Eng

Multivariate data analysis and machine learning classification have become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensing elements must be a fair compromise between a period sufficiently long to output a meaningful information pattern and sufficiently short to minimize training time for practical applications. Particularly when a reactivity’s kinetics differ from the thermodynamics in sensitive materials, finding the best compromise to get the most from the data is not obvious. Here, we investigate the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for sensing elements to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffices to recognize molecular gases by principal component analysis with the same materials. Especially for online inference, this study shows that a good sensing array is not an array of good sensors, and that new figures of merit should be defined for sensing hardware using machine learning pattern recognition rather than metrology. Full article

A quantitative evaluation of the musical timbre and its variations is important for the analysis of audio recordings and computer-aided music composition. Using the FFT acoustic descriptors and their representation in an abstract timbral space, variations in a sample of monophonic sounds of chordophones (violin, cello) and aerophones (trumpet, transverse flute, and clarinet) sounds are analyzed. It is concluded that the FFT acoustic descriptors allow us to distinguish the timbral variations in the musical dynamics, including crescendo and vibrato. Furthermore, using the Random Forest algorithm, it is shown that the FFT-Acoustic provides a statistically significant classification to distinguish musical instruments, families of instruments, and dynamics. We observed an improvement in the FFT-Acoustic descriptors when classifying pitch compared to some timbral features of Librosa. Full article

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app “GeoVision”, achieves remarkable performance during its training phase with a mean accuracy of 95% and a precision of 98%. The model successfully predicts claystone, marl, and sandstone classes with high precision scores. Testing on new data yields an overall accuracy of 95%, providing valuable insights and setting a benchmark for future efforts. To address the limitations of current methodologies, such as time lags and lack of real-time data, we utilize drilling data as a unique endeavor to predict lithology. Our approach integrates nine drilling parameters, going beyond the narrow focus on the rate of penetration (ROP) often seen in previous research. The model was trained and evaluated using the open Volve field dataset, and careful data preprocessing was performed to reduce features, balance the sample distribution, and ensure an unbiased dataset. The innovative methodology demonstrates exceptional performance and offers substantial advantages for real-time geosteering. The accessibility of our models is enhanced through the user-friendly web app “GeoVision”, enabling effective utilization by drilling engineers and marking a significant advancement in the field. Full article

Adopting a multiscale approach is crucial for optimizing urban and building performance, prompting inquiries about the link between a technology’s local efficiency (building scale) and its broader impact (city-wide). To investigate this correlation and devise effective strategies for enhancing building and city energy performance, we experimentally examined a commercial nano-ceramic Thermal Barrier Coating (TBC) on a small-scale building and assessed numerically its influence on mitigating Urban Heat Islands (UHIs) at a city scale, translated in our case by the use of the thermal comfort index: the Universal Thermal Climate Index (UTCI). Our results reveal that the coating significantly curbs heat transfer locally, reducing surface temperatures by over 50 ${}^{\circ }$C compared to traditional roofs and attenuating more than 70% of heat flux, potentially alleviating air conditioning demands and associated urban heat effects. However, implementing such coatings across a city does not notably advance overall efficiency and might trigger minor overheating on thermal perception. Hence, while nano-ceramic coatings indirectly aid UHI mitigation, they are not a standalone fix; instead, an integrated strategy involving efficient coatings, sustainable urban planning, and increased vegetation emerges as the optimal path toward creating enduringly sustainable, pleasant, and efficient urban environments to counter urban heat challenges effectively. Full article

Mining plays a pivotal role in economies worldwide, contributing to employment, infrastructure, and the supply of essential raw materials. Chile’s global mining powerhouse, particularly in copper production, exemplifies this industry’s economic significance. The supplier selection process in the mining industry, a complex and multifaceted task, is explored in detail, specifically focusing on explosives procurement, a critical component for mineral extraction. The paper underscores the importance of informed supplier selection decisions, especially for SMEs, which often need more resources and capabilities for efficient management. To address these challenges, the study proposes applying the Analytic Hierarchical Process (AHP), a multi-criteria decision-making methodology, to identify and prioritize the criteria and sub-criteria pertinent to choosing explosives suppliers. A case study in the Coquimbo Region, Chile, involving SMEs in the mining sector is the empirical foundation for this research. Our research highlights that the foremost criterion for SMEs in the Coquimbo Region’s mining sector is “relationship with the environment and communities”. This reflects the national context of mining community tensions and the rising environmental standards and social expectations, which can profoundly impact mining operations. “Quality of products and services” is the second most crucial criterion, underscoring SMEs’ drive to enhance productivity and efficiency. “Contractual compliance” follows closely, signifying the integration of SMEs into broader social and environmental sustainability efforts. Conversely, “innovation” ranks as the least relevant criterion, indicating that SMEs prioritize traditional processes due to limited resources and cost constraints. These insights are valuable for mining supplier company managers, emphasizing the need for sustainability, corporate social responsibility, and management control systems. Full article

