This study investigates the optimal energy mix for industrial complexes by integrating renewable energy systems, decarbonization strategies, and sector coupling technologies, using actual load data from the Balan Industrial Complex in Korea. Five different energy scenarios were analyzed, ranging from a conventional base case (Scenario 1) to progressively more advanced configurations incorporating renewable sources (Scenarios 2 to 5). The findings reveal that Scenario 5, which combines sector coupling, hydrogen-based systems, and decarbonization technologies, is the most cost-effective and environmentally sustainable, achieving the lowest Net Present Cost (NPC) and significantly reducing CO2 emissions. To enhance the study’s relevance, electricity prices and CO2 emission costs from Korea, the United States, and Germany were considered, revealing that regional electricity tariffs and carbon pricing mechanisms greatly influence the economic feasibility of the proposed energy systems. Despite the higher initial investment for renewable-based setups, Scenario 5 proved to be the most economically viable over the long term when factoring in operational costs and emission penalties, underlining the potential for renewable integration to achieve both cost savings and environmental benefits. These results provide valuable insights for policymakers and industry leaders aiming to adopt sustainable, cost-effective energy solutions, highlighting the necessity of tailored strategies to optimize energy systems for industrial applications. This research thus offers a meaningful contribution to the field of sustainable energy transitions, suggesting that sector coupling and renewable integration hold significant promise for enhancing the efficiency and sustainability of energy systems in diverse industrial areas.

This study proposes a simplified and fully renewable energy system, composed of two intermittent energy sources (wind and solar) and a long-duration energy storage technology using pumped hydro storage. The impact of intermittency on the medium- and long-term design of the energy matrix is evaluated using the OSeMOSYS model. The findings indicate that omitting intermittency results in a significant underestimation of costs and an inability to manage the variability of renewable energies effectively. Incorporating intermittency, although increasing the installed capacity and the amount of wasted energy, enhances the system's reliability. The inclusion of energy storage demonstrates the need to redistribute installed capacity in favor of solar energy to meet higher daytime demand. The study concludes that considering intermittency and storage is crucial for improving the accuracy of energy models, reducing losses, and optimizing operational costs in renewable energy-based systems.

This study presents an optimized design for a Segmented Sinusoidal Parameter Winding with Magnetic Wedge Variable Reluctance Resolver (SSPWMW-VRR), addressing challenges like winding asymmetry and harmonic distortion in conventional designs. By integrating Particle Swarm Optimization (PSO) for winding design, Magnetic Equivalent Circuit (MEC) analysis for leakage flux, and machine learning techniques (XGBoost and Multi-Layer Perceptron), the stator slot shape was fine-tuned for improved accuracy. XGBoost outperformed MLP in prediction accuracy with a Mean Absolute Error (MAE) of 0.1172. Finite Element Analysis (FEA) simulations and experimental validation demonstrated a reduction in position errors from ±30’ in conventional VRRs to ±5’ in the optimized design, along with significant harmonic reduction.

Aluminum-based hybrid nanocomposites, particularly Al6061 alloy, have gained prominence in the scientific community due to their unique properties, such as high strength, low density, and good corrosion resistance. The production of these nanocomposites involves incorporating reinforcing nanoparticles into the matrix to improve its mechanical and thermal properties. Al6061 hybrid nanocomposites were manufactured using powder metallurgy. Silicon carbide (SiC) ceramic nanoparticles and carbon nanotubes (CNTs) were used as reinforcements. Characterization of the Al6061 hybrid nanocomposites involved analysis of their mechanical properties, such as hardness and tensile strength, as well as their micro and nanometric structures. Techniques such as optical microscopy (OM) and scanning electron microscopy (SEM) with electron backscatter diffraction (EBSD) were used to study the distribution of nanoparticles, the grain size of the microstructure, and the presence of defects in the matrix. The results showed that adding reinforcing nanoparticles can significantly improve the properties of Al6061, depending on the amount and type of nanoparticles. However, the agglomeration of the nanoparticles and the difficulty of uniform dispersion remain challenges in manufacturing these nanocomposites. The microstructural evaluation revealed significant grain refinement and improved homogeneity in the hybrid composites, resulting in enhanced mechanical performance. Tensile tests demonstrated that the hybrid Al6061/CNTs/SiC composite exhibited the highest tensile strength of 104 MPa, compared to 63 MPa for the unreinforced Al6061 matrix.

In an attempt to replace corroding mild steel components in aggressive mine water environ-ments, nickel-chromium-iron (Hastelloy® G30), copper-nickel-tin (ToughMet® 3), and co-balt-chromium-tungsten (Stellite® 6B) hard alloys were selected due to their excellent corrosion resistance, but they remain challenged in many acidic environments. The corrosion behaviours of these alloys were compared and investigated in synthetic mine water with varying pH levels (6, 3, 1) using potentiodynamic polarisation. Microstructural, hardness, and XRD analyses were conducted. Stellite® 6B exhibited superior corrosion resistance across all pH values, outperform-ing both Hastelloy® G30 and ToughMet® 3. With the lowest corrosion rates and comparable high hardness, Stellite® 6B emerges as a promising choice for replacing mild steel pump components in aggressive mine water environments.

In Europe, non-residential building stock built before building energy codes consumes more energy and resources than new buildings. Existing educational buildings comprise 17% of this outdated stock. These buildings can be retrofitted to create a conducive learning environment that can improve students’ comfort. Facade renovation has become the first choice for elevating the building performance of the education stock because it is less expensive than other alternatives. This study develops a methodology to optimize facade renovation solutions including: 1) preparation, 2) simulations of the simplified model using local shading, and 3) modelling realistic optimized facade design. This study evaluates visual comfort by considering multiple-dimensional metrics such as useful daylight illuminance (UDI), annual sunlight exposure (ASE), illuminance uniformity and the daylighting factor. The methodology was first applied to improve the facade proposal “Roof to Façade” in the project Waste-based Intelligent Solar Control Devices for Envelope Refurbishment (WiSeR) and simulations with the software DesignBuilder to design tile system facades. The results illustrate that implementing these solutions could efficiently improve indoor visual comfort by considering multiple daylight metrics. Moreover, for a constant gaps surface, the facade distribution with staggered gaps performs better in terms of the daylighting factor, solar gains and uniformity than more continuously connected gaps.

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided.

Single crystal nickel-based superalloys are extensively used in turbine blade applications 1 due to their superior creep resistance compared to their polycrystalline counterparts. With the high 2 creep resistance, High Cycle Fatigue (HCF) and Low Cycle Fatigue (Single crystal nickel-based 3 superalloys are extensively used in turbine blade applications due to their superior creep resistance 4 compared to polycrystalline counterparts. As a result, fatigue becomes the primary failure mechanism 5 in such applications. Specifically, High Cycle Fatigue (HCF) and Low Cycle Fatigue (LCF) are of 6 significant concern under these conditions. This study investigates the fatigue life prediction of 7 CMSX-4 using a combined crystal plasticity and lifetime assessment model. The constitutive crystal 8 plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while 9 a modified Chaboche damage model is used for lifetime assessment, incorporating anisotropy by 10 focusing on cleavage stresses on active slip planes. Both qualitative and quantitative data obtained 11 from HCF experiments on single crystal superalloys with notched geometry were used to validate 12 the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy 13 of lifetime predictions across varying temperatures by analyzing the stress-strain data obtained from 14 simulations. The integration of crystal plasticity, damage mechanics, and ANNs resulted in accurate 15 predictions of fatigue life and crack initiation points under complex loading conditions of single 16 crystal superalloys.

The present paper addresses dynamic risks in the automotive industry factories, specifically the car lead-acid batteries manufacturing area. The main analyzed risk is represented by the fire risk. Battery manufacturing process is described and analyzed from the fire risk perspective and the hazard areas are identified. The investigation methodology uses case studies for different lead-acid battery formation processes, combined with 3D simulations using PyroSym platform, and it is based on the author's experience in the battery manufacturing field for about ten years. The results of the case studies are compared using the same inputs, but different specific process conditions, and conclusions are formulated. To avoid the fires and mitigate the risk, a series of actions are proposed in the last sections. As a general conclusion, the actual research demonstrates that the complex and dynamic risks from the automotive industry, associated with Industry 5.0 must be analyzed using combined methods, classical and modern, including 3D simulations, to get great results.

This article documents the rammed earth construction practices undertaken by Wang Shu’s team during the “Tiles Hill — New Reception Center for the Xiangshan Campus” project. This building not only embodies the principle of “learning from nature” inherent in traditional vernacular construction but also profoundly interprets the concept of “the craftsmanship way”. Traditional rammed earth techniques represent a sustainable construction method, with a core focus on material composition and building techniques. Through more than a decade of continuous research, the team, combining modern technology, explored how to revive this nearly forgotten vernacular construction method in contemporary architecture. The article further highlights that the recyclability of rammed earth techniques is not only reflected in the reuse of materials but also in the material‘s inherent friendliness and non-destructive nature to the natural environment. This process showcases the fusion of traditional craftsmanship with modern construction technology, as well as the inheritance and innovation of the artisan spirit.

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand what is the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics, and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what are the currently open theoretical and numerical challenges in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided to ``model-based'' methods and ``model-free'' methods, in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles and energy storage.

This paper proposes a multifunctional soft robotic gripper for a Dobot robot to handle sensitive products. The gripper is based on pneumatic network (PneuNet) bending actuators. In this work, two different models of PneuNet actuators have been studied, designed, simulated, experimentally tested, and validated using two different techniques (3D printing and molding) and three different materials: FilaFlex (3D printed), Elastosil M4601 and Dragonskin Fast 10 silicones (with molds). A new soft gripper design for the Dobot robot is presented, and a new design/production approach with molds is proposed to obtain the gripper's PneuNet multifunctional actuators. It also describes a new control approach that is used to control the PneuNet actuators and gripper function, using compressed air generated by a small compressor/air pump, a pressure sensor, a mini valve, etc., and executing on a low-cost controller board – Arduino UNO. This paper presents the main simulation and experimental results of this research work.

