 Chinese provinces from 2019 to 2024, we apply a Two-Stage Least Squares (2SLS) approach, instrumenting digital infrastructure with the 1984 provincial fixed-line telephone penetration rate (first-stage F-statistic = 82.15) to address endogeneity concerns. The results reveal a clear asymmetry between quantity and quality outcomes. Digital infrastructure significantly increases total output (coefficient = 1.540, p < 0.01), primarily through improved market coordination rather than productivity gains. However, it produces no statistically discernible effect on unit output value. This divergence suggests that agricultural digitalization follows a stage-dependent pattern: basic connectivity effectively relaxes constraints on production scaling but is insufficient on its own to shift producers toward higher-value activities. Consequently, scale expansion may proceed without corresponding value creation, raising concerns for long-term economic and environmental sustainability. Achieving genuine agricultural upgrading therefore requires complementary investments in institutional capacity, downstream processing, and brand development alongside digital infrastructure deployment. Full article

Urban emptiness is a recurring spatial condition across contemporary cities, resulting from long-term planning decisions, functional transformations, and shifting socio-economic dynamics. Urban voids are often interpreted as signs of failure or neglect; however, they also represent flexible and open-ended spaces embedded within everyday urban environments. This study develops and tests the Adaptive Void Assessment Framework (AVAF), a five-dimensional typological instrument applied to n = 33 urban voids identified through a systematic grid-based field survey (100 × 100 m resolution) in the central urban zone of Nitra, Slovakia (March 2025–January 2026). The framework evaluates sites across nine indicators spanning openness, social appropriation, ecological succession, temporal persistence, and institutional flexibility, yielding composite Adaptivity Index scores and four dominant adaptive regimes. The findings demonstrate that 34% of identified voids function in a socially active regime while 14% exhibit ecological dominance, with a moderate positive correlation identified between temporal persistence and adaptive capacity (r = 0.46, p < 0.05). This challenges conventional deficit-based classifications and reframes urban voids as active components of the urban metabolism capable of enhancing ecological connectivity and spatial flexibility within post-industrial urban landscapes. This reframes urban voids from residual outcomes of urbanization to spaces with potential for green integration within sustainable contemporary cities. Full article

The Wannachawee Recipe (WCR) is a traditional Thai herbal formulation with a clinical history of use in psoriasis. An observational study conducted at Prapokklao Hospital reported that 93% of psoriasis patients showed good clinical responses. However, the absence of standardized quality control parameters remains a critical barrier to its pharmaceutical reproducibility, safety, and integration into mainstream clinical practice. This study established robust quality specifications and a phytochemical profiling for WCR, in accordance with the Thai Herbal Pharmacopoeia (THP) guidelines, to support its development from traditional use to a standardized therapeutic agent. A multimodal analytical approach was employed, integrating microscopic characterization, physicochemical evaluation, and advanced instrumental techniques. Phytochemical characterization was conducted using High-Performance Liquid Chromatography (HPLC) fingerprinting and Compact Mass Spectrometry (CMS). A validated HPLC method was developed to quantify trans-p-coumaryl alcohol, a key bioactive marker. Anti-inflammatory activity was further assessed by measuring inhibition of nitric oxide production. Physicochemical analysis established rigorous benchmarks, including ethanol-soluble extractive (8.73 ± 0.15% w/w), water-soluble extractive (18.89 ± 0.09% w/w), and loss on drying (<10%), which ensure long-term stability and microbial safety. CMS analysis successfully identified key chemical constituents, including alpha-amyrin, stemone, protocatechuic acid, and trans-p-coumaryl alcohol. HPLC fingerprinting demonstrated high batch-to-batch consistency, while quantitative analysis determined a trans-p-coumaryl alcohol content of 8.77 mg/g extract. Critically, biological evaluation showed that WCR exhibited potent anti-inflammatory activity by inhibiting nitric oxide production, with a superior inhibitory effect compared with the reference drug indomethacin. This study provides a preliminary scientific framework for the standardization of WCR. It defines precise quality specifications and a potential bioactive marker, establishing the rigor needed for regulatory certification and industrial production. This work connects traditional Thai medicine with evidence-based pharmaceutics, positioning WCR as a promising therapy for psoriasis. Full article

In the last decade, as cities increasingly seek sustainable development pathways within the cultural and creative economy, cultural-creative events have gained prominence as strategic instruments for urban transformation. Among them, city design weeks have emerged as complex service systems that connect creative industries, urban governance, and tourism development. This research aims to understand how cultural-creative events (represented by design weeks) facilitate sustainable tourism development from a service design perspective. Adopting a qualitative comparative research design, the study examines 30 design weeks selected through a cross-validated process with the World Design Weeks global network and UNESCO City of Design network. Data from 2020 to 2025 is collected primarily through expert interviews, official reports, and media materials in relation to the United Nations Sustainable Development Goals (SDGs). Grounded in the service design perspective, four Service Design Levels are summarized into 17 assessment dimensions, and experts applied Likert scale to evaluate the relative service intensity of each case. Through cross-case analysis, the findings reveal four distinct models of design weeks, reflecting different configurations of service intensity and strategic orientation. The study contributes theoretically by extending service design theory to cultural-creative tourism research, and practically by providing guidance for the organizers of cultural-creative events seeking to support sustainable city tourism development. Future research may incorporate quantitative impact assessments to further refine these models. Full article

