Eng

This work aimed to evaluate the accuracy of analytical models for predicting the behavior of concrete-filled steel tubular (CFST) columns via finite element analysis coupled with physical nonlinearity. The methodology involved an extensive review of experimental tests from the literature, numerical modeling of columns with different configurations, and a comparison of the results obtained with available experimental data. Several characteristics were evaluated, such as the load capacity, confinement factor, and relative slenderness. The numerical model agreed well with the experimental results, with a less than 10% relative error. The results indicated that analytical models of the Chinese (GB 50936) and European (EC4) codes overestimated some load capacity values (up to 14.9% and 8.7%, respectively). In comparison, the American (AISC 360) and Brazilian (NBR 8800) standards underestimated the ultimate loads (23.3% and 31.6%, respectively). An approach coefficient β is proposed, contributing to safer and more efficient design practices in structural engineering. Full article

The incorporation of recycled glass fiber reinforced polymer (rGFRP) in cementitious materials is an interesting recycling and valorization method. However, this incorporation generally results in a significant loss of workability, often compensated by an adjustment of the water to cement ratio, which can affect mechanical performance, particularly compressive strength. The aim of this paper is to examine the effect of different size fractions of rGFRP (0.063 mm, 0.16 mm, 0.63 mm, 1.25 mm and 2 mm) on the mechanical strengths of cement mortars with a mixing method that is likely to maintain the workability of the mix without adjusting the water to cement ratio. For this, a substitution rate of 10% (in volume of sand by rGFRP), supposed to induce workability loss, is chosen. A pre-mixing of rGFRP with water before adding cement and sand is performed and allows for the workability to be maintained without increasing the water content. The results show that compressive and flexural strengths are almost maintained compared with reference mortar for two rGFRP size fractions (2 mm and 0/2 mm). For the 2 mm fraction, a slight improvement (3%) in flexural strength after 7 months of curing and a 5% reduction in compressive strength are observed. After 7 months of curing, fibers or clusters of rGFRP are still observed, although they are not alkali-resistant. Full article

End-to-end traceability offers significant opportunities for product lifecycle visibility, sustainability enhancement, and regulatory compliance in product management. However, it faces challenges in data integration and management, supplier collaboration, cost and complexity, and the sharing of information across the supply chain. Productization refers to the representation of a product and connects commercial and technical aspects to the systemic perspective of product management. This includes a focus on the engineering lifecycle with inherent linkages to product data. The product management perspective, specifically in relation to the connection between end-to-end traceability and the productization concept, has not been extensively studied. This study explores the role of both productization and traceability in the context of end-to-end traceability. It combines an extensive literature review and an empirical example of applying productization logic across company borders to support end-to-end traceability. The key findings indicate that productization logic with a product structure focus can support end-to-end traceability in product management by providing consistency and a foundation for tracking both technical and operational data across the engineering lifecycle of a product. By focusing on productization, companies can overcome traceability challenges and unlock the benefits of end-to-end traceability. Full article

This investigation investigates the influence of a variety of data augmentation techniques on sentiment analysis in low-resource languages, with a particular emphasis on Bulgarian, Croatian, Slovak, and Slovene. The following primary research topic is addressed: is it possible to improve sentiment analysis efficacy in low-resource languages through data augmentation? Our sub-questions look at how different augmentation methods affect performance, how effective WordNet-based augmentation is compared to other methods, and whether lemma-based augmentation techniques can be used, especially for Croatian sentiment tasks. The sentiment-labelled evaluations in the selected languages are included in our data sources, which were curated with additional annotations to standardise labels and mitigate ambiguities. Our findings show that techniques like replacing words with synonyms, masked language model (MLM)-based generation, and permuting and combining sentences can only make training datasets slightly bigger. However, they provide limited improvements in model accuracy for low-resource language sentiment classification. WordNet-based techniques, in particular, exhibit a marginally superior performance compared to other methods; however, they fail to substantially improve classification scores. From a practical perspective, this study emphasises that conventional augmentation techniques may require refinement to address the complex linguistic features that are inherent to low-resource languages, particularly in mixed-sentiment and context-rich instances. Theoretically, our results indicate that future research should concentrate on the development of augmentation strategies that introduce novel syntactic structures rather than solely relying on lexical variations, as current models may not effectively leverage synonymic or lemmatised data. These insights emphasise the nuanced requirements for meaningful data augmentation in low-resource linguistic settings and contribute to the advancement of sentiment analysis approaches. Full article

