Search Results (735)

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

Automated short-answer grading (ASAG) in educational contexts faces a fundamental trade-off between predictive performance, interpretability, and methodological transparency, particularly under data-constrained educational settings. While recent approaches rely on deep learning architectures, these models require large annotated datasets and offer limited transparency, restricting their applicability in authentic classroom environments. This study proposes a fully specified and interpretable machine learning pipeline for ASAG across multiple educational concepts. The approach is based on a shared TF–IDF representation and evaluates three linear classifiers—Logistic Regression, Multinomial Naïve Bayes, and Linear Support Vector Machines—under a stratified cross-validation framework adapted to small datasets. Model performance is assessed using accuracy, precision, recall, and F1-score. Statistical comparisons using the Wilcoxon signed-rank test indicate exploratory evidence of statistically significant differences between classifiers, although the observed differences remain small in practical magnitude. Additionally, the methodology incorporates token-level analysis to identify discriminative lexical patterns and examine consensus across classifiers. To enhance interpretability, tokens are presented using a bilingual Spanish/English representation while preserving the original feature space. The results across ten concept-specific datasets show consistent performance across models (accuracy ≈ 0.82–0.88) and reveal stable lexical patterns consistently associated with model predictions of correctness. The findings highlight that lightweight, interpretable models can provide consistent and reliable performance under resource-constrained educational conditions. The proposed framework contributes a stability-oriented and interpretable evaluation paradigm for ASAG, offering a practical alternative to data-intensive approaches in educational assessment. It is intended as a methodological reference protocol rather than a performance benchmark. The findings should be interpreted as evidence of within-context consistency instead of broad external generalization. Full article

The protection of confidential information is a worldwide challenge, and block ciphers are the most reliable option by which data security is accomplished. To the best of our knowledge, this type of research is being performed for the first time, unlocking new avenues for block cipher design research, shifting the paradigm from cryptographer-designed ciphers to computationally generated. We propose a computational model named Block Cipher Design Generation Model (BCDGM) to generate a complete design of novel block ciphers and their primitives, such as cipher structures, S-boxes, inverse S-boxes, P-boxes, round functions, half-round functions, and derived keys, in an automated manner without the participation of cryptographers. To accomplish this goal, BCDGM only requires high-quality quantum random bits as input to generate a myriad of new block ciphers. A quantum circuit is designed over the International Business Machines Corporation (IBM) Quantum Santiago computer to generate high-quality random bits. BCDGM itself and all its generated cipher structures and primitives are invariably transparent, unreproducible, and nondeterministic due to their sole reliance on quantum random bits. Furthermore, every decision made by BCDGM is randomized. As a result, potential vulnerabilities and attacks that exist in other ciphers are bypassed in BCDGM-generated ciphers. Extensive experimentation was conducted, generating more than fifty thousand new block cipher designs tested over 107 terabytes of data. Generated ciphers are compared with twelve reputable standard block ciphers, including five AES competition finalists. The results show that the proposed block ciphers occasionally surpass standard ciphers and achieve equivalent security strength in many cases. The implementation of the proposed model is publicly available. Full article

Introduction: The growing integration of artificial intelligence (AI) into recruitment and selection is reshaping how organizations identify, evaluate, and choose talent. Although prior research emphasizes improvements in efficiency and automated decision-making, concerns related to fairness, transparency, trust, and applicant experience remain insufficiently resolved. Despite increasing scholarly attention, the field continues to evolve in a fragmented manner. This scoping review addresses this gap by systematically mapping and synthesizing the literature on the advantages and challenges of AI-based recruitment and selection, considering both organizational outcomes and human implications. Materials and Methods: A scoping review was conducted following established methodological frameworks. A structured search and screening process across major academic databases resulted in a final corpus of 33 peer-reviewed studies. The analysis combined descriptive mapping with a hybrid thematic synthesis organized around five dimensions: efficiency and decision support, bias and fairness, transparency and trust, applicant experience, and governance and ethics. Results: The evidence indicates that AI-based recruitment enhances efficiency, scalability, and consistency in decision processes. At the same time, these benefits are accompanied by challenges related to algorithmic bias, limited interpretability, reduced trust, and concerns about procedural fairness. The findings highlight a persistent interdependence between performance outcomes and legitimacy-related responses. Conclusions: This review proposes a socio-technical framework that explains AI-based recruitment as a system shaped by the interaction between technological design, human judgment, and governance structures. The results underscore the importance of integrating oversight, transparency, and ethical accountability to support responsible and sustainable implementation. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

