A novel approach to optimizing turbomachinery performance is presented in this chapter, which integrates Computational Fluid Dynamics (CFD) simulations, an artificial neural network (ANN) with a modified Non-dominated Sorting Genetic Algorithm II (NSGA-II). Using this enhanced algorithm, substantial performance improvements can be achieved through geometric optimization. The effectiveness of this method is demonstrated in two case studies: an inducer for a centrifugal pump and a Savonius wind turbine. The inducer is designed to maximize head coefficient, hydraulic efficiency, and net positive suction head by optimizing factors such as inlet and outlet blade angles. The optimization process employs the Group Method of Data Handling (GMDH) for objective function modeling, followed by Pareto front plotting and TOPSIS for identifying trade-off optimal points. Results indicate significant improvements in head coefficient, hydraulic efficiency, and NPSHR by 14.3%, 0.3%, and 30.2%, respectively. Twist angle, aspect ratio, and overlap ratio are also optimized for wind turbine blades. The power coefficient is enhanced by 5.32%, the torque coefficient by 13.74%, and the rotational speed by 0.071% through multi-objective optimization. As a result of this approach, efficiency improves, and valuable insights into the optimal design principles for turbomachinery components are also gained.

The growing use of proton-exchange membrane fuel cells (PEMFC) in hybrid propulsion systems, aimed at replacing traditional internal combustion engines and reducing greenhouse gas emis-sions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial, which has led to a growing emphasis on developing energy management systems (EMS) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydro-gen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention to reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches —control-based, optimization-based and deterministic rule-based— are con-ducted to verify the effectiveness of the proposed strategy. Simulation results indicated that EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distri-bution between the fuel cell and energy storage systems.

This paper provides an alternative Slope-Deflection equation for performing the static analysis of the indeterminate structures. While the conventional Slope-Deflection method introduced in undergraduate structural analysis course involves the use of fixed end moments in the Slope-Deflection equations, the proposed method employs the slope of a simply supported beam to obtain the unknown moments at the joints. Three numerical examples of continuous beams with different load configurations are chosen and the efficacy of the proposed equations is demonstrated. From the perspective of undergraduate structural analysis course in Civil Engineering education, the approach proposed in this paper offers two advantages: (1) prior knowledge of fixed beams is not required and (2) the concepts gained from determinate structural analysis are directly applicable.

To obtain high quality grain refiner, a new Al-5Ti-1B-1RE master alloy grain refiner was synthesised by melt-matching method. Thermodynamic calculations and kinetic analyses, which were seriously missing from the study of key issues during the preparation process, were investigated. The microstructure and properties, comparison of the refining effect of different grain refiners on industrial pure aluminium and its refining mechanism were analysed with the help of X-ray diffraction (XRD), optical microscopy (OM), scanning electron microscopy (SEM) and energy spectroscopy, transmission electron microscopy (TEM) and an electronic universal testing machine (CSS-44100). The experimental results show that the second-phase particles in the Al-5Ti-1B-1RE master alloy matrix synthesised by the melt-matching method are mainly TiB2、 A13Ti、Ti2A120RE, etc. The magnitude of the free energy ΔG of the synthesis reaction is calculated to be ΔGTiB2<ΔGAl3Ti<ΔGTi2A120RE. When the temperature is low (undercooling ΔT is large), the nucleation rate N mainly depends on the kinetic atomic diffusion activation energy Q, while the thermodynamic nucleation work becomes a secondary factor, and vice versa. The industrial pure aluminum refined by the new grain refiner has almost transformed from coarse columnar crystals to fine equiaxed crystals, with an average grain size of 70.2 μm, which was 36.18% and 20.66% smaller than that refined by domestic and imported Al-Ti-B wire master alloy grain refiner, and its mechanical properties of tensile strength σb was increased by 11.94%, 8.29%, and elongation at break δ was improved by 31.79%, 17.41%, respectively. The main mechanism for refining pure aluminum in Al-5Ti-1B-1RE master alloy is the formation of TiA13 on TiB2 particles, the release of RE atoms from Ti2A120RE phase surrounding TiAl3, and the partial transformation into TiA13 phase, which promotes the dual nucleation refinement mechanism and the pinning effect of rare earth elements on grain boundaries.

