Search Results (349)

The convergence of Artificial Intelligence and the Internet of Things has given rise to the Artificial Intelligence of Things (AIoT), which enables connected systems to operate with greater autonomy, adaptability, and contextual awareness. In agriculture, this evolution supports precision farming, improves resource allocation, and strengthens climate resilience by enhancing the capacity of farming systems to anticipate, absorb, and recover from environmental shocks. This review provides a structured synthesis of the transition from IoT-based monitoring to AIoT-driven intelligent agriculture and examines key applications such as smart irrigation, pest and disease detection, soil and crop health assessment, yield prediction, and livestock management. To ensure methodological rigor and transparency, this study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search and multi-stage screening procedure was conducted across major scholarly repositories, resulting in a curated selection of studies published between 2018 and 2025. These sources were analyzed thematically to identify technological enablers, implementation barriers, and contextual factors affecting adoption particularly within low-income countries where infrastructural constraints, limited digital capacity, and economic disparities shape AIoT deployment. Building on these insights, the article proposes an AIoT architecture tailored to resource-constrained agricultural environments. The architecture integrates sensing technologies, connectivity layers, edge intelligence, data processing pipelines, and decision-support mechanisms, and is supported by governance, data stewardship, and capacity-building frameworks. By combining systematic evidence with conceptual analysis, this review offers a comprehensive perspective on the transformative potential of AIoT in advancing sustainable, inclusive, and intelligent food production systems. Full article

The high costs and time-consuming nature of space exploration missions are among the major barriers to studying deep space. The lack of samples and limited information make such studies challenging, highlighting the need for innovative solutions, including advanced data-mining techniques and tools such as geochemical modeling, as strategies for overcoming challenges in data scarcity. Geochemical modeling is a powerful tool for understanding the processes that govern the composition and distribution of elements and compounds in a system. In cosmology, space geochemical modeling could support cosmochemistry by simulating the evolution of the atmospheres, crusts, and interiors of astronomical objects and predicting the geochemical conditions of their surfaces or subsurfaces. This study uniquely focuses on the geochemical modeling of celestial bodies beyond Mars, fills a significant gap in the literature, and provides a vision of what has been done by analyzing, categorizing, and providing the critical points of these research objectives, exploring geochemical modeling aspects, and outcomes. To systematically trace the intellectual structure of this field, this study follows the PRISMA guidelines for systematic reviews. It includes a structured screening process that uses bibliographic methods to identify relevant studies. To this end, we developed the Custom Bibliometric Analyses Toolkit (CBAT), which includes modules for keyword extraction, targeted thematic mapping, and visual network representation. This toolkit enables the precise identification and analysis of relevant studies, providing a robust methodological framework for future research. Europa, Titan, and Enceladus are among the most studied celestial bodies, with spectrometry and thermodynamic models as the most prevalent methods, supported by tools such as FREZCHEM, PHREEQC, and CHNOSZ. By exploring geochemical modeling solutions, our systematic review serves to inform future exploration of distant celestial bodies and assist in ambitious questions such as habitability and the potential for extraterrestrial life in the outer solar system. Full article

The rapid expansion of real-world medical data is driving a transformative shift toward integrating artificial intelligence (AI) with propensity score matching (PSM) to enhance clinical research. While AI provides advanced capabilities in diagnostics and prediction, PSM serves as a critical statistical tool for mitigating confounding bias in quasi-experimental studies, thereby approximating the reliability of randomized controlled trials. This study utilized synthetic thematic analysis (STA) and bibliometric mapping via VOSviewer and Bibliometrix to analyze 433 documents retrieved from the Scopus database. The findings reveal an exponential growth in this field between 2020 and 2024, with the United States and China emerging as the primary contributors to global research output. Four central thematic clusters were identified: prediction, cancer management, diagnostics, and deep learning. The integration is bidirectional, characterized by AI algorithms optimizing propensity score estimation and PSM frameworks being used to enhance AI-driven models. This methodological convergence is significantly improving the rigour of observational studies, particularly in complex clinical domains such as cardiovascular disease and chronic illness management. Ultimately, the AI-PSM symbiosis represents a critical trend in medical informatics, refining the accuracy of predictive modelling and strengthening the evidentiary value of real-world data in global health research. Full article

