Search Results (10,473)

In recent decades, higher education has seen significant transformations due to rapid technological advancements that have challenged traditional teaching and learning practices. As digital technologies become increasingly integrated into education, institutions are adopting innovative approaches to enhance teaching effectiveness, student engagement, and learning outcomes. Many real-world activities have transitioned into the virtual world, bringing unprecedented challenges to all sectors, particularly in education, leading to the rapid adoption of technology-supported teaching and learning solutions. The fusion of the digital and physical worlds has facilitated the development of the metaverse, a virtual, three-dimensional space in which students can interact with each other and with professors. This “phygital” environment enhances engagement and cooperation while providing innovative and interactive methods for students to experience and understand complex concepts. This paper aims to examine the impact of metaverse integration in open-access higher education institutions and explore students’ perspectives on its potential applications in learning and teaching. The study utilized a convergent parallel mixed-methods approach over two academic years (2022–2023 and 2023–2024) among students aged 18–25 enrolled at a Moroccan public university. Data collected through questionnaires and focus group interviews reveal varied perceptions of the metaverse, highlighting its potential benefits, challenges, and the influence of students’ social, economic, and intellectual contexts on its acceptance. Full article

Universities play a pivotal role in fostering sustainable practices, yet limited evidence is available on how this commitment is translated into digital communication within Latin American higher education. To address this gap, this study examines how Ecuadorian public universities communicate sustainability through digital media, using a mixed-methods design that integrates content analysis, correlational analysis, and expert-judgment validation to develop educommunicational strategies for university communication offices, sustainability units, and higher-education policymakers. Content analysis was conducted on 733 sustainability-related publications (102 on institutional websites and 631 on social media—Facebook, Instagram, X, YouTube, and TikTok) from 34 Ecuadorian public universities. Publications were classified into internal and external institutional-performance categories and assessed in terms of publication frequency and engagement rate; Spearman correlations were used to examine associations between audience size and posting activity. The findings reveal limited visibility of sustainability topics, fragmented communication strategies, and a predominance of Facebook and Instagram; only three universities maintain a dedicated sustainability section on their website. Four educommunicational strategies, validated through expert judgment, are proposed as a framework for transforming university digital communication and aligning it more closely with institutional sustainability commitments. Full article

Conventional valve-controlled hydraulic excavator boom drive systems suffer from low energy conversion efficiency and severe throttling losses. To address this issue, this paper proposes a pump-controlled digital hydraulic drive system based on a Direct Energy Recovery Digital Pump (DERDP). The DERDP integrates hydraulic pumping and boom potential energy recovery within a single unit, enabling both energy recuperation and precise flow regulation. An AMESim simulation model of the system is developed and validated against experimental measurements from a dedicated hardware-in-the-loop test bench. Using the validated model, the operational characteristics are investigated under varying boom load pressures, pump displacement fractions, and motor displacement fractions; the drive and energy recovery efficiencies are systematically evaluated across different operating conditions. Simulation results show that the accumulator stores 2834 J per lowering cycle and releases 2756 J per lifting cycle, reducing motor energy consumption from 5413 J to 2317–2903 J. The system-level energy recovery efficiency reaches 50–60%. These findings offer practical guidance for implementing digital hydraulic solutions in construction machinery. Full article

The Pantanal features complex vegetation in terms of physiognomy; its original cover is increasingly replaced by agricultural and livestock land use. Given its importance and the resulting impacts on biodiversity and the physical environment, the biome was included in the Brazilian deforestation monitoring project. Accordingly, the objective is to develop a methodology and map native vegetation loss within the biome to generate strategic information for monitoring purposes. Medium-spatial-resolution imagery from the Landsat satellite series, specifically the TM (Landsat 5), ETM+ (Landsat 7), and OLI (Landsat 8) sensors were used, covering the period from 2000 to 2021. Using Terra Amazon software, the images were enhanced, and a region-based segmentation algorithm was applied. The hybrid interpretation (combining digital and visual methods) classified only instances of natural vegetation loss, regardless of subsequent land use. A specific interpretation key was developed based on standard image interpretation elements. Accuracy assessment employed a stratified random sampling approach based on the adopted categories (Natural Vegetation and Vegetation Loss by year). The study produced a time series of Vegetation Loss from 2000 to 2021, achieving an overall accuracy of 94.5% (±1.21%). The producer’s and user’s accuracy for natural vegetation are 98.6% (±0.38%) and 97.3% (±1.40%), respectively. The methodological distinction lies in the use and integration of modern technologies (drones, satellite imagery, GIS, WebGIS, and geospatial data platforms), the identification and analysis of regional dynamics, the hybrid interpretation method (manual + digital), and field verifications adapting traditional PRODES deforestation monitoring protocols to overcome the unique hydrological, seasonal, and structural idiosyncrasies of the Pantanal. Regarding regional dynamics, the analysis considers landscape changes driven by wet and dry seasons, land management practices (such as native pasture clearing, cattle grazing, and the use of fire), the replacement of native grasslands with exotic pastures, and the classification of wetlands (temporary ponds, floodplains, and drainage channels) within the mapped Vegetation Loss polygons. Full article

