Search Results (4,049)

Intelligent Transportation Systems (ITS) increasingly combine sensing, communication, computation, data platforms, and mobility services. This article presents a structured, literature-based mapping review of ITS applications from a standards-oriented perspective. The study analyzes 42 studies organized into five thematic groups and evaluated through 63 article–standard assessments using selected ITU-T Recommendations as an analytical lens. The rubric used in this work examined whether each study reported, or allowed reviewers to infer, evidence on architecture, data handling, interoperability, security and privacy, deployment assumptions, and digital-twin capabilities. Partial alignment was the most frequent outcome, accounting for 25 of 63 article–standard assessments (39.7%). At group level, satisfactory or optimal alignment occurred in 5 of 8 assessments (62.5%) in the digital-twin group and in 3 of 13 (23.1%) in the Big Data group; in the latter, 6 of 13 assessments (46.2%) showed limited or no alignment. Stronger alignment was usually found when studies described architectures, data flows, sensing mechanisms, service workflows, physical–virtual modeling, or system-management components relevant to the Recommendation, and weaker alignment when they focused mainly on algorithms, datasets, prediction accuracy, authentication, or secure dissemination without sufficient detail on interfaces, data governance, gateway roles, deployment conditions, or platform integration. The review proposes a five-dimension standards-facing reporting checklist addressing interoperability, data lifecycle and governance, security and privacy, operational readiness, and standards-facing evidence. It supports traceable reporting through explicit evidence-status categories and locations, and can be implemented as a Standards and Interoperability Reporting Statement (SIRS) for authors, reviewers, and editors. Overall, the findings show that standards-oriented assessment depends not only on technical performance but also on explicit and traceable integration evidence, while the proposed reporting profile provides a practical mechanism for making such evidence more systematically visible in future ITS studies. Full article

This article exposes how Chile permits photovoltaic plants on its land of highest agricultural capability, withdrawing the material basis of long-term food security for at least a generation, in full legality. Linking environmental-assessment records, fiscal cadastral microdata and the CIREN capability survey, I document 135 approved plants sited dominantly on Class I–III soil, 5836 declared hectares; elite soil covers 18.2% of the farmed landscape yet hosts 42.8% of the approved plants, 2.35 times its share. That the system processes this tension without friction reveals the socio-legal infrastructure of property, whose dephysicalisation erases the land’s food-producing capability from every register of decision. The regime fuses three securities (legal certainty, juridical security and land-tenure security), serving as the foundation of the energy transition. The fusion shields the proprietor and confines all protection to the voluntary, visible in the compensations proponents offer when the Agriculture and Livestock Service (SAG) objects. A documentary census of the permitting files supplies the new evidence. The SAG objects in writing to the loss of productive agricultural land in 64 of 136 approved files, and no objection prevents approval; in 21 a favourable qualification is issued with the objection still standing. The Service refutes, with measurements of its own, proponents who declare their soil poor, up to Class I, and the projects are approved nonetheless. If rural land is to be protected as a public good, voluntariness must yield to obligation. Full article

The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To address this problem in discrete location query scenarios, this paper proposes a single-point location privacy protection method based on Q-R tree retrieval and differential privacy, termed QRDPP. QRDPP combines the adaptive spatial partitioning capability of a Q-tree with the minimum bounding rectangle (MBR)-based indexing capability of an R-tree. It applies an improved geometric privacy budget allocation strategy to leaf nodes and an arithmetic allocation strategy to non-leaf nodes, followed by Laplace perturbation of the corresponding location data and node information. For continuous trajectory query scenarios, this paper proposes a spatiotemporal generalization and differential privacy method, termed STG-DPTP, to address inadequate temporal protection, inappropriate generalization, and trajectory distortion. STG-DPTP performs hierarchical spatiotemporal clustering, separately models temporal and spatial distributions using Gaussian kernel density estimation, dynamically optimizes bandwidth parameters through Bayesian optimization, selects representative candidate subsets using the exponential mechanism, and generates protected trajectories through constrained sampling. Experiments on the GeoLife dataset evaluate the proposed methods in terms of query accuracy, computational efficiency, spatial trajectory similarity, reconstruction error, adversarial uncertainty, and temporal preservation. The results show that QRDPP improves the utility and efficiency of privacy-preserving spatial queries, while STG-DPTP better preserves the spatial distribution, trajectory structure, and temporal characteristics of the original data under the adopted differential privacy framework. Full article

