Search Results (417)

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

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings of modern enterprise resource planning systems (ERPs) were considered in the context of Warehouse 4.0. Based on the problematic areas of material accounting in manufacturing enterprises, a typical workplace was taken as a basis, which creates a gray area for warehouse systems and does not provide the opportunity of quality-managing the company’s inventory. The main tool for collecting and processing data from the workplace was the neural network. A mobile application was proposed for processing and converting the collected data for the decision-maker on material management. The YOLOv8 convolutional neural network was used to identify materials and production parts. A laboratory experiment was conducted using 3D-printed models of commercially available products at the SmartTechLab laboratory of the Technical University of Košice to evaluate the system’s effectiveness. The data from the network evaluation was obtained with the help of the ONNX format of the network for further use in conjunction with the C++ OpenCV library. The results were normalized and illustrated by diagrams. The designed system works on the principle of client–server communication; it can be easily integrated into the enterprise resource planning system. The proposed system has potential for further development, such as the expansion of the product database, facilitating efficient interaction with production systems in accordance with the circular economy, Warehouse 4.0, and lean manufacturing principles. Full article

Reliability is an essential factor for the operation of the Switched Reluctance Motor (SRM) drive. Electric vehicles operate in harsh environments, which may degrade the operation of power converters. These failure modes include transistor open- and short-circuits, freewheeling diode open- and short-circuits, and DC-link capacitor failures. This work presents a performance analysis of an in-wheel SRM for an electric vehicle under short-circuit (SC) and open-circuit (OC) faults of a modified Neutral-Point-Clamped Asymmetric Half-Bridge (NPC-AHB) Converter. The SRM is modeled as an in-wheel electric vehicle. A separate vehicle model attached to the motor is also developed for validation and performance of the NPC-AHB under different faulty scenarios. The performance of the modified NPC-AHB is also compared with that of a conventional AHB under faulty conditions for an in-wheel 8/6 SRM. The performance indicators such as torque, speed, current, and flux are presented from MATLAB/Simulink 2023b numerical simulations. Full article

This research emphasizes the effect of using Eisenia foetida in vermicompost for power generation in microbial fuel cells (MFCs). By accelerating the organic decomposition, the bioenergy generation is improved. A vermicompost-microbial fuel cell employing electrogenic microorganisms was used to convert chemical energy into electrical energy. In this work, substrates of black soil, tree bark, leaves, eggshells, and ground tomatoes were used. The vermicompost MFC has a copper cathode and a stainless steel anode. In this study, the performance of MFCs was evaluated using different numbers of Eisenia foetida specimens, with three specimens (MFC_W3), five specimens (MFC_W5), and seven specimens (MFC_W7). Our key findings show that by increasing the number of Eisenia foetida specimens does not bring higher power densities; as a result, the best power density was observed in MFC_W3 and MFC_W5 at the end of the fourth week, both presenting a total of five Eisenia foetida specimens with a power density of 192 mW m⁻². Therefore, optimal results were found when 330 g of substrate and five Eisenia foetida specimens were used to achieve a maximum current density of 900 mW m⁻² and a maximum power density of 192 mW m⁻². This type of microbial fuel cell can be considered as an alternative for power generation with a significantly reduced environmental impact, considering the use of organic waste. It can be considered a game-changer in waste management and bioenergy projects. Full article

The dietary recommendations for individuals with diabetes focus on maintaining a balanced nutritional intake to manage blood sugar levels. This study suggests a nutritional strategy to improve glycemic control based on an analysis of a dietary optimization problem. The goal is to minimize the overall glycemic loads (GLs) of specific foods. Two variations of the particle swarm optimization (PSO) method, as well as random quantum process optimization (GQPSO), are introduced. The findings demonstrate that the quantum and random methods are more effective than the traditional techniques in reducing the glycemic loads of diets and addressing nutritional deficiencies while also aligning nutrient intake with the recommended levels. The resolution of this diet optimization model, executed multiple times with adjustments to the parameters of both methods, enables dynamic exploration and provides a wide range of diverse and effective food choices. Full article

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop, and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work, in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K- and M-type pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant. Full article

