Torque and angular velocity fluctuations during idle and low-speed operation decrease drivetrain efficiency, increase vibration, and impose irregular loading on coupled systems such as hybrid powertrain generators and conventional transmissions. Building upon a previously validated balancing cam mechanism, this research presents a friction-aware redesign methodology that generates manufacturable cam profiles while preserving the torque characteristics necessary for effective compensation. Two target torque definitions are considered for cam synthesis: a smoothed profile derived from experimentally measured angular velocity and a cycle-resolved profile obtained from engine simulation. To account for friction effects, the selected target torque is reformulated to incorporate the parasitic torque introduced by the mechanism. Manufacturability constraints are then applied to ensure compatibility with the available cam-radius range and follower-stroke limit, while retaining regions of high compensation. The redesigned cam is subsequently implemented on a single-cylinder engine and evaluated through simulations and laboratory measurements. The assessment quantifies torque ripple and angular velocity fluctuation and evaluates the effect of incorporating estimated parasitic torque into cam-profile synthesis. Relative to the previously validated cam profile, the redesigned profile reduced angular-velocity standard deviation fluctuation by 50% and torque peak-to-peak by 44%. These improvements were achieved while maintaining manufacturable geometry and stable follower motion.

Background: Engineering education often separates technical design from production planning, although professional product development requires these forms of reasoning to evolve together.Aim: This article analyses a consultancy-based interdisciplinary project-based learning modelimplemented between the “Introduction to Electromechanical Project” (IEP) and the “Introductionto Industrial Project” (IIP) courses.Method: The study follows an implementation-oriented qualitative descriptive case-study design, supported by documentary analysis of project briefs, milestone criteria, student presentations, meeting minutes, consultant reports, photographs, and an anonymous satisfaction questionnaire.Findings: The documentary evidence shows how two IEP groups developed rover-based project concepts while six IIP consultant groups translated those concepts into production -planning, manufacturability, quality-control, logistics, and cost scenarios. Questionnaire results from 25 of 32students indicated favourable perceptions, especially for skills development, teamwork, communication, and problem-solving, while also identifying coordination between courses as the main operational challenge. Contribution: The article provides a transferable implementation framework for integrating technical design and production planning through structured inter-course consultancy.

The agro-industrial sector faces challenges due to post harvest waste and labor shortages, requiring advanced real-time automation for high-throughput fruit quality control. In response, this work presents LITecS AiVision system, a computer vision ecosystem designed for real-time automated fruit selection. The system integrates customized data acquisition hardware with controlled lighting and global shutter cameras. Comparative analysis indicates that the YOLO family offers superior operational efficiency, specifically, the YOLO26-n achieved high FPS with a latency of 1.006 ms, ideal for Edge AI systems. For anomaly detection, Cascade R-CNN obtained the highest recall (0.929), although at a higher computational cost. To ensure identity persistence in multi-camera systems, we proposed an early fusion tracking method based on homographic projection and a momentum-smoothed velocity model. This approach significantly outperformed generic algorithms, achieving an IDF1 of 99.60

Various technologies, including the Internet of Things (IoT) and artificial intelligence (AI), have been increasingly applied in Occupational Safety and Health (OSH). Digitalization has the potential to improve OSH through real-time monitoring and data-driven decision-making, particularly in hazardous workplaces. However, important research gaps remain regarding the use of sensor data for proactive decision-making, the integration of data privacy mechanisms into monitoring systems, and the evaluation of such solutions in real workplace environments. This paper presents OSH Guardian, an integrated monitoring system designed for individualized occupational environmental risk assessment and management. The study adopts a technological research methodology involving the design and evaluation of an innovative solution for a practical OSH problem. The system combines environmental measurements and worker health profiles to generate personalized alerts and support preventive decision-making. It also incorporates data privacy mechanisms aligned with the General Data Protection Regulation (GDPR). Field tests were conducted in real construction and building maintenance environments. The results revealed environmental exposure levels substantially above recommended thresholds. The highest recorded temperature exceeded the established limit by 46%, while the highest ultraviolet radiation (UVR) exposure reached ten times the recommended threshold. In addition, workers operating at the same site experienced substantially different exposure levels to the same environmental agents. These findings highlight the importance of individualized environmental monitoring and demonstrate the potential of personalized and privacy-aware monitoring systems to support proactive OSH strategies in hazardous workplaces.