The building sector is responsible for a significant share of global greenhouse gas emissions, raw material usage, and waste generation, driving the need for new circular design strategies. Among these, Design for Disassembly (DfD) promotes the reuse, repair, and recycling of building components. However, existing quantitative DfD assessment methodologies generally require extensive preliminary studies, which limit their practical use. This article presents a new quantitative DfD assessment methodology developed within the EU-funded INFINITE project, which aims to provide designers with a simple yet robust tool to evaluate the detachability potential of building integrated systems without requiring prior environmental studies. This methodology has been designed to evaluate specific DfD scores for maintenance, reuse, and recycling, using the mass and lifespan of products or systems as weighting factors. The tool was tested and validated on several systems developed during the INFINITE project. In the specific case of the Building Integrated Solar Thermal (BIST) system, it successfully identified key design improvements—such as enhanced accessibility for maintenance operations and optimized component connections. Industrial partners reported high usability and recognized the tool as a valuable decision-support instrument during early development phases. Nevertheless, the assessment methodology also revealed some limitations related to the assessment of the specific components and end-of-life scenarios, and to the absence of a holistic evaluation of trade-offs between mass-based score and environmental impacts. Full article

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article

In both research and industrial settings, it is often necessary to expand the input/output channels of measurement instruments using relay-based multiplexer boards. In research activities in particular, the need for a highly flexible and easily configurable solution frequently leads to the development of customized systems. To address this challenge, we developed a system optimized for automated direct current (DC) measurements. The result is based on a $4\times 4$ switching platform that simplifies measurement procedures that require instrument routing. The platform is based on a custom-designed circuit board controlled by a microcontroller. We selected bistable relays to guarantee contact stability after switching. We finally developed a system architecture that allows for straightforward expansion and scalability by connecting multiple platforms. We share both the hardware design source files and the firmware source code on GitHub with the open-source community. This work presents the design and development of the proposed system, followed by the performance evaluation. Finally, we present a test of our designed system applied to a specific case study: the DC analysis of complex resistive networks through multi-point resistance measurements using only a single voltmeter and current source. Full article

This study aims to investigate the impact of green finance on corporate sector investments and their associated environmental outcomes. The authors collected cross-sectional survey data with a sample of four hundred firms selected from the five green-relevant industries in an emerging economy. The results indicate that, over the last three years, seventy percent of firms have accessed at least one green instrument. Overall, the firms under study indicate that PKR 3.4 million is being allocated to green finance, and PKR 2.7 million is spent on CAPEX. However, each million PKR is associated with a ten percent capital expenditure, which exhibits the highest adoption of the renewable energy sector, while the manufacturing sector has the lowest adoption. Regression results depict that Greenhouse gas reduction is only achievable if expenditure on R&D is ensured for environmental gains. This study indicates a declining incremental impact when green finance exceeds PKR 5.00 million, suggesting that firms’ limitations in utilizing the additional amount may be a factor. Financially constrained firms achieve stronger environmental goals, confirming that strict criteria to finance projects show more responsibility and discipline in executing projects. However, small- and medium-sized firms are confronted with barriers, such as lack of information and transaction costs. The findings of this study highlight the need for a multi-layered regulatory framework, innovation-driven incentives, and fintech integration to fully realize the potential of green finance. The outcome enables financial institutions, sustainability practitioners, and regulators to connect financial markets, national climate, and development goals. Full article

The development of electronics education requires continuously keeping pace with changes in pedagogical methods, hardware, and software, as well as with emerging laboratory concepts such as remote, virtual, and augmented reality. This paper proposes an open, curriculum-integrated approach to embed ICT competencies, specifically computer networking and programming, into electronics education and assesses its impact. We developed a networked measurement and information-processing system that students not only use but also learn about every aspect of its operation across our electronics courses. The system uses LXI-capable measurement instruments connected using principles of standard computer networks, enabling built-in remote-access capabilities and high scalability. Software for communication, instrument control, and purpose-built functions for electronics applications is implemented in Python 3. The software also handles data acquisition and local or remote post-processing. In this way, the system is open, versatile, and readily adaptable to future instrument developments. Integrating this system into the curriculum demonstrably enhanced students’ ICT, digital, and entrepreneurial competencies, aligning with the European Commission competency frameworks. Evaluation, using purpose-built questionnaires, indicated strong student reception across multiple system aspects and confirmed the approach relevance and applicability from the perspective of industry stakeholders. Full article

Non-food geographical indications (GIs) are emerging as strategic policy instruments in the European Union after Regulation (EU) 2023/2411 extended protection to craft and industrial products. While the literature on agri-food GIs is extensive, empirical and comparative evidence on non-food GIs remains scarce and fragmented. This study addresses this gap by constructing a harmonised dataset, combining 132 registered and 380 potential non-food GIs identified by EUIPO (512 in total across the EU). Using secondary institutional data, descriptive and comparative statistics, and a hierarchical clustering (Ward, squared Euclidean distance) on normalised indicators total GIs, GIs per million inhabitants (GI/POP), and GIs per € billion of GDP (GI/GDP), the analysis identifies three country typologies differing by scale and intensity. Results reveal a strong geographical concentration in Southern Europe but also unexpectedly high intensity in smaller or mid-sized economies such as Portugal, Cyprus, and Slovenia. A forward-looking scenario analysis based on Cost of Non-Europe (CoNE) estimates suggests that the full implementation of the new Regulation could generate 284,000–338,000 new jobs and € 37–50 billion in additional intra-EU trade. The study contributes to EU policy debates by introducing comparative indicators (GI/POP, GI/GDP) as monitoring tools for evidence-based policymaking and by highlighting the role of non-food GIs as hybrid institutions connecting industrial competitiveness, cultural identity, and sustainability transitions. Full article

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article