Accurately estimating project duration is critical for the successful delivery of construction projects, particularly in the dynamic and resource-constrained environment of South Africa. This study aims to assess the key factors influencing construction project duration in the South African context through the development of a regression model. By analyzing data from a diverse range of construction projects, the research identifies significant variables such as project size, complexity, location, resource availability, and stakeholder involvement that contribute to delays or expedited completion. The regression model is designed to quantify the impact of these factors, offering a predictive tool that can enhance project planning and scheduling accuracy. The findings provide valuable insights for project managers, contractors, and policymakers, enabling them to make informed decisions that optimize project timelines and improve the overall efficiency of the construction industry in South Africa.

In the field of Thermal Non-Destructive Testing and Evaluation (TNDT&E), active thermography gained popularity due to its fast, wide area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression favourable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, experimental investigation has been carried out on a hardened steel sample used in ship building industry with a flat-bottom hole simulated defect using the Frequency Modulated Thermal Wave Imaging (FMTWI) technique. And the defect detection capabilities of FMTWI have been investigated from various statistical post processing approaches and compared by taking Signal to Noise Ration (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, the pulse compression has been carried out using different methods: offset removal with polynomial curve fitting, and Principal Component Analysis (PCA) which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimen used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS's 0.035 W/(m·K), leading to higher energy savings. Calcu-lations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of pol-yurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS's 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

The present study examines the relationship between thermal and configurational entropy in two resistors in parallel and in series. The objective is to introduce entropy in electric circuits analysis by considering the impact of system geometry on energy conversion in the circuit. Thermal en-tropy is derived from thermodynamics, whereas configurational entropy is derived from network modelling. It is observed that the relationship between thermal entropy and configurational en-tropy varies depending on the configuration of the resistors. In parallel resistors, thermal entropy decreases with configurational entropy, while in series resistors, the opposite is true. The impli-cations of the maximum power transfer theorem and constructal law are discussed. The entropy generation for resistors at different temperatures was evaluated, and it was found that the con-sideration of resistor configurational entropy change was necessary for consistency. Furthermore, for the sake of generalization, a similar behavior was observed in time-dependent circuits, either for resistor-capacitor circuits or circuits involving degradation.

The analysis of the liquefaction potential of the ground is a fundamental characterization in areas with continuous seismic activity, such as Ecuador. Geotechnical liquefaction studies are usually approached from the application of dynamic penetration tests, which pose problems both in their execution and in their evaluation. The proposed research involves analyzing dynamic penetration tests and microtremor geophysical surveys (Horizontal to Vertical Spectral Ratio, HVSR technique) at the base of the San Marcos dam, a reservoir destined for the irrigation water located in Cayambe (Ecuador). Based on the investigations performed at the time of construction of the dam (drilling and geophysical refraction profiles) and the application of 20 microtremor observation stations performed by the HVSR technique, an analysis of the Safety Factor of liquefaction (SFliq) has been conducted, proposed by Youd and Idriss in 2001, and based on the values of the Standard Penetration Test (SPT) applied in granular materials (sands). In addition, the vulnerability index (Kg) proposed by Nakamura in 1989 was analyzed through the HVSR passive seismic records related to the Ground Shear Strain (GSS). The results obtained in the HVSR surveys indicate the presence of a zone of about 100 m length in the central part of the foot of the dam, whose GSS values are identified with a condition of susceptibility to liquefaction. In the same area, the analysis applied from SPT essays in the P-8A drill hole executed in said area also shows a potential susceptibility to liquefaction in earthquake conditions greater than a moment magnitude (Mw) of 4.5, which could occur in the environment, for example, with a new activity condition of the nearby Cayambe volcano or even from an earthquake from the vicinity fractured zone.

Magneto Hydrodynamic (MHD) propulsion presents an innovative and environmentally friendly alternative to conventional marine propulsion systems. By utilizing electromagnetic fields to generate thrust, MHD propulsion reduces both noise and air pollution, making it an appealing option for the future of commercial shipping. However, the widespread adoption of MHD technology has been limited by several significant challenges. High energy demands, particularly for generating the necessary magnetic fields, require advanced power sources such as nuclear reactors or large-scale batteries, which are impractical for most commercial vessels. Additionally, the cost of installing and maintaining the high-powered electromagnets and energy infrastructure remains prohibitively expensive. Furthermore, material durability is another concern, as system components such as electrodes are exposed to corrosion and wear due to their interaction with seawater and high electrical currents. By addressing these constraints, MHD propulsion has the potential to transform the commercial maritime industry, contributing to more sustainable and less polluting marine transportation. This paper explores the feasibility of overcoming these challenges through advancements in cost-effective energy efficient methods. We identify the primary limiting factor for the practical implementation of magnetohydrodynamic (MHD) propulsion systems as the magnetic field strength. Drawing comparisons with similar applications, such as magnetic resonance imaging (MRI) technologies, we explore advancements in generating strong, homogenous magnetic fields over large volumes. Insights from these research areas provide a foundation for improving the scalability and efficiency of magnetic field generation in MHD systems.

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. 1 Estimating the RUL enables the assessment of health bearing, maintenance planning, and signifi- 2 cant cost reduction, thereby fostering industrial development. Existing models rely on traditional 3 feature engineering, with feature changes because operating conditions pose a major challenge to 4 the generalization of RUL prediction models. This study focuses on neural network-based feature 5 engineering and the downstream prediction of the RUL, eliminating the need for specific prior 6 knowledge and simplifying the development and maintenance of models. Initially, a convolutional 7 neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional 8 long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation 9 characteristics of the engineered features and predict the RUL through regression. Finally, the study 10 examines the influence of operating conditions in the model and integrates domain adaptation to 11 minimize differences in feature distribution, thereby enhancing the model’s generalizability for the 12 RUL prediction.

Although new industrialized construction technologies have the potential to improve industry performance by optimizing work processes and improving the working environment, the application of these technologies in the construction industry is still in its infancy, and its technical direction is not yet set. This study aims to put forward a theoretical framework for evaluating the level of new-type building industrialization of construction engineering projects, conduct integrated research on the existing domestic and foreign literatures on new-type building industrialization and intelligent construction, preliminarily construct the index system, and determine the evaluation index system of new-type building industrialization projects through the Delphi method. The index weight is determined by the combination of order relation analysis and entropy weight method. The evaluation model is constructed by matter-element theory and cloud model, and the evaluation grade standard is proposed. The research results will help guide the evaluation of new building industrialization projects, and provide a judgment reference for determining the development trend and direction of new building industrialization technologies in the future, and help formulate data-driven industrial policies to promote the adoption of new industrialization technologies and promote the digital transformation of the construction industry.

The deterministic generation of robust soliton comb has significant meaning for the optical frequency combs to be widely used in various applications. As a novel form of microcomb, Soliton crystal holds the advantages of easy generation, high conversion efficiency, and excellent thermal robustness. Here, we report the turnkey deterministic generation of ‘Palm-like’ soliton crystal with a free-running scheme. The robustness of the turnkey soliton crystal generation is also investigated in multiple aspects, including the success rate, the thermal robustness, and the long-term stability. The experiment results reveal our turnkey soliton crystal can achieve nearly a 100% success rate with a power variation less than ±1.5 dB over one hundred trials of two samples, is insensitive to thermal effects, and is robust to the environment during four-hour laboratory time.

Wind-solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out that was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers, which locally perform the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multiphysical systems.

This paper investigates the leader-following H∞ consensus of fractional-order multi-agent systems (FOMASs) under input saturation via the output feedback. Based on the bounded real lemma for FOSs, the sufficient conditions of H∞ consensus for FOMASs are provided in α∈0,1 and 1,2, respectively. Furthermore, the iterative linear matrix inequalities (ILMIs) approaches are applied for solving quadratic matrix inequalities (QMIs). The ILMI algorithms show a method to derive initial values and transform QMIs into LMIs. Mathematical tools are employed to transform the input saturation issue into optimal solution of LMIs for estimating stable regions. The ILMI algorithms avoid the conditional constraints on matrix variables during the LMIs construction and reduce conservatism. The approach does not disassemble the entire MASs by transformations to the Laplacian matrix, instead adopting a holistic analytical perspective to obtain gain matrices. In the end, numerical examples are conducted to validate the efficiency of the approach.

In this paper, a passive wireless sensor is designed and developed in terms of an enhanced multiple bit application. The proposed sensor is in the formula of UWB working at 3.1GHz~10.6GHz band, and the carrying of multiple bits information is realized by the combination of multiple sensor units operated in the principle of Spectral-Temporal Joint Coding and Modulation. A prototype of the proposed sensor is configured by four UWB sensor units, each is specified by modulation upon the UWB pulse in time domain and, simultaneously, upon the UWB spectrum in frequency domain, jointly coded digitally by 1/0 data delivering totally the enhanced digital capacity up to 8 bits. Simulation and measurement verify the validation of the UWB sensor with effectiveness of data delivery in the scenarios where remote, contactless and battery-free applications are necessary.

Hydrogen combustion in internal combustion engines is a strongly awaited technology on the way to carbon neutrality, but still with major challenges to takle. There are plenty of combustion anomalies like preignition, backfire, knock and lube oil ignition that come along with the technology, hence not yet controllable. To gain more knowledge about their origin, optical engines and CFD simulations are well established methods to investigate and develop knew combustion processes. Hydrogen’s wide flammability range and its short quenching distance bring new challenges. However, optical engines give the best insight into the real combustion behavior. Therefore, their understanding and proper modeling in the simulation is of great importance to calibrate the simulation models accurately. For optical engines there are diverging opinions on how to deal with their blow-by losses. Due to their lack of oil different sealing mechanisms exist and those seem to be crucial for the question on how or wether the blow-by has to be modelled. proposed a numerical multi-zone blow-by model whereas the opinion at is to neglect those losses.

