Search Results (285)

The Amazon rainforest, spanning multiple countries in South America, is the world’s largest equatorial expanse, housing a vast array of relatively unknown plant and animal species. Encompassing the planet’s greatest flora, the Amazon offers a tremendous variety of plants from which natural lignocellulosic fibers (NLFs) can be extracted. In this century, NLFs, which have long been utilized by indigenous populations of the Amazon, have garnered interest as potential reinforcements for composites, whether polymer- or cement-based, in various technical applications such as packaging, construction, automotive products, and ballistic armor. A comparison with synthetic materials like glass, carbon, and aramid fibers, as well as other established NLFs, highlights the cost and specific property advantages of Amazon natural fibers (ANFs). Notably, the sustainable cultivation and extraction of ANFs, as alternatives to deforestation and livestock pasture, contribute to the preservation of the Amazon rainforest. This review article provides a comprehensive examination of recent studies directly related to ANF-reinforced polymer matrix composites. The specific advantages, proposed applications, and reported challenges are highlighted, shedding light on the potential of these unique natural fibers. Full article

This study investigates the biodegradation of Reactive Red 141 (RR 141), an azo dye prevalent in the textile industry, by bacteria isolated from activated sludge in a textile effluent treatment plant. RR 141, characterized by nitrogen–nitrogen double bonds (-N=N-), contributes to environmental issues when improperly disposed of in textile effluents, leading to reduced oxygen levels in water bodies, diminished sunlight penetration, and the formation of potentially carcinogenic and mutagenic aromatic amines. This research focuses on identifying bacteria from activated sludge with the potential to decolorize RR 141. Microbiological identification employs MALDI-TOF-MS, known for its precision and rapid identification of environmental bacteria, enhancing treatment efficiency. Results highlight Bacillus thuringiensis and Kosakonia radicincitans as the most promising strains for RR 141 decolorization. Analysis of micro-organisms in activated sludge and database exploration suggests a correlation between these strains and the decolorization process. It is worth noting that this is the first report on the potential use of K. radicincitans for azo dye decolorization. Three distinct culture media—BHI, MSG, and MS—were assessed to investigate their impact on RR 141 decolorization. Notably, BHI and MSG media, incorporating a carbon source, facilitated the bacterial growth of both tested species (B. thuringiensis and K. radicincitans), a phenomenon absent in the MS medium. This observation suggests that the bacteria exhibit limited capability to utilize RR 141 dye as a carbon source, pointing towards the influence of the culture medium on the discoloration process. The study evaluates performance kinetics, decolorization capacity through UV-VIS spectrophotometry, potential degradation pathways via HPLC-MS analysis, phytotoxicity, and enzymatic activity identification. B. thuringiensis and K. radicincitans exhibit potential in decolorizing RR141, with 38% and 26% removal individually in 120 h. As a consortium, they achieved 36% removal in 12 h, primarily through biosorption rather than biodegradation, as indicated by HPLC-MS analyses. In conclusion, the research emphasizes the importance of exploring bacteria from activated sludge to optimize azo dye degradation in textile effluents. B. thuringiensis and K. radicincitans emerge as promising candidates for bioremediation, and the application of MALDI-TOF-MS proves invaluable for rapid and precise bacteria identification. Full article

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight’s remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. Full article

Protein engineering is the design and modification of protein structures to optimize their functions or create novel functionalities for applications in biotechnology, medicine or industry. It represents an essential scientific solution for many of the environmental and societal challenges ahead of us, such as polymer degradation. Unlike traditional chemical methods, enzyme-mediated degradation is selective and environmentally friendly and requires milder conditions. Computational methods will play a critical role in developing such solutions by enabling more efficient bioprospecting of natural polymer-degrading enzymes. They provide structural information, generate mechanistic studies, and formulate new hypotheses, facilitating the modeling and modification of these biocatalysts through enzyme engineering. The recent development of pluriZymes constitutes an example, providing a rational mechanism to integrate different biochemical processes into one single enzyme. In this review, we summarize our recent efforts in this line and introduce our early work towards polymer degradation using a pluriZyme-like technology, including our latest development in PET nanoparticle degradation. Moreover, we provide a comprehensive recipe for developing one’s own pluriZyme so that different laboratories can experiment with them and establish new limits. With modest computational resources and with help from this review, your first pluriZyme is one step closer. Full article