Chromium (Cr) exists in multiple oxidation states, with Cr(III) and Cr(VI) being the most environmentally and industrially relevant due to their distinct toxicity profiles and chemical behaviour. This study presents a comprehensive method that combines chemical simulation modelling, emission spectroscopy for quantification, and the controlled laboratory production of Cr species. Key findings include that acid digestion effectively extracted the Cr(III) and total Cr species, while thermodynamic modelling forecasted their stability and speciation under various environmental conditions. Thematic analysis indicates that the current quantification of Cr species is still in early development and remains centralized. Mineralogical and surface investigations showed that samples 1 and 2 have a BET surface area below 1 m²/g, whereas samples 3 and 4 exceed this. All samples are crystalline, with approximately 54.3 weight percent Cr₂O₃, 7.3 weight percent SiO₂, 17.75 weight percent of MgO, and 8.3 weight percent Al₂O₃, suggesting Al and Fe²⁺ replacement of Cr in the spinel structure. Computational fluid dynamics (CFD) modelling revealed that longer residence times are necessary for higher Cr metallization under H₂-CH₄-reducing conditions, and accurately predicted carbon deposition on pellets. These results demonstrate that CFD can optimize the H₂:CH₄ ratio to minimize carbon deposition and enhance gas transport to reaction sites. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

Landslides are a persistent hazard in the tectonically active Central Himalaya, frequently affecting roads and settlements. However, quantitative assessments of their spatial drivers have remained limited. This study investigated landslide susceptibility along a 90 km section of the Uttarkashi–Gangotri highway to identify dominant triggering factors and generate a reliable risk map. We applied the AHP–GIS framework, guided by a multi-criteria decision-making approach. Nine thematic parameters, such as slope, geology, lineament density, drainage density, proximity to roads, rainfall, aspect, curvature, and land use/land cover were utilised to quantify their relative influence on slope failure. Results showed that slope (23%), geology (22%), and lineament density (21%) were the most influential factors. Secondary contributions came from drainage density (9%), proximity to roads (8%), and rainfall (>231 mm). The susceptibility map was validated using 105 landslide inventory points, with 64 events (61%) located in very high-risk zones and 31 (30%) in high-risk zones. The model achieved a predictive accuracy of 0.817 based on the Area Under the Curve (AUC) metric. High-risk zones are aligned with steep slopes (30–50°), convex curvatures, and barren land, particularly near infrastructure. These findings provide a scientific tool for hazard mitigation and support disaster risk reduction in similar mountainous regions worldwide, contributing to safer infrastructure development. Full article

This paper presents the results of assessing the influence of siltation factors in 23 ponds in one of the most agriculturally developed macro-regions of European Russia—the Central Russian Upland. Key natural and anthropogenic factors determining the intensity of pond siltation have been identified, and a typification of ponds has been developed to predict the rate of accumulation of bottom sediments in them. For the typification, statistical methods such as correlation analysis (Spearman’s coefficient), cluster and factor analysis, and the Random Forest machine learning algorithm were used. Correlation analysis revealed that the percentage of catchment cultivation has a significant effect (r = 0.55, p < 0.01) on the volume of bottom sediments, while soil loss (r = 0.47, p < 0.05) and vertical terrain dissection (r = 0.43, p < 0.05) have a moderate effect. The most important factors in the siltation process are the average slope of the catchment (24.5%), the percentage of cultivated soils (18.8%), and the average annual soil loss (14.1%). All factors were grouped into three clusters, which explained 77.8% of the variance. As a result, four pond types were identified, differing in their dominant limiting factors: pond hydrological characteristics, catchment morphometry, and the degree of anthropogenic transformation of the catchment. Verification of the typification was carried out based on the calculation of annual soil losses considering the sediment delivery coefficient; the discrepancies between the calculated and actual pond sediment volumes were 1.2–10.0%. The proposed approach, which recommends a multi-scale assessment of potential sediment formation volumes using remote sensing data and thematic mapping, offers heuristic potential for identifying the most degraded water bodies. This enables the planning of priority sites and rehabilitation measures for their restoration within the framework of regional soil and water conservation programs. Full article