Artificial intelligence (AI) is reshaping the hospitality industry at an unprecedented pace. However, existing hospitality research has overwhelmingly concentrated on customer-facing applications, including service robots, chatbots, personalization, and revenue management, while largely overlooking the internal operational systems that sustain hotel performance. As a result, the concept of AI maturity within back-of-house hotel operations remains theoretically undefined, fragmented across functional domains, and lacks an integrated framework for assessment. This study addresses this critical gap by asking a fundamental research question: What does AI maturity actually mean for hotel back-of-house operations? Drawing upon a systematic literature review following the PRISMA protocol, this study synthesizes evidence from 18 studies spanning the interdisciplinary fields of hospitality management, operations management, information systems, and artificial intelligence to examine how AI is transforming core internal hotel functions. The review identifies current applications, implementation patterns, organizational enablers, barriers to adoption, and emerging trends across human resource management, procurement, finance and accounting, inventory management, housekeeping planning, maintenance, energy management, and managerial decision support. Building on these findings, the study develops the Artificial Intelligence Maturity in Back-of-House Operations (AIM-BoH) Framework, a domain-specific conceptual framework designed to conceptualize AI maturity across hotel back-of-house functions. The framework conceptualizes AI maturity as a multidimensional organizational capability encompassing technological adoption, process automation, decision intelligence, data readiness, human–AI collaboration, governance and ethical preparedness, and measurable operational outcomes. By moving beyond technology-centric perspectives, the framework provides a comprehensive model for understanding how AI creates organizational value through the integration of internal hotel processes. The proposed framework advances hospitality literature by establishing a common theoretical foundation for understanding AI maturity in internal hotel operations while offering hotel executives a structured conceptual lens for considering organizational capability development in the planning of digital transformation initiatives. The article concludes by proposing a research agenda for the empirical validation, refinement, and cross-cultural application of the AIM-BoH Framework, positioning it as a reference model for future hospitality AI research and practice. Full article

Reflective cracking is one of the primary causes of premature deterioration in asphalt-overlaid Portland cement concrete (PCC) pavements, reducing service life and increasing maintenance costs. Since crack initiation begins within the underlying PCC layer before becoming visible at the pavement surface, conventional inspection methods have limited capability for early damage detection and continuous monitoring. This study presents a hybrid experimental–numerical framework for monitoring and interpreting bottom-up reflective cracking by integrating Optical Frequency Domain Reflectometry (OFDR)-based Distributed Fiber-Optic Sensing (DFOS), laboratory-scale three-point bending tests, and finite element (ABAQUS) modeling. Rectangular and semi-cylindrical asphalt-overlaid PCC specimens were instrumented with surface-bonded distributed optical fibers arranged in a serpentine sensing layout with approximately 20 mm spacing to continuously monitor strain evolution during flexural loading. In both tested specimen configurations, the three-point bending tests produced bottom-up crack initiation at the predefined notch within the PCC layer, followed by crack propagation toward the asphalt overlay. Crack-width measurements showed that the maximum crack opening occurred near the notch and progressively decreased toward the asphalt overlay, consistent with the expected flexural stress distribution. The OFDR-based DFOS system successfully identified localized strain concentrations associated with crack initiation and propagation, demonstrating its capability for continuous distributed monitoring of fracture evolution. Finite-element simulations identified tensile stress and strain-localization patterns that showed good qualitative spatial correspondence with the experimentally observed cracking region and distributed strain measurements. The combined experimental, sensing, and numerical results demonstrate the proposed framework’s capability to monitor and interpret bottom-up reflective cracking and highlight the potential of OFDR-based distributed fiber-optic sensing for structural health monitoring, condition assessment, and future field-scale monitoring of rehabilitated concrete pavements. Full article