Pipelines are regularly trenched and then forgotten, just allowed to run till failure and producing an overall incredibly poorly run system that is ripe for improvements. One of the most needed areas for improvement are proactive methods for condition assessment of existing pipelines. This can be divided into two main categories: proactive condition assessment technologies and nonstructural proactive condition improvement technologies. The objective of this paper is to identify the best option available on the market for both these areas of proactive condition assessment for existing pipelines. The compared proactive condition assessment technologies include Acoustic Resonance Testing, Ultrasonic Thickness Measurement Testing, Phased Array Ultrasonic Testing, Pulsed Eddy Current Testing, and the Multisensor ROV. The technologies compared for nonstructural proactive condition improvement were Jetting, Pigging and also included the main requirements for a proper catalog of pipe information that can be used to produce a ranked system for assessing which pipes are at highest risk for failure and can be used to efficiently direct resources for pipe maintenance. The final conclusion was that Acoustic Resonance Testing and the Multisensor ROV are the two best products available for the proactive condition assessment of existing pipelines available on the market from their versatility of application and amount of information gathered during pipe inspection. The best option available in the group of nonstructural proactive condition improvement technologies was pigging for its incredibly low risk and low social impact as well as potential for improvement in the near future. It was also concluded that every agency would be better off with a catalog of the pipe material, age, length, diameter, and burial depth to know the location of every pipe an agency owns as well as to rank every pipe in terms of risk of failure and impact of said failure to most efficiently direct resources.

The behavior of concrete beams reinforced with steel and Glass Fiber Reinforced Polymer (GFRP) rebars, without shear reinforcement is investigated in the current study. This study includes testing experimentally of twenty-four steel and GFRP Reinforced Concrete (RC) beams cast with different percentage of fibers. The beams were split into two types, steel and GFRP RC beams. Beams of different fiber ratios (0, 0.5, 1.5 and 2.5%) were developed and tested, in two groups, each consists of twelve specimens, steel and GFRP reinforced RC beams. Both steel and GFRP RC beams were cast as singly reinforced with 10 mm diameter longitudinal bars at the bottom. In order to ensure that the beam to fail under shear, the top reinforcement and stirrups are removed. The concrete cover was kept as 30 mm. Three concrete grades (M30, M60 and M80) were examined for different dosage of fibers for both steel and GFRP RC beams. The size of the beam samples was kept as 150 ×200×1500 mm. The key parameters were the form of reinforcement material (steel and GFRP), compressive strength of concrete and fiber percentage. Failure mode, ultimate load carrying capacity, mid-span deflection, strain in concrete and reinforcement (steel and GFRP) of the beams have been reported. It is found that the effect of using fibers and higher grade of concrete is more pronounced in improving stiffness of GFRP RC beams compared to steel RC beams. Hence it can be concluded that GFRP rebars can be used as an efficient type of reinforcement in RC beams.

A previous research showed the 1755 tsunami would inundate the Marginal Avenue, the railway and the low ground area of Caxias, Oeiras municipality, Portugal. Thus, in this study several proposals for coastal protection are presented. A new Lidar data of local topography allowed the construction of a more accurate DEM, including the representation of the streets of Caxias downtown, to be used in the tsunami numerical model. The results were combined with field survey on Caxias and other coastal areas of Portugal and Japan. The new calculations show the low ground areas up to 6 m in height area inundated by the tsunami. However, with the new proposal DEM the tsunami inundation is restricted to the beaches. The proposal includes the construction of a new sea wall with 7 m in height at Caxias beach, the increase in height of the Barcarena Stream levees and the elevation of the sidewalk at São Bruno beach, with a gentle slope up to 7 m in height. It is also proposed the construction of a third bridge over the Barcarena Stream for pedestrian ranging from 5 to 7 m in height that will serve as a gate for the incoming tsunami waves.