The critical importance of effective industrial symbiosis is emphasized in the rapidly evolving landscape of manufacturing, energy, and environmental sustainability. This study employs the Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology to examine and outline the complex interrelationships among critical success factors (CSFs) pivotal for the successful implementation of industrial symbiosis. Key findings indicate that leadership and technology are the most significant causal CSFs, driving positive outcomes in waste reduction, environmental impact, and economic growth, identified as primary effect factors. Leadership emerges as the predominant influence, guiding strategic alignment, fostering a collaborative and sustainable organizational culture, and affecting all other CSFs. Technological integration acts both as a direct driver of operational efficiency and as a mediator of leadership’s influence, enabling optimized resource flows and data-driven decision-making. Additional CSFs such as clear communication, enhanced training and education, and policy and regulatory support also serve as essential mediators connecting leadership to key outcomes. This research outlines an actionable pathway for stakeholders, including policymakers, engineers, and corporate executives, to strategically prioritize and utilize these CSFs to promote more resilient and sustainable industrial ecosystems. Full article

Apple by-products (i.e., peels) are often thrown away, yet they are highly nutritious and provide numerous advantages as they contain a variety of nutrients such as vitamins, minerals, and antioxidants. Apple peels also comprise a high level of antioxidants, particularly polyphenols and flavonoids. This research aimed to determine the most efficacious extraction techniques and parameters to accomplish maximum bioactive compounds recovery from apple peels. Several extractions were conducted, including stirring, ultrasonication, and pulsed electric field-assisted extractions. Response surface methodology and several factors such as temperature, extraction duration, and solvent composition were considered to have a major impact on the isolation of bioactive compounds. The findings indicated that the most practical and efficient approach was to combine the pulsed electric field process with ultrasonication and stirring at 80 °C for 30 min, while 75% aqueous ethanol comprised the optimal solvent concentration, demonstrating the critical role of the solvent in optimizing extraction efficiency. The optimal conditions were obtained through response surface methodology with a statistical significance of p < 0.05. The extract exhibited a total polyphenolic content (TPC) of 17.23 mg gallic acid equivalents (GAE) per g of dry weight (dw), an ascorbic acid content (AAC) of 3.99 mg/g dw, and antioxidant activity of 130.87 μmol ascorbic acid equivalents (AAE)/g dw, as determined by FRAP and 95.38 μmol AAE/g dw from the DPPH assay. The measured antioxidant activity highlighted the significant potential of apple peels as a cost-effective source of exceptionally potent extracts. Full article

Aquaculture is one of the key economic activities to reduce food shortages worldwide. Water recirculation systems using pumps are crucial to maintain oxygenation and water quality, consuming about 35% of the total energy in this economic activity. This research proposes a multiple linear regression mathematical model to optimize oxygenation systems in intensive shrimp aquaculture by reducing energy consumption and minimizing water changes in ponds. The proposed model is key to optimizing the operation of pumping systems, allowing us to significantly reduce water turnover without compromising dissolved oxygen levels as a function of key variables such as water turnover volume, biomass, solar radiation (0–1200 W/m²), water temperature (20 °C–32 °C), phytoplankton levels (0–1,000,000 cells/ml), zooplankton (0–500,000 cells/ml), and wind speed (0–15 m/s). These variables are integrated into the model, managing to explain 94.02% of the variation in dissolved oxygen, with an R² of 92.9%, which adjusts the system conditions in real time, reducing the impact of environmental fluctuations on water quality. This leads to an estimated annual energy savings of 106,397.5 kWh, with a total consumption of 663.8 MWh. The research contributes to the development of a mathematical approach that not only improves oxygenation prediction, but also minimizes the use of water resources, improving the sustainability and profitability of shrimp farming systems, and is a robust tool that maximizes operational efficiency in intensive aquaculture, particularly where water and energy management are critical. Full article

