Abstract: Growing concerns over greenhouse gas (GHG) emissions have positioned hydrogen fuel cell buses (HFCBs) as a promising alternative for sustainable urban mobility. By elimi-nating tailpipe emissions and enabling significant reductions in well-to-wheel GHG in-tensities when hydrogen is sourced from renewables, HFCBs can contribute to im-proved urban air quality, energy diversification, and alignment with climate goals. De-spite these benefits, large-scale adoption faces challenges related to production costs, hy-drogen infra-structure, and efficiency improvements across the supply chain. Life Cycle Assessment (LCA) provides a valuable framework to assess these trade-offs holistically, capturing en-vironmental, economic, and social dimensions of HFCB deployment. How-ever, incon-sistencies in system boundaries, functional units, and impact categories high-light the need for more standardized and comprehensive methodologies. This paper ex-amines the potential of hydrogen buses by synthesizing evidence from peer-reviewed studies and identifying opportunities for integration into urban fleets. Findings suggest that when combined with robust LCA approaches, hydrogen buses offer a pathway to-ward decar-bonized, cleaner, and more resilient public transport systems. Strategic adop-tion could not only enhance environmental performance but also foster innovation, infra-structure de-velopment, and long-term economic viability, positioning HFCBs as a corner-stone of sus-tainable urban transportation transitions.

Abstract: Drones have long been explored for supply. While several systems offering small pay-loads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, these challenges are evaluated from a conceptual modelling perspective to forecast their applicability once these barriers are overcome. The study uses technology trend modelling and bibliometric activity map-ping methodologies to predict the applicability of specific technologies that are cur-rently identified as operational challenges. Specifically for supply drones, trends in technological improvements of battery technology and aircraft control are modelled to project effects and focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and con-straints of larger supply drones. A case study, conceptual design of a supply drone large enough to move a shipping container, is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance.

Abstract: Anterior cervical discectomy and fusion (ACDF) and cervical vertebrectomy with interbody fusion (CVIF) are commonly performed spine procedures that rely on anterior plate fixation for stability. This biomechanical study evaluated the effects of screw length, plate length, and posterior fixation augmentation on the stability of anterior cervical fixation constructs under worst-case conditions. Using osteopenic-grade polyurethane foam blocks, four construct configurations representing discectomy and corpectomy models were tested with varying anterior plate lengths (22 mm, 34 mm), screw lengths (14 mm, 16 mm), and the addition of posterior fixation. Static and fatigue testing were per-formed using methods adapted from ASTM F1717, with fatigue run-out defined at 1.2 million cycles to simulate the fusion period. Static testing demonstrated the lowest yield load in constructs using a short plate with shorter screws. Fatigue testing showed that increasing screw length from 14 to 16 mm increased maximum fatigue load by 1.6-fold, while the addition of posterior fixation increased fatigue capacity by 3.6-fold. Increasing plate length resulted in a modest reduction in fatigue performance. These findings indicate that posterior fixation provides the greatest improvement in construct fatigue resistance, while increased screw length offers a less invasive means of enhancing stability, informing surgical strategies for high-risk ACDF and CVIF cases.

Abstract: In Louisiana’s marsh creation projects designed to mitigate wetland loss, riverine sediments are hydraulically dredged and transported through pipelines. These dredged materials are extremely soft, with moisture contents well above 100%, resulting in significant consolidation settlements even under minimal self-weight loads. Conventional one-dimensional (1-D) oedometer consolidation tests are commonly used to assess consolidation behavior; however, they are limited to soils with much lower moisture contents. At higher moisture levels, the soft slurry tends to overflow due to the weight of the standard stainless-steel dial cap and porous stone, which together apply a seating pressure of 1.07 kPa (0.01 TSF). This study presents a modified oedometer setup utilizing 3D-printed dial caps made from lightweight materials such as polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS), reducing the seating pressure to 0.21 kPa (0.002 TSF). This modification enables testing of dredged soils with moisture contents up to 100% without overflow. Settling column tests were also integrated with the modified oedometer tests, allowing for the development of void ratio–effective stress relationships spanning from 0.02 kPa (0.0002 TSF) to 107.25 kPa (1 TSF). The results demonstrate that combining settling column and modified oedometer tests provides an effective approach for evaluating the consolidation behavior of high-moisture slurry soils.