The science of catalysis has a direct impact on the world economy and the energy environment that positively affects the environmental ecosystem of our universe. Any catalyst, before being tested in a reaction, must undergo a specific characterization protocol to simulate its behavior under reaction conditions. In this work, these steps that must be carried out are presented, both generically and with examples, to the support and to the catalyst itself before and after the reaction. The first stage consists of knowing the textural and structural properties of the support used for the preparation of the catalysts. The specific surface area and the pore volume are fundamental properties, measured by N₂ adsorption at −196 °C when preparing the catalyst, dispersing the active phase, and allowing the diffusion and reaction of the reactants and products on its surface. If knowing the structure of the catalyst is important to control its behavior against a reaction, being able to analyze the catalyst used under the reaction conditions is essential to have knowledge about what has happened inside the catalytic reactor. The most common characterization techniques in heterogeneous catalysis laboratories are those described in this work. As an application example, the catalytic conversion of CO₂ to CH₄ has been selected and summarized in this work. In this case, the synthesis and characterization of Cu and Ni catalysts supported on two Al₂O₃ with different textural properties, 92 and 310 m²/g, that allow for obtaining various metallic dispersions, between 3.3 and 25.5%, is described. The catalytic behavior of these materials is evaluated from the CO₂ methanation reaction, as well as their stability from the properties they present before and after the reaction. Full article

Because textile industries are intensely water-consuming and generate a huge quantity of wastewater, the present study examines the scope of using solar thermal technology to treat wastewater from textile industries. A hybrid technology, comprising a compound parabolic concentrator-based solar thermal system in conjunction with a Membrane Distillation (MD) system, is experimented with for wastewater treatment in textile industries. The MD system requires a water temperature of around 90 °C for efficient functioning. The advanced MD technology using waste heat combined with solar heat to meet the system’s thermal load is technologically evaluated for an experimental textile industry in India. Moreover, the present study critically analyses the techno economics of the proposed hybrid technology. A detailed financial analysis has revealed that, besides technological superiority, the recommended technology is also financially rewarding for wastewater treatment in the textile industry. To cope with the delayed payback period, financial incentives are recommended so that the system becomes a lucrative technological option. Full article

The business world is becoming more competitive. Therefore, it is crucial to increase the flexibility of production by decreasing the time used in the processes of preparing the production lines for new items’ production, reducing changeover and setup times. This paper presents a case study where the main goal is to reduce the setup time of welding robots. Single Minute Exchange of Die (SMED) was implemented, using other tools such as the Spaghetti Diagram, ERCS Analysis (Eliminate, Rearrange, Combine, Simplify), Gemba Walk, Standardized Work, Flowcharts, and Pareto Diagram. The setup time decreased by 36% in the welding robots studied, decreasing the motions by 43% during the changeover process and reducing the time from the categories: “transportation”, “main”, “other”, and “waiting”. In addition to SMED implementation, this study offers an integrated study of several Lean tools and Quality tools to achieve the maximum reduction of changeover and setup times. Full article

Background: Artificial Intelligence has been an area of great interest and investment in the industrial sector, offering numerous possibilities to enhance efficiency and accuracy in production processes. In this regard, this study aimed to identify the adoption challenges of Artificial Intelligence and determine which of these challenges apply to the industrial context of an emerging economy, considering the aspects of Industry 4.0. Methods: To achieve this objective, a literature review was conducted, and a survey was carried out among professionals in the industrial field operating within the Brazilian context. The collected data were analyzed using a quantitative approach through Cronbach’s alpha and the Lawshe method. Results: The results indicate that to enhance the adoption of Artificial Intelligence in the industrial context of an emerging economy, taking into account the needs of Industry 4.0, it is important to prioritize overcoming challenges such as “Lack of clarity in return on investment,” “Organizational culture,” “Acceptance of AI by workers,” “Quantity and quality of data,” and “Data protection”. Conclusions: Therefore, based on the achieved results, it can be concluded that they contribute to the development of strategies and practical actions aimed at successfully driving the adoption of Artificial Intelligence in the industrial sector of developing countries, aligning with the principles and needs of Industry 4.0. Full article