Background: Case marking serves as a crucial cue in sentence processing, enabling the prediction of upcoming arguments, thematic roles, and event structure. Cross-linguistic studies have revealed language-specific variations in case processing, with differences observed between nominative–accusative and ergative languages, albeit with limited data from the latter. Objective: To this end, we investigated case processing in Hindi compound light verb constructions, leveraging its split-ergative system. Methods: An ERP study was conducted with twenty-four native Hindi speakers, wherein the subject case (ergative or nominative) either matched or mismatched with the aspect marking on the light verb (perfective or imperfective). Results: The results revealed distinct ERP effects depending upon the subject case: a P600 effect for ergative case violations at the imperfective light verb and a biphasic N400-P600 effect for nominative case violations at the perfective light verb. Conclusions: These findings suggest underlying neurophysiological differences in the processing of ergative versus nominative case alignment within light verb structures. Moving forward, a closer examination of structure-specific neurophysiological variation can help bridge the gap between typological distributions and their neural underpinnings. Full article

Industrialised construction (IC) represents a foundational strategy for overcoming entrenched productivity constraints and supply shortfalls in the housing sector. By enabling the mass production and mass customisation of advanced kit-of-parts systems, IC supports more efficient, predictable, scalable, and sustainable building delivery through integrated, standardised, and digitally enabled processes. However, adoption remains uneven due to fragmentation across regulatory, organisational, and technological systems. This paper presents a systematic literature review and thematic synthesis of the literature published between 2000 and 2025 to examine performance outcomes, adoption trends, digital integration maturity, and emerging platform-based design for manufacture and assembly (P-DfMA) approaches, and the main drivers. The review shows that significant performance gains are achievable, including notable reductions in construction time and cost variability, along with substantial reductions in material waste, together with measurable improvements in quality, safety, and delivery predictability. However, widespread uptake of IC remains constrained. This is largely driven by regulatory misalignment, rigid and bespoke procurement and delivery models, inconsistent and unstable supply chain capacity, and the lack of standardised components and integrated digital workflows. Building on these insights, this paper examines the key enablers required for sector-wide transformation toward an ecosystem that supports standardised kit-of-parts solutions, digitally driven design-to-production workflows, and aligned policy and procurement frameworks that are capable of delivering scalable and repeatable industrialised housing. The findings provide a consolidated evidence base and identify the key enablers for policymakers, industry stakeholders, and researchers working to move from project-centred delivery models to platform-based, digitally integrated, and industrialised construction systems. We searched Scopus, Web of Science, ScienceDirect, and Google Scholar, complemented by targeted industry and policy repositories; the searches were last updated on 1 December 2025. After screening, 117 sources were included. The review was not registered, and no review protocol was prepared. Full article

This article presents an academic literature analysis of 75 Industry 4.0 (I4.0) and Industry 5.0 (I5.0) maturity models published between 2020 and 2024, examining their empirical validation, sectoral scope, geographical origin, and stated applicability to developing-country contexts. The study combines descriptive profiling, contingency-table analyses with exact tests and effect sizes, and a large-scale synthesis of 562 research gaps reported by model authors. Knowledge production is highly concentrated in single-country studies (77.3%) and in developed economies, while most models do not explicitly or implicitly document applicability to developing-country settings (approximately 83%). Empirical validation practices are uneven, with multiple-case studies (33.3%) and surveys (24.0%) dominating, and sectoral coverage is strongly skewed toward manufacturing, limiting transferability to other sectors relevant for emerging economies. A statistically detectable association is observed between the development level of the model’s country of origin and the presence of applicability statements (${\chi }^2$ = 17.13, $p<0.05$, moderate effect size), whereas authorship configuration shows no substantive association. Thematic analysis of reported gaps highlights persistent deficits in empirical rigor, sectoral breadth, SME orientation, operationalization of human-centric and sustainability dimensions associated with Industry 5.0, availability of implementation tools, and longitudinal or predictive evidence. The article concludes by outlining a research agenda focused on context-aware validation designs, broader sectoral grounding, and greater transparency and reproducibility, supported by open access to all underlying data, codebooks, and taxonomies. Full article

