Eng, Volume 5, Issue 1 (March 2024) – 28 articles

Ubiquitous street lighting is essential for urban areas. While nowadays, LED-based “smart lamps” are commercially available, municipalities can only switch to them in the long run due to financial constraints. Especially, older types of lamps require frequent bulb replacements to maintain the lighting infrastructure’s function. To speed up the detection of defects and enable better planning, a non-invasively retrofittable IoT sensor solution is proposed that monitors lamps for defects via visible light sensors, communicates measurement data wirelessly to a central location via LoRaWAN, and processes and visualizes the resulting information centrally. The sensor nodes are capable of automatically adjusting to shifting day- and nighttimes thanks to a second sensor monitoring ambient light. The work specifically addresses aspects of energy efficiency essential to the battery-powered operation of the sensor nodes. Besides design considerations and implementation details, the paper also summarizes the experimental validation of the system by way of an extensive field trial and expounds upon further experiences from it. Full article

In order to successfully achieve mass production in NAND flash memory, a novel test procedure has been proposed to electrically detect and screen the channel hole defects, such as Not-Open, Bowing, and Bending, which are unique in high-density 3D NAND flash memory. Since channel hole defects lead to catastrophic failure (i.e., malfunction of basic NAND operations), detecting and screening defects in advance is one of the key challenges of guaranteeing the quality of flash products in the NAND manufacturing process. Based on analysis of the physical and electrical mechanisms of the channel hole defect, we have developed a two-step test procedure that consists of pattern-based and stress-based screen methodologies. By optimizing test patterns depending on the type of defect, the pattern-based screen is effective for detecting the type of Hard channel hole defects. The stress-based screen is carefully implemented to detect hidden Soft channel hole defects without degrading the reliability of NAND flash memory. In addition, we have attempted to further optimize the current version of our technique to minimize test time overhead, thus enabling 72.2% improvement in total test time. Experimental results using real 160 3D NAND flash chips show that our technique can efficiently detect and screen out various types of channel hole defects with minimum test time and negligible degradation in the flash reliability. Full article

The revised Energy Performance of Buildings Directive (EPBD) recognizes nearly zero-energy buildings (nZEB) and building renovation as essential steps in the decarbonization of the built environment. A thorough understanding of existing buildings is a prerequisite for improving their thermal performance and ensuring that interventions are based on pre-existing conditions. This study investigates the use of thermography as a tool for identifying construction solutions and assessing the thermal performance of buildings. Initially, it addresses the benefits and limitations of this technique, as well as some results of a qualitative analysis and standard application of this technology. Specific conditions for capturing images on-site were identified, along with the relevant factors for interpreting thermograms under natural conditions. These images enabled the identification of previous works, changes in buildings, and the use of different materials and construction techniques, thereby contributing to the characterization of buildings. Consequently, they can be used in the diagnostic phase to enhance the accuracy of intervention solutions based on a better understanding of existing conditions. Full article

Samples of Sr₄Al₆O₁₂SO₄ are obtained through a solid-state reaction of Al₂O₃, SrSO₄, and SrCO₃. The samples are then made into 1 and 4 cm pellets by compacting them at 100MPa and sintering them at 1400 °C for 4 h. The compound is analyzed using X-ray diffraction. Static immersion and wettability tests are carried out to evaluate corrosion resistance in contact with Al–Si. Corrosion tests are conducted by immersing the samples at 800, 900, and 1000 °C for 24, 50, and 100 h, while wettability is studied at 900, 1000, and 1100 °C for 2 h. Afterwards, the samples are subject to metallographic preparation. The samples are then analyzed using optical microscopy, scanning electron microscopy, and image analysis. In general, reaction products consisting of alumina, spinel, oxides, and sulfates are found. The contact angles obtained are between 124° and 135°. It is concluded that the Sr₄Al₆O₁₂SO₄ ceramic substrate is resistant to corrosion by the Al–Si alloy because of the slight thickness of the reaction products found in the samples (73 μm), considering the severe conditions of the experiment: 1000 °C and 100 h of isothermal temperature. Furthermore, Sr₄Al₆O₁₂SO₄ is not wettable by Al–Si alloys. These results suggest that the ceramic substrate could be used in the refractory industry, possibly as an additive to commercial refractory ceramics. For future work, it is recommended to carry out the same study with the aluminum–magnesium alloy and as an additive in commercial refractory ceramics. Full article