Digital news organizations increasingly adopt generative artificial intelligence (GenAI) under conditions of economic strain and platform dependency. While AI integration is often framed as a strategy for operational efficiency, its institutional implications extend beyond productivity gains. This study examines how different governance approaches to GenAI adoption—specifically variations in transparency, disclosure, and oversight practices—correspond to shifts in audience engagement and financial performance. Using a comparative mixed-methods design, we analyze three prominent cases between 2022 and 2025—CNET, Gizmodo, and The New York Times—representing, respectively, covert AI use with limited disclosure, transparent but poorly managed deployment, and proactive ethical and legally grounded positioning. To operationalize audience stability, we introduce two behavioral indicators: the Engagement Resilience Index (ERI), measuring depth and consistency of user engagement; and the Market Turbulence Ratio (MTR), capturing post-incident volatility in audience behavior. The findings indicate that AI deployment strategies associated with limited disclosure or weak governance correspond with increased engagement instability and revenue contraction, whereas approaches framed through institutional accountability and ethical positioning align with more stable or positive performance trajectories. The results suggest that AI integration functions not merely as a technological shift but as a governance-mediated signal interpreted by audiences in economic terms. These dynamics highlight the centrality of institutional trust in shaping the sustainability of digital journalism in the age of automation. Full article

Background/Objectives: Lung cancer is one of the most common and lethal malignancies worldwide. Early detection remains challenging due to its variable biological behavior. Computed tomography (CT) is the primary imaging method used for early detection. However, the manual interpretation of CT scans is constrained by several challenges such as reliance on expert experience, increasing clinical workload, and considerable variability among observers. Methods: This study introduces an explainable transformer-based framework capable of distinguishing among the three principal clinical categories of lung cancer (small-cell lung cancer, non-small-cell lung cancer, and normal) while simultaneously generating automated radiology reports from CT images. In contrast to conventional single-slice methodologies, the proposed model employs a multi-slice volumetric encoding strategy that captures spatial continuity and anatomical relationships across the CT slices. Visual features extracted by a ViT-based encoder are transformed into a compact patient-level representation through a Learnable Query Attention Pooling (LQAP) mechanism, and this unified representation is subsequently used for both three-class prediction and report generation with a GPT-2-based decoder. To enhance explainability, slice-wise Grad-CAM maps are produced, visually highlighting the anatomical cues that guide the model’s decisions. Results: Experiments conducted on the newly curated LungCA dataset comprising 767 patients demonstrate that the model achieves 97.40% accuracy in the Turkish (TR) reporting scenario and 94.81% accuracy in the English (EN) scenario, alongside strong alignment with human-written reports in BLEU, ROUGE, METEOR, and CIDEr metrics. Conclusions: The findings demonstrate that the proposed multi-slice transformer framework achieves robust performance in both classification and radiology report generation, enhances transparency throughout the decision-making process, and provides a robust artificial intelligence solution capable of effectively supporting clinical workflows in lung cancer assessment. Full article