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH²AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC²AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively. Full article

In recent years, the assessment of damage scenarios in urban communities has become one of the central themes in local government policies, aimed at promoting effective seismic risk mitigation and improving the efficiency of rescue systems to manage emergencies. In Italy, the seismic hazard has become a topical issue since the 1982 Irpinia earthquake, and several ventures have been promoted to face the seismic mitigation of complex residential districts. The objective of this research is to define the damage scenarios of the city of Florence, where 97% of the building stock is designed without anti-seismic prescriptions. The urban vulnerability of Florence has been assessed based on the current approaches available in the technical literature, combined with the knowledge provided by the recent investigation on the subsoil. Once the possible damage scenarios have been defined, the resilience of the area has been determined, and the population involved in the evacuation has been estimated. Full article

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). The ISIC dataset is balanced through oversampling in classification, and preprocessing techniques such as data augmentation and post-processing are applied in segmentation to increase robustness. The ensemble model outperformed individual models, achieving a Dice Coefficient of 0.93, an IoU of 0.90, and an accuracy of 0.95 for segmentation, with 90% accuracy on the original dataset and 99% on the balanced dataset for classification. The use of ensemble models and balanced datasets proved highly effective in improving the accuracy and reliability of automated skin lesion analysis, supporting dermatologists in early detection efforts. Full article

Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs. Full article

In the field of modern drive technology, conventional form-fit shaft-hub connections, such as the standardized keyway connection, are reaching their mechanical limits due to the space-saving design. The trochoidal profiles are elegant modern shaft-hub connections with a compact design for high-power transmission. This article deals with an analytical approach to determining the stress state in trochoidal profiles under shear bending. The solution completes the existing analytical attempts at the load cases of pure bending and torsion. Similar to the torsional loading case, a conformal mapping must be found that can completely transform the unit circle to the non-circle profile area. The conformal mapping function is deduced from the contour equation of the profile. To check the analytical results, in addition, numerical investigations were carried out. The results of the complementary numerical studies show very good agreement with the analytical solutions. The equations derived for the maximum stresses enable a reliable and cost-effective design of the profile shafts subjected to shear force loading. Full article

Cardiovascular diseases pose a significant global health threat, with atrial fibrillation representing a critical precursor to more severe heart conditions. In this work, a multimodality-based deep learning model has been developed for diagnosing atrial fibrillation using an embedded system consisting of a Raspberry Pi 4B, an ESP8266 microcontroller, and an AD8232 single-lead ECG sensor to capture real-time ECG data. Our approach leverages a deep learning model that is capable of distinguishing atrial fibrillation from normal ECG signals. The proposed method involves real-time ECG signal acquisition and employs a multimodal model trained on the PTB-XL dataset. This model utilizes a multi-step approach combining a CNN–bidirectional LSTM for numerical ECG series tabular data and VGG16 for image-based ECG representations. A fusion layer is incorporated into the multimodal CNN-BiLSTM + VGG16 model to enhance atrial fibrillation detection, achieving state-of-the-art results with a precision of 94.07% and an F1 score of 0.94. This study demonstrates the efficacy of a multimodal approach in improving the real-time diagnosis of cardiovascular diseases. Furthermore, for edge devices, we have distilled knowledge to train a smaller student model, CNN-BiLSTM, using a larger CNN-BiLSTM model as a teacher, which achieves an accuracy of 83.21% with 0.85 s detection latency. Our work represents a significant advancement towards efficient and preventative cardiovascular health management. Full article