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector. Full article

Background: Missed nursing care (MNCs) is a global issue with the potential to threaten patient safety and is often associated with a stressful work environment. Although stress-related work among clinical nurses is associated with MNCs, the correlation remains limited. Objective: This systematic review aimed to assess and synthesize available scientific evidence regarding the correlation between stress-related work among clinical nurses and the incidence of MNCs in hospital settings. Methods: This review was conducted according to the PRISMA guidelines. The PubMed, Scopus, and EBSCO databases were systematically searched for articles published between January 2014 and June 2025. Primary studies with quantitative, qualitative, or mixed-methods designs that examined the relationship between stress-related work and MNCs among hospital nurses were included. The data obtained were extracted and analyzed using thematic approaches. Results: A total of 244 articles were identified from the three databases. Seven studies, conducted in different countries met the inclusion criteria. All studies used cross-sectional designs. The results showed that most study reported stress-related work, emotional fatigue, and burnout were significantly positively related to the frequency of MNCs (p < 0.05). The most frequently missed types of nursing care include monitoring vital signs, skin/wound care, and oral care. Conclusions: The evidence suggests that stress-related work among nurses has significant potential to predict MNCs. Interventions that focus on mitigating work stress by improving the work environment and optimizing workload are crucial for improving quality of care and patient safety. Full article

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

The growing pressure on industrial organizations to align digital transformation with sustainability objectives has intensified the need to systematically understand the role of emerging digital technologies in sustainable industrial development. The accelerated digitalization of industrial ecosystems has positioned the Internet of Things (IoT) as a critical enabler of corporate sustainability within Industry 4.0. However, evidence on how IoT contributes to environmental, social, and economic performance remains fragmented. This study conducts a systematic literature review following PRISMA 2020 guidelines to consolidate the scientific advances linking IoT with sustainable corporate management. The search covered 2009–2025 and included publications indexed in Scopus, EBSCO Essential, and MDPI, identifying 65 empirical and conceptual studies that met the inclusion criteria. Bibliometric analyses—such as keyword co-occurrence mapping and temporal heatmaps—were performed using VOSviewer v. 2023 to detect dominant research clusters and emerging thematic trajectories. Results reveal four domains in which IoT significantly influences sustainability: (1) resource-efficient operations enabled by real-time sensing and predictive analytics; (2) energy optimization and green digital transformation initiatives; (3) circular-economy practices supported by data-driven decision-making; and (4) the integration of IoT with Green Human Resource Management to strengthen environmentally responsible organizational cultures. Despite these advances, gaps persist related to Latin American contexts, theoretical integration, and longitudinal assessment. This study proposes a conceptual model illustrating how IoT-enabled technologies enhance corporate sustainability and offers strategic insights for aligning Industry 4.0 transformations with the Sustainable Development Goals (SDGs), particularly SDGs 7, 9, and 12. Full article

While the technological foundation for sludge valorization (anaerobic digestion and pyrolysis) is mature, a significant disconnect exists between traditional research and the advanced application of artificial intelligence. This study identifies that Machine Learning (ML) remains in a peripheral position, representing an untapped frontier for achieving predictive and circular systems. The methodology involved a quantitative bibliometric analysis of 190 Scopus-indexed documents (2005–2025). We analyzed indicators of scientific production, collaboration, and thematic evolution using Bibliometrix and VOSviewer 1.6.20. The results reveal a rapidly growing research field, predominantly led by Chinese institutions. The temporal analysis projects a productivity peak around 2033. Core topics include established technologies like anaerobic digestion and pyrolysis. However, network and keyword analyses reveal an emerging trend toward hydrothermal processes and, crucially, the early incorporation of ML. However, ML still occupies a peripheral position within the main scientific discourse, highlighting a gap between traditional research and the advanced application of artificial intelligence. The study systematizes existing knowledge and demonstrates that, although the technological foundation is mature, the deep integration of ML represents the future frontier for achieving sludge valorization systems that are more predictive, efficient, and aligned with the principles of the circular economy. Full article