Solar electric and solar thermal energies are often considered as part of the solution to the current energy emergency. In the present work, a study of the characteristic length of tubes in flat photovoltaic-thermal (PVT) hybrid solar devices has been carried out using three heat transfer fluids: water, Emim-Ac ionic liquid and ionanofluid of graphene nanoparticles suspended in the above ionic liquid. Experimental measurements and numerical simulations have been performed using the HEATT® platform. Slower fluid velocities are recommended if high fluid outlet temperatures are the goal of the application, but this penalizes the overall thermal energy production. The characteristic process length, or most efficient distance at which heat transfer occurs, is not typically achieved in parallel tube PVT collectors; coil-type panels can improve the thermal and temperature performance of INFs in PVT solar collectors depending on the application.

In the realm of digitalization, data play a crucial role across various scientific fields, from physics to engineering. Analyzing data with advanced technologies like artificial intelligence enhances en-gineers' ability to make informed judgments on product quality. This study aligns with the digital-ization trend by implementing formula generation, generalization, and optimization using genetic algorithms and three machine learning approaches: support vector regression, Gaussian process regression, and artificial neural networks, on a grinding process dataset from a previous study on UNS S34700 steel. The study introduces new approaches by considering three different grinding wheels and four cooling and lubrication solutions as influential process parameters, previously considered qualitative and fixed in earlier literature. Additionally, seven different depths of cut were implemented to measure surface roughness and grinding forces (tangential and normal). Qualitative data were encoded into quantitative data using standard techniques. By enhancing genetic algorithm optimization, a relational formula with 25 coefficients was developed to describe the relationship between input parameters and outputs. Three machine learning algorithms were evaluated for predicting the relationship between predictors and responses. The flexibility and scalability of these methods, combined with the relational formula, provide a sophisticated ap-proach for future expansions. The formula evaluation achieved around 85% accuracy for surface roughness and 90% for grinding forces. In machine learning approaches, the GPR method reached model stability with an R² of around 0.98 and a mean accuracy of 93%, while the ANN reached an R² of around 0.96 and a mean accuracy of 90%. This shows that machine learning approaches are more suitable than formula generalization. The scalability and flexibility of machine learning help generalize with more data variety, while the formula evaluation only accepted current data sets. Furthermore, ML methods achieve high accuracy and are suitable for predicting future data, aligning with digitalization and trend prediction.

This work investigates mixed convection flow over a vertical thin needle with variable surface heat, mass, and microbial flux, incorporating the influence of gyrotactic microorganisms. The governing partial differential equations are transformed into ordinary differential equations using appropriate similarity transformations. These transformed equations are then numerically solved employing MATLAB's Bvp4c solver. The study's primary focus lies in examining the influence of various dimensionless parameters, including the mixed convection parameter, power-law index, buoyancy parameters, bioconvection parameters, and needle size parameter, on the velocity, temperature, concentration, and microbe profiles. The results demonstrate that these parameters significantly impact surface (wall) temperature, surface fluid concentration, surface motile microbe concentration, and the corresponding velocity, temperature, concentration, and microorganism profiles. The study's findings offer valuable insights into the intricate dynamics of mixed convection flow with bioconvection and have potential applications in diverse fields such as biomedicine and engineering.

This study presents a synthesizer topology based on a DLL and programmable frequency multiplier for 5G+ communication systems. The proposed synthesizer comprises a 512-phase DLL, an intermediate frequency generator (IFG), and an RF frequency multiplier (RFFM). The 512-phase DLL provides 512 delayed pulses through a chain of 256 delay units and single-to-differential complementary converters (S2DCs). The IFG comprises I/Q-multiplexers, I/Q-accumulators, an XOR, and an S2DC. The I/Q-multiplexer outputs switch to the phase lag or lead waveforms at every rising or falling edge of the outputs, which makes the I/Q-multiplexer output frequency, fMX, programmable. The IF, fIF, is two times fMX, and fIF is up-converted to RF, fRF, through the RFFM. When reference clock frequency, fref is 156.25 MHz, the fIF range is 156.863–312.5 MHz and the fRF dynamic range is approximately 1.89–9.96 GHz. The channel resolution range is 3.698–38.609 MHz. Consequently, the proposed synthesizer provides a wide 134 % output frequency bandwidth and a finer channel resolution smaller than fref. The presented synthesizer is fabricated in a 65-nm CMOS process. The total power consumption is 15 mW, and the rms jitter integrated from 12 kHz to 20 MHz measured as 83 fs.

Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels. As a part of the sustainable planning and effective management of water resources, it is crucial to assess the existing as well as future groundwater level (GWL) condition. In the current study, an attempt was made to model and forecast GWL using artificial neural networks (ANN) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA incorporating exogenous variables (ARIMAX) were adopted as the time series models. Kushtia district in Bangladesh was selected as the case study area, and GWL data of five monitoring wells in the study are used to demonstrate the modeling exercise. Rainfall was taken as the exogeneous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. The performance of each time series and ANN model was assessed based on various model evaluation criteria. It was evident from the results that the multivariate ARIMAX model (SSE of 15.361) performed better than the univariate ARIMA model with an SSE of 17.217 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the time series and ANN models, it was found that the ANN-based model outperformed the time series models with the enhanced forecasting accuracy (SSE of 9.894). Results also exhibit a significant correlation coefficient value R of 0.995 (ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Thus, the ANN approach was not only carried out for model building and simulation but also to provide a valuable tool for managing water resources amidst changing environmental conditions.

Zr-2.5%Nb Pressure tubes (PT) house uranium fuel bundles in the fuel channels of CANDU® nuclear reactors. Preventing a failure mode caused by contact of the PT with an outer calandria tube (CT) is performed by inspection using eddy current (EC) testing and ultrasonic testing (UT) to measure the PT-CT gap. EC gap measurements are particularly sensitive to circumferential variation of the PT’s electrical resistivity, due to microstructural variations. A full-factorial experiment was performed to examine the statistical significance of variations in EC test parameters and manufacturing conditions on average circumferential electrical resistivity of as-manufactured PTs. It was found that 79% of the variance in the data could be attributed to variations caused by any of the test factors or combinations of test factors. The parameters that accounted for the majority of the variance were; 1) heat treatment (HT); 2) HT and EC frequency; 3) probe inner or outer surface placement; and 4) EC frequency. Measurements of circumferential resistivity showed up to ± 2.3% variation from the average of either surface. HT caused average PT resistivity to decrease at a rate of 1.53±0.08 (μΩ⋅cm)/log⁡(hr) and 1.1±0.4 (μΩ⋅cm)/log⁡(hr) for inner and outer PT surfaces, respectively. Results are correlated with literature reported differences in average β_Zr ribbon thickness in the axial-transverse cross-section between inner and outer PT surfaces. Results demonstrate potential for EC based resistivity measurements to characterize variations and changes in microstructure of Zr-2.5%Nb PT material.

We developed and applied a new calibration method to make the measurement more accurate with our multi-color ellipsometric mapping tool from cheap parts. Ellipsometry is an optical technique that measures the relative change in polarization state of the measurement beam induced by reflection from or transmission though a sample. During the conventional ellipsometric measurement, the data collection is relatively slower and measures one spot at a time, so mapping needs long time compared with our new optical mapping equipment made by an ordinary color LED monitor and a polarization sensitive camera. The angle-of-incidence and the incident polarization state is varied point-by-point so a special optical calibration method is needed. Three SiO2 samples with different thicknesses were used to determine point-by-point the angle-of-incidence and rho corrections. After the calibration, another SiO2 sample was measured and analyzed using the calibrated corrections and this sample further was independently measured using a conventional spectroscopic ellipsometer. The difference between the two measured thickness maps is less than 1 nm. Our optical mapping tool from cheap parts is faster and covers wider area samples in relative to the conventional ellipsometers and these correction enhancements further signify its performance.

Cyclic straining simulations using incompatibility-incorporated crystal plasticity-FEM, which exhibit PSB ladder structure evolutions as detailed in Part I, are coupled with diffusion analyses of produced vacancies. We introduce a new vacancy source model based on the Field Theory of Multiscale Plasticity (FTMP), interpreting the relationship between the incompatibility rate and the flux of dislocation density as edge dipole annihilation processes. Both direct and indirect coupling diffusion analyses, with and without cyclic straining, demonstrate that varying incompatibility rates tend to further promote vacancy diffusion, leading to surface grooving, enhanced extension rates, and eventual transition to cracks. The findings reveal that: (i) the evolved PSB ladder structure serves as a site for vacancy formation, (ii) it provides a diffusion path toward the specimen surface, and (iii) it significantly enhances groove extension rates. These factors effectively facilitate the transition from a "groove" to a "crack," evidenced by the abrupt acceleration of the extension rate, mirroring systematic experimental observations by Nakai et al. These achievements validate the FTMP's capability to simulate complex phenomena and significantly deepen our understanding of slip band-fatigue crack transition mechanisms.

The sciences related to production engineering consider many aspects related to cyber physical systems (CPS), in particular robots, treating them as implementations of the Industry 4.0 con-cept. The construction of CPS remains the domain of various engineering disciplines - mechan-ics, control theory or computer science. The problem of communication occurring during the implementation of the control of a single robot understood as a CPS object may concern the ex-change of data within a CPS or between different CPSs. In this area, software and hardware compatibility problems arise. Therefore, the paper presents a modular application for the ex-change of process data between mechatronic devices such as robots, which solves this problem. The work is complemented by an example of the use of the presented application in communi-cation between a mobile wheeled robot with Macanum wheels and a quadruped robot based on a motion capture system. It was assumed that the control of the wheeled robot would be realized from the perspective of implementing the basic robotics task of driving to a target, while the quadruped robot, representing a moving target, together with the motion capture system, would constitute a CPS whose outputs are incompatible in a software and hardware sense. With this choice of example, main attention was focused on the particular importance of multidisci-plinary in the context of the communication problem and also provide an application that al-lows for a modular solution to the problem of software/hardware compatibility in the commu-nication problem. This, in turn, is of interest to researchers associated with various engineering disciplines.