Abstract: Mechatronics developed under the influence of the Third Industrial Revolution and was a discipline that provided methods and tools for the development of industrial robots, advanced machine tools, mobile phones, and automobiles, among other sophis-ticated products. With the emergence of Industry 4.0 in 2011, mechatronics has be-come indispensable, as traditional production systems are being transformed into cyber-physical systems (CPS), some of which are composed of sophisticated technolo-gies such as Digital Twins (DT) and sophisticated robots, among others. In 2020, the Fifth Industrial Revolution began, giving rise to so-called Human Cyber-Physical Sys-tems and promoting the use of Cobots in industries. Because today's industrial world is influenced by three active industrial revolutions and two transitions, it is possible to find machines and production systems that were designed with different principles and for different purposes, making it necessary to propose a classification that allows each system to be located according to the premises of its respective industrial revolu-tion. This article analyzes the evolution of mechatronics and proposes a classification of machines and production systems based on the premises of each industrial revolu-tion. The objective is to determine the influence of mechatronics on the different types of machines that exist today and analyze its implications.

Abstract: Technologies for home movement rehabilitation after stroke are rapidly expanding. However, for consumers, the number and nature of available products are unclear, and the information provided by device manufacturers varies widely. To understand this landscape, we used the U.S. Food and Drug Administration (FDA) database to identify devices for stroke rehabilitation suitable for home use. We then surveyed 13 individuals with stroke to determine what information they most wanted about home-based reha-bilitation devices and contacted manufacturers to obtain those details. Thirteen FDA codes were associated with stroke rehabilitation devices, encompassing 58 devices produced by 41 companies. Nearly half were categorized under two codes: QKC (in-teractive rehabilitation exercise devices) and GZI (neuromuscular stimulators). Among devices for which information was available, 71% received FDA clearance after 2015, and 23% cost under $1,000. The top information priorities for individuals with stroke were required usage to achieve therapeutic benefit, expected benefit, ease of use, and motivational features. Despite repeated outreach, only 44% of companies responded to our queries; among those that did, details were vague and variable. These results con-firm that a large and growing number of FDA-approved devices are now available for home-based post-stroke motor rehabilitation. We further identify a need to establish industry standards for reporting ease of use, motivational effectiveness, and dose–response characteristics to help the intended consumers select appropriate technolo-gies. We include the dataset for future reference.

Abstract: This research aimed to optimize the continuous monitoring of residual chlorine in the Potable Water System (PWS) managed by the Water and Sanitation Management Board (JASS) of the Sinsicap district, through the implementation of a low-cost technology, thus contributing to Sustainable Development Goal 6 (SDG 6). The study was developed using an applied approach, with an experimental design and explanatory scope. A prototype was designed and validated, consisting of an I2C/UART chlorine sensor, a PCB board, an SD module, and an LCD screen, programmed to record automatic readings three times a day at four points in the distribution network. The data obtained were analyzed using SPSS software, applying one-sample t-tests and calibration correlations. The results showed a significant correlation (R² = 0.983; p < 0.004), ensuring compliance with the sanitary standard (0.5-1.5 mg/L). Furthermore, the system achieved 95% availability and cost savings of 90.83% compared to commercial equipment. It is concluded that the developed technology improves the efficiency, accuracy, and sustainability of chlorine monitoring, representing a viable and replicable alternative for the country’s Water and Sanitation Management Boards (JASS).

