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In recent decades, significant efforts have been devoted to constructing energy-efficient buildings, providing comfortable indoor environments. However, measures such as enhanced airtightness, while reducing infiltration through the building envelope, might consequently reduce natural ventilation. This reduction is a critical concern because natural ventilation is an essential factor in controlling indoor air quality (IAQ), and its diminution could therefore worsen IAQ. Sick building syndrome has emerged as a term used to describe health hazards linked to the time spent indoors but with no particular cause. Since people spend most of their time indoors, the demand for continuous and real-time IAQ management to reduce human exposure to pollutants has increased considerably. In this context, low-cost sensors (LCS) for IAQ monitoring have become popular, driven by recent technological advancements and increased awareness regarding indoor air pollution and its negative health impacts. Although LCS do not meet the performance requirements of reference and regulatory equipment, they provide informative measurements, offering high-resolution monitoring, emission source identification, exposure mitigation, real-time IAQ assessment, and energy efficiency management. This perspective article proposes a general model for LCS systems (and subsystems) implementation and presents a prospective analysis of their strengths and limitations for IAQ management, reviews the literature regarding sensor system technologies, and offers design recommendations. It provides valuable insights for researchers and practitioners in the field of IAQ and discusses future trends. Full article

Research on microplastics (MPs) in agricultural soils has received increasing attention due to their potential ecological risks and adverse effects on the food chain. Recently, geostatistical approaches have been increasingly used to assess the spatial distribution of MPs in soils. Therefore, this study aims to predict the abundance of MPs in the soil of an agricultural micro-watershed using geostatistical methods and to produce a continuous map of the interpolated MPs. Soil samples were collected, and MP abundance was determined using the density separation method. Subsequently, exploratory data analysis was conducted, followed by the construction of the experimental semivariogram, theoretical variogram model fitting, ordinary kriging interpolation, cross-validation and, inverse distance weighting (IDW) interpolation. MPs were detected in all samples, with average abundances of 3826, 2553, and 3407 pieces kg⁻¹ in forest, pasture, and agricultural land use systems, respectively. The experimental semivariogram showed that the spatial distribution of MPs has a weak spatial dependence structure. The Kriging and IDW maps showed a distribution of MPs in the range of 600 to 7400 pieces kg⁻¹, with higher concentrations of MPs for forest and agricultural areas. Additionally, the map reveals a high abundance of MPs, with greater concentrations in depressions and areas near roads and urban centers, allowing for identifying critical points within the micro-watershed. This study offers important insights into the presence of MPs across various land uses, emphasizing the need for proactive measures to prevent and mitigate their accumulation in soil. Full article

The importance of safety culture in high-risk sectors such as railway transport has gained increasing prominence, particularly within the evolving European regulatory landscape. Commission Delegated Regulation (EU) 2018/762 requires railway organisations to establish strategies for the continuous improvement of safety culture, emphasizing both behavioural and systemic dimensions of safety. This paper presents a structured literature review and proposes a conceptual model of performance indicators designed to support the implementation of these strategies in railway enterprises. Drawing on established continuous improvement methodologies—Kaizen, Six Sigma, and the DMAIC (Define–Measure–Analyse–Improve–Control) framework—the model aligns with Safety Management System (SMS) and Maintenance Management System (MMS) processes. The proposed indicators encompass domains such as risk assessment, change management, employee competence, incident reporting, and system monitoring. The model aims to transform railway organisations into learning systems capable of proactively adapting to emerging risks, including those related to cybersecurity as addressed by the NIS2 Directive. Through a structured literature review and conceptual synthesis, this study provides a theoretical foundation for the integration of continuous improvement and sustainability in safety management. The findings offer practical guidance for policymakers and railway operators seeking to strengthen data-driven, resilient, and sustainable transport safety governance in the European context. Full article

Delamination remains a critical failure mode in carbon fiber-reinforced polymer (CFRP) composites, particularly under cyclic loading in aerospace and automotive applications. This study explores whether nanoscale reinforcement with graphene-based materials can enhance delamination resistance and identifies the most effective nanofiller type. Two distinct graphene nanospecies—reduced graphene oxide (rGO) and carboxyl-functionalized graphene nanoplatelets (HDPlas)—were incorporated at 0.5 wt% into CFRP laminates and tested under static and fatigue mode I loading using double cantilever beam (DCB) tests. Both nanofillers enhanced interlaminar fracture toughness compared to the neat composite: rGO improved the energy release rate by 36%, while HDPlas achieved a remarkable 67% enhancement. Fatigue testing showed even stronger effects, with the fatigue threshold energy release rate rising by 24% for rGO and 67% for HDPlas, leading to a fivefold increase in fatigue life for HDPlas-modified laminates. A compliance calibration method enabled continuous monitoring of crack growth over one million cycles. Fractography analysis using scanning electron microscopy revealed that both nanofillers activated crack bifurcation, enhancing energy dissipation. However, the HDPlas system further exhibited extensive nanoparticle pull-out, creating a more tortuous crack path and superior resistance to crack initiation and growth under cyclic loading. Full article