This paper gives an overview of the operating characteristics of the railway interline power flow controller (RIPFC) regarding the capability of transferring active power between two sections of an electrified railway line separated by a neutral zone and proposes its use for compensating the power factor at the substation instead of regulating the voltage level at the neutral zone. The basic analysis is based on simplified steady-state models for the energy supply architecture, while detailed time-domain simulations are used for more realistic tests. The paper mainly focus on active power balancing between two neighbouring substations and the global losses in the system. Other functionalities of the RIPFC system are also analysed, like reactive power compensation at the substations. The paper presents the main operating principles of the system, shows results for some representative scenarios (generic and reduced) and discusses the results. The most relevant conclusions are related to substation active power balancing and peak shaving, power factor compensation in the substation, voltage stability at the neutral zone and system power losses. Full article

This study aims to investigate the impact of spatial rainfall distribution scenarios from ground observation stations on runoff simulation using hydrological modeling specific to the Rainfall-Runoff-Inundation (RRI) model. The RRI model was applied with six different spatial distribution scenarios of input rainfall, including Inverse Distance Weight (IDW), Thiessen polygon (TSP), Surface Polynomial (SPL), Simple kriging (SKG), and Ordinary kriging (OKG), to simulate the runoff of a 13,000 km² watershed, namely the Nan River Basin in Thailand. This study utilized data from the 2014 storm event, incorporating temporal information from 28 rainfall stations to estimate rainfall in the spatial distribution scenarios. The six statistics, Volume Bias, Peak Bias, Root Mean Square Error, Correlation, and Mean Bias, were used to determine the accuracy of the estimated rainfall and runoff. Overall, the Simple kriging (SKG) method outperformed the other scenarios based on the statistical values to validate with measured rainfall data. Similarly, SKG demonstrated the closest match between simulated and observed runoff, achieving the highest correlation (0.803), the lowest Root Mean Square Error (164.48 cms), and high Nash-Sutcliffe Efficiency coefficient (0.499) values. This research underscores the practical significance of spatial interpolation methods, such as SKG, in combination with digital elevation models (DEMs) and landuse/soil type datasets, in delivering reliable runoff simulations considering the RRI model on the river basin scale. Full article

Transformers are the cornerstone of natural language processing and other much more complicated sequential modelling tasks. The training of these models, however, requires an enormous number of computations, with substantial economic and environmental impacts. An accurate estimation of the computational complexity of training would allow us to be aware in advance about the associated latency and energy consumption. Furthermore, with the advent of forward learning workloads, an estimation of the computational complexity of such neural network topologies is required in order to reliably compare backpropagation with these advanced learning procedures. This work describes a mathematical approach, independent from the deployment on a specific target, for estimating the complexity of training a transformer model. Hence, the equations used during backpropagation and forward learning algorithms are derived for each layer and their complexity is expressed in the form of MACCs and FLOPs. By adding all of these together accordingly to their embodiment into a complete topology and the learning rule taken into account, the total complexity of the desired transformer workload can be estimated. Full article