In the present study, the concept of utilizing two circular cam-track disks, of the same central angle, in combination with one circular roller is presented. The roller is restrained to move within a vertical groove, and at the same time it rotates with rolling-contact on both cam tracks. When the upper cam is fully travelled by the roller, the same occurs with the lower one, despite their different lengths. Therefore, during the rolling contact, the two cams always sweep the same central angle. The aforementioned configuration of the two circular arcs may be considered as a unit cell, which can be repeated an even number of times, and when folded forms a closed circular groove between two cam-track disks. For better understanding, a manufactured prototype and 3D CAD-models have been developed. The operation of this setup as a gearless automotive differential is demonstrated by performing two bench experiments, which are then explained by a simplified mechanical model. The latter focuses on the implementation of the principle of the inclined plane, in which an upper limit of the inclination angle is imposed in accordance with the coefficient of friction at the friction disks. Previous patents on gearless differentials are discussed and other possible applications in mechanical engineering are outlined. Full article

This article examines the comparative effectiveness of three indoor node localization techniques—Multilateration, the Weighted Centroid algorithm, and Grid-based Received Signal Strength (RSS)—in wireless networking applications. The comparison is based on their performance against localization accuracy using RSS Indicator (RSSI) data in three experiments. The experiments utilized internally generated or real-world datasets with RSSI values for the unknown tag nodes. The datasets were obtained from various sources and evaluated in different scenarios to determine the efficiency of the three localization techniques. The results were evaluated and compared using mean error and standard deviation metrics. The findings indicate that trilateration achieves superior localization accuracy and precision in a Bluetooth Low Energy (BLE) environment compared to Wi-Fi and ZigBee. The Centroid technique showed the highest resistance to noise and outliers but is positioned biased (unlike Trilateration). Besides that, the Grid-based RSS technique is highly sensitive to noise, and theoretical RSS. These findings can greatly assist researchers and network operators in carefully selecting the most suitable localization technique for their wireless networking applications, taking into account the specific wireless technology utilized and their unique needs and limitations. Full article

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results to those obtained for a battery electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments, reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, from 0% to 100%, for each mode. This methodology optimizes the simulation results for the current driving patterns in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, a fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes. Full article

As photovoltaics (PV), also known as solar electricity, has been growing over the years, the energy markets have been gradually moving toward decentralization. However, recent media accusations suggest that decentralized renewable energy is slowly becoming unpopular because of the hidden fees being charged to owners of installed PV systems. In response, this paper investigates the potential for alternative approaches to incentivize owners using peer-to-peer (P2P) sharing. This study provides an analytical comparison between the use of the P2P mechanism, the net-metering mechanism, and a combination of these in the commercial sector. Through the use of a simulation, this case study presents the possible outcomes of the implementation of these models in a microgrid. Using technical and economic indexes the comparison was made by looking at the following indexes: peak power, energy balance, economic benefit, and transaction index. Based on a microgrid of 28 commercial buildings, readings of consumption were taken at intervals of one hour, and a Python model was made to find PV size and compare trading mechanisms. It was found that the combination of P2P and net-metering had the best overall performance, followed by net-metering itself, with the best season being all for both, and summer for net-metering by itself. This shows that a P2P model implemented in a microgrid helps create more energy balance, although the combination would achieve the highest performance. This study can be used by policymakers for proposing renewable energy policies and regulations that are more beneficial to all prosumers and consumers. Full article

Lateral distortional buckling (LDB) is an instability phenomenon characteristic of steel-concrete composite beams (SCCB) that occurs in the presence of hogging moments in regions close to internal supports. The LDB behavior in SCCB is not yet fully understood. The procedures for determining the LDB strength are based on the classic lateral torsional buckling theory or on the inverted U-frame model. In addition, the standard procedures make use of the classic design curves of the SSRC (Structural Stability Research Council) and ECCS (European Convention for Constructional Steelwork) developed to analyze the stability behavior of steel elements. However, studies indicate that the use of the same empirical curves obtained for the analysis of steel elements leads to the conservative results of the LDB strength in SCCB. Therefore, this article aims to assess the LDB strength in SCCB through the development of post-buckling numerical analysis using the ABAQUS software. In the parametric study, four types of steel with different mechanical properties were analyzed. In addition, the I-section, the unrestrained length, and the reinforcement rate in the concrete slab were varied. The results showed the influence of the steel type on the LDB strength and deviations from the standard procedures. A small influence of the longitudinal reinforcement area variation was verified in the LDB strength in the FE analyses; however, this factor is significantly important in the standard procedures, causing considerable divergences. These results can provide a reference for future research and specification reviews. Full article