Handling delicate products with high variability in shape and texture remains a key limitation of rigid industrial end-effectors. This paper evaluates three Fin Ray Effect (FRE)-inspired soft gripper geometries: Straight, Beak, and Constant Curve, manufactured by 3D printing and assessed through coupled numerical and experimental analysis. Finite element simulations were conducted in Ansys using a hyperelastic material representation of TPU, and a cylindrical object was subjected to a prescribed displacement of 50 mm under low-friction contact to quantify deformation patterns, stress/strain fields, and reaction forces. Experimental tests were performed using a dedicated fixture on a universal testing machine, replicating the gripper arrangement and measuring force–displacement response for cross-validation. Results show clear geometry-dependent trade-offs: the Straight design delivers the highest reaction force and is therefore suitable for tasks requiring firmer retention; the Beak design provides the lowest force and stress, supporting gentler interaction with delicate or fragile objects; and the Constant Curve design offers an intermediate solution with more uniform contact pressure distribution. This work serves as a foundation for optimising gripper design to improve handling capabilities across diverse applications.

Decelerating climate change requires a shift away from conventional production practices across industries, including agriculture, to meet the United Nations Sustainable Development Goals (SDGs). Agricultural sustainability has therefore gained growing interest, and growing evidence suggests that frameworks and computer-based decision-making support, known as Decision Support Tools (DSTs), can enhance sustainability. This study analyses the functionalities and methodologies of DSTs designed to assess sustainability at the farm level. A systematic literature review was conducted following the PRISMA methodology to identify (1) key characteristics of operational DSTs, (2) SDG-related areas requiring enhanced decision-making support, and (3) the extent to which DST implementation improves sustainability. Results indicate that while DST applications frequently present expected outputs, recommendations, improved management scenarios, or identify vulnerable areas, there is a notable lack of studies evaluating how these tools translate information into practical, long-term actions for sustainable development at the farm scale. The TOMCAST model was the only DST reporting direct evidence of efficacy through reduced disease incidence and yield losses. Most reviewed DSTs target resource-use optimisation (SDG 12), whereas tools assessing biodiversity (SDG 15) and social impacts remain scarce. By identifying methodological gaps and underrepresented sustainability dimensions, this review provides both a diagnosis of current limitations and a roadmap for developing more implementation-oriented, SDG-aligned tools. Future research should adopt participatory approaches and assess the short- and long-term impacts, accessibility, operability, and costs associated with DST implementation and maintenance, while expanding the integration of Agriculture 4.0 and 5.0 technologies beyond remote sensing.

Torque and velocity fluctuations in internal combustion engines (ICEs), particularly during idle and low-speed operation, can reduce efficiency, increase vibration, and impose mechanical stress on coupled systems. This work presents the design, simulation, and experimental validation of a passive balancing cam mechanism developed to mitigate fluctuations in single-cylinder internal combustion engines (ICEs). The system consists of a cam and a spring-loaded follower that synchronizes with the engine cycle to store and release energy, generating a compensatory torque that stabilizes rotational speed. The mechanism was implemented on a single-cylinder Honda® engine and evaluated through simulations and laboratory tests under idle conditions. Results demonstrate a reduction in torque ripple amplitude of approximately 54% and standard deviation of 50%, as well as a decrease in angular speed fluctuation amplitude of about 43% and standard deviation of 42%, resulting in significantly smoother engine behavior. These improvements also address longstanding limitations in traditional powertrains, which often rely on heavy flywheels or electronically controlled dampers to manage rotational irregularities. Such solutions increase system complexity, weight, and energy losses. In contrast, the proposed passive mechanism offers a simpler, more efficient alternative, requiring no external control or energy input. Its effectiveness in stabilizing engine output makes it especially suited for integration into hybrid electric systems, where consistent generator performance and low mechanical noise are critical for efficient battery charging and protection of sensitive electronic components.