The present study investigated how two types of face masks affected seven acoustic parameters, commonly used in forensic speaker recognition, in several languages. Reading samples from an excerpt of “The Little Prince” were recorded by volunteers of both genders speaking in Lithuanian, Croatian, Romanian, Turkish, Ukrainian, Portuguese, Georgian, Hungarian, Spanish, Russian, Polish, and German, without a protection mask, wearing a surgical mask, and wearing an FFP2 mask. In addition, recordings from mobile and landline communications in some of the mentioned languages were also obtained. A total of 860 volunteers were recorded. The dataset is part of the Forensic Multilingual Voices Database (FMVD), developed under the “Competency, Education, Research, Testing, Accreditation, and Innovation in Forensic Science” (CERTAIN-FORS) project, funded by the European Union (EU) and coordinated by the European Network of Forensic Science Institutes (ENFSI). The results showed that face masks have an impact on the studied acoustic parameters and that the effects vary with mask type, sex, language, and recording channel.

Dehydration of hydrogen gas is one of the important steps in many industrial purposes thus, drying systems have been developed with high-efficiency and relative effect. In this article, the basic principles and design of a three-bed TSA (Temperature Swing Adsorption) dryer for dehydration operation of hydrogen gas drying are described in detail. The text commences with an in-depth explanation of the basic principles behind TSA technology such as adsorption and desorption mechanisms, thermodynamic considerations and material selection for adsorbents. This paper also deals with the detailed design of a three-bed TSA dryer, explaining about various fabricating details that influences both performance and total operability. The third part focuses the operational phase, and especially in cycle time, regeneration strategy and efficiency of energy. Advanced optimisation techniques are employed to lower energy consumption, increase throughput capacity and improve overall system. This detailed study will be of great help for engineers and investigators working on TSA systems design and optimization to dehydrate hydrogen gas, contributing towards the betterment in this important field dealing with industrial gas processing.

This study focuses on enhancing the prediction of flow characteristics in gas pipelines under hydrate-forming conditions using a validated CFD model. Four-Quadrants approach was introduced to analyse the relationship between gas and water flowrates, and to provide further insights into hydrate formation, agglomeration, deposition, and pipeline plugging. The analysis considered subcooling temperatures, water volume fractions, and gas velocities, to assess the risk of hydrate plugging in subsea gas pipelines. Results indicate that higher subcooling temperatures significantly increase the risk of hydrate formation and plugging, especially at lower gas velocities. At subcooling temperatures above 7.1 K, hydrate agglomeration and deposition become more pronounced, leading to a greater risk of plugging. Similarly, water volume fractions above 0.06 were found to increase the likelihood of hydrate blockages. However, higher gas velocities were shown to reduce this risk by promoting the sloughing of hydrates and preventing deposition. The findings align with existing literature and offer practical insights for flow assurance in gas pipeline operations.

Several stochastic H∞ filters for estimating the attitude of a rigid body from line-of-sight measurements and rate gyro readings are developed. The measurements are corrupted by white noise with unknown variances. Our approach consists of estimating the quaternion while attenuating the transmission gain from the unknown variances and initial errors to the current estimation error. The time-varying H∞ gains are computed through differential and algebraic linear matrix inequalities whose parameters are independent of the state. The case of a gyro drift is addressed, too. Extensive Monte Carlo simulations show that the proposed stochastic H∞ quaternion filters perform well for a wide range of noise variances. The actual attenuation, which improves with the noise level and is worst in the noise-free case, is better than the guaranteed attenuation by one order of magnitude. The proposed stochastic H∞ filter produces smaller biases than a standard quaternion Kalman filter and similar standard deviations at large noise levels. An essential advantage of this H∞ filter is that the gains are independent of the quaternion, which makes it insensitive to modeling