Abstract: The rapid adoption of electric vehicles (EVs) necessitates advanced energy management systems to mitigate grid instability caused by fluctuating charging demand. This study proposes an attention-based Long Short-Term Memory (LSTM) model for predicting EV charging load and optimizing energy allocation. The model leverages historical data from the Adaptive Charging Network (ACN) dataset, incorporating preprocessing techniques such as missing value imputation, feature scaling, and one-hot encoding to enhance data quality. Experimental results demonstrate that the attention-based LSTM outperforms conventional deep learning and machine learning algorithms, achieving a mean squared error (MSE) of 0.0099, mean absolute percentage error (MAPE) of 2.8%, and an accuracy of 98.2%. The model effectively captures temporal dependencies and identifies peak demand periods, enabling efficient integration of renewable energy sources and reducing operational costs. This research highlights the critical role of data preprocessing and advanced deep learning architectures in sustainable energy management for EV charging infrastructure.

Abstract: Background: The automotive industry is undergoing a deep transformation driven by 9 the global green transition. This change follows divergent trajectories in developed and 10 emerging markets due to differences in regulation, infrastructure, and economic con- 11 straints. The research methodology is adapted to incorporate different factors of influence 12 and contraints. The research applies a structured Failure Mode and Effects Analysis 13 (FMEA) based on IEC 60812:2018 and AIAG & VDA (2019), and integrates the Analytic 14 Hierarchy Process (AHP) to prioritize corrective measures. Concepts from adaptive risk 15 management, informed by expert consensus and literature-backed data, are also used to 16 interpret dynamic behavior of supply chains and market volatility. The comparative anal- 17 ysis successfully highlights the systematic RPN Divergence between market types, reveal- 18 ing critical differences in failure mode profiles, risk priorities, and capacity to adopt miti- 19 gation strategies. The hybrid FMEA-AHP approach reduces subjectivity and provides 20 transparent prioritization tailored to market maturity. The integrated methodology sup- 21 ports decision-making in electrification programs and offers a robust framework for 22 benchmarking complex transition processes across regions.

Abstract: The use of Silicon Carbide (SiC) MOSFETs significantly improves converter perfor-mance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodolog-ical synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the depend-ence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prog-nosis tools for SiC MOSFETs remains an ongoing research challenge.

Abstract:

Over the past two decades, the transition from conventional power networks to smart grids has accelerated, driven by advances in digital communication and intelligent control technologies. Smart grids integrate sensing devices, automated metering, and data-driven management systems, producing large volumes of heterogeneous information across all operational layers. This review examines 220 publications from the last twenty years, highlighting major research trends, classifying works by publication type, and identifying the most influential journals and conferences. It also summarizes the contribution of each reviewed work and categorizes the analytical methods and smart-grid-related topics addressed. Finally, the paper outlines emerging challenges and future research directions that can further enhance the role of big data analytics in next-generation smart grids.

Abstract: High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the relationship between distributed capacitance and coupling capacitance, a resistive–capacitive signal input circuit is designed to address scenarios involving large distributed capacitance in engineering applications. Furthermore, to improve measurement accuracy, a phase shift vibration method is introduced based on the investigation of the effect of relative phase shifts in mixed-frequency signals. The proposed circuit architecture and data processing method are simple in design and have been verified through experimental prototype, demonstrating stable long-term performance under room-temperature conditions.

Abstract: This study investigates the chemical composition, liquefaction behavior, and polyurethane foam (PU) properties of two lignocellulosic biomasses, Red Angico (Anadenanthera colubrina) and Mahogany (Swietenia macrophylla), as potential sources of bio-based polyols. Detailed chemical characterization revealed that Red Angico has high α-cellulose (48.44%) and moderate hemicellulose (25.68%) content, while Mahogany shows the inverse, with high hemicellulose (56.11%) and low cellulose (18.24%), influencing their reactivity during liquefaction. Liquefaction trials using a polyalcohol system (glycerol:ethylene glycol) demonstrated higher conversion efficiency for Mahogany, reaching 93.4% at 180 °C in 60 minutes, compared to 73.9% for Red Angico. Hydroxyl value analysis revealed increasing functionality for Mahogany polyols with time, whereas Red Angico showed declining values, indicating possible recondensation reactions. PU foams were synthesized using the resulting polyols, with compressive strength and modulus increasing with isocyanate index. Red Angico foams, despite lower OH values, displayed superior mechanical performance, attributed to their lower hydroxyl content favoring optimal crosslinking. Water content, used as a chemical blowing agent, negatively impacted compressive strength for both foams due to increased porosity. Results highlight the species-specific influence of chemical composition on liquefaction behavior and foam performance, suggesting tailored processing conditions are essential for maximizing bio-based PU properties.