Green technological innovation is important to the sustainable development of manufacturing. As a core driver of transition toward sustainable production patterns, green innovation helps firms reduce environmental externalities while maintaining economic competitiveness. Drawing on the Resource-Based View and the Knowledge-Based View, this study examines the association between digital-transformation disclosure and green technological innovation using 16,231 firm-year observations for Chinese A-share listed manufacturing firms from 2015 to 2022. Green technological innovation is measured by green patent applications, and the baseline specifications use negative binomial models with year and industry fixed effects, province-clustered standard errors, and a full control set that includes firm size. Digital-transformation disclosure is positively associated with green patent applications. Parallel-mediation estimates and province-clustered Bootstrap confidence intervals identify significant indirect effects through inter-organizational knowledge flow, R&D expenditure intensity, and the R&D personnel share. In terms of point estimates, the R&D personnel indirect effect is the largest of the three, followed by knowledge flow and R&D expenditure; however, only the R&D personnel versus R&D expenditure difference is statistically significant, and the three channels are better characterized as complementary. The difference between the knowledge-flow and R&D-expenditure effects is marginally significant. The hypothesized inverted-U moderating pattern of market competition is not statistically significant in the preferred specification with industry and year fixed effects. Split-sample estimates show directional heterogeneity across region, ownership, and firm size, but formal interaction tests do not reject equality of the digital transformation disclosure coefficients across groups. These results document robust associations rather than definitive causal effects and highlight the complementary roles of knowledge recombination, R&D expenditure, and technical personnel in green patenting. These findings carry implications for sustainability-oriented research and management practice by documenting channels through which digital-transformation disclosure is associated with green patent activity in the manufacturing sector. Full article

Professional orientation has become an essential objective of higher education because university courses are increasingly expected to prepare students for professional practice as well as academic achievement. This study examined students’ perceptions of the professional orientation of a university Probability Theory and Mathematical Statistics course using a seven-dimensional framework. Data were collected from 242 undergraduate students enrolled in the Artificial Intelligence and Data Analysis bachelor’s program at a university in Kazakhstan using a 37-item questionnaire that measured both the current and the needed levels of professional orientation. The instrument demonstrated good-to-excellent reliability and confirmatory factor analysis provided support for the proposed multidimensional measurement structure. Linear mixed-effects models, importance–performance priority analysis, and repeated-measures marginal models were used for data analysis. Students evaluated the needed level significantly higher than the current level across all seven dimensions, showing a consistent professional-orientation gap. Descriptively, Software and Digital Tools showed the largest observed gap, while Assignments and Learning Activities was classified as a curriculum-improvement priority using the sample-specific priority matrix. Students without previous statistics coursework, first-year students, and those with lower mathematical confidence reported significantly larger gaps than their counterparts. Students with previous statistical-software experience also reported greater unmet needs across all dimensions. The findings illustrate a potentially useful multidimensional framework for evaluating professionally oriented statistics courses and offer practical direction for curriculum redesign through stronger addition of authentic learning activities, software-supported data analysis, and professionally relevant applications. Full article

Carbon deposition during catalytic methane pyrolysis under induction heating governs reactor operability and carbon recovery; however, the available datasets are sparse. In this study, literature-derived experimental data were curated and split into training, validation and test sets (70%, 15%, 15%) to develop physics-informed neural networks (PINNs) that predict the deposited carbon mass (M_C) from the operating variables. Two models were proposed: PINN-M₁, which embeds an Arrhenius-type carbon formation rate in the loss function, and PINN-M₂, which constrains the time derivative of the M_C to match the measured carbon formation rate (CFR). Both models shared an optimized neural network architecture and were benchmarked against a classic feedforward neural network (FNN). PINN-M₁ achieved the closest agreement with the experiments (MAE∼0.104 g, MSE∼0.018 g², R²∼0.993), outperforming FNN (MAE∼0.152 g, R²∼0.987) and PINN-M₂ (MAE∼0.121 g, R²∼0.984). Loss tracking indicated rapid convergence as well as the predictions of the mass of carbon deposited. The results demonstrate that embedding physically meaningful rate expressions can improve accuracy and generalization in scarce-data methane pyrolysis modelling, providing a practical surrogate for optimizing operating conditions and supporting future digital twin development. Full article

