Abstract: Large amounts of high-quality data is required for the training of Artificial Intelligence (AI) models, which are indeed cumbersome to curate and perform quality assurance via human intervention. Moreover, models trained using erroneous data (human errors, data faults) can cause significant problems in real-world applications. This paper proposes an automated cleaning framework and quality assurance strategy for 2D object detection datasets. The proposed cleaning method was designed according to the ISO/IEC 25012 data quality standards, and uses multiple AI models to filter anomalies and missing data. In addition, it balances out the statistical unevenness in the dataset, such as the class distribution and object size distribution. Thereby ensuring the quality of the training dataset and examining the relationship between the amount of data required for enhanced performance in terms of detection. The experiments were conducted using popular datasets for autonomous driving, including KITTI, Waymo, nuScenes and publicly available datasets from South Korea. An automated data cleaning framework was employed to remove anomalous and redundant data, resulting in a reliable dataset for training. The automated data pruning and assurance system demonstrated the ability to substantially decrease the time and resources needed for manual data inspection.

Abstract: The deployment of utility-scale hybrid wind-solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from available data, addressing key challenges such as: (1) spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) cost dependencies of different components and system characteristics. When conducting such analyses at regional or national scales, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, and proposes a simplified methodology for country-level analysis.

Abstract: As conventional oil resources decline, optimizing the development of tight reservoirs has become critical for sustaining production. Horizontal wells with artificial fractures offer a promising solution, but improper water injection often leads to uneven waterflooding, particularly in irregular horizontal-vertical well systems—a common challenge in fields like China’s Fuxian oilfield. This study tackles this issue by introducing a practical and effective method to optimize water injection flow rates, significantly enhancing oil recovery in such complex well patterns. Through advanced numerical modeling and three-dimensional flow visualization, we analyze sweep efficiency and water breakthrough risks, categorizing the horizontal well’s drainage area into three distinct regions, each requiring tailored injection rates. Using a representative model with one horizontal well and three vertical wells, we demonstrate that adjusting the flow rate ratio among injectors to 6:3:1 (instead of a uniform 1:1:1) boosts cumulative oil production by an additional 2997.6 m³. These findings provide field engineers with a actionable strategy to improve waterflooding efficiency, directly increasing recoverable reserves and economic viability in tight reservoirs. The proposed approach has immediate relevance for oilfield operations, offering a scalable solution to maximize recovery in similar unconventional reservoirs worldwide.

Abstract: Solar radiation forecasting is critical for optimizing renewable energy systems, particularly in regions with high solar potential like Kuwait. This paper presents a theoretical framework for a hybrid forecasting system that combines fuzzy logic and neural networks to predict solar radiation with high accuracy. The proposed system leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to handle the inherent uncertainty and variability in meteorological data. While the study is primarily theoretical due to limitations in data and resources, it provides a comprehensive review of existing methods and highlights the potential of hybrid systems for improving solar radiation forecasting. The paper concludes with a discussion of the limitations and suggests future work involving experiments to validate the proposed framework.

Abstract: This study evaluates Lolium perenne press juice as a sustainable substrate for Single-Cell Protein (SCP) production using Kluyveromyces marxianus. Key fermentation parameters were systematically optimized, including microbial reduction, dilution ratios, temperature, and nutrient supplementation. Pasteurization at 75 °C preserved essential nutrients better than autoclaving, resulting in a 27.8% increase in biomass yield. A 1:2 dilution of press juice enhanced fermentation efficiency, achieving 20.2% higher biomass despite lower initial sugar content. Cultivation at 30 °C enabled sustained substrate utilization and outperformed 40 °C fermentation, increasing final biomass by 43.4%. Nutrient supplementation with yeast extract, peptone, and glucose led to the highest biomass yield, with a 71% increase compared to unsupplemented juice. Press juice from the tetraploid variety Explosion consistently outperformed the diploid Honroso, especially when harvested early, reaching up to 16.62 g·L⁻¹ biomass. Early harvests promoted faster growth, while late harvests exhibited higher biomass yield coefficients due to improved sugar-to-biomass conversion. Compared to conventional YM medium, fermentation with L. perenne press juice achieved up to a threefold increase in biomass yield. These findings highlight the potential of grass-based substrates for efficient SCP production and demonstrate how agricultural parameters like variety and harvest timing influence both quantity and quality. The approach supports circular bioeconomy strategies by valorizing underutilized biomass through microbial fermentation.