This paper considers the application of a scenario simulation technique to assess subsurface drip irrigation system efficiency while using it to irrigate crops grown under raised bed technology. For simulating purposes, we use a model based on the two-dimensional Richards equation stated in terms of water head in a curvilinear domain. Solutions to problems are obtained using a finite-difference scheme with dynamic time step change. Using the data from pressure measurements obtained while growing potatoes on sandy loess soil in production conditions, we performed a calibration of the model using the particle swarm optimization algorithm. Further, the accuracy of the model was tested and average absolute errors in the range from 3.16 to 5.29 kPa were obtained. Having a calibrated model, we performed a series of simulations with different irrigation pipeline placements determining the configuration under which water losses are minimal. The simulated configuration, under which infiltration losses were minimal, was the installation of pipelines under the raised bed at the depth of 10 cm below the soil surface. The results confirm that the applied technique can be used for decision-making support while designing subsurface drip irrigation systems combined with raised bed growing technology. Full article

Random Numbers are widely employed in cryptography and security applications. This paper presents a novel approach to generate high-quality random bitstreams by harnessing the inherent noise properties of ring oscillators. We implemented ring oscillators with varying numbers of stages (3, 5, and 7), different geometries and different startup voltages in Cadence and recorded their total output power, which includes the cumulative noise effects. Subsequently, we exported these power measurements to MATLAB, where we applied a Fast Fourier Transform (FFT)-based technique to extract the total noise characteristics for each ring oscillator. Using the obtained noise data, we generated separate random bitstreams of 10 million bits for the 3-stage, 5-stage, and 7-stage ring oscillators. The final random bitstream, consisting of 10 million bits, was created by performing a bitwise XOR operation on the bitstreams generated by each ring oscillator. The degree of randomness of the generated bitstreams was assessed using the NIST 800-22 statistical test suite. Remarkably, the final random bitstream exhibited strong robustness and suitability for cryptographic applications. This innovative approach leverages the noise properties of ring oscillators to create reliable random bitstreams, offering potential applications in secure communications and cryptography. The results highlight the feasibility of using ring oscillators as noise sources for random bit generation and underscore their effectiveness in meeting stringent randomness criteria. Full article

The main aim of this study was to identify and evaluate the characteristic properties of bitumen modified with algae. Two types of algae, each with distinct gradation and origin, were employed for this investigation. For each type of algae (noted as chlorella and microchlorella), three blends were created with varying algae contents (5%, 10%, and 15% by weight of bitumen), utilizing a 70/100 reference bitumen as the virgin material and a basis for comparison. The properties of the blends were investigated using the Penetration, Softening Point, Elastic Recovery, Force Ductility, Dynamic Viscosity, and Storage Stability tests, both before and after short-term ageing (TFOT). The test results were then used to calculate the Activation Energy (E_a), Viscosity-Temperature Susceptibility (VTS) Index, and Mixing Temperature (T_mixing), along with their respective Pearson Correlation Coefficient (PCC) and R² and p-values. The main finding of the study was that the addition of a low algae content of 5% caused a change in the classification of the unaged bitumen from 70/100 to 50/70 according to EN 12591 and thus hardened the reference bitumen. Additionally, a strong linear statistical correlation was observed between E_a and the VTS index, suggesting that these values should be considered when characterizing the temperature susceptibility of algae-modified bitumen. Full article

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners. Full article

Structures are recommended to be designed and constructed with the integration of structural health monitoring techniques to ensure that they can dissipate a large amount of energy without considerable damage when subjected to earthquakes. Hysteretic (H), friction (F), viscous (V), and viscoelastic (VE) dampers were employed in this study to observe the response of buildings using the commercially available software ETABS. The effect of different dampers along with configurations on three prototype concrete buildings (3, 5, and 10-storey) was studied by performing a time history analysis. Initially, the response of the buildings was observed in terms of storey drifts, base shear, and displacement without using dampers, while gradually increasing the damping ratio from 0 to 40%. Subsequently, the response of the buildings was evaluated in terms of displacements and base shear using various types of dampers with different configurations. The analysis results demonstrated that the effectiveness of viscous and viscoelastic dampers is higher for 3 and 5-storey buildings, while friction and hysteresis dampers are more suitable for 10-storey buildings. This information enables informed decisions regarding the performance and maintenance of dampers, contributing to the overall resilience and durability of structures in seismic events. Full article

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury. Full article

Based on a very fast numerical procedure for the determination of the subsurface stress field beneath frictional contacts of axisymmetric elastic bodies under arbitrary 2D oblique loading, the contact mechanical influences of loading parameters and contact profile geometry on the Smith–Watson–Topper (SWT) fatigue crack initiation parameter in elastic fretting contacts with superimposed normal and tangential oscillations are studied in detail. The efficiency of the stress calculation allows for a comprehensive physical analysis of the multi-dimensional parameter space of influencing variables. It is found that a superimposed normal oscillation of the contact can significantly increase or decrease the SWT parameter, depending on the initial phase difference and frequency ratio between the normal and tangential oscillation. Written in proper non-dimensional variables, the rounded flat punch always exhibits smaller values of the SWT parameter, compared to a full paraboloid with the same curvature, while the truncated paraboloid exhibits larger values. A small superimposed profile waviness also significantly increased or decreased the SWT parameter, depending on the amplitude and wave length of the waviness. While both the load protocol and the profile geometry can significantly alter the SWT parameter, the coupling between both influencing factors is weak. Full article