Mandatory Environmental, Social, and Governance (ESG) disclosure requirements—anchored in Corporate Sustainability Reporting Directive (CSRD), International Sustainability Standards Board (ISSB), and Task Force on Climate-related Financial Disclosures (TCFD)—have placed unprecedented demands on supply chain data quality and auditability. Blockchain technology, combining immutability, decentralised governance, and smart contract automation, has emerged as a candidate infrastructure for addressing verification deficits across multi-tier supply chains. To our knowledge, no prior systematic review has simultaneously examined the blockchain specifically for formal ESG transparency and sustainability reporting across all three ESG dimensions within the post-CSRD mandatory reporting landscape. This study presents a systematic literature review (PRISMA 2020). Scopus and Web of Science searches identified 1,166 records (2016–2026); after deduplication, 761 unique records were screened, and after blinded screening (κ = 0.84), 96 studies were included. Five blockchain application typologies are identified (T1–T5), spanning provenance tracing, smart contract compliance, carbon accounting, supplier data aggregation, and ESG disclosure systems. A structural asymmetry is identified: governance is addressed in 96% of studies (77.1% under the strictest G-CONFIRMED recoding; 95.8% under the moderate interpretation, including borderline cases), the environmental pillar in 49%, and the social dimension in 21%, explained through institutional theory, with significant implications for CSRD and Corporate Sustainability Due Diligence Directive (CSDDD). Key barriers include scalability, interoperability, and the blockchain–GDPR (General Data Protection Regulation) tension. Three principal contributions are made: (i) a systematic typology of blockchain for ESG transparency; (ii) institutional-theory explanation of ESG dimension asymmetry; and (iii) a research agenda centred on AI–blockchain convergence and post-CSRD empirical studies. The review is limited to English-language peer-reviewed literature. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

Assessing hydropower potential in ungauged basins requires consistent derivation of key hydrological variables from heterogeneous geospatial and climatic data. Conventional GIS-based approaches often rely on fragmented, user-dependent workflows, limiting reproducibility and comparability. This study presents HYDROPOT, a web-based geospatial framework for the automated and reproducible extraction of hydrologically relevant basin descriptors for regional-scale hydropower screening. The platform integrates centralized datasets with server-side geoprocessing to delineate upstream catchments and compute quantitative basin descriptors, including drainage area (2–400 km²), Curve Number (CN), concentration time, and spatially aggregated monthly thermo-pluviometric variables derived from 95 stations over the 2004–2022 period. These descriptors provide essential inputs for rainfall–runoff modeling and preliminary discharge estimation, thereby supporting (although not directly performing) the assessment of water availability in ungauged basins. By eliminating manual preprocessing, HYDROPOT ensures consistent and reproducible analyses, reducing user-induced variability and improving comparability across applications, without implying increased predictive accuracy. The framework, applied to the Lazio Region (Central Italy) over the 2004–2022 period, enables rapid and transparent screening of river reaches, offering a scalable decision-support tool for preliminary, input-based screening in early-stage small hydropower planning. Full article

Automated Speed Enforcement (ASE), a widely known speed management strategy, extends beyond its safety benefits and is shaped by public trust, broader governance, and policy frameworks. This study evaluated public opinions of the ASE program in school zones in Georgia, United States, which has recently undergone multiple policy changes. An online survey was conducted targeting Georgia drivers aged 18 years or older, which gathered 502 responses from a representative sample based on exposure, direct school connections, and sociodemographic factors. Respondents indicated their agreement levels on a Likert scale across multiple statements about ASE and their thoughts on enhancing the program’s transparency, trustworthiness, and fairness. Data analysis was conducted using descriptive statistical techniques and cross-classification. Among all respondents, 71 percent supported the program, and among individuals who had driven through speed-enforced school zones, 81 percent reported that ASE led them to reduce speeds. Issuing the citation to the actual driver at the time of violation, publicizing revenue allocation and utilization, publicizing safety benefits, and clearly posting the speed limits and the hours under evaluation were among the key concerns. These findings highlight the significance of integrating public perceptions into ASE policy, identifying areas needing improvement, and promoting community-endorsed traffic safety interventions. Full article

In the context of global value chain restructuring and accelerating digital transformation, enterprise competition is increasingly shifting toward sustainable systemic efficiency centered on supply chain operations. Although financial robotic process automation (RPA), as a critical technology enabling financial digitalization, has been widely adopted by firms, its impact on sustainable supply chain operational efficiency (SCOE) and the underlying transmission mechanisms remains underexplored. Drawing on data from Chinese A-share listed firms spanning the period from 2015 to 2024, we investigate the effect of RPA adoption on SCOE. Our analysis reveals that RPA adoption significantly improves firm SCOE, with the effect being more pronounced among non-state-owned enterprises, firms located in eastern and central regions, non-high-tech firms, and large enterprises. Moreover, we identify two underlying mechanisms—enhanced information transparency and optimized capital utilization—as primary channels through which RPA enhances supply chain performance. Further analysis indicates that supply chain concentration (SCC) positively moderates the relationship between RPA adoption and SCOE. These findings provide practical implications for enterprise digital transformation and sustainable supply chain development. Full article