The coarse-grained heat-affected zone (CGHAZ) is a critical brittle region in welded reactor pressure vessel steels, and phosphorus segregation at prior-austenite grain boundaries can further impair its low-temperature toughness during long-term service. Although phosphorus-induced embrittlement has been established for SA508-4N base metal, the quantitative relationship between grain-boundary phosphorus segregation and the ductile-to-brittle transition temperature (DBTT) in the CGHAZ—and the role of its distinct bainitic microstructure relative to the base metal—remains insufficiently understood. Here, a CGHAZ was simulated in P-doped SA508-4N steel and thermally aged at 500, 530, and 560 °C to establish different equilibrium segregation levels. Optical metallography, Vickers hardness testing, Charpy impact testing, and Auger electron spectroscopy were used to correlate microstructure, hardness, DBTT, and grain-boundary phosphorus concentration. As the aging temperature increased from 500 to 560 °C, the grain-boundary phosphorus concentration decreased from 21.40 to 18.46 at. %, while the DBTT decreased from −53 to −91 °C. The nearly unchanged hardness excludes hardening as the principal cause, demonstrating that the toughness variation is governed predominantly by non-hardening embrittlement associated with phosphorus segregation. The DBTT exhibited a strong positive linear correlation with the equilibrium grain-boundary phosphorus concentration. Moreover, at a comparable prior-austenite grain size, hardness, and phosphorus segregation level, the CGHAZ showed a higher DBTT than the base metal, which is attributed to the lower crack-deflection capability of tempered bainite compared with tempered martensite. These results fill the quantitative gap linking phosphorus segregation to CGHAZ embrittlement and provide a basis for assessing the long-term integrity of SA508-4N welded joints. Full article

Future transportation networks may require aerial communication platforms capable of providing flexible and reliable services to vehicular terminals. In conventional unmanned aerial vehicle (UAV) communication systems, the antenna geometry is commonly treated as fixed, which limits the attainable directional gain when the relative geometry between the UAV and users changes significantly. This work considered a UAV equipped with a mechanically reconfigurable antenna array and studied its joint motion and transmission control under finite-blocklength communication. A sequential optimization problem was formulated to maximize the accumulated user throughput by jointly optimizing the UAV trajectory, the array orientations, and the transmit beamforming vectors, subject to the UAV kinematic constraints, the UPA orientation constraints, and the transmission energy budget. The resulting problem involves nonlinear coupling among platform motion, antenna pointing, beamforming, and finite-blocklength rate expressions, making conventional optimization computationally demanding. To obtain an adaptive control policy, a soft actor–critic-based deep reinforcement learning method was developed. The simulation results showed that jointly controlling the UAV mobility, array orientation, and beamforming improves the achievable finite-blocklength transmission performance compared with benchmark schemes, demonstrating the effectiveness of the proposed framework in enhancing reliable data delivery for UAV-assisted transportation infrastructure applications. Full article

General-text translation is converging toward near-full automation by large language models (LLMs), yet the translation of OTT multimedia content still demands a deliberately designed division of labor between humans and AI; this asymmetry is the starting point of this review. Korea occupies a distinctive position in this transformation: the global success of Korean content has created an asymmetric, outbound-heavy translation market in which linguistically complex Korean-source material must be localized into dozens of languages at speed, while the domestic language-service industry undergoes a structural shift from human translation to machine-translation post-editing. This paper reviews three research streams that have developed largely in isolation, namely LLM translation quality and automatic evaluation, human–AI collaboration and knowledge-worker productivity, and audiovisual translation, and integrates them through an operations-management input–process–output–outcome framework tailored to the Korean OTT context. The review finds that LLM-based post-editing improves draft quality and productivity, but that honorific register, culture-bound expressions, and speaker-relational meaning, all pervasive in Korean dialogue, lie largely outside current LLM competence. Three testable propositions are derived on how Korean-specific linguistic density conditions the productivity–quality trade-off, and a research agenda is proposed with implications for job redesign and capability development in language service providers, OTT platforms, and language-focused higher education. Designing this division of labor is a management problem before it is a technological one, and its answers lie where language expertise and operations management meet. Full article