The study examines decentralized waste treatment in an urban setting with a high-density population of 2500 inhab./km². The co-digestion of food and garden waste was assumed by using several mid-size digesters, while centralized biogas and digestate valorization was considered. The studied configuration generates electricity and thermal energy, covering 1.3% of the residential electricity demand and 3.2% of thermal demand. The use of double-turbocharged engines under the most favorable scenario aids cities in reaching sustainability goals. However, the location of treatment plants is a factor that may raise social discomfort and cause a nuisance to citizens. Locating waste plants near residential areas causes discomfort due to possible odors, gaseous emissions, and housing market distortions. Such problematic aspects must be addressed for the decentralized alternative to work. These factors are of great relevance and must be given a practical solution if the circular economic model is to be implemented by considering the insertion of waste streams into the production system and generating local energy sources and raw materials. Full article

The authors present a case study of the investigation of a transient signal that appeared in the testing of a radar receiver. The characteristics of the test conditions and data are first discussed. The authors then proceed to outline the methods for detecting and analyzing transients in the data. For this, they consider several methods based on modern signal processing and evaluate their utility. The initial method used for identifying transients is based on computer vision techniques, specifically, thresholding spectrograms into binary images, morphological processing, and object boundary extraction. The authors also consider deep learning methods and methods related to optimal statistical detection. For the latter approach, since the transient in this case was chirp-like, the method of maximum likelihood is used to estimate its parameters. Each approach is evaluated, followed by a discussion of how the results could be extended to analysis and detection of other types of transient radio-frequency interference (RFI). The authors find that computer vision, deep learning, and statistical detection methods are all useful. However, each is best used at different stages of the investigation when a transient appears in data. Computer vision is particularly useful when little is known about the transient, while traditional statistically optimal detection can be quite accurate once the structure of the transient is known and its parameters estimated. Full article

Thermal images are highly dependent on outside environmental conditions. This paper proposes a method for improving the accuracy of the measured outside temperature on buildings with different surrounding parameters, such as air humidity, external temperature, and distance to the object. A model was proposed for improving thermal image quality based on KMeans and the modified generative adversarial network (GAN) structure. It uses a set of images collected for objects exposed to different outside conditions in terms of the required weather recommendations for the measurements. This method improves the diagnosis of thermal deficiencies in buildings. Its results point to the probability that areas of heat loss match multiple infrared measurements with inconsistent contrast for the same object. The model shows that comparable accuracy and higher matching were reached. This model enables effective and accurate infrared image analysis for buildings where repeated survey output shows large discrepancies in measured surface temperatures due to material properties. Full article

The tomato processing industry is focused on product yield maximization, keeping energy costs and waste effluents to a minimum while maintaining high product quality. In our study, cold atmospheric plasma (CAP) pretreatment enhanced tomato processing to facilitate peelability, a specific peeling process, and enhance peel drying. Peeling force analysis determined that CAP pretreatment of whole tomatoes improved peelability under the conditions used. The specific peeling force after CAP treatment decreased by more than three times. It was observed that cold atmospheric plasma pretreatment reduced the duration of infrared drying of tomato peels by 18.2%. Along with that, a positive effect on the reduction of the specific energy consumption of peel drying was shown for CAP-pretreated tomato peels. The obtained data show that the technology of cold atmospheric plasma pretreatment, in particular, when processing whole tomatoes and tomato peels, has a promising application in industry, as it can significantly reduce the specific energy consumption for peeling and drying procedures. Full article

This paper begins with an introduction to porcelain tiles. A review of the major scientific and technological features of mullite-based porcelain tiles (MPTs) and anorthite-based porcelain tiles (APTs), focusing primarily on the raw material, processing, phase evolution and mechanical behavior, is then presented. Based on the porcelain tile firing behavior and a series of physical and chemical changes that can occur, a comprehensive comparison is described. In the last part, the prospects for further developments related to MPTs and APTs are discussed. Full article