The three-dimensional (3D) product model has become a tool that has transitioned from a legacy instrument, used in design, to an emerging technology applied to production and assembly processes. As this evolution has occurred, the need has developed to understand the value of deploying the 3D product model beyond the design phase. This research answers the question and solves the problem, does electronic documentation inclusive of the 3D product model add to the production workers’ ability to complete the production task? To answer this question, the methods used were that the research team tested how accurately and quickly a production and assembly team could build the product using interactive, electronic documentation, including the 3D product model, as a means to understand the design intent as opposed to printed bills of materials (BOMs) and two-dimensional (2D) paper drawings. The conclusions that can be drawn from this research are that the research found statistically significant improvements in the production throughput time (~10%), reductions in the direct labor hours per unit (~14%), and retained quality levels, when deploying electronic documentation, including the 3D product model, into the production and assembly processes. Through the deployment of the interactive 3D product model electronic documentation to the production floor, the organization also took a step towards creating a digital twin of the produced product and laid a foundation for the further adoption of Industry 4.0 practices. The novelty of the work and the areas where it goes beyond previous efforts in the literature concerns the current body of knowledge that does not demonstrate a repeatable methodology through which industry and other researchers can replicate the experiment on demonstrating economic value when deploying the 3D product model to production and assembly processes. In this paper, the authors aim to build on prior work to demonstrate a repeatable methodology for determining the economic value of 3D product model deployment in production and assembly processes through applied research. Full article

The sigma-delta modulator (SDM) is one of the well-established data converter architectures. It is well-known for achieving a high signal-to-noise ratio (SNR). In the SDM, the integrators in the loop filter could suffer from overloading if the signal swing exceeds its maximum level, which leads to performance and SNR degradation. Thus, scaling the system coefficients is needed, such that there is no overloading for the integrators. In this work, we present a systematic general method that could be used for scaling the signal swings in the continuous-time low-pass sigma-delta modulator (SDM). The proposed method can be applied to any continuous-time low-pass SDM architecture, and it includes the scaling of all the possible combinations of the system coefficients. Moreover, an open-source Simulink-based toolbox that includes the systematic method is presented. This toolbox could help the designer to execute the scaling process and the simulations in an efficient way. In addition to that, a design example is discussed to illustrate the proposed method, wherein the presented toolbox is used for simulations, and the simulation results are shown. Full article

Consuming fruits and vegetables necessitates discarding the inedible parts, which raises issues such as waste management and contamination of the environment. Numerous studies have been conducted in recent years in an effort to identify alternatives that reduce the negative effects of food and agricultural waste. This study aims to investigate the polyphenol recovery and the antioxidant capacity of overripe banana peel through cloud point extraction (CPE), which is a green method. The optimal conditions of the CPE were three steps of CPE with 10% w/v lecithin, which was used as surfactant, pH 7, and a salt concentration of 15% at 45 °C for 20 min. The total polyphenol content (TPC) was determined to be 541.25 mg GAE/L, the total flavonoid content (TFC) was determined to be 226.38 mg RtE/L, and FRAP and DPPH assays were found to be 2.52 and 2.91 mmol AAE/L, respectively. According to the results, the antioxidant compounds from banana peels can effectively be extracted with the proposed CPE procedure. The as-prepared extracts can potentially be used as food additives to improve human well-being and even as feed additives for a similar purpose. Full article

The digital integrated circuit (IC) testing model module is applied in this study to simulate the fabrication and testing of integrated circuits. The yield and quality of ICs are analyzed by assuming that the wafer devices under test conditions are normal probability distributions. The difficulties of testing and verification become increasingly great as the design function of the chip becomes remarkably complex. Conversely, the automotive industry chip supply chain has been substantially affected since the COVID-19 outbreak. The shortage of chips in the auto-market has always existed; therefore, increasing available chips under a limited production capacity has become a top priority. Therefore, this study applies the digital integrated circuit testing model (DITM) and proposes a retest plan. This method does not require considerable time to collect large wafer data, nor does it require additional hardware equipment. Furthermore, the required test quality parameters are set, and the test is repeated on the device by adjusting the test guardband (TGB). Moreover, three retesting schemes are proposed to improve the IC test quality (Y_q) and test yield (Y_t) to meet the requirements of consumers for product quality. A set of 2021 IEEE International Roadmap for Devices and Systems (IRDS) parameters is used to demonstrate the three proposed retesting schemes. The simulation results from the 2021 IRDS data prove that the retest method can effectively improve the test yield (Y_t). A comparison of the estimated results of the three retest methods shows that using the repeat test method can maximize the test yield without sacrificing the test quality (Y_q). By contrast, repeat testing can indeed improve the test yield (Y_t) by 14% or more. Moreover, the increase in sellable ICs not only increases additional earnings for corporations, but also alleviates the current global shortage of automotive ICs. Full article