Micro, small, and medium-sized enterprises (MSMEs) increasingly adopt digital technologies, yet converting this exposure into enterprise performance (ENP) requires multidimensional readiness capabilities. Evaluating these capabilities remains complex as existing studies often separate expert judgement, measurement validation, linear association, and explainable prediction. This study therefore develops an integrated analytical framework comprising two sequential phases. First, 22 experts contextualised the seven capability domains through a three-round Delphi process, followed by fuzzy TOPSIS to derive priorities under linguistic uncertainty. Second, cross-sectional survey data from a non-probability convenience sample of 610 eligible MSME respondents in Thailand underwent Confirmatory Factor Analysis (CFA) to validate the measurement structure, followed by hierarchical regression to establish a conventional baseline of adjusted linear associations. Within the second phase, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were subsequently applied for internal out-of-sample evaluation and model interpretation. The findings show that experts assigned the highest priorities to digital financial services and digital financial access. The XGBoost model achieved a test $R^2$ of 0.6303, compared with 0.6282 for the OLS benchmark, indicating only a modest predictive improvement. Test-set SHAP identified social media use (mean |SHAP| = 0.2027), adaptive financial resilience, and financial management practice as the leading group of capability-level predictive contributors. The evidence is observational and does not support causal inference. This study contributes an integrated, measurement-validated, explainable framework for capability assessment. Expert-based priorities and SHAP-based predictive contributions provide complementary rather than equivalent forms of evidence. Full article

The textile, apparel, and fashion (TAF) industry generates significant environmental burdens across its entire supply chain. As the global textile recycling market expands, a systematic framework for classifying exhibitors and assessing their sustainability-related characteristics is increasingly needed. This study analyzes exhibitors at the 2026 Textiles Recycling Expo USA, the first specialized textile recycling exhibition in North America, to develop an exploratory sustainability assessment framework. Using qualitative content analysis, 78 exhibiting companies were categorized into four functional groups: (1) Hard-tech Infrastructure, (2) Chemical & Material Innovation, (3) Logistics & Waste Management, and (4) Knowledge & Support Services. Based on a review of sustainability assessment literature in the TAF industry, a four-dimensional framework was developed, encompassing Material Renewability, Process Sustainability, End-of-Life Options, and Technical & Digital Attributes. For a preliminary pilot application, 11 companies were purposively selected and evaluated by eight experts using defined key performance indicators (KPIs). The total scores ranged from 10.3 to 16.3 out of 20, with ESO RECYCLING Società Benefit receiving the highest overall score (16.3), followed by MARGASA (15.8). Across the evaluated companies, Technical & Digital Attributes generally showed relatively lower scores than the other dimensions, indicating comparatively limited publicly evidenced digital traceability capabilities. These findings demonstrate the preliminary applicability of the proposed framework for characterizing heterogeneous exhibitors while highlighting the need for further refinement and validation using larger and more diverse samples. Full article