Allium vegetables, including Welsh onion and onion, are important seasoning and processing crops worldwide. However, their production still relies heavily on manual labor because of their complex agronomic characteristics and the limited adaptability of existing machinery. Small seed size, fragile seedlings, narrow planting spacing, considerable variation in underground harvest organs, and high requirements for product quality present substantial challenges for mechanized production. With the continuous decline in agricultural labor availability and increasing production costs, the development of precision, intelligent, and full-process mechanization systems adapted to the biological characteristics of Allium vegetables has become essential for improving production efficiency and industrial sustainability. This review summarizes recent advances in mechanized production technologies and intelligent equipment for Allium vegetables, covering agronomic foundations and planting systems, precision seeding and nursery production, automatic transplanting, intelligent field management, mechanized harvesting, and digital technology applications. Advances in seed pelleting, pneumatic precision metering, and seed physical property-based parameter optimization have improved seeding accuracy and uniformity for small-seeded crops. Automated transplanting technologies, including oriented bulb planting, paper-pot seedling transplanting, and robotic seedling picking and placement, provide promising solutions for reducing labor requirements, although improvements are still needed in seedling recognition, pickup reliability, placement accuracy, and soil-covering coordination. Recent developments in field management have shifted from single mechanical or chemical operations toward integrated precision approaches involving intelligent mechanical weeding, mulch-based weed control, soil sensor-based monitoring, model-driven irrigation and fertilization regulation, biological control, and UAV- and satellite-assisted crop monitoring. Mechanized harvesting technologies have progressed through improvements in digging, soil separation, clamping and conveying, root and leaf cutting, windrowing, and collection systems. Among these processes, precise control of digging depth, soil disturbance, clamping force, and component synchronization remains critical for improving harvesting efficiency and reducing mechanical damage. Furthermore, emerging digital technologies, including machine vision, RTK positioning, LiDAR, hyperspectral sensing, multisource sensor fusion, and machine learning, are accelerating the transition of Allium vegetable machinery toward intelligent perception, autonomous navigation, and adaptive operation. However, challenges remain, including insufficient integration between agronomic requirements and machinery design, limited robustness of perception systems under complex field conditions, unclear mechanisms of harvest damage, inadequate equipment adaptability for small-scale and hilly production areas, and the lack of unified evaluation standards. Future research should focus on the coordinated development of varieties, cultivation practices, agricultural machinery, and digital technologies, with particular emphasis on high-speed precision seeding, flexible automatic transplanting, intelligent narrow-row crop management, low-damage harvesting, and closed-loop control based on multisource agricultural information. The establishment of standardized, lightweight, modular, and intelligent mechanized production systems will provide important support for the sustainable, high-quality, and large-scale development of the Allium vegetable industry. Full article

A minimally monitored clinical protocol demonstrated preliminary short-term feasibility and tolerability: 18/23 (78.3%) fitted patients were confirmed compliant, with 18/20 (90%) respondent compliance and three patients of unknown status, without serious adverse events. Longer follow-up studies are needed to assess sustained compliance throughout the pre-cranioplasty period. Future research should standardise materials and outcome measures to inform clinical practice guidelines. Background/Objectives: 3D-printed externally applied cranial orthosis (PEACO) represents an emerging solution for temporary cranial protection, though evidence remains limited regarding real-world implementation using minimally monitored clinical protocols. Methods: Retrospective cohort study conducted at a 121-bed rehabilitation centre in Singapore (November 2022–November 2025). Adults >21 years, >3 weeks post-DC with healed wounds and available caregivers were included. PEACO devices were individually designed from CT stereotaxis scans using contralateral hemi-cranium mirroring and fabricated using Ultra-durable Resin. Rehabilitation therapists fitted devices using progressive wearing schedules. T0 outcomes (30 min post-fitting) included therapist-observed pain, redness, skin imprints, and pruritus. T1 outcomes (at least 2 weeks post-fitting) included wearing duration, cosmesis ratings (0–10 scale), compliance, and adverse events via digital surveys. Exploratory comparisons using descriptive analysis were performed to compare outcomes from 2021 (n = 10) and 2025 (n = 23) cohorts. Results: Twenty-three patients (14 males, median age 56.0 years) were fitted with PEACO at median 67 days post-DC, (13 traumatic brain injury, 10 stroke). At T0, primary reprinting for poor fit was required in 3/23 (13.0%), 18/23 (78.3%) fitted patients were confirmed compliant, 18/20 (90%) were compliant with responder feedback; 2/20 (10.0%) were non-compliant, with three patients of unknown status. At T0, 9/23 (39.1%) had therapist-observed mild, transient adverse events, with no reported wound dehiscence. At T1, among 18 respondents: 16/18 (88.9%) wore PEACO ≤4 h/day, 7/18 (38.9%) reported self-limiting discomfort or transient skin changes at T1, median cosmesis rating was 6.0/10. At T1 assessment, no falls with head impact occurred. Clinical Trials Registration: ClinicalTrials.gov NCT07122752. Full article