Abstract: Multi-key homomorphic encryption is widely applied into outsourced computing and privacy-preserving applications in multi-user scenarios. However, the existence of CRS weakens the ability of users to independently generate public keys, and it is difficult to implement in decentralized systems or scenarios with low trust requirements. In order to reduce excessive reliance on public parameters, a multi-key homomorphic encryption scheme without pre-setting CRS is proposed based on a distributed key generation protocol. The proposed scheme does not require the pre-generation and distribution of CRS, which enhances the security and decentralization of the scheme. Furthermore, in order to further protect the plaintext privacy from each user, by embedding the specified target user into the ciphertext, this paper proposes an enhanced multi-key homomorphic encryption scheme that only allows only the target user to decrypt. Finally, this paper applies the proposed lattice-based multi-key homomorphic encryption scheme into the data submission stage of the perceived users, and thereby proposes a crowd-sensing scheme with privacy preservation.

Abstract: The refurbishment of school buildings offers the opportunity to reduce energy consumption and carbon emissions, which positively influences the reduction of environmental impact. It is also important to remember to maintain or enhance the comfort of the users of such buildings. This paper presents a systematic review of the state of the art on current trends and low-carbon technical, operational and behavioural methods used in the refurbishment of school buildings in cool temperate climates. This subject matter is positioned at the interface of architecture and environmental engineering. This study identifies the most commonly used active and passive refurbishment methods, as well as the research gaps and problems of applied solutions, and demonstrates the most likely and cost-effective optimisation directions in existing schools.

Abstract: This paper analyzes the process of energy transition from traditional solid fuel heating to an air-to-air (A2A) heat pump-based heating system. Special emphasis was placed on the implementation of new technologies for improved management of energy systems aiming to elevate both comfort levels and energy efficiency. This paper explores the use of open-source Home Assistant software as an integration platform for home automation, designed to manage smart home devices while preserving local control, user privacy and increasing cybersecurity. The proposed hardware platform includes a Raspberry Pi with appropriate IoT modules, providing a flexible and economically viable solution for household needs.

Abstract: Organizations strive to maximize efficiency in their manufacturing processes, yet they must also consider broader repercussions, as industrial activity directly impacts the environment and society. The adoption of innovative technologies and initiatives to mitigate this impact is therefore essential. Traditional asset maintenance plays a critical role in ensuring high equipment availability, but there is a clear need to evolve toward predictive and sustainable maintenance strategies to enhance reliability, safety, and equipment lifespan. This shift redefines maintenance itself, aligning it with Circular Economy principles and Industry 4.0 solutions to prevent unplanned downtime, reduce failures, and improve personnel and facility safety. This research examines the transition from traditional preventive maintenance to predictive and sustainable maintenance in a real-world industrial context, comparing the design of a neural network with the ARIMAX technique to develop reliable predictive models. The study aims to facilitate a paradigm shift by proposing a predictive model that reduces unplanned shutdowns and optimizes spare parts and labor utilization. The practical application focuses on a hydrogen compressor in the petrochemical industry, demonstrating the model’s potential for operational and sustainability improvements.