In this work, we propose a novel transformer-based deep learning model for the design of electromagnetically induced transparency (EIT) metasurfaces, which consists of a forward network to predict transmission spectra from structural parameters and an inverse network to retrieve structural parameters from target spectra. To train the model, we generated a dataset of 23,500 samples by automating CST simulations. The well-trained model can predict all seven structural parameters of an EIT metasurface from a given target spectrum within milliseconds, achieving a mean square error (MSE) of 8.49 × 10⁻⁴ at convergence. The mean errors between the predicted data and target parameters remain below 0.25 μm. The relative spectral error (RSE) is employed to evaluate the discrepancy between the spectra from predicted structures and the targets, with a maximum RSE of 0.57%. Benchmarking against two other neural networks confirms the superior predictive capability and accuracy of our model. Furthermore, the method not only streamlines EIT metasurface design but is readily adaptable to diverse metasurface devices across the electromagnetic spectrum, establishing a versatile platform for metasurface inverse design. Full article

Digital maturity models are widely used to support transformation; however, many remain organization-level, lack transparency, and are only weakly linked to implementation prioritization. These limitations are especially consequential in purchasing, where maturity may vary substantially across activities and sub-processes. This study develops a process-level digital maturity assessment-and-roadmapping (DEMA) artifact for purchasing. Within the broader DEMA architecture, this study develops and evaluates only the Smart Business Processes component, while retaining Digital Strategy and Infrastructure as a contextual architectural layer. Drawing on design science research and maturity model development guidance, DEMA was developed through literature synthesis, iterative expert involvement over approximately 18 months, and structured refinement conducted in approximately 20 sessions. The artifact was refined through three anonymized pilot applications in electronics manufacturing SMEs and then demonstrated through a focal case application in an electronics SME that used an ERP system but lacked a purchasing-specific digital transformation roadmap. Evaluation was utility-oriented rather than psychometric, focusing on whether the artifact could (i) generate differentiated capability profiles across purchasing subprocesses, (ii) improve item clarity, stage interpretation, and scoring logic through pilot-based refinement, and (iii) translate assessment results into feasible targets, priorities, and sequenced roadmap actions under facilitated conditions. To provide bounded but direct validation evidence, the study also included a lightweight two-rater consistency and interpretability check on a representative subset of 24 items, together with a structured diagnosis-to-roadmap traceability review of six representative items. The results showed moderate exact agreement, perfect adjacent agreement, positive weighted inter-rater agreement for ordinal ratings, and favorable interpretability scores. Together, these findings provide bounded empirical support for the artifact’s practical consistency and usability within the study’s development-oriented scope. Unlike reflective survey scales, the DEMA is evaluated here as a staged, prescriptive maturity grid. Accordingly, the methodological emphasis is on interpretability, traceability, and assessment-to-action usefulness in facilitated use rather than psychometric scale validation. The DEMA integrates a fully disclosed 70-item staged instrument with explicit scoring, dual-target setting, dependency-aware prioritization, and a structured implementation methodology. In the focal case, the artifact revealed uneven maturity profiles across capabilities, distinguished between current and target capability states, and supported the prioritization of concrete intervention areas, such as data-entry automation/RPA, digital tool budgeting, remote-access improvement, and analytics-related training. Rather than pursuing psychometric scale validation, this study presents a transparent, implementation-oriented artifact for purchasing and shows how process-level maturity diagnosis can be translated into roadmap development in guided-application settings. Therefore, the contribution is design-oriented and practice-facing rather than a claim of broad theoretical advancement or comparative superiority over existing maturity frameworks. Full article