The global transportation sector is transitioning towards renewable energy to combat climate change, with biodiesel playing a critical role. Significant research over the past decades has focused on enhancing biodiesel through novel feedstocks and production methods. The defense community, a major diesel consumer, is particularly interested in biodiesel to support national sustainability goals while also leveraging the benefits of the new technology, including the ability to produce biodiesel locally at the point of need. This paper sets out to review recent advances in biodiesel technology and aligning them with the needs of the defense communities. By doing so, this paper provides insight into the challenges, benefits, and technical feasibility for the two primary consumers of military diesel fuel—naval ships and ground vehicles. For naval applications, algae-based biodiesel shows promise due to its potential for local production near ports. Advances in genetic engineering and cultivation are crucial for increasing lipid content and reducing costs. Innovative methods such as microwave-assisted transesterification and artificial neural networks for optimization could further enhance economic viability. In military ground vehicles, locally produced biodiesel could sustain operations by minimizing supply chain dependencies. Efforts are ongoing to develop mobile production facilities and improve feedstock diversity and methanol-independent transesterification processes. Overall, advancements in biodiesel production from various feedstocks and innovative techniques are poised to significantly benefit the military sector, promoting sustainability and operational efficiency. Full article

The production process requires massive amounts of minerals, fossil fuels, and energy. The efficient use of energy and natural resources appears to be crucial to the state of affairs. It should be noted that the post-consumer management of solar power plant elements results in a certain amount of power and matter, as well as harmful effects on the natural world. The major goal of this study was to examine the environmental effect of the solar power plant throughout its life cycle, taking into consideration the depletion of natural materials and mineral resources, using the ReCiPe 2016 model. A life cycle study was performed on an actual 2 MW solar power facility located in northern Poland. This study was conducted using the ReCiPe 2016 model and the Life Cycle Assessment (LCA) methodology. The analyzed renewable energy system’s impact was assessed utilizing 22 impact categories, focusing mostly on the depletion of natural resources. A Life Cycle Assessment was conducted for two post-consumer development scenarios (landfill and recycling). This research focuses on the full solar power plant, not just the photovoltaic panels. Recycling, as a kind of post-consumer development, can provide major environmental benefits and minimize negative environmental consequences throughout the solar power plant’s life cycle. The exceedingly harmful effects can be evident in losses related to water and the aquatic environment. The obtained study findings enabled the development of sustainable-friendly recommendations towards the continuous advancement of the life cycle of solar power plants, thereby reducing the use of rare earth minerals. Full article

Different direct solar harvesting systems for daylighting are being explored to achieve high uniform illumination deep within buildings at minimal cost. A promising solution to make these systems cost-effective is the use of plastic optical fibers (POFs). However, heat-related issues with low-cost POFs need to be addressed for the widespread adoption of efficient daylighting technologies. Previous studies have explored solutions for this overheating problem, but their effectiveness remains uncertain. This study proposes a low-cost fiber optic daylighting system integrated with a newly patented mechanical component designed to secure the fiber optic bundle at the focal point, providing three levels of heat filtration while ensuring uniform illumination. Our methodology involves selecting a small area, installing the setup, and measuring both heat and light readings, followed by validation through software simulations. The operational principle of this technology is explained, and experimental tests using lux meters and infrared thermometers were conducted to investigate the system’s characteristics. The three-level heat filtration device reduces temperature by approximately 35 °C at the surface of the optical fiber, and the average illumination of the room is around 400 lux. These results were further verified using RELUX simulation software. The findings demonstrate the promising potential of this new device in solar heat filtration and achieving uniform illumination. Recommendations for mitigating overheating damage and exploring heat filtering possibilities in new parabolic solar daylighting systems for further research are also provided. Full article