Abstract: The engineering profession in South Africa plays a significant role in driving innovation, infrastructure development, and economic growth. However, there is high attrition rate within the engineering skills pipeline which impacts the development of a sustainable engineering workforce in the country. This study aimed to address high attrition rates using a multifaceted approach with the academic institutions and the Engineering Council of South Africa. A mixed methods research design was used and data was be collected through qualitative surveys and questionnaires. The sample included n=10 academic staff and n=263 registered engineering candidates. The qualitative data was analysed using thematic analysis, while the quantitative data was analysed using SPSS. The study identified foundational gaps in basic education, institutional barriers and language proficiency as critical contributors to attrition. The Kruskal-Wallis test revealed no statistically significant differences for financial-related items. A recurring theme in the narratives was the call for ECSA to evolve beyond its current compliance-driven role and become a proactive enabler of transformation and socio-academic support. Based on the findings, the study recommends the development of strategies for enhancing the collaboration between ECSA and higher education institutions to foster a more sustainable engineering pipeline.

Abstract: Three-dimensional (3D) printing, also known as additive manufacturing, is rapidly transforming the healthcare landscape by enabling the creation of patient-specific solutions across a wide range of clinical applications. This review explores recent advancements in 3D printing technologies and their implementation in surgical planning, implant fabrication, drug delivery systems, and bioprinting. A major highlight is the role of 3D printing in custom prosthetics design, where the technology allows for highly personalized, anatomically accurate prosthetic limbs that improve functionality, comfort, and aesthetic integration. By leveraging digital scanning and computer-aided design (CAD), prosthetics can be fabricated rapidly and cost-effectively, making them accessible even in low-resource settings. In pharmaceutical science, 3D printing enables the development of complex drug delivery systems tailored to individual pharmacokinetic profiles, enhancing therapeutic efficacy and patient compliance. Bioprinting, another emerging domain, holds promise for tissue regeneration and organ fabrication using living cells and bioinks. Despite its transformative potential, the adoption of 3D printing faces challenges such as regulatory hurdles, material limitations, and the need for interdisciplinary training. Nonetheless, ongoing innovations and regulatory progress suggest a promising future for 3D printing in delivering personalized, efficient, and accessible healthcare solutions—including the growing field of custom prosthetics.

Abstract: Due to the nature of composites, the ability to accurately locate low-energy impacts is crucial for Structural Health Monitoring (SHM) in the aerospace sector. For this purpose, several techniques have been developed in the past and, among them, Artificial Intelligence (AI) has demonstrated promising results with high performance. The non-linear behaviour of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configuration have been introduced; making accurate location predictions. However, the black-box nature of AI poses a challenge in the aerospace field, where reliability, trustworthiness, and validation capability are paramount. To overcome this problem, eXplainable Artificial Intelligence (XAI) techniques emerge as a solution, enhancing model transparency, trust, and validation. This research places a previously trained Impact-Locator-AI under the spotlight, revealing whether it is truly reliable and worthy of application in aerospace industry.

Abstract: As one of the main routes of vaccine production, cell suspension culture technology has become an inevitable trend in the development of biopharmaceutical industry. Its main advantage is that it can maximize the product quality while achieving high yield through accurate and effective process control means. As the basic material needed in the process of cell suspension culture, cell culture medium mainly provides energy through substances such as glucose. In order to obtain high-quality and high-performance cell products, it is necessary to continuously monitor the culture process. Traditional biochemical method can achieve the purpose of monitoring, but the detection cycle is long which making it hard to meet the needs of real-time monitoring. In contrast, Near-infrared (NIR) spectroscopy can detect glucose concentration in cell culture medium in real time without loss and it has the advantages of fast, accurate, and high sensitivity. In this study, a partial least squares regression (PLSR) model was established based on the near infrared spectrum of cell medium containing a certain amount of glucose to verify its validity and feasibility. Efforts have been made on the processing of sample outlier, spectrum preprocessing and wavelength optimization method. And good prediction results were obtained with the coefficient of determination (R2) of the model was 0.998, the cross-validation mean square error (RMSECV) was 0.0036 and the residual prediction deviation (RPD) value was 4.15. On this basis, in order to further improve the prediction accuracy of the model, this paper introduced glutamine variables to establish a multi-component fusion model and further optimized the combined wavelength selection method. The results showed that the model could achieve better prediction accuracy when using our proposed optimization method which indicated that the method of real-time detection of glucose concentration in cell medium based on near infrared spectroscopy was feasible.