Abstract: Nuclear power plants must be safe, reliable and ensure the planned supply of electricity. The metal of the equipment operates in conditions of high pressure values and fluid tem-peratures, in radiation and in corrosive working environments. These factors cause deg-radation of the metal mechanical properties. Defects appear in the structure and this leads to a decrease in the operability of the equipment and to compromise the NPP safety. In the "worst case", the facilities could be torn apart and a nuclear accident could occur. The mechanical characteristics of the metal should be periodically examined. The topic in this article is the study of stress intensity factor for defects on the inner surface of the reactor vessel. The metals on the inner surfaces of the vessels of two nuclear reactors were studied by a visual method. Stress intensity coefficients have been calculated at the site of defects, taking into account the influence of the embrittlement factors. The results are compared with the requirements of the strength standards. The purpose of the investigation is to check whether the facility can continue to be operated safely. The algorithm can be used as a methodology for periodically examining defects detected during the operation of the unit.

Abstract: Microplastic quantification in marine vegetated ecosystems remains analytically demanding, yet little is known about the environmental footprint of the laboratory procedures required to isolate and measure these particles. This study applies Life Cycle Assessment (LCA) to laboratory analytical workflows for microplastics quantification, focusing exclusively on sample preparation and analytical procedures rather than natural environmental absorption or fate processes, in two ecologically relevant matrices: (i) pelagic algae (Sargassum) and (ii) seagrass biomass. Using openLCA 2.5 and the ReCiPe Midpoint (H) v1.13 method, the analysis integrates foreground inventories of digestion, filtration, drying, and spectroscopic identification, combined with background datasets from OzLCI2019, ELCD 3.2 and USDA. Results show substantially higher impacts for the algae scenario, particularly in climate change, human toxicity, ionising radiation and particulate matter formation, largely driven by longer digestion times, increased reagent use and higher energy demand during sample pre-treatment. Conversely, the seagrass scenario exhibited lower burdens per functional unit due to reduced organic complexity and shorter laboratory processing requirements. These findings highlight the importance of matrix-specific methodological choices and the influence of background datasets on impact profiles. The study provides a first benchmark for the environmental performance of microplastic analytical workflows and underscores the need for harmonised, low-impact laboratory protocols to support sustainable monitoring of microplastic pollution in marine ecosystems.

Abstract: The need for simpler, yet accurate and physically sound, methods to predict the lift and pressure distributions over asymmetric delta wings, particularly at high angles of attack with attached shock wave, motivates the development of an alternative approach presented in this paper. By employing a geometric transformation and postulating a functional similarity between linear and nonlinear solutions, a straightforward algebraic technique for pressure estimation is developed. This approach bridges the solution in the central nonuniform flow region to the exact solutions in the uniform flow regions with attached shock waves near the leading edges, in a manner analogous to methods used for supersonic starting flow at high incidence. The method is shown to reproduce established results for both symmetric and yawed delta wings within a limited error. It yields a compact, explicit expression for the normal force coefficient, formulated as a weighted average of the pressure coefficients from the two uniform flow regions. A pathway for extending the approach to the upper surface, where the uniform flow is governed by swept Prandtl-Meyer relations is also outlined. Although classical analytical approaches for delta wings were established decades ago, the proposed method provides a tractable alternative tool for modern fast engineering analysis.