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while the second ones are associated with cavitation. It is possible to use computational algorithms to monitor both failures in CPs. In this study, two different problems in pumps, the defective impeller and cavitation, have been considered. When a CP is working in a faulty condition, it generates vibrations that can be measured using piezoelectric sensors. Collected data can be analyzed to extract time- and frequency-domain data. Interpreting the time-domain data showed that distinguishing the type of defect is not possible. However, indicators like kurtosis, skewness, mean, and variance can be used as input for the multi-layer perceptron (MLP) algorithm to classify pump faults. This study presents a detailed discussion of the vibration-based method outcomes, emphasizing the benefits and drawbacks of the multi-layer perceptron method. The results show that the suggested algorithm can identify the occurrence of different faults and quantify their severity during pump operation in real time. Full article

Changes in soil fertility have led to a decline in crop production, making it challenging for farmers to select the best crops based on soil conditions. Accurate crop prediction can significantly enhance crop productivity, and machine learning plays a crucial role in this process. Crop forecasting is influenced by soil, geographic, and environmental characteristics, with feature selection being essential for identifying suitable crops. In this study, we developed a real-time soil fertility analyzer to obtain the real-time values of soil parameters such as potassium, phosphorus, nitrogen content, temperature, pH, moisture content, and electrical conductivity. The crops examined were coconut, ginger, plantain, and tapioca. The data collected from this analysis served as the dataset for different training and testing classification algorithms for crop prediction using 100 soil samples. Among the algorithms tested, the k-nearest neighbors (KNN) algorithm demonstrated the highest performance, with an accuracy of 84%, precision of 85%, recall of 88.8%, and specificity of 92.4%. These results indicate that machine learning, combined with real-time soil analysis, can effectively predict suitable crops, enhancing crop productivity and aiding farmers in making informed decisions. This approach can revolutionize traditional farming practices by providing precise, data-driven insights into crop selection, ultimately improving agricultural efficiency and sustainability. Full article

Nanofluids have gained significant attention as a promising solution to several challenges in drilling operations. Nanoparticles, due to their exclusive properties such as high specific surface area, strong adsorption potential, and excellent thermal conductivity, offer significant potential to improve the efficiency and performance of drilling processes. Regardless of the advancements in drilling fluids and techniques that have improved borehole stability, hole cleaning, and extreme operational condition (HTHP) management, limitations still persist. This review discusses a detailed summary of existing research on the application of nanofluids in drilling, exploring their types, properties, and specific uses in areas such as fluid loss control, wellbore stability, and thermal management. It also reports the challenges and future potential of nanotechnology in drilling, including nanoparticle stability, environmental considerations, and cost concerns. By synthesizing current research and highlighting gaps for further study, this review intends to guide researchers and industry professionals in effectively integrating nanofluid usage to optimize drilling practices and support a more sustainable energy future. Full article

The development of the oil industry and the fossil fuel economy has historically improved the quality of life for many people, but it has also led to significant environmental degradation. As a response, the concept of ‘sustainable development’ has gained prominence recently, emphasizing the importance of balancing economic progress with environmental protection. Among the many environmental challenges we face today, preserving water resources is one of the most pressing. To tackle this issue, researchers are focusing on strategies to reduce water consumption and enhance the efficiency of wastewater treatment. In this context, the present review explores recent advancements in a novel coupled treatment process that integrates adsorption in activated carbon fiber (ACF) and photocatalytic oxidation using TiO₂ to remove micropollutants from wastewater. This innovative approach would allow for the in situ and continuous regeneration of ACF with TiO₂ photocatalysis, increasing the oxidative degradation efficiencies of the supported semiconductor thanks to the adsorbent material, all under the possibility of a durable and low-cost process using solar radiation. In addition, this is vital for meeting regulatory standards, protecting aquatic ecosystems, and safeguarding human health. Full article

This paper investigates the use of neural networks to predict characteristic parameters of the grease application process pressure curve. A combination of two feed-forward neural networks was used to estimate both the value and the standard deviation of selected features. Several neuron configurations were tested and evaluated in their capability to make a probabilistic estimation of the lubricant’s parameters. The value network was trained with a dataset containing the full set of features and with a dataset containing its average values. As expected, the full network was able to predict noisy features well, while the average network made smoother predictions. This is also represented by the networks’ R2 values which are 0.781 for the full network and 0.737 for the mean network. Several further neuron configurations were tested to find the smallest possible configuration. The analysis showed that three or more neurons deliver the best fit over all features, while one or two neurons are not sufficient for prediction. The results showed that the grease application process pressure curve via pressure valves can be estimated by using neural networks. Full article