Abstract: As fuel efficiency and operational costs become critical concerns in the growing general aviation sector, drag reduction technologies for small aircraft have gained paramount importance. This review critically examines the current state of nanosecond laser etching technology for microgroove-based drag reduction on aircraft skin materials. We synthesize advancements in numerical simulations and experimental approaches while addressing key challenges such as precision control of micrometer-level morphology, thermal-induced microcracks, and material fatigue. A multidisciplinary framework integrating multi-physics modeling and fatigue life prediction is proposed to bridge the gap between laboratory research and industrial implementation. Our analysis highlights that optimized microgrooves can reduce aerodynamic drag in controlled experiments, yet scalability and long-term durability remain critical barriers. This work provides actionable insights for advancing nanosecond laser etching toward certification-ready solutions in small aircraft manufacturing.

Abstract: Resource depletion and environmental degradation have resulted from the substantial increase in the use of natural aggregates and construction materials brought on by the growing demand for infrastructure development. Road building using mining waste has become a viable substitute that reduces the buildup of industrial waste while providing ecological and economic advantages. In order to assess the appropriateness of several mining waste materials for use in road building, this study investigates their engineering characteristics. These materials include slag, fly ash, tailings, waste rock, and overburden. To ensure long-term performance in pavement construc-tions, it evaluates their tensile and compressive strength, resistance to abrasion, durability under freeze-thaw cycles, and chemical stability. The review indicate that waste rock and slag have excellent mechanical strength and durability, which makes them suitable alternatives to conventional aggregates for high-traffic roadways. Despite their need for stabilization, fly ash and tailings have important pozzolanic qualities that improve subgrade reinforcement and soil stability. When properly processed, overburden mate-rials can be used again for subbase layers and embankment building. This review also assesses the environmental effects, such as acid production and leachability, to make sure that the use of mining waste complies with legal and sustainable requirements. This review paper helps to re-duce landfill disposal, minimize carbon emissions, and promote circular economy concepts in the construction sector by maximizing the use of mining waste in road building. The findings show that mining by-products have the potential to be widely used in infrastructure projects as an economical and ecologically friendly substitute for traditional materials. To enable broad adop-tion, future studies should concentrate on improving stabilizing methods, long-term field per-formance tracking, and policy frameworks.

Abstract: An area of study for reluctance motors is the analytical computation of their magnetic field distribution, from which other quantities such as phase inductances and output torque may be derived. While numerical or iterative methods exist, analytical derivations of reluctance motor magnetic fields from first principles result in solutions that are less time-consuming to implement while requiring only the motor dimensions, without the need to re-discretize the motor model. A discussion of analytical derivations is scarce in the current literature whose focus is usually on their direct application in various motor models. This work then aims to review and examine the major mathematical techniques and assumptions used to derive the magnetostatic field distribution in reluctance motors. The methods under review are air gap permeance, magnetic potential, and conformal mapping techniques and are applied to machines with conventional structures. Other relevant approaches that can extend these techniques are also presented. Segmented motor designs have also emerged given their advantages and so must be systematically studied. These segmented topologies may require a new set of boundary conditions before these techniques can be applied. Thus, the methods’ possible avenues of application to segmented motor are discussed as well.

Abstract: The gradual deterioration of underground water infrastructure requires constant Condition Assessment (CA) and Condition Monitoring (CM) to prevent catastrophic failures, reduce Non-Revenue Water Loss (NRWL), and avoid costly unexpected repairs. Because water utilities manage large and dispersed systems with tight budgets, strategies for optimal selection and placement of technologies that harness maximum benefits and performance, are essential. This article introduces a framework and methods for an innovative and unified approach for optimally selecting and placing CM technology. The approach is underpinned by an R-E-R-A-V (Redundant, Established, Reliable, Accurate, and Viable) principle and asset management concepts. The proposed method is supported by a thorough review of CA and CM technology, and common approaches for CM technology deployment. The proposed unified approach evaluates CM selection with Technology Readiness Levels (TRL), and Spherical Fuzzy Analytic Hierarchy Process (SFAHP). CM placement is evaluated with k-Nearest Neighbors (kNN), tuned with topological and physical pipeline system features. A Cluster Distance Factor (CDF) derived from OPTICS (Ordering Points to Identify the Clustering Structure) is introduced as a measure to evaluate pipe segment vulnerability due to proximity failure prone areas. Data sources from technology selection and placement analyses are integrated through an algorithm framed in asset management concepts. The proposed approach for optimally placing CM technology on a benchmark network (Net3) revealed that 25% of the pipe segments require monitoring to prevent 95.7% of expected failures in a period of 11 years. The benefits of a unified approach are discussed and areas of future exploratory research are explained to encourage additional applications.