The rapid rise of artificial intelligence (AI) confronts education with a question that is philosophical rather than technical: not how schools should use AI, but what education is ultimately for once knowledge is no longer scarce. This article addresses that question through a single, extended argument rather than an empirical report. It proceeds from a historical observation, that the modern transmission model of schooling rested on the scarcity of knowledge, and shows, drawing on the pragmatist philosophy of Charles S. Peirce, William James, and above all John Dewey, that AI dissolves this scarcity and thereby reopens the deepest question about the aims of education. The article’s central claim is that the most consequential change AI brings to education is not technological but teleological: as knowledge shifts from a scarce resource to a pervasive condition, it can no longer function as the destination of education and must be repositioned as a condition for the fuller aim of human formation. To clarify what “the full development of the human person” means, an idea too often asserted rather than defined, the article brings Dewey’s concept of growth into dialogue with the German tradition of Bildung, with Gert Biesta’s account of subjectification, and with Nick Bostrom’s Deep Utopia, whose “purpose problem” sharpens, by pushing to the limit, what remains distinctively human when instrumental tasks are automated. The argument closes by weighing and answering four objections. In doing so, the article extends pragmatist philosophy of education into the age of AI and proposes that the value of education be judged by its capacity to form persons who can use knowledge and AI to judge wisely, act responsibly, and keep developing in a changing world. The Vietnamese context provides a particularly relevant setting for this discussion, as the country’s strong commitment to educational development and digital transformation offers an important opportunity to place emerging AI capabilities in the service of human growth and the broader purposes of education. Full article

This study presents a five-state functional model for assessing the reliability of electric vehicle charging infrastructure from the perspective of charging-service capability. The model distinguishes full functionality, three levels of degradation, and the loss of the basic charging function. State changes are described using a time-homogeneous continuous-time Markov process that includes progressive degradation, sudden functional loss, and restoration to state S0. On this basis, MTTF, MTTR, and MTBF are reinterpreted in relation to first attainment, restoration trajectories, and complete functional cycles. The reference calculations determine stationary state probabilities, annual residence times, and the effectiveness of restoration before reaching S4. The results show that a low stationary probability of S4 may still correspond to a meaningful annual period of service unavailability, while most functional cycles end with restoration from intermediate states. The proposed framework provides a reproducible basis for analysing EV charging infrastructure and can be applied using transition intensities estimated from operator records. Full article

Sustainable and demand-adaptive railway operation requires frameworks capable of aligning service capacity with time-varying demand while ensuring safe, operationally feasible, and energy-efficient service. This paper proposes an integrated control-and-planning framework for modular pod-based railway operation based on virtual coupling. The framework combines a convoy control layer, which ensures safe and dynamically feasible virtually coupled operation, with a planning layer formulated as a mixed-integer linear programming (MILP) model for daily service allocation and convoy sizing. This hierarchical framework combines dynamically feasible convoy-control simulations with service-level planning to adapt capacity to passenger demand. The proposed methodology is evaluated through comparative simulations under peak-hour, shoulder-period, and off-peak demand scenarios, as well as over a daily schedule of 20 services. Its performance is compared with a conventional fixed-composition diesel–electric multiple unit (DEMU)-based operation. Results show that the pod-based configuration increases energy consumption under peak-hour conditions, remains comparable during shoulder periods, and substantially reduces energy consumption in off-peak operation, achieving a 57% saving in that regime. At the daily level, total energy consumption decreases from 3864 kWh to 3075 kWh, corresponding to a 20% reduction. These findings indicate that the main value of the proposed framework lies in transforming convoy composition into a demand-adaptive operational variable, thereby improving energy performance at the daily system level while preserving the safe and dynamically feasible operation of virtually coupled pod formations. Full article

As the core equipment for in situ deep-sea scientific research, the design quality and operational performance of the sampling platform determine the efficiency of deep-sea operations, including long-term continuous observation, high-quality sampling and preservation, and in situ experimental studies. To address technical bottlenecks commonly observed in conventional platforms, including track sinkage, excessive motion drag, and low propulsion efficiency, this study proposes the design and development of a novel sampling platform with tracked-propeller dual-mode propulsion, deployable from either a surface vessel or a large manned submersible, capable of high-throughput, multi-sequence fidelity water sampling, in situ sediment incubation, and seabed mudstone sampling in deep sea. Through hydrodynamic performance analysis and design optimization, both lightweight design and drag reduction were achieved. Furthermore, load verification of the main frame under multiple operating conditions was conducted; results demonstrate that the structure meets strength and stiffness requirements and ensures reliability in typical service environments. This study provides a theoretical basis and technical reference for the development of similar deep-sea sampling platforms and holds substantial engineering application value. Full article

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions. Full article