Human anatomy teaching is undergoing a rapid transformation driven by digital innovation, immersive visualization, open multiscale datasets, and changing learner profiles, while donor-based dissection nevertheless remains essential for understanding human anatomical variability and tissue properties and may contribute to students’ professional identity formation when supported by structured reflection. In this narrative review, we synthesize peer-reviewed literature, prioritizing contributions most relevant to current educational practice and governance. We discuss how technology-enhanced resources can extend learning before, during, and after laboratory sessions through manipulable visualization, repetition, and tighter links to imaging and data literacy. We also examine the expanding clinical, research, and innovation roles of donor-based anatomy laboratories and the associated need for explicit governance (biosafety, access control, documentation, and transparency regarding secondary uses). Ethical literacy is treated as a core learning outcome, including inclusive representation, consent and donation law awareness, and critical engagement with plastination and public display. Finally, we consider how attention fragmentation and AI-enabled tools may support personalization and feedback, yet also introduce risks such as automation bias and superficial recognition if not paired with verification and reasoning-based tasks. Overall, the future of human anatomy education lies in purposeful integration of donor-based, digital, immersive, and computational modalities within an adaptive learning ecosystem that serves learners across disciplines while maintaining robust ethical and professional standards. Full article

Supply chain management (SCM) optimisation is a critical but under-examined pathway for delivering circular economy outcomes in the construction sector, particularly in geographically remote and small- and medium-sized enterprise-dominated markets such as New Zealand (NZ). This study investigates how SCM practices, such as digital tools, early contractor involvement, strategic partnerships, and reverse logistics, can be optimised to improve circular performance in NZ-built environment projects. A pragmatist, abductive mixed-methods design combined 75 case studies, a 28-case quantitative subset, 15 elite semi-structured interviews and documentary evidence. Reflexive Thematic Analysis was integrated with descriptive statistics, cross-tabulation, non-parametric testing, and configuration archetype analysis. Coordination and partnerships are the most frequently observed SCM lever, appearing in 46.7% of the 75 cases and in all four cases in the highest waste-diversion band, while digital tools are seldom deployed as the primary lever. Two operational metrics summarise these configurations: a Waste Hierarchy Index (WHI) of 77.6%, calculated across the 66 cases with an assignable waste hierarchy position, and a Digital SCM Adoption Rate (DSAR) of 15.1%, calculated across the 73 cases with a stated primary lever. Together they indicate an upstream but digitally uneven pattern of optimisation. The study contributes a configuration-based method for comparing SCM levers in circular construction, together with the first NZ evidence base of this kind, and finds that procurement reform rather than digital investment is the key constraint on circular performance. Practical implications include embedding early contractor involvement and integrated design in public procurement, funding sector-wide digital interoperability rather than firm-level tools, and extending site waste management plan reporting. Priority future research is longitudinal, scale-stratified testing of the configuration archetypes identified in this paper. Full article

We investigate how to integrate AI-generated content (AIGC) into project-based teaching (PBT) in higher vocational colleges to address the limitations of traditional models and advance digital transformation in engineering education. AIGC generation systems in PBT provide teaching resources, personalized skill guidance, and project process management. Improvement was observed in learning performance and project outcomes by 15%, confirming that AIGC fosters engineering practice, innovation, and collaboration. The results show that AIGC can be utilized in vocational engineering education and talent cultivation. Full article