Agricultural production in southern Angola faces challenges due to unsustainable practices, including inefficient use of water, fertilizers, and machinery, resulting in low yields and environmental degradation. Therefore, clear and measurable indicators are needed to guide farmers toward more sustainable practices. The scientific literature insufficiently addresses this issue, leaving a significant gap in the evaluation of key performance indicators (KPIs) that can guide good agricultural practices (GAPs) adapted to the context of southern Angola, with the goal of promoting a more resilient and sustainable agricultural sector. So, the objective of this study is to identify and assess KPIs capable of supporting the selection of GAPs suitable for maize, potato, and tomato cultivation in the context of southern Angolan agriculture. A systematic literature review (SLR) was conducted, screening 2720 articles and selecting 14 studies that met defined inclusion criteria. Five KPIs were identified as the most relevant: gross margin, net profit, water use efficiency, nitrogen use efficiency, and machine energy. These indicators were analyzed and standardized to evaluate their contribution to sustainability across different GAPs. Results show that organic fertilizers are the most sustainable option for maize, drip irrigation for potatoes, and crop rotation for tomatoes in southern Angola because of their efficiency in low-resource environments. A clear, simple, and effective representation of the KPIs was developed to be useful in communicating to farmers and policy makers on the selection of the best GAPs in the cultivation of different crops. The study proposes a validated KPI-based methodology for assessing sustainable agricultural practices in developing regions such as southern Angola, aiming to lead to greater self-sufficiency and economic stability in this sector.

Soft robotic systems are increasingly being developed for applications that require handling fragile or irregularly shaped objects, such as in agriculture, medical robotics, and industrial automation. This paper presents a self-sensing soft finger fabricated entirely from conductive thermoplastic polyurethane (TPU), combining adaptive grasping capabilities with integrated piezoresistive force sensing. Inspired by the Fin Ray Effect, the finger eliminates the need for external sensors by using its structure for force measurement. Additive manufacturing through Fused Filament Fabrication (FFF) was used to produce the finger, offering an efficient and cost-effective fabrication process suitable for scalable applications. The finger's sensing performance was validated through experimental tests that employed a calibrated load cell to measure applied forces while simultaneously recording the analog output of the self-sensing finger. A Moving Median filter was applied to reduce noise in the analog data, resulting in smoother and more interpretable readings. The results confirmed the reliability of the soft finger under different gripper closure percentages and speeds, demonstrating its capability to track force variations accurately. This study also identifies some challenges, such as inconsistencies in the readings at lower force levels, and outlines possible future improvements, including the integration of multi-material printing to isolate structural elements and enhance sensing differentiation. The results highlight the potential of the self-sensing soft finger for practical applications in delicate object manipulation, particularly in fields like fruit handling, and automated manufacturing systems.

This study examines the establishment of a Hub for Circular Economy and Industrial Symbiosis (HUB-CEIS) centred on a forest biomass waste plant in Fundão, Portugal, presenting an innovative model for rural industrial symbiosis, circular economy governance, and sustainable waste management. Designed as a strategic node within a reverse supply chain, the hub facilitates the conversion of solid waste into renewable energy and high-value co-products, including green hydrogen, tailored for industrial and agricultural applications, with an estimated 120 ktCO2/year reduction and 60 direct jobs. Aligned with the United Nations (UN) Sustainable Development Goals (SDGs) and the Paris Agreement, this initiative addresses global challenges such as decarbonization, resource efficiency, and the energy transition. Employing a mixed research methodology, this study integrates a comprehensive literature review, in-depth stakeholder interviews, and comparative case study analysis to formulate a governance framework fostering regional partnerships between industry, government, and local communities. The findings highlight Fundão’s potential to become a benchmark for rural industrial symbiosis, offering a replicable model for circularity in non-urban contexts, with a projected investment of USD 60 M. Special emphasis is placed on the green hydrogen value chain, positioning it as a key enabler for regional sustainability. This research underscores the importance of cross-sectoral collaboration in achieving scalable and efficient waste recovery processes. By delivering practical insights and a robust governance structure, the study contributes to the circular economy literature, providing actionable strategies for implementing rural reverse supply chains. Beyond validating waste valorization and renewable energy production, the proposed hub establishes a blueprint for sustainable rural industrial development, promoting long-term industrial symbiosis integration.

The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance of reverse supply chains for solid waste (RSCSW) within a circular economy framework. However, the existing literature lacks a systematic assessment of the effectiveness of these methods compared to traditional waste management practices. This study conducts a systematic literature review (SLR), following PRISMA guidelines and the P.I.C.O. framework, to investigate how MCDA and AI can optimize governance, operational efficiency, and the sustainability of RSCSW. After collecting 1139 articles, 22 were selected and used for analysis. The results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, and genetic algorithms, enhance decision-making automation, reduce costs, and improve waste traceability. Nevertheless, regulatory barriers and technological challenges still hinder large-scale adoption. This study proposes an innovative framework to address these gaps and drive evidence-based public policies. The findings provide guidelines for policymakers and managers, contributing to the Sustainable Development Goals (SDGs) agenda and advancements in circular economy governance.