Ultrasonic additive manufacturing (UAM) is a process for three-dimensional printing of metal foil stock that can produce near-net shape metallic parts. This work details the development of an energy-based tool to identify relationships between input energy, stored energy in the interface microstructure, and the strength of the weld interface in UAM. The stored energy in the grain boundaries of the crystallized grains in the interface microstructure are estimated using the Read-Shockley relationship. The energy stored in the interface is found to be positively correlated with the resulting weld strength. An energy flow diagram is developed to map the flow of energy from the welder to the workpiece and quantify the key participating energies such as the energy of plastic deformation, energy stored in the interface microstructure, energy required for asperity collapse, and heat generation. A better understanding of the flow of energy in UAM can assist in optimizing the process to maximize the portion of input energy from the welder that is used for bond formation.

This paper presents an advanced methodology for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (COregistered Multi-temPorAl Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach as implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformation in dynamic environments, such as the Algeciras port. Results indicate that ISCE3-MiaplPy delivers significantly denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces.

In this study, we successfully reproduced the PSB with laddered patterning, showcasing the predictive capability of the framework of Field Theory of Multiscale Plasticity (FTMP), without relying on ad hoc models, intricate mathematical models, or elaborate finite element discretization. The FTMP-incorporated CP-FEM simulation not only reasonably replicates the experimentally observed laddered morphology and PSB but also effectively simulates surface roughening and grooving, independent of vacancy formation and diffusion. These results highlight the significance of laddered morphology and set the stage for further investigations into the effects of vacancy formation in future studies in Part II. Leveraging incompatibility tensor-based degrees of freedom, the FTMP framework offers exceptional capabilities for natural modeling dislocation substructures typically overlooked in conventional approaches, positioning it as a transformative tool for advancing our understanding of the mechanism that dictate slip band-fatigue crack transitions.

Modifying surface topography to prevent surface-induced thrombosis in cardiovascular implants allows endothelialization, which is the natural thrombo-resistance of the blood-contacting surfaces and is deemed to be the only long-term hemocompatible material. We have adapted a simulation framework to predict platelet deposition on the modified surface and developed an optimization strategy to promote endothelial retention and limit platelet deposition. Under supraphysiological bulk shear stress, a maximum of 79% linear coverage was achieved. This study concludes that the addition of microtrenches promotes endothelial retention and can be improved through optimal selection of geometric parameters.

Urban public space comfort is vital for enhancing the quality of life and perceptions of livability, especially as climate change increasingly impacts outdoor thermal environments. This study investigates the use of ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors like age and gender. While ENVI-met is often used to analyze urban microclimates, there is limited research on its application for simulating heat emission variations across different demographic groups. The findings highlight significant disparities in thermal stress vulnerability: elderly individuals are at higher risk due to elevated maximum PET values, while children experience broader ranges of thermal stress and higher baseline values. Despite cooling effects in the evening, children continue to face greater stress. The study emphasizes the need for tailored management strategies, as morning PET values are consistent but afternoon data reveals children’s ongoing vulnerability. Overall, the research calls for further investigation to enhance urban planning, thermal comfort strategies, and public health interventions amid rising temperatures. It advocates for expanding ENVI-met's capabilities to include diverse demographic factors, promoting a more comprehensive approach to urban resilience and well-being.

Robot grasp modeling and implementation is one of essential abilities for a robot with embodied artificial intelligence. However, most existing deep learning-based grasp methods suffer a large number of parameters and heavy computation overheads. To address this issue, by fully exploiting the complementary capabilities of both CNNs and Transformers, we propose a smart grasp deep network with one-way fusion strategy via context path and spatial path (SGNet), which enjoys a lightweight structure, fast inference speed and easy deployment on devices with limited computation resources. Specifically, the context path employs lightweight depthwise separable convolution to achieve fast down-sampling while a novel DSFormer module mainly by integrating Transformer is to extract global and context-rich features. The spatial path efficiently fuses feature information from the context path in one-way manner and generate high-resolution feature maps via point-by-point convolution operations. Experimental results show the proposed model with only 1M parameters has a significantly overall performance, achieving 99.4% accuracy on Cornell dataset and 93.4% accuracy on Jacquard dataset, as well as within 12.5ms inference time.

As the cornerstone of severe accident management strategy, either in-vessel or ex-vessel retention of core melt (IVR or EVR) plays a pivotal role in the stabilization and termination of a severe accident, and ultimately secures the safety goal of “Practical elimination of large radioactive release” for light water reactors. In contrast to the IVR measures that are more or less identical in reactor designs, the EVR measures are quite different from design to design. This paper was intended to give a critical review on the EVR measures adopted in the reactor designs of VVER-1000, EPR, ESBWR, EU-APR1400 and APWR. The review study began with a general description of the existing EVR measures, including their principles, operational procedures and research efforts. We then focused our discussions on the pros and cons of each EVR measure, through the comparisons with the IVR and with the others in terms of simplicity, reliability and economy. We finally tried to identify the remaining issues and uncertainties in the qualification of the EVR measures, based on which potential design improvements and future research needs were recommended.

Background/Objectives: Infusion pumps are critical in clinical settings, precisely delivering fluids such as medications and blood products. However, maintaining accuracy and preventing hemolysis is essential when administering blood, particularly to neonates. Conventional pumps may cause hemolysis due to pressure variations during transfusion. This study compares the blood transfusion accuracy and hemolysis rate between the conventional pump, Terufusion TE-LM700 (Terufusion) and the novel cylinder-type infusion pump, Anyfusion H-100 (Anyfusion); Methods: The study evaluated six different infusion rates (10-180cc/hr) and conducted 57 transfusion trials, 20 of which used a 1:1 blood-saline dilution. Blood transfusion accuracy was measured using research-grade packed red blood cells, and hemolysis rates were assessed before and after transfusion by chi-square tests and independent sample t-tests; Results: Anyfusion demonstrated an average transfusion error rate of 3.77%, compared to 4.00% for the Terufusion, with no statistically significant difference in hemolysis rates (p=0.697). Bland-Altman plots confirmed equivalent performance, with hemolysis rates of 0.566 ± 0.095% for Anyfusion and 0.518 ± 0.126% for Terufusion; Conclusions: Anyfusion provides accurate and reliable blood transfusion performance comparable to Terufusion, with no significant difference in hemolysis rates. Its integration of syringe and infusion methods shows potential for safer and more efficient transfusion practices, especially in pediatric and emergency settings.

Keywords: Security robot; kalman filtering; millimeter wave; inertial navigation system.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.

Lidar and camera sensors are widely utilized in autonomous vehicles (AVs) and robotics due to their complementary sensing capabilities—lidar provides accurate depth information, while cameras capture rich visual data. However, effective fusion of these sensors requires precise calibration, which is often challenging due to the differences in sensing modalities. This paper presents a self-calibration algorithm based on Singular Value Decomposition (SVD) to address this problem. Singular Value Decomposition is a powerful matrix factorization technique that helps identify key calibration parameters by decomposing matrices into singular vectors and values, representing essential calibration data. Our method not only automates the calibration process but also provides a reliable mechanism for detecting calibration errors, ensuring long-term accuracy and robustness in sensor fusion. The algorithm simplifies deployment in real-world applications by eliminating the need for manual calibration or external targets. Experimental results show that our approach improves sensor alignment and fusion accuracy, making it particularly useful for AV and robotic systems where maintaining precise calibration is critical.

Water desalination technology has emerged as a critical area of research, particularly with the advent of more cost-effective alternatives to conventional methods such as reverse osmosis and thermal evaporation. Given the vital importance of water for life and the scarcity of potable water for agriculture and livestock—especially in the Kingdom of Saudi Arabia—the capacitative deionization (CDI) method for removing salt from water is highlighted as the most economical choice compared to other techniques. CDI applies a voltage difference across two porous electrodes to extract salt ions from saline water. This study will investigate water desalination using CDI, utilizing a compact D.C. power source under 5 volts and a standard current of 2 amperes. We will convert waste materials like sunflower seeds, peanut shells, and rice husks into activated carbon through carbonization and chemical activation to improve its pore structure. Critical parameters for desalination, including voltage, flow rate, and total dissolved solids (TDS) concentration, have been established. The initial TDS levels are set at 2000, 1500, 1000, and 500 ppm, with flow rates of 38.2, 16.8, and 9.5 mL/min across various voltage settings of 1.5, 2, and 2.5 volts, applicable to both direct and inverse desalination methods. Results indicate that desalination processes function effectively at a flow rate of 9.5 mL/min, achieving a performance efficiency of 65%, while a voltage of 2.5 volts yields a performance level of 44%.

This study delves into the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, crucial conduits for global trade. It explores the correlation between regional instability, as exemplified by Houthi actions from 1st attack to a merchant vessel (November 19, 2023) to February 5, 2024, and alterations in maritime traffic patterns, alongside the operational efficiency of these vital maritime arteries. Employing descriptive statistics, qualitative, and geospatial analytical methods, the research examines recent trends in maritime traffic and incident reports, shedding light on the spatial and geopolitical challenges faced by one of the world's most critical trade routes. The findings reveal a marked decline in maritime activity in the Gulf of Aden and Suez Canal, attributed to security concerns stemming from Houthi attacks, with a significant shift towards alternative routes, notably around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade dynamics and the critical nature of maintaining uninterrupted maritime flow. The study also highlights the economic ramifications, including increased operational costs and freight rates due to longer transit times and enhanced security measures. It concludes with a call for improved maritime security protocols and international cooperation to safeguard these strategic maritime pathways. This research contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, emphasizing the intertwined nature of global trade, security, and the urgent need for collective action in the face of evolving geopolitical challenges.