This work explains that the Coulomb elastic process on the nucleus is a major source of single-event effects (SEE) for protons within the energy range of 1–10 MeV. The infinite range of Coulomb interactions implies an exceptionally high recoil probability. This research seeks to extend the investigations under which the elastic process becomes significant in the energy deposition process by providing a simplified methodology to evaluate the elastic contribution impact on the reliability of electronics. The goal is to derive a method to provide a simple way to calculate and predict the SEE cross-section. At very low energy, we observe a significant increase in the proton differential cross-section. The use of a direct Monte Carlo approach would mainly trigger low energy recoiling ions, and a very long calculation time would be necessary to observe the tail of the spectrum. In this sense, this work provides a simple methodology to calculate the SEE cross-section. The single-event upset (SEU) cross-section results demonstrate a good agreement with the experimental data in terms of shape and order of magnitude for different technological nodes. Full article

One major challenge of today’s product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled. Full article

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. Full article

The aim of this study is to examine the effect of the partial replacement of sand by Glass Fiber-Reinforced Polymer (GFRP) waste on the mechanical properties of cement mortars. Compressive and flexural tests were carried out on mortars containing 0, 3, 5, 10, and 15% (by volume) of GFRP waste. It appears that the incorporation of 3% GFRP waste did not significantly affect the mechanical strength. However, further increasing the GFRP waste content led to a reduction in the mechanical strength. The flexural strength seemed less affected than the compressive strength, since the decrease in flexural strength at a 10% replacement was only 37%, while it was 54% for the compressive strength. However, an improvement in the toughness of the mortar with an increase in the substitution rate was observed. The reference sample displayed a flexural toughness of 0.351 N·m, while the mortar incorporating 15% of GFRP exhibited a flexural toughness of 0.642 N·m. The reuse of GFRP waste in cementitious materials, therefore, constitutes an interesting recycling solution. Full article

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques. Full article

At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends. Full article

Urban drainage and waterlogging prevention are critical components of urban water management systems, as they help to mitigate the risks of flooding and water damage in cities. The accurate collection of liquid level and flow rate data at the end of these systems is crucial for their effective monitoring and management. However, existing water equipment for this purpose has several shortcomings, including limited accuracy, inflexibility, and difficulty in operation under specific working conditions. A new type of multi-parameter flexible smart water gauge was developed to address these issues. This technology uses underwater simulation robot technology and is designed to overcome the deficiencies of existing water equipment. The flexibility of the gauge allows it to be adapted to different working conditions, ensuring accurate data collection even in challenging environments. The accuracy of the new water gauge was tested through a series of experiments, and the results showed that it was highly accurate in measuring both liquid level and flow rate. This new technology has the potential to be a key tool in smart water conservancy, enabling the more efficient and accurate monitoring of water levels and flow rates. By providing a new solution to the problem of collecting terminal equipment for urban drainage and waterlogging prevention, this technology can help to improve the resilience and sustainability of urban water management systems. Full article

Acceptable zircon for composite formulation in the aerospace industry requires that the mineral contains a minimum of 65% zirconia (ZrO₂). Despite having vast deposits of zircon, Nigeria’s aerospace industry has historically relied primarily on imported mild steel tubes for solid rocket motor cases (SRMCs) construction, resulting in three major challenges: low strength-to-weight ratio, pressure, and temperature containment. In this study, the Arikya zircon deposit located in northern Nigeria was investigated with the aim of upgrading low-grade zircon ore using magnetic and gravity separation processes for use in composite formulation for SRMCs. The dry high-intensity magnetic separator (DHIMS) produced a ZrO₂ grade of 52.48%, recovery of 57.99%, and an enrichment ratio of 0.78 with a separation efficiency of 0.56, while the air-floating separator (AFS) generated the highest of 65.52% ZrO₂ grade with 70.81% recovery and enrichment ratio of 1.25 with a separation efficiency of 0.25. The ZrO₂ content increased from 40.77 to 65.52% after beneficiation. Iron oxide and titanium dioxide contaminants at 0.73 and 0.83% were reduced to 0.66 and 0.54%, respectively, while the specific gravity increased from 4.4 to 4.6 g/cm³. The ZrO₂ content and specific gravity were improved to the minimum standard specified for zirconia-reinforced composite application and competed effectively with industrially/globally accepted zircon. These results demonstrated the efficacy of combining DHIMS and AFS to upgrade the low-grade zircon ore from Arikya, Nasarawa State. Full article

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