Abstract: This work was conducted in an industrial context within a garment manufac- 1 turing company under the Innovation Pact for the Digital Transition of the Textile and 2 Clothing Sector in Portugal. While recent developments focus on recycling fabrics post-use, 3 this study addresses textile surpluses generated during manufacturing. Currently, the 4 transportation and separation of these surpluses rely on manual labor, leading to errors 5 and inefficiencies. The objective is to describe the implementation of an automated system 6 to enhance sustainability in this process. There is limited research on the design and im- 7 pact of such automation. This paper presents a case study detailing a viable solution for 8 managing textile surpluses (covering separation, transportation, sorting, and storage) to re- 9 duce inefficiencies. Key performance indicators assess the implementation, demonstrating 10 quantitative improvements over the initial process. The project enhances the company’s 11 intralogistics, reducing the time required to collect and process textile waste while allowing 12 workers to focus on value-added tasks. By automating transportation and separation, the 13 system optimizes resource use and minimizes waste. This study contributes to sustainable 14 textile waste management by showcasing the benefits of automation in handling cutting 15 surpluses, aligning with industry efforts toward digital transformation and environmental 16 responsibility.

Abstract: The goal of this study was to quantify the sensitivity of the radial spring rate of a basketball, and the damping ratios of the first two modes of vibration of the basketball interacting with a Gared rim, to the change of inflation pressure. The chosen method of empirical modal analysis was impulse-response, where taps of an impact hammer provided the excitation and an accelerometer measured the response. The ANSYS 2025R1 Workbench modal analysis system was used to compliment the empirical modal analysis, by providing additional insight regarding the first two modes of vibration of the basketball-rim system. Our hypothesis was that the radial spring rate of the basketball would have a high positive correlation, and the damping ratios of the first two modes of vibration of the basketball and rim would have a high negative correlation, with respect to basketball inflation pressure. This study introduced studying the rotation of the modal eigenvectors with respect to the change of inflation pressure to better understand the physics of basketball-rim vibrations.

Abstract: This comprehensive review explores the profound influence of capacitor technologies on circuit design efficiency, performance characteristics, and environmental sustainability. Capacitors, as fundamental passive components, play a pivotal role in electrical circuits by facilitating functions such as filtering, energy storage, and coupling [1]. The choice of capacitor type significantly impacts circuit performance, affecting parameters including capacitance, frequency response, temperature stability, and voltage handling. Advancements in capacitor technology, such as integration into integrated circuits (IC), enhance circuit efficiency by reducing parasitic effects and improving signal integrity [2]. The environmental impact of capacitors is substantial, with significant implications for material extraction, manufacturing, and disposal. As the electronics industry evolves, adopting sustainable practices like recycling and repurposing capacitors becomes crucial. This review integrates lifecycle assessments and comparative analyses of disposal methods to provide insights into designing and managing capacitor technologies for a greener environment. This literature review contributes to the development of more sustainable electronic systems by highlighting the interplay between capacitor technology, circuit design efficiency, and environmental stewardship. Optimizing capacitor selection and disposal practices enhances circuit performance while minimizing environmental impact, aligning with broader efforts to reduce electronic waste and foster a sustainable electronics industry. This review supports the advancement of environmentally conscious circuit design practices through a comprehensive understanding of capacitor technologies and their lifecycle implications.