Digital-twin (DT) technology has evolved into a fundamental paradigm for integrating physical assets, processes, and systems with their dynamic digital counterparts, enabling real-time monitoring, simulation, prediction, optimization, and intelligent decision-making. This study presents a systematic review of 230 peer-reviewed articles published between 2018 and 2026, examining the evolution, application domains, enabling technologies, implementation challenges, and emerging research directions of DTs across manufacturing, healthcare, smart cities, and energy systems. The review synthesizes the growing convergence of DTs with artificial intelligence (AI), the Internet of Things (IoT), blockchain, edge computing, cloud computing, and cyber–physical systems, highlighting their role in developing intelligent, interconnected, and data-driven environments. Across the four domains, the principal challenges identified include heterogeneous and distributed data integration, cybersecurity and privacy, physical–virtual synchronization, interoperability, scalability, model fidelity, and computational and communication constraints. The review further identifies eight emerging research directions, including AI-enabled autonomous DTs, blockchain-assisted trust and security mechanisms, federated learning, 6G-enabled DT networks, adaptive and self-learning architectures, and sustainable and energy-efficient DT infrastructures. To complement the literature synthesis with practical evidence, two experimentally validated DT prototypes developed at the Laboratory of Advanced Electronic Systems (LSEA) and the Laboratory of Applied Automation and Industrial Diagnostics (LAADI), Algeria, are presented. The first integrates CIROS Studio, VGG16, and Node-RED for drilling-operation quality control, achieving an accuracy improvement from 98.63% to 99.83% following fine-tuning. The second combines Factory I/O, MobileNet, fuzzy logic, and a Gradio-based human–machine interface for conveyor-belt sorting, achieving true-positive and true-negative rates of 99.90% and 99.10%, respectively. The combined findings demonstrate that the integration of DTs with AI, industrial automation, and simulation can substantially enhance monitoring, prediction, quality control, and intelligent decision-making. At the same time, they emphasize the need for interoperable, secure, scalable, computationally efficient, and energy-aware architectures to enable reliable real-time deployment of DTs across heterogeneous application domains. Full article

Random numbers play a vital role in modern information security, where quantum random number generators (QRNGs) based on single-photon avalanche diodes (SPADs) offer a promising physical platform for quantum random-number generation. However, practical SPAD QRNGs can exhibit temporal correlation arising from finite photon-event statistics and detector nonidealities. To overcome these limitations, this paper presents a high-speed, 256-channel SPAD QRNG chip, featuring a 256 × 512-pixel array integrated with a rolling-sampling time-division multiplexing architecture. By virtue of the rolling-sampling mechanism, the single-pixel sampling interval is extended to 40.96 μs, which substantially reduces the measured temporal correlation by increasing the per-pixel revisit interval while preserving aggregate throughput. Within one complete operating cycle, the chip generates 256 random bits simultaneously, achieving a maximum aggregate raw output bitrate of 3.2 Gb/s without digital post-processing. An FPGA-based test platform was constructed for experimental verification. Measurements demonstrate that under standard indoor illumination (8 W/m²), the generated raw bitstreams pass all 15 NIST SP 800–22 statistical randomness tests at 3.2 Gb/s without post-processing. Furthermore, under controlled 530 nm illumination, the measured lag-1 serial autocorrelation coefficient decreases below the 10⁻³ reference level at an incident optical power of 11.6 μW. The proposed chip offers a low-optical-power, high-throughput solution for integrated hardware security. Full article

Smart tourism technologies are increasingly transforming how tourists interact with destinations, yet the mechanisms through which technology quality contributes to memorable tourism experiences remain insufficiently understood. Drawing on Stimulus–Organism–Response (S-O-R) theory, this study examines the relationships among smart tourism technology quality (STTQ), smart destination perception (SDP), tourist psychological empowerment (TPE), and memorable tourism experience (MTE). Data were collected from tourists visiting selected Egyptian destinations characterized by extensive use of digital and smart tourism technologies. The proposed model was assessed using partial least squares structural equation modeling. The findings revealed that STTQ positively relates to MTE, SDP, and TPE, while both SDP and TPE positively relate to MTE. Furthermore, the results indicate significant indirect associations between STTQ and MTE through SDP and TPE. The findings extend S-O-R theory by demonstrating that smart tourism technology quality can influence memorable experiences through both destination-oriented cognitive perceptions and tourist-oriented psychological empowerment. The study contributes to smart tourism research by integrating technological quality, destination smartness, and tourist agency within a unified framework and offers practical implications for designing technology-enabled destination experiences. Full article