This study investigates the mechanical properties of thermoplastic polyurethane (TPU) 60A, which is a flexible material that can be used to produce soft robotic grippers using additive manufacturing. Tensile tests were conducted under ISO 37 and ISO 527 standards to assess the effects of different printing orientations (0°, 45°, −45°, 90°, and quasi-isotropic) and test speeds (2 mm/min, 20 mm/min, and 200 mm/min) on the material’s performance. While the printing orientations at 0° and quasi-isotropic provided similar performance, the quasi-isotropic orientation demonstrated the most balanced mechanical behavior, establishing it as the optimal choice for robust and predictable performance, particularly for computational simulations. TPU 60A’s flexibility further emphasizes its suitability for handling delicate objects in industrial and agricultural applications, where damage prevention is critical. Computational simulations using the finite element method were conducted. To verify the accuracy of the models, a comparison was made between the average stresses of the tensile test and the computational predictions. The relative errors of force and displacement are lower than 5%. So, the constitutive model can accurately represent the material’s mechanical behavior, making it suitable for computational simulations with this material. The analysis of strain rates provided valuable insights into optimizing production processes for enhanced mechanical strength. The study highlights the importance of tailored printing parameters to achieve mechanical uniformity, suggesting improvements such as biaxial testing and G-code optimization for variable thickness deposition. Overall, the research study offers comprehensive guidelines for future design and manufacturing techniques in soft robotics.

Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices.

Demand defrosting is a well-established strategy for improving defrost efficiency in refrigeration systems, and resistive frost-detection sensors provide a cost-effective means of enabling such control. However, copper electrode resistive sensors operating in water-based environments are susceptible to electrolysis, which degrades electrode integrity and compromises measurement reliability. This study investigates the impact of electrolysis on sensor performance and evaluates the effectiveness of an intermittent powering method in mitigating these effects. Two sensor excitation methods were experimentally tested: continuous voltage application and pulsed DC excitation with a 1.7% duty cycle. The results demonstrated that continuous excitation caused erratic measurements and electrode degradation due to ongoing electrochemical reactions. In contrast, the pulsed DC excitation method stopped observable electrolysis effects, leading to stable measurements and improved sensor longevity. These findings highlight pulsed DC excitation as a practical and effective solution for enhancing the accuracy and durability of resistive frost-detection sensors, making them more suitable for long-term use in commercial refrigeration system evaporators.

In Portugal, cheese holds a prominent position as a major dairy product, with traditional varieties enjoying widespread acclaim. A number of these cheeses have earned Protected Designations of Origin status, showcasing their unique qualities and regional significance. Notable examples include “Serra da Estrela”, “Serpa”, and “Terrincho”. The production of cheese relies heavily on heating and cooling processes, which account for a substantial portion of the total energy consumed. This research endeavour undertakes a detailed description and analysis of traditional cheesemaking practices within Portugal’s interior central region, with a particular emphasis on the economic and energetic efficiency of refrigeration systems. For this purpose, thirty-one traditional cheese production facilities were examined and classified into two distinct groups: Traditional Industrial Producers and Traditional Handmade Producers. The analysis was conducted through two separate case studies. The findings reveal that a significant 58% of the energy consumed by these facilities is attributed to electrically powered cooling systems, encompassing components such as fans, compressed air systems, and illumination. Within the production processes, fuel combustion, primarily naphtha or propane, serves the purpose of water heating and steam generation. Based on energy consumption reports, the Specific Energy Consumption of electricity was determined to be 0.283 kWh/lRM for TIP and 0.169 kWh/lRM for THP. Furthermore, several linear regression models were developed to explore the relationships between parameters such as cold room volume, compressor power, and raw material quantity. The study also identified key factors contributing to reduced energy efficiency within the facilities. These factors include inadequate insulation of buildings and cold rooms, outdated and poorly maintained refrigeration equipment situated in suboptimal locations, and cold rooms and compressors that are oversized and not optimised for efficient operation.