Hoof and leg problems in racehorses can cause serious injuries and decrease their value. Although therapeutic shoeing using special horseshoes can increase the effectiveness of veterinary care, it is labor-intensive and burdensome for farriers. A three-dimensional (3D) printed horseshoe fabricated by additive manufacturing has high design flexibility for use in special horseshoes. However, the mechanical properties of 3D printed plastics for use as horseshoes remain unclear. In this study, a proposed 3D printed plastic was subjected to degradation tests under the simulated equine growth environment, and changes in strength during the period of use were investigated. It was found that the strength of polylactic acid and polycarbonate, which are commonly used for 3D printing, was not significantly affected by the environment. Full article

Previous studies have demonstrated that the inclusion of tire-derived aggregate (TDA) enhances the damping, ductility, and toughness of concrete mixtures. The effectiveness of tire-derived aggregate as a ductile material with a higher damping ratio and lower density in buckling-restrained braces has been examined at California State University’s Structures Laboratory (CSU). Through experimental and theoretical investigations, this study compares the structural application of buckling-restrained braces with TDA and with conventional concrete infill subjected to various ground motions as well as artificial excitations. The evaluations include modeling a full-scale experimental setup equipped with a single-leg BRB utilizing ETABS 2016 and OpenSees 2000 software. The effectiveness of the application is demonstrated through a comparison of accelerations, displacements, stiffness, and damping ratios between TDA and concrete filling. Additionally, a design guideline for TDA-filled buckling-restrained braced frames is provided. Full article

Underground coal gasification (UCG) is the process of directly recovering energy as combustible gases such as hydrogen and carbon monoxide by combusting unmined coal resources in situ. The UCG process is an invisible phenomenon, in which fracturing activity at high temperature (>1000 °C) in coal seams expands the gasification zone and increases the combustible components of the product gas. However, excessive expansion of the gasification zone may cause environmental problems such as gas leakage, deformation of the surrounding ground, and groundwater pollution. Therefore, visualization of the gasification zone of UCG is required for both improving gasification efficiency and developing UCG systems with low environmental impact. In this study, the large-scale model UCG experiments conducted on a laboratory scale (size: 625 mm × 650 mm × 2792 mm (H × W × L)) were carried out to discuss the visualization of the gasification reaction zone of coal in UCG by Acoustic Emission (AE) technique with uniaxial and triaxial acceleration transducers. As the results of temperature monitoring and AE source location analysis show, AE sources are located near the high-temperature zone (>1000 °C). In addition, the located AE sources move and expand with the movement and expansion of the high-temperature zone. AE measurement can be a useful technique for monitoring the progress of the UCG reaction zone. AE measurement with triaxial sensors is also useful to predict a high-temperature zone though the measurable range, which has to be considered. Full article

Collaborative robots have experienced low acceptance in applications, especially in industry. This fact has attracted the attention of researchers and practitioners, who point to different causes for this limited acceptance. One of the main reasons is the difficulty in converging on suitable methods for modeling collaborative interactions between robots and their surrounding context during the requirements phase. These interactions must be elicited and modeled during the requirements stage to maximize value creation through collaboration. Formal verification is necessary, taking into account the risks of human-robot interaction. However, such modeling is often absent in collaborative robot design, and choosing an appropriate approach remains an open problem. This paper addresses this problem using a model-based requirements cycle where the value creation is detached to provide direct analysis, possible optimization, and formal verification. The general process integrates with the general model-based requirements engineering of the remaining system. This service system approach relies on a goal-oriented requirements approach, and specific algorithms were developed to transfer goal-oriented diagrams into Petri Nets—to provide formal process verification. A case study illustrates the application of the proposed method on a collaborative robot used in a university hospital environment. Full article