Avoiding global dependence on fossil oils, as well as improving the environmental impact during energy production, are factors that drive research into renewable energies. Considering lignocellulosic biomass residues as a raw material for gasification, a thermochemical process that converts lignocellulosic resources into synthesis gas (H2, CO, CH4 and CO2) is an alternative under study, due to its relatively low costs, high efficiency and the wide variety of applications of synthesis gas. Fortunately, there are still fields for its improvement and technological development. Gasification, for example: Different types of lignocellulosic biomass, such as sugarcane bagasse, wheat straw, pine sawdust or corn cob, differ in their physical, chemical and morphological properties, which can affect the characteristics of the gasification process. This work uses the methodology of generalized stoichiometry and mass and atomic balances in the gasification reactor, to predict the composition of syngas produced by gasification of both individual substrates and mixtures. The results provide useful information for the design and operation of gasification reactors, with their operating region between 2.0 bar and 4.5 bar, and 1024 K and 1224 K, understanding the effects of different types of biomasses, its humidity and molecular weight on the operation and performance of the process.

The present paper focuses on the practical application of software tools for calculating and verifying trusses systems. It covers externally and internally statically indeterminate trusses systems and compares results from two software tools. The MATLAB Simscape – Multibody simulation tool, specially designed for mechanical systems, is utilized to calculate the trusses system. The model is optimized to minimize errors compared to theoretical calculations. By using blocks, it is possible to build a system, including repeating parts quickly.

Optical communication systems pose significant challenges, including the effects of nonlinear noises. The nonlinearities, including Kerr-induced phase noise, become more problematic in m-QAM as the orden of the format increases becoming a highly densed set of data-symbols and therefore, requiring advanced signal processing for successful separation of symbols at the demodulation stage. Machine learning techniques have recently been applied to improve signal integrity in such scenarios. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in -90, -100, and -110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.

The study examines the effects of transitioning to hydrogen production in the National Capital Region (NCR) and Palawan Province, Philippines, focusing on technology, environment, and stakeholder impact. The research, conducted through a July 2022 survey, aimed to assess public awareness, knowledge, risk perception, and acceptance of hydrogen and its environmentally friendly variant, green hydrogen, infrastructure. Disparities were found between urban NCR and rural Palawan, with lower awareness in Palawan. Safety concerns were highlighted, with NCR respondents generally considering hydrogen production safe, while Palawan respondents had mixed feelings, particularly regarding nuclear-based hydrogen generation. The report emphasizes the potential ecological advantages of hydrogen technology but highlights potential issues concerning water usage and land impacts. It suggests targeted public awareness campaigns, robust safety assurance programs, regional pilot projects, and integrated environmental plans to facilitate the seamless integration of hydrogen technology into the Philippines' energy portfolio. This collective effort aims to help the country meet climate action obligations, foster sustainable development, and enhance energy resilience.

The incremental sheet forming is born as an excellent alternative to other material forming pro-cedures to deform incrementally flat metal sheets into the preferred complex three-dimensional profile. The main characteristics of this process are its versatility and cost-effectiveness; in addition, it allows for reaching greater formability, compared to conventional sheet forming processes. Re-cently, its use has been extended to polymers and composites. The following review aims to present the current state of the art of the incremental sheet forming of polycarbonate, an outstanding en-gineering plastic, starting from the first studies on the feasibility of this process applied to polymers. Attention is given to the advantages, drawbacks and main applications of the incrementally formed polycarbonate sheets, as well as the influence of the process parameters and the toolpath strategies on features like formability, forming forces, deformation and failure mechanisms, geometric accu-racy, surface quality, etc. Moreover, new hybrid forming methods for process optimization are presented. Finally, a discussion is provided on the technical challenges and research directions for incremental sheet forming of polycarbonate, and more generally thermoplastics, in the future. Therefore, this review aims to offer an extensive overview of the incremental forming of polycar-bonate sheets, useful to academic and industrial researchers who work on this topic.

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant buses is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

The potential of technical lignins slow pyrolysis is briefly summarized. The pyrolysis process, which can be self-sustained by burning the co-produced gas, can primarily produce high-quality biochar in significant amounts, to be used as a fuel and as a reductant in metallurgy. Together, significant amounts of bio-oil can be produced, rich in guaiacols, which are commercial and expensive chemicals produced today via petrochemical routes and used in pharmacology, food chemistry, polymer chemistry, etc. Such compounds or bio-oil itself can also be converted by hydrodeoxygenation into biofuels.

In the context of enhanced decision-making related to Industry 4.0 and 5.0, this work examines the first step to implement a Digital Twin (DT) in a discrete manufacturing firm. The DT will be required to be adequately integrated with the information systems, especially the Manufacturing Execution System (MES), because the virtual counterpart of the DT itself, a Discrete Event Simulator (DES) model, will exploit MES data for the validation and the monitoring. The objective of the DT is to enhance the decision-making related to production planning, in particular, achieving better on-time delivery to customers. Therefore, the DT intends to depict material flows within the production department to enhance monitoring and control, facilitating the prompt identification of deviations from the plan, and supporting the decision-makers, enabling a more responsive and informed management of delay alerts. The first goal to achieve the DT implementation and integration is to establish a conceptual framework that improves material flow data synchronization. A conceptual integration and implementation framework for the DT will be proposed and discussed, underlying the technical decisions chosen to achieve the functional and integration requirements.

Experimental studies and numerical analyses were conducted to estimate the laminar flame speeds of butyl ether, n-octane, iso-octane, and 1-octene at temperatures of 373 and 443 K and pressures ranging from 0.03 to 0.3 MPa in a constant-volume combustion chamber. Iso-octane had the slowest laminar flame speed whereas butyl ether had the highest. The LLNL model for large hydrocarbons and the Cai model for butyl ether were combined to describe the chemical kinetic mechanism for the test fuels. The simulation data calculated with the constructed model agree with the experimental data. The discrepancies in the laminar flame speeds of the four fuels were clarified from the perspectives of thermodynamics, diffusion, and chemical kinetics. The results reveal that the thermodynamics and chemical kinetics were dominant. Butyl ether had the highest laminar flame speed. This was attributed to it having the highest adiabatic flame temperature and a higher concentration of H and OH radicals, which have a high reactivity in a flame. Iso-octane produces the highest concentration of CH3, which inhibits flame propagation, so that it had the slowest laminar flame speed.

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind en-ergy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to forecast wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, evaluated us-ing metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their ca-pabilities and responses to preprocessing, as well as each model's reaction in terms of statistical performance deviation indicators. Ultimately, the preprocessing and the data analysis show that Random Forest regression is more suitable for weather condition datasets than the other two re-gression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy forecasting.

This numerical study investigates the impact of reaction temperature, molar ratio of CH4:CO2, and catalyst porosity (p) on H2 yield and H2 selectivity during biogas dry reforming over a Ni/Cr catalyst. Using COMSOL Multiphysics, we conducted detailed simulations to elucidate the underlying reaction characteristics. Our findings reveal that increasing p significantly enhances H2 production and H2 selectivity, while simultaneously reducing CO selectivity. This effect is attributed to the improved mass transfer within the catalyst bed, leading to more efficient reactant conversion and product formation. Additionally, we observed a strong correlation between higher reaction temperatures and increased H2 yield and H2 selectivity, confirming the endothermic nature of the reforming process. By optimizing these operational parameters, our results suggest that Ni/Cr catalysts can be effectively employed for the sustainable production of H2 from biogas.

In the fourth industrial revolution, artificial intelligence (AI) and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning (RL). These advanced ML techniques have recently garnered significant attention due to their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML’s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.

The position accuracy of polishing tool affects the surface quality of the polished aspheric surface. The contact deformation among the polishing tool, abrasives and aspheric part can cause a displacement, in turn will cause a position error of polishing tool, this will lead to a significant change of the polishing force. In order to resolve this error, this paper proposed a method of normal displacement compensation for NC polishing system by controlling the polishing force. Firstly, the coupling principle between polishing force and position of polishing tool are expounded, the relationship between normal displacement and deformation is analyzed, based on Hertz theory, a model of normal displacement is established. Then, on the basis of decoupled polishing system developed, a normal displacement compensation method was proposed. Finally, a group of comparative experiments was carried out to verify the effectiveness of the proposed method. Compared with no displacement compensation, when the part was polished with normal displacement compensation method, the value of roughness decreases from 0.4µm to 0.21µm, and the unevenness coefficients of surface roughness decreases from 112.5% to 19%. The experimental results show that the polishing quality is improved greatly and the aspheric surfaces can be polished more uniformly with method proposed in this paper.

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the Backstepping Control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance.

The transformative influence of digital engineering on photonic technologies is significant, es-pecially regarding advancements in laser processing through digital models, artificial intelli-gence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material de-formation and defects. The integration of AI further refines these models, improving productiv-ity and quality in applications such as micro-machining and cladding. Additionally, the combi-nation of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manu-facturing, offering significant improvements in process optimization, defect reduction, and sys-tem efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solu-tions for complex production environments.