Adult learner dropout is adults’ withdrawal or stop-out from formal or non-formal educational programs before successful completion. For adult learners, withdrawal often manifests as stop-out or temporary disengagement rather than permanent attrition, reflecting the episodic nature of their participation. Unlike traditional students, adult learners must often balance multiple life responsibilities—employment, caregiving, financial obligations, and community roles—while also pursuing education or training. Their vulnerability to attrition is further exacerbated by these overlapping demands, particularly when educational programs do not accommodate their situational and motivational needs. Adult learner dropout therefore requires a more dynamic understanding of persistence as a continuous negotiation between internal and external demands. Participation in online education has significantly expanded over the past two decades, particularly during and after the COVID-19 pandemic, as adult learners increasingly engage with digital platforms for work and communication. This exposure has enhanced their digital fluency, transforming their expectations and experiences of online learning. Thus, the underlying factors that influence adult learner dropout have also shifted—moving beyond technological and access-related barriers to instructional quality, engagement design, and relevance issues. In this evolving landscape, adult learner dropout can no longer be regarded as isolated or individual events. It is a systemic phenomenon emerging from dynamic interactions among psychological, pedagogical, contextual, and institutional factors. Full article

Skin is constantly exposed to UV radiation. While topical sunscreens are the main preventative measure, oral photoprotective agents are emerging as promising systemic adjuncts, offering uniform, continuous protection. This study presents the results of two clinical trials designed to evaluate the efficacy of supplementation with a standardized coriander (Coriandrum sativum L.) seed oil (CSO) in mitigating UV-induced skin damage, in comparison with a placebo. The first trial investigated the effects of CSO supplementation on women with reactive skin, assessing UVA+B-induced skin erythema and tumor necrosis factor-alpha (TNF-α) release. The second trial included women of all skin types and, in addition to the outcomes mentioned above, examined UVA-induced lipoperoxidation. Measurements were taken before and after 56 days of supplementation. CSO supplementation led to a significant reduction in UV-induced skin erythema and associated TNF-α levels in both cohorts, with decreases of 11.8% and 24.1% in the reactive skin group and 18.1% and 18.7% in the cohort with all skin types, respectively. In women of all skin types, UV-induced skin lipoperoxidation was reduced by 31.9% at 4 h and by 69.9% at 24 h post-exposure. To the best of our knowledge, this is the first study reporting the photoprotective efficacy of CSO. This finding is attributed to CSO’s high petroselinic acid content and its known anti-inflammatory properties. Full article

Compared to the traditional non-load-bearing multi-legged robots, the heavy-duty multi-legged robots typically not only have larger body weight, larger volume, and larger load ratio but also require greater energy dissipation. Traditional path planning often focuses on the problem of finding the shortest path. However, the substantial load capacity and multi-jointed structure of heavy-duty multi-legged robots impose stringent requirements on path smoothness. Consequently, the smoothness requirement makes the traditional A* algorithm unsuitable for applications where low-energy operation is critical. An improved low-energy path planning method based on the A* algorithm is presented for an electrically driven heavy-duty six-legged robot. Then, the environment is discretized by using the grid method to facilitate path searching. To address the path zigzagging problem caused by the traditional A* algorithm, the Bézier curve smoothing technique is adopted. The continuous curvature transitions are employed to significantly improve the smoothness of path. The heuristic function in the A* algorithm is enhanced through a dynamic weight adjustment mechanism. The nonlinear suppression strategy is introduced to prevent data changes and improve the robustness of the algorithm. The effectiveness of the proposed method is verified through the MATLAB simulation platform system. The simulation experiments show that, in various environments with different obstacle densities (0.17–0.37%), compared with the traditional A* algorithm, the method proposed in this paper reduces the average path length by 7.2%, the number of turning points by 25.9%, and the energy consumption by 5.75%. The proposed improved A* algorithm can significantly overcome the problem of insufficient smoothness in traditional A* algorithms and reduce the number of nodes generated by the control data stack, which improves the optimization efficiency during path planning. As a result, the heavy-duty six-legged robots can walk farther and operate for longer periods of time while carrying the limited energy sources. Full article