Consumer dissatisfaction and damage are increasing worldwide due to the increase in defects caused by the decline in housing quality, and disputes over housing defects are expanding. The number of housing units, a representative standard related to housing quality, is used in Canada, Japan, and Korea. Generally, quality costs increase as the number of housing units increases, and each country’s laws apply stricter management standards. Therefore, the quality is expected to be better as the number of units increases. In 2020, South Korea added a new regulation requiring inspections by a quality inspection team by a public institution only when building housing complexes with more than 300 households. There is a debate about whether this direction of regulation is appropriate. This study examines whether the number of households is being used appropriately as a criterion related to housing quality. It aims to determine whether the limit of 300 households is appropriate for distinguishing housing quality. In addition, since the contractor’s role is vital in housing construction, the contractor’s capabilities and supply–demand relationship were also considered as factors affecting housing quality. The ratio of defect repair costs to construction costs was used as a quality measure for 285 housing complexes in Korea. Generally, the lower the defect repair–construction costs ratio, the better the quality. A comparative study was conducted through a variance analysis on the scale of 300 households and the status of the contractor’s capability, whether they were among the top 10 construction companies with excellent construction performance, and whether a sole contract was made. The results showed that the quality was better in the cases with 300 or more households than in the cases with fewer than 300 households. The quality was better in the cases built by higher-ranking contractors than in those built by other contractors, but there was no difference according to supply-and-demand relationships. The results of the comprehensive analysis indicated that the quality was better when higher-ranking contractors built housing complexes with 300 or more households than when lower-ranking contractors built housing complexes with fewer than 300 households. Therefore, the direction of the Korean regulation requiring quality inspections for housing complexes with more than 300 households is incorrect and should be improved to regulate housing complexes with fewer than 300 households, and of low quality. In addition, the standard of determining housing quality based solely on the number of households should be revised, and the direction should be changed to strengthen quality control and the public supervision of housing built by low-capacity contractors. If the results of this study are utilized with this view in mind, a reasonable system to protect housing consumers will be promoted. Full article

Wastewater Treatment Plants (WWTPs) encompass a range of processes from preliminary to advanced stages. Conventional treatments are increasingly inadequate for handling emergent pollutants, particularly organic compounds with carcinogenic potential that pose risks to aquifers. Recent advancements prioritize integrating Advanced Oxidation Processes (AOPs) and adsorbents with conventional methods to effectively retain organic pollutants and enhance mineralization. There is a growing preference for non-chemical or minimally chemical approaches. Innovations such as combining ozone and other biological processes with photo-sono-assisted methods, alongside integrating AOPs with adsorbents, are promising. These approaches leverage catalyst-assisted reactions to optimize oxidation efficiency. This review aims to provide a holistic perspective on WWTP processes, spanning wastewater intake to the production of potable water, highlighting key technologies, operational challenges, and future trends. The focus is on advancing sustainable practices and enhancing treatment efficacy to safeguard water quality and address evolving environmental concerns effectively. Full article

This paper presents an efficient and secure method for updating firmware in IoT devices using LoRaWAN network resources and communication protocols. The proposed method involves dividing the firmware into fragments, storing them in the application server’s database, and transmitting them to remote IoT devices via downlink messages, without necessitating any changes to the device’s class. This approach can be replicated across any IoT LoRaWAN device, offering a robust and scalable solution for large-scale firmware updates while ensuring data security and integrity. The proposed method significantly reduces the downtime of IoT devices and enhances the energy efficiency of the update process. The method was validated by updating a block in the program memory, associated to a specific functionality of the IoT end device. The associated Intel Hex file was segmented into 17 LoRaWAN downlink frames with an average size of 46 bytes. Upon receiving the complete firmware update, the microcontroller employs self-programming techniques that restrict the update process to specific rows of the program memory, avoiding interruptions or reboots. The update process was successfully completed in 51.33 ms, resulting in a downtime of 16.88 ms. This method demonstrates improved energy efficiency compared to existing solutions while preserving the communication network’s capacity, making it an adequate solution for remote devices in LoRaWAN networks. Full article