Abstract: Two-phase ejectors are a promising alternative for improving the performance of direct expansion vapor compression refrigeration systems, especially in transcritical applica-tions. Extensive literature has been produced on modeling, simulations and experiments related to two-phase ejectors. In particular, 0D models have proven to offer a trade-off be-tween simplicity and precision. In these models, there remains significant uncertainty re-garding the estimation of the two-phase speed of sound and the choking conditions at the primary nozzle throat. These choking conditions have a considerable impact on the throat geometry. This study proposes a novel approach that relies solely on conservation equa-tions (mass and energy) to determine the thermodynamic conditions at the throat for de-sign purposes. The results of the proposed approach and 13 other approaches reported in the literature were compared with published experiments data regarding the throat diam-eter and pressure. The proposed approach showed robust when validated against three experimental cases, predicting the throat diameter and pressure of a primary convergent–divergent nozzle with deviations of -8 % and +15 %, while the other approaches exhibited larger deviations of -12 % to +574% and -73 % to +21 %, respectively. Moreover, the pro-posed approach reliably generates a convergent-divergent nozzle configuration across a wide range of operating conditions, including variations in primary and secondary pres-sures as well as variations in primary nozzle efficiency using R1234yf and CO2 as work-ing fluids.

Abstract: This review explores the link between engineering and creativity, challenging the perception gap between structured training and creative fields. It reframes human creativity insights from prominent scholars to inform the development of AI systems capable of creative problem-solving. The paper translates abstract and philosophical models into structured, computationally tractable frameworks to bridge human creativity research and machine learning applications. The review focuses on four core frameworks to guide AI design: Wallas’s Four-Stage Process, Rhodes’ Four Ps Model, Simonton’s Creativity-as-Influence Model, and Runco’s prevailing framework. It traces the historical progression of creativity research from early efforts by Guilford and Torrance to later dynamic frameworks by Amabile and Csikszentmihalyi. The document discusses how these models, which evolved from abstract theorizing to structured, multidimensional constructs, provide a foundation for examining and applying creativity within technical domains. It also addresses the growing integration of AI, distinguishing between human creativity and artificial creativity produced by machines. The forward-looking perspective suggests an augmentative role for AI within hybrid human-AI workflows. Ultimately, the review aims to provide a blueprint for developing AI systems that move beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities, capitalizing on the synergy between creativity science and AI.

Abstract: The growing complexity of BIM (Building Information Model) models leads to perfor-mance issues, extended file loading times, and difficulties in cross-industry coordina-tion. One of the main factors reducing performance are so-called "heavy" library com-ponents (families in Revit), characterized by excessive geometric complexity, a large number of instances, or improper optimization. Currently, the identification of such components is based mainly on the experience of designers and manual inspection of models, which is time-consuming and prone to errors. This article presents a new tool, HeavyFamilies, which automates the detection and analysis of heavy library compo-nents in BIM models. The tool uses a multi-criteria analysis method, evaluating com-ponents based on five key parameters: number of instances, geometry complexity, number of walls and edges, and estimated file size. Each parameter is weighed ac-cording to its impact on model performance. The developed solution has been imple-mented as a pyRevit plugin for Autodesk Revit, offering a graphical interface with a tabular summary of results, a CSV export function, and visualization of detected components directly in the model. Validation of the tool on real BIM projects has demonstrated its effectiveness in identifying components with a weight index exceed-ing the threshold of 200, allowing designers to prioritize optimization efforts. The HeavyFamilies tool is a practical contribution to the field of BIM model optimization, enabling a systematic approach to managing model performance in complex construc-tion projects and supporting the development of smart cities.

Abstract: Post-disaster reconstruction remains largely excluded from circular-economy ap-proaches. This gap is particularly evident in earthquake-affected inner territories, where reconstruction faces severe logistical constraints—especially rubble manage-ment—and where debris is often composed of materials closely tied to local building cultures and community identities. In these contexts, rebuilding still follows linear, emergency-driven models that treat rubble primarily as waste. This study introduces Rubble as a Material Bank (RMB), a digital–material framework that reconceptualises earthquake rubble as a traceable and programmable resource for circular reconstruc-tion. RMB defines a rubble-to-component chain integrating material characterisation, data-driven management, robotic fabrication, and reversible architectural design. Se-lected downstream segments are experimentally validated through the TRAP project, developed within the European TARGET-X program. The experimentation focuses on extrusion-based fabrication of dry-assembled wall components using rubble-derived aggregates. Results show that digitally governed workflows can enable material reuse while revealing technical and regulatory constraints on large-scale implementation.