In the context of dual-carbon goals, Park-Level Integrated Energy Systems (PIES) are pivotal for enhancing renewable energy integration and promoting clean, efficient energy use. However, the inherent non-linearity from multi-energy coupling and the high dimensionality of operational data present substantial challenges for conventional scheduling optimization methods. To overcome these obstacles, this paper introduces a novel multi-objective scheduling framework for PIES leveraging deep reinforcement learning. We innovatively formulate the scheduling task as a Markov Decision Process (MDP) and employ the Trust Region Policy Optimization (TRPO) algorithm, which is adept at handling continuous action spaces. The state and action spaces are meticulously designed according to system constraints and user demands. A comprehensive reward function is then established to concurrently pursue three objectives: minimum operating cost, minimum carbon emissions, and maximum exergy efficiency. Through comparative analyses against other AI-based algorithms, our results demonstrate that the proposed method significantly lowers operating costs and carbon footprint while enhancing overall exergy efficiency. This validates the model’s effectiveness and superiority in addressing the complex multi-objective scheduling challenges inherent in modern energy systems. Full article

Continuous ethanol fermentation is crucial for renewable bio-manufacturing, but delay-induced ethanol inhibition triggers self-oscillations via Hopf bifurcations, undermining productivity and stability. This study investigates instability mechanisms and proposes a washout-filter-aided control strategy. Using Hopf bifurcation theory, the critical delay time τ_c (20.97 h) was quantified, and it confirmed that τ > τ_c (intrinsic τ = 21.72 h) induces oscillations. Closed-loop analysis reveals that the filter extends τ_c to 25.57 h (e.g., K = 2, d = 0.5), expanding the stability margin by modulating ethanol dynamics through phase-shifted feedback. Numerical simulations and experimental validation demonstrate effective oscillation suppression, maintaining steady-state substrate (S* = 84.32 g/L), biomass (X* = 6.92 g/L), and ethanol (P* = 22.02 g/L) concentrations without sacrificing productivity. Unlike conventional methods, the strategy retains the system’s equilibrium structure, resists noise, and requires no additional hardware. This work bridges bifurcation analysis with practical control, offering a robust, scalable solution for industrial continuous ethanol production to mitigate delay-induced instabilities. Full article

This paper presents a traffic-driven scaling framework for a digital twin proxy pool (DTPP) in vehicular edge computing (VEC), designed to eliminate the latency and synchronization issues inherent in conventional digital twin (DT) migration approaches. The core innovation lies in replacing the migration of vehicle DTs between edge servers (ESs) with instantaneous switching within a pre-allocated pool of DT proxies, thereby achieving zero migration latency and continuous synchronization. The proposed architecture differentiates between short-term DTs (SDTs) hosted in edge-side in-memory databases for real-time, low-latency services, and long-term DTs (LDTs) in the cloud for historical data aggregation. A queuing-theoretic model formulates the DTPP as an M/M/c system, deriving a closed-form lower bound for the minimum number of proxies required to satisfy a predefined queuing-delay constraint, thus transforming quality-of-service targets into analytically computable resource allocations. The scaling mechanism operates on a cloud–edge collaborative principle: a cloud-based predictor, employing a TCN-Transformer fusion model, forecasts hourly traffic arrival rates to set a baseline proxy count, while edge-side managers perform monotonic, 5 min scale-ups based on real-time monitoring to absorb sudden traffic bursts without causing service jitter. Extensive evaluations were conducted using the PeMS dataset. The TCN-Transformer predictor significantly outperforms single-model baselines, achieving a mean absolute percentage error (MAPE) of 17.83%. More importantly, dynamic scaling at the ES reduces delay violation rates substantially—for instance, from 13.57% under static provisioning to just 1.35% when the minimum proxy count is 2—confirming the system’s ability to maintain service quality under highly dynamic conditions. These findings shows that the DTPP framework provides a robust solution for resource-efficient and latency-guaranteed DT services in VEC. Full article

Background: Many everyday electronic devices incorporate embedded computers, allowing them to offer advanced functions such as Internet connectivity or the execution of artificial intelligence algorithms, giving rise to Tiny Machine Learning (TinyML) and Edge AI applications. In these contexts, models must be both efficient and explainable, especially when they are intended for systems that must be understood, interpreted, validated, or certified by humans in contrast to other approaches that are less interpretable. Among these algorithms, linguistic fuzzy systems have traditionally been valued for their interpretability and their ability to represent uncertainty with low computational cost, making them a relevant choice for embedded intelligence. However, in dynamic and changing environments, it is essential that these models can continuously adapt. While there are fuzzy approaches capable of adapting to changing conditions, few studies explicitly address their adaptation and optimization in resource-constrained devices. Methods: This paper focuses on this challenge and presents a lightweight evolutionary strategy, based on a micro genetic algorithm, adapted for constrained hardware online on-device tuning of linguistic (Mamdani-type) fuzzy models, while preserving their interpretability. Results: A prototype implementation on an embedded platform demonstrates the feasibility of the approach and highlights its potential to bring explainable self-adaptation to TinyML and Edge AI scenarios. Conclusions: The main contribution lies in showing how an appropriate integration of carefully chosen tuning mechanisms and model structure enables efficient on-device adaptation under severe resource constraints, making continuous linguistic adjustment feasible within TinyML systems. Full article