The application of joint sensing and communications (JSACs) technology in air-ground networks, which include unmanned aerial vehicles (UAVs), offers unique opportunities for improving both sensing and communication performances. However, this type of networks are also sensitive to the peculiar characteristics of the aerial communications environment, which include shadowing and scattering caused by man-made structures. This paper investigates an aerial JSAC network and proposes a UAV-selection strategy that is shown to improve the communication performance. We first derive analytical expressions for the received signal-to-interference ratio for both communication and sensing functions. These expressions are then used to analyze the outage and coverage probability of the communication part, as well as the ergodic radar estimation information rate and the detection probability of the sensing part. Moreover, a performance trade-off is investigated under the assumption of a total bandwidth constraint. The presented results reveal the impact of shadowing severity and interference on the system’s performance.

This paper proposes a design for a platform-based Multi-phase Interleaved Synchronous Buck Converter (MISBC) using Silicon (Si) based Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). A custom platform was developed to compare the theoretical performance of a MISBC circuit simulated with Multisim and compared it with a prototype that was built at Western Sydney University. The work disclosed in this manuscript describes some steps adopted during the selection of each component and technical considerations taken during the design of the Printed Circuit Board (PCB). The platform designed has a maximum power output of 260 Watts, with a buck reduction of the nominal voltage from 97 Volts to 24 Volts at a maximum switching frequency of 50 kHz. This switching frequency is achieved with an open-loop circuit configuration coupled with synchronized signal generators, used to validate the dead band required between the activation of each MOSFET implemented in a half bridge configuration. A summary of the results based on the duty cycle required to achieve the buck voltage desired highlights the advantages of each operating mode of the MISBC circuit, where the theoretical performance is compared against the data acquired during functional evaluations of the prototype, allowing further interpretations towards the optimal control algorithm required to maximize the performance output of MISBC circuits.

This study aims to investigate the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI). A place-based approach utilizing Geographic Information Systems (GIS) integrates spatial data and satellite imagery on the urban environment, human ac-tivity, demographics, and climate conditions, facilitating a deeper understanding of UHI dy-namics. Data-driven modelling with Machine Learning algorithms were developed to identify key variables influencing UHI effects, such as building density, greening extent, and surfaces’ albedo. A comparative pre- and post-intervention analysis in Turin (Italy), focusing on the Teksid ex-industrial area, was investigated. This analysis reveals a reduction in surface UHI intensity of -0.94 in summer and -0.54 in winter, highlighting the positive impact of mitigation strategies employed in the regeneration plan. The implications for urban and territorial policies were in-vestigated, focusing on urban planning tools for UHI mitigation. Specifically, the Local Climate Zones method was examined and compared with actual urban morphologies to find more effective and tailored solutions for policy makers and urban planners. The combined approach of geo-graphical and satellite information, Machine Learning models and Local Climate Zones joins quantitative and qualitative data, allowing to find more tailored, effective and alternative solu-tions in critical contexts such as the urban ones.

HTA introduced by Annett and Duncan is widely recognized for breaking down complex tasks into manageable subtasks, data, and quantitative analysis confirming its effectiveness are limited. By combining the HTA with the NASA Workload Index (NASA-TLX), a subjective workload assessment tool, this study aims to provide a comprehensive assessment of task performance and workload. The project involved assembling a three-jaw chuck with HTA for process definition and subtasks, while NASA-TLX was used for workload determination. Data were collected through questionnaires and observation forms and analyzed to determine the impact of HTA on task performance and workload distribution. The results show that HTA effectively identifies task complexity and bottlenecks, which facilitates task optimization and improves performance. The NASA-TLX integration provides critical insights into individuals' subjective workload and targets areas where task performance and resource allocation can be improved. This study shows that combining HTA and NASA-TLX provides a holistic approach to understanding and improving task performance, ultimately leading to better user experience and productivity.

Technological advancements and evolving government regulations are driving the adoption of construction equipment powered by renewable energy sources, aiming to significantly reduce emissions. Electrifying non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) with telescopic booms, presents stability challenges. Replacing diesel engines with electric motors and battery packs shifts the centre of gravity, necessitating precise load capacity determinations via load charts to ensure safe operation. This paper introduces a virtual model developed using multi-physic modelling with Bond Graph methodology. The model encompasses crucial components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, requiring three-dimensional treatment for spatial dynamics resolution. An illustrative example, supported by experimental data, demonstrates the model's capabilities. Special focus is placed on calculating ground wheel reaction forces and hydraulic self-leveling of the attachment fork. Numerical results validate the BG-3D simulation model of the telehandler with 20-Sim as a promising tool for estimating stability limits with satisfactory precision and predicting dynamic behavior across various operating conditions. Moreover, the paper explores potential extensions of the model, such as integrating the virtual vehicle model with a platform of variable inclination in subsequent phases which aims to evaluate telehandler longitudinal and lateral stability, aligning with the ISO 22915 standard and operator safety.

Inconel 718 is a widely popular aerospace superalloy known for its high-temperature performance and resistance to oxidation, creep, and corrosion. Traditional manufacturing methods, like casting and powder metallurgy, face challenges with intricate shapes that can result in porosity and uniformity issues. On the other hand, Additive Manufacturing (AM) techniques such as Powder Bed Fusion (PBF) and Direct Energy Deposition (DED) can allow the creation of intricate single-part components to reduce weight and maintain structural integrity. However, AM- parts often exhibit directional solidification, leading to anisotropic properties and potential crack propagation sites. To address this, post-processing treatments like HIP and heat treatment are necessary. This study explores the effects of the raster and stochastic spot melt scanning strategies on the microstructural and mechanical properties of IN718 parts fabricated using Electron Beam Powder Bed Fusion (EB-PBF). The research demonstrates that raster scanning produces columnar grains with higher aspect ratios. In contrast, stochastic spot melt scanning promotes the formation of equiaxed grains, enhancing microstructural refinement and reducing anisotropy. Microhardness values also show variation in strength in build direction, denoting changes in mechanical performance due to scanning strategies. These findings emphasize the critical role of scanning strategies in optimizing the EB-PBF process for improved material properties.

This paper proposes a methodology for multi-criteria calibration of a thermo-mechanical model for numerical simulation of welding in vacuum. A finite element model of a steel plate is created. Experimental and computational data are obtained. An inverse problem is formulated for the vector identification of five calibration parameters from the heat flow model. They are evaluated for adequacy with controlled accuracy according to four criteria. An optimization problem is solved using a two-step interactive procedure. The parameter space studying method (PSI) has been applied to the study of multidimensional regions by means of quasi-uniform sounding. A Pareto-optimal set is defined. It is used to determine reduced ranked Pareto subsets by -selection. Salukvadze optimum is also determined.

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

This study examines the rationale and perceptions surrounding AI in military technologies, focusing on its application in autonomous weapons systems (AWS), intelligence, surveillance, and reconnaissance (ISR), and decision-support systems (DSS). AI models, such as unmanned aerial and ground platforms, showed high mission success rates, with 92% mission accomplishment, 95% true positive threat identification, and low false alarms. AI's effectiveness surpassed human operators, particularly in response time and threat detection. A global survey revealed mixed opinions: two-thirds believed AI enhances national security, yet 70% opposed autonomous weapons due to ethical concerns, particularly civilian casualties. The study highlights AI's advantages in improving decision-making speed and accuracy but acknowledges challenges such as ethical considerations and public trust in autonomous systems. Future research should address ethical AI models, international humanitarian law (IHL) standards in military operations, human-AI interaction in hazardous environments, and policies related to military robotics and accountability. Understanding and enhancing public awareness of AI in military contexts is also essential.

This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation, to minimize the inherent randomness associated with training neural networks. The refined LSTM network achieved a weighted average accuracy of 84%, demonstrating a high level of agreement between the network's identified stationary phases and those manually determined by researchers. This result suggests that LSTM networks can reliably mimic manual identification processes in tribological data, providing a promising avenue for automating data analysis. The study underscores the potential of neural networks to transcend their traditional role in predictive modeling within tribology, opening up new possibilities for their application across a broader spectrum of tasks within the field.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.

The retrofitting of existing reinforced concrete (RC) buildings with Cross-Laminated Timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a pre-defined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

This paper presents the development and testing of the passive Almaty Ankle Exoskeleton V.2. The device features a new design with three electric linear actuators, and the shin and foot platforms are connected by links with spherical and universal joints. Functional testing demonstrates the prototype's ability to operate effectively within the natural range of ankle joint movements. Preliminary results indicate that the exoskeleton can reduce calf muscle activation in the work limb where the device is applied. In this the passive exoskeleton is designed for dorsiflexion and plantarflexion, whose operation with numerical results is highlighted by focusing on parameters such as angular velocity, linear acceleration, and angles. These measurements provide valuable information about the exoskeleton's performance and efficiency.

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of the electrical equipment. In particular, modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of Deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. Particularly in the paper, different Neural networks used in scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modelling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

This paper presents RMSPowerSims.jl, an open-source Julia package for time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, fault analysis, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances. The package has been verified against DIgSILENT PowerFactory and has produced results that are in close agreement with those obtained using PowerFactory. RMSPowerSims.jl is available under an open-source license and can be downloaded from the Julia package registry.

A large amount of sea ice exists in the polar regions, which is prone to ship-ice collisions leading to ship breakage and sinking events. Complex environmental factors pose challenges to navigation safety, and it is particularly important to perceive the polar environment and make decisions in advance. In this paper, You only look once (YOLOv5) is targeted to be optimized according to the characteristics of ice remote sensing images, which includes adding Squeeze-and-Excitation (SE) attention mechanism, improving spatial pyramid pooling, and replacing Flexible Rectified Linear Unit (FReLU) activation function. In order to verify the effect of the optimized algorithm, ablation experiments are set up, and the accuracy of the optimized algorithm is improved by 3.5% com-pared with the original YOLOv5. Comparison experiments with other target identification algo-rithms are conducted, and the results show that the optimized YOLOv5 has higher accuracy and better robustness, and can identify small target sea ice that is difficult to identify in large-size re-mote sensing images. Polar ship path planning based on the identification results. The identifica-tion output of remote sensing ice images is used as the input for the construction of path planning maps, and raster maps corresponding to the actual polar scenes are built. Taking the path length, the number of ship turns, and the sea ice risk value as the objective function, the Any-Angle Path Planning on Grids (Theta*) algorithm is used to simulate the path planning under different sea conditions with different sea ice concentration, and a path with a shorter length, a smoother path, and a higher safety coefficient is found.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. The study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives upon conventional rigid body structures. In this arena, biomimetic soft systems are playing an important role for modeling and controlling the human hand kinematics without the restrictions of rigid mechanical joints, while having an entire deformable body with limitless points of actuation. In this paper, we address the computational limitations for modeling large-scale articulated systems for soft robotic exoskeletons, by integrating a parallel architecture to compute the exoskeleton’s dynamics equations of motion (EoM), obtaining a computation with O(log2n) complexity for the highly-articulated n degrees of freedom.

The transport sector is heavily relying on the use of fossil fuels that are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity, through e-fuels production, are emerging to power the transport sector. To ensure environmental benefits over this transition, an accurate estimation of the impact of the use of electricity is needed. This work presents a temporal analysis of carbon intensity of e-fuels using grid electricity in countries members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for to produce low-carbon e-fuels according to the European Union legislative framework. This was achieved, building on top of the existing EcoDynElec tool, by developing EcoDynElec_xr a python tool enabling, with an hourly time resolution, the calculation, visualisation and the analysis of the historical time-series of electricity mix from the ENTSO-E. The results highlight that, in 2023, a minority of European countries were reaching low carbon intensity of electricity that enables the use of grid electricity for green electrolytic hydrogen production. The methodological hypothesis to consider the consumed electricity mix instead of the production mix, and the considered time step, are of paramount importance and drastically impact the potential load factor of green hydrogen production. The developed tool and calculated data are released under an open-source license to ensure transparency, results reproducibility, reuse on newer data, or for other purposes.

The increasing global demand for biodiesel is due to the urgent need to replace mineral diesel with a fuel based on renewable energy sources. In this scenario, biocatalysis stands out as an efficient and environmentally friendly alternative to chemical catalysis. When associated with the use of immobilized enzymes, which allow their reuse in the process of obtaining biofuel, this approach makes production sustainable and reduces costs. In this sense, we investigated the use of the lipase produced by an Amazonian endophytic fungus in immobilized form as a biocatalyst in the transesterification of waste cooking oil for biodiesel production. The fungus Endomelanconiopsis endophytica QAT_7AC demonstrated high production of lipase. The lipolytic extract was precipitated in ethanol, which increased the specific enzyme activity by 28%. The lipolytic extract and the precipitated lipolytic extract were immobilized in calcium alginate beads. Immobilization efficiency was over 89%. The immobilized biocatalysts showed thermal stability (30-50 °C) and were used in the production of biodiesel using waste cooking oil and ethanol. It was possible to reuse them for up to four reaction cycles, with yields greater than 70%. These results prove the efficiency of immobilized biocatalysts in the production of ethyl esters from waste oils.

The connection between a prefabricated reinforced concrete column and a pocket foundation is a case treated from a general perspective in the European Standard named EN 1992-1-1, and when the structural engineer deals with the dimensioning or verification of the connection, he tackles several unknowns. The present work aims to fill in the missing information by presenting detailed calculation models based on the strut-and-tie method for 4 of the widely used pocket foundations: pedestal pocket foundation with smooth, rough or keyed internal walls, and pad foundation with pocket possessing keyed internal walls. Additionally, detailed design prescriptions applicable to seismic areas are given. Afterwards, as a case study, a pocket foundation is designed in all 4 variations, having the structural design particularities, similitudes and differences pointed out. Finally, to conclude the advantages and disadvantages are mentioned for pocket foundations in respect to the type of internal wall surface used. Quantifiable data based on the case study undertaken is available.

Anaerobic digestion has the potential to convert organic waste materials into valuable energy. At the same time, using press water from biomass materials for energy generation, while taking advantage of the resulting cake for other purposes is an emerging approach. Therefore, this study aimed to investigate the residual potential expected from a typical biogas feedstock in the West African context after it has been mechanically separated into liquid and solid phases. Hence, in this study, the rumen contents of ruminants (cow, goat and sheep) and their proportionate ratios were obtained from an abattoir in Ghana. Resource characterisation of the waste samples were carried out in the Central laboratory of the HFR, Germany. Anaerobic batch tests for biogas (biomethane) yield determination were set-up using the Hohenheim Biogas Yield Test (HBT). Inoculum used was obtained from an inoculum production unit at the Hohenheim University biogas laboratory. The trial involved two different forms of the sample: mixture of rumen contents, press water, and inoculum, each in four (4) replicates. The trial was carried out at a mesophilic temperature of 37°C. Results obtained over a seventy (70) day period were transformed into biogas yields. Overall, the results show that the current contents are suitable for biogas generation as an option, as opposed to the current form of disposal at a refuse dump. However, using these mixtures in their original forms is more technically viable than using press water without further treatment.

This research seeks to provide local governments with a comprehensive alternative for the proper maintenance of roads, focusing on the surface and functional evaluation of pavements. It com-pares conventional methods of the International Roughness Index (IRI) and the Pavement Condi-tion Index (PCI) with novel methodologies that employ smart technologies. Road maintenance is essential for economic development, facilitating the circulation of services and resources. This re-search analyzes the efficiency of smart technologies in the maintenance of local roads in Arequipa, taking as a case study a 2 km section of the AR-780 highway in Polobaya. The International Roughness Indices (IRI) obtained through the Merlin Roughness Meter and the Roadroid applica-tion were compared, finding a minimum variation of 4.007% in the left lane and 8.5% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost differ-ence of 220.11 soles. Both methods classified the road condition (PSI) as GOOD, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with con-ventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.89%. The cost difference between the methodologies was 1,047.73 soles, and the use of the drone proved to be 10 times faster than visual inspection. Finally, it can be said that this study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so that the actors in charge of the roads can make decisions based on science and thus con-tribute to the well-being of the population.

In the DC distribution networks, DC bus voltage is maintained by the grid-connected converter, the controllability and reliability of the grid-connected converter significantly affects the bus voltage characteristic. To address the problem of limited stability and frequent oscillations, this paper proposes a state feedback control method for the AC/DC converter. Conventional AC/DC converter adopts the voltage-current double-closed-loop control structure with the proportional integral (PI) controllers, which is the equivalent of the typical type II control system, but the typical type II control system cannot fully settle the stability and immunity problems. In contrast, the state feedback control strategy not only achieves the control objectives of the traditional double-closed-loop control, but also reduce the AC/DC converter system model to a typical I-type system, which improves the stability, thus enhances the oscillation suppression capability of the bus voltage. By selecting multiple state variables and designing the converter pole configuration range, the proposed single-loop state feedback control method manages to optimize both the dynamic and steady-state performances of the grid-connected AC/DC converter. Finally, the effectiveness of the proposed single-loop state feedback control strategy has been verified by both the simulation and experiment results.

Assessing handling qualities is crucial for ensuring the safety and operational efficiency of aircraft control characteristics. The growing interest in Urban Air Mobility (UAM) has increased the focus on electric Vertical Take-Off and Landing (eVTOL) aircraft; however, a comprehensive assessment of eVTOL handling qualities remains a challenge. This paper proposed a handling qualities framework to assess eVTOL handling qualities, integrating pilot compensation, task performance, and qualitative comments. An experiment was conducted, where eye-tracking data and subjective ratings from 23 participants as they performed various Mission Task Elements (MTEs) in an eVTOL simulator were recorded. The relationship between pilot compensation and task workload was investigated based on eye metrics. Data mining results revealed that pilots' eye movement patterns and workload perception change when performing Mission Task Elements (MTEs) that involve aircraft deficiencies. Additionally, pupil size, pupil diameter, iris diameter, interpupillary distance, iris-to-pupil ratio, and gaze entropy are found to be correlated with both handling qualities and task workload. Furthermore, a handling qualities and pilot workload recognition model is developed based on Long-Short Term Memory (LSTM), which is subsequently trained and evaluated with experimental data, achieving an accuracy of 97%. A case study was conducted to validate the effectiveness of the proposed framework. Overall, the proposed framework addresses the limitations of the existing Handling Qualities Rating Method (HQRM), offering a more comprehensive approach to handling qualities assessment.

Anaerobic digestion followed by microalgal cultivation is considered a promising renewable alternative for the production of biomethane with reduced effluent generation, thus lowering the environmental impact. In this arrangement, in addition to generating energy, the microalgae act by potentiating the refinement of the effluents generated via anaerobic digestion (digestates). In this study, the microalga Tetradesmus obliquus was cultivated in photobioreactors with the final digestate resulting from the codigestion of Arundo donax L. plant biomass and cattle wastewater. The biotechnological route used was efficient, and biogas production ranged from 50.20 to 94.69 mL gVS-1. The first-order kinetic model with variable dependence (FOMT) provided the best fit for the biogas production data. In the microalgal posttreatment, the removal values ranged from 81.5 to 93.8% for chemical oxygen demand, 92.0 to 95.3% for N-NH4+, and 41.7 to 83.3% for PO43- after 26 days. The macromolecular composition of the algal biomass reached lipid contents ranging from 33.4 to 42.7%. Thus, the proposed process mediated by microalgae can be considered promising for the bioremediation and recovery of effluents produced by agriculture through the use of microalgal biomass for bioproduct production.

Ventricular Assist Devices (VADs) are designed to provide adequate blood flow to patients suffering from heart failure. Once implanted, the patient becomes dependent on the VAD, making it unacceptable to have situations with the potential to cause harm to patients on circulatory support. The VADs can be categorized as Critical Systems (CS), as device malfunction or Adverse Events (AEs) can result in harm to the patient, including the risk of death. Undesirable situations can occur in two ways: deviations in the functionality of the VAD linked to failures or defects or deviations in the patient's physiological responses associated with AEs. The AEs can result in hospitalization, causing permanent harm to the patient, including the risk of death. Patient care is provided through in-person consultations, and reports of incidents are submitted through Medical Device Reports (MDRs), monitored by the Food and Drug Administration (FDA). However, the crucial issue is that there is no real-time monitoring of AEs or supervision of patients. Therefore, an Open System for Supervising Critical Adverse Processes in Patients with Implanted Ventricular Assist Devices (OSCVAD) is proposed based on horizontal and vertical integration architecture. A method for structuring the processes in this system is proposed to address its complexity, involving different domains of operation: device, patient, and medical team, as well as the possible interactions between the entities that comprise the OSCVAD. In this context, the formalism of Interpreted Petri Nets was employed to synthesize diagnostic models for AEs and intervention models based on Intervention Protocols, utilizing the medical team's knowledge.

An analysis of the current state of development in the agro-industrial complex (AIC) has established that freeze-drying is the most effective method for processing raw materials entering the drying stage. However, this drying method is hindered by the lack of modern, high-performance, energy-efficient drying technologies and equipment. The production of freeze-dried agricultural products will stimulate the development of AIC sectors. The implementation scheme of an automatic control system for a vacuum freeze-drying unit is considered, representing a promising and environmentally friendly direction in the processing of agricultural products. The drying equipment includes the following components and systems: a drying chamber, a cooling system, a vacuum pump, a control system, and a heating system. The product is placed on shelves in the drying chamber and frozen to a temperature that ensures the removal of 75-80% of the moisture contained in the product. The vacuum pump creates a pressure of 10-30 Pa and evacuates the spent air from the sublimator. Water vapor condensation occurs in the desublimator, and at the final stage of the drying process, the heating elements are activated to remove the remaining moisture. To validate the reliability of controlling the operating modes of the sublimator, experimental studies were conducted on the temperature dependence of the product on the surface and in the center over time. The graphs show temperature changes on the surface and in the center of the product throughout the drying process. The research results indicate that at a pressure of 20±5 Pa, the desublimator temperature should be below -20°C. The surface temperature of the product during the sublimation stage should be within the range of -25±5°C. Drying is completed when the chamber temperature reaches no more than 40°C, and the surface temperature of the product is around 30°C.

A basic limitation of the periodogram as frequency estimator is that any of its significant peaks may be produced by a diffuse (or spread) frequency component instead of a pure one. Diffuse components are common in applications such as channel estimation in which a given periodogram peak reveals the presence of a complex multipath distribution (unresolvable propagation paths or diffuse scattering, for example). We present a method to detect the presence of a diffuse component in a given peak based on analyzing the projection of the data vector onto the span of the signature’s derivatives up to a given order. Fundamentally, a diffuse component is detected if the energy in the derivatives’ subspace is too high at the peak’s frequency, and its spread is estimated as the ratio between this last energy and the peak’s energy. The method is based on exploiting the signature’s Vandermonde structure through the properties of discrete Chebyshev polynomials. We also present an efficient numerical procedure for computing the data component in the derivatives’ span based on barycentric interpolation. The paper contains a numerical assessment of the proposed estimator and detector.

Geological storage of carbon dioxide (CO2) is a crucial technology for mitigating global temperature rise. Near-depleted edge-bottom water reservoirs are attractive targets for CO2 storage, as they can not only enhance oil recovery (EOR) but also provide important potential candidates for geological storage. This study investigated CO2 enhanced oil recovery and storage for a typical near-depleted edge-bottom water reservoir that had been developed for a long time with a recovery factor of 51.93%. To improve the oil recovery and CO2 storage, new production scenarios are explored. At the near-depleted stage, by comparing the four different scenarios of water injection, gas injection, water alternating gas injection, and bi-directional injection, the highest additional recovery of 8.64% is achieved by the bi-directional injection scenario. Increasing the injection pressure leads to a higher gas-oil ratio and liquid production rate. After shifting from the near-depleted to depleted stage, the most effective approach to improve CO2 storage capacity is to increase reservoir pressure. At 1.4 times the initial reservoir pressure, the maximum storage capacity is 6.52×108 m3. However, excessive pressures boosting poses potential storage and leakage risks. Therefore, lower injection rates and longer intermittent injections are expected to achieve a larger amount of long-term CO2 storage. Through the numerical simulation study, a gas injection rate of 80,000 m3/day and a schedule of 4-6 years injection with 1 year shut-in was shown to be effective for the case considered. During 31 years of CO2 injection, the percentage dissolved CO2 increased from 5.46% to 6.23% during the near-depleted period, and to 7.76% during the depleted period. This study acts as a guide for the CO2 geological storage of typical near-depleted edge-bottom water reservoirs.

With many Americans eager to resume travel after prolonged restrictions from the coronavirus pandemic, car rentals are on the rise. According to Auto Rental News, “2021 U.S. Car Rental Revenue Climbs 21% Year-Over-Year” (Brown, 2021). In conjunction with increased car rental, and associated travel, we can expect to see an increase in cyber security and data privacy attacks. In the digital age, enterprises have a responsibility to protect consumers’ personally identifiable information (PII). As it pertains to rental vehicles, when consumers connect their mobile devices to a rental vehicle’s Entertainment and Infotainment System or utilize the built-in navigation feature, they do so, unbeknownst to themselves that the information from their devices is transferring to the vehicle, where it will remain, unless properly expunged from the system. A large majority of consumers are blissfully unaware that their PII is being stored, and how easy it would be for an adversary to potentially breach their data security and privacy. To expand upon this hypothesis, I conducted a further investigation on the subject, by partaking in the rental of three different vehicles, each belonging to a different manufacturer (Nissan, Chrysler, and Maserati), from three different vehicle rental companies (Enterprise, Hertz and Turo). I then, as a consumer, tested my hypothesis to determine if, I was able to easily breach the prior consumers’ data security and privacy by viewing devices that had previously connected to the vehicle, as well as any of the previous consumers’ personal information. The results were undeniable; the PII of the previous consumers is not protected.

The aim of this research work consisted of assessing the effects of the COVID-19 pandemic on the interurban public transport system in a rural region with a sparse population density, considering the number of tickets sold and passengers in each locality, as well as the different connecting lines. From a methodological point of view and with the intention of identifying patterns to explain the behaviour of both routes and passengers, a series of variables were selected, becoming determining factors that sought to offer a solution to the search for a common trend. Additionally, data processing by means of statistical analysis and the application of Geographic Information Systems (GIS) tools complemented the procedure. The results obtained in the investigation were provided both by municipality and by interurban route. An interesting finding of the research was the uneven recovery of the municipalities. The localities closest to the attractor nucleus have recovered more quickly to pre-pandemic mobility levels due to their geographical proximity, larger population, higher income per household and the need to access certain public services. In terms of routes, all lines showed significant decreases in ticket sales, although with variations. Although passenger numbers have shown a gradual recovery, the initial loss was considerable, and pre-pandemic normality has not been completely achieved. The authors consider that future research should include other alternative means of transport in these interurban areas, incorporating variables to characterise passengers, such as age, gender...etc.

Wind energy is one of the main drivers of the renewable energy market. The wind turbine gearbox, used as a multiplier - is one of the main components directly related to wind turbine efficiency and lifespan. Therefore, the strict control of the gearbox project and its manufacturing processes or even minor improvements in this component heavily and positively impact energy production/generation over time. This work diagnoses a 1.6 MW gearbox fracture mechanics to identify its main reason and how the chain of events took place, leading to the catastrophic failure. The diagnoses underwent numerical simulation (Finite Element Analysis - FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses between the teeth, the region with the crack nucleated was the top land (tip of the tooth). The most likely factors that led to the premature failure were the missed lubrication for the planetary bearings, lack of gear raw material cleanliness, and excessive case depth at the tip of the sun shaft gear teeth. With the sun gear, planet bearings, and planet gears broken in pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, ending up as a premature gearbox failure.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis on the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

The effect of zirconium modified with silica dioxide in the conductivity of modified nanocomposite membrane in proton exchange membrane fuel cells (PEMFCs) application. In this study silica-zirconia nanoparticles were successful synthesized by precipitation method. The surface area and surface morphology of Si-ZrO2 nanoparticles were observed under BET analysis, X-ray diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscope (HRTEM). SEM and XRD results show that silica was well incorporated into ZrO2 nanoparticles with the mixed shape of nanorods and nanosphere. HRTEM shows a crystallised small nanoparticle with average diameter about 5–8 nm. The element analysis (EDX) confirms the presence of silica and zirconia nanoparticles as well as oxygen on the surface. The addition of silica has impact on the surface area and pore size of Si-ZrO2 nanoparticles, with Si-ZrO2 nanoparticles having a higher surface area of 185 m2/g when calcinated at 600 °C. It was shown that the Si-ZrO2 can be used as an impregnating membrane to enhance the conductivity as observed by cyclic voltammetry (CV), with a rectangular shape that exhibiting the best capacitance behaviour.

In this study, the structural behavior of a short threaded anchor with a 20 mm diameter and an embedment length of 50 mm (2.5Ø) in UHPFRC is investigated using non-linear Finite Element models. UHPFRC is assumed to exhibit tensile strain-hardening behavior, with tensile strengths of 7 MPa and 11 MPa, respectively. The modelled anchor was subjected to a continuously increasing uniaxial pull-out force. The results indicate that the fracture mechanism of threaded anchors in UHPFRC is primarily characterized by the formation of a tensile membrane within the UHPFRC, which acts as the main resisting element against the pull-out force. Additionally, the influence of UHPFRC's tensile properties on the pull-out behavior and ultimate resistance of the threaded anchors was determined.