The intelligent decision-making systems of Autonomous Underwater Vehicles (AUVs) are undergoing a significant transformation, shifting from traditional control theories to data-driven paradigms. Deep learning (DL) serves as the primary driving force behind this evolution; however, its application in complex and unstructured underwater environments continues to present unique challenges. To systematically analyze the development, current obstacles, and future directions of DL-enhanced AUV decision-making systems, this paper proposes an innovative ‘four-module’ decomposition framework consisting of information processing, understanding, judgment, and output. This framework enables a structured review of the progression of DL technologies across each stage of the AUV decision-making information flow. To further bridge the gap between theoretical advancements and practical implementation, we introduce a task complexity–environment uncertainty four-quadrant analytical matrix, offering strategic guidance for selecting appropriate DL architectures across diverse operational scenarios. Additionally, this work identifies key challenges in the field as well as anticipates future developments to solve these challenges. This paper aims to provide researchers and engineers with a comprehensive and strategic overview to support the design and optimization of next-generation AUV decision-making architectures. Full article

White-matter development during fetal life represents one of the most vulnerable processes to environmental disruption, as it relies on the precisely timed proliferation, migration, and differentiation of oligodendrocyte lineage cells. Among environmental threats, exposure to toxic compounds contained in tobacco smoke and vaping aerosols represents a major yet preventable risk during pregnancy. Despite growing awareness, tobacco smoking remains widespread, and a substantial proportion of the population—including pregnant women—continues to perceive electronic nicotine delivery systems (ENDS) as less harmful, a misconception that contributes to persistent prenatal exposure. These products expose the fetus to numerous substances that readily cross the placenta and reach the developing brain, including compounds with endocrine-disrupting activity, where they interfere with white-matter development. Epidemiological and neuroimaging studies consistently reveal microstructural alterations in white matter that correlate with long-term cognitive and behavioral impairments in offspring exposed in utero. These alterations may arise from both nicotine-specific pathways and the actions of other toxicants in cigarette smoke and ENDS aerosols that cross the placenta and disrupt white-matter emergence and maturation. Preclinical research provides mechanistic insight: nicotine acts directly on nicotinic acetylcholine receptors (nAChRs) in oligodendrocyte precursor cells, disrupting calcium signaling and differentiation, while additional constituents of smoke and vaping aerosols also affect astrocyte and microglial function and disturb the extracellular milieu required for proper myelination. Full article

Geometric inaccuracies in the design of X-ray baggage scanners can lead to significant image artifacts, such as banding and discontinuities, which compromise security screening effectiveness. Although comprehensive commercial solutions are available, constructing a custom X-ray scanner requires the precise alignment of detector arrays. This is a complex and time-consuming process when performed manually. The core of the proposed method is a computational model that calculates the optimal position and orientation for each detector card based on user-defined scanner dimensions and hardware parameters. To validate the geometry created with this method, its performance was compared against flat and arc-shaped geometries. The results demonstrate that the proposed method successfully generates geometries that produce continuous and artifact-free images. The study concludes that the developed software tool provides a robust and practical solution, significantly simplifying the complex task of scanner construction and accelerating the development of reliable, custom X-ray inspection systems. Full article

The increasing size and weight of deep-water topside modules necessitate reliable and efficient installation methods. The twin-barge float-over technique presents a viable alternative to conventional heavy-lift operations; however, its critical tri-vessel load transfer phase involves complex hydrodynamic interactions and continuous load redistribution that are not adequately captured by traditional staged analyses. This study develops a fully coupled time-domain dynamic model to simulate this process. The framework integrates multi-body potential flow hydrodynamics, mooring and fender systems, and Deck Support Units (DSUs). A novel continuous mass-point variation method is introduced to replicate progressive ballasting and the dynamic load transfer from single- to dual-barge support. Numerical simulations under representative sea states reveal significant narrow-gap resonance effects, direction-dependent motion amplification, and transient DSU load peaks that are overlooked in conventional quasi-static approaches. Beam-sea conditions are found to induce the largest lateral DSU loads and the highest risk of barge misalignment. The proposed framework demonstrates superior capability in predicting motion responses and load transitions, thereby providing critical technical support for the safe and efficient application of twin-barge float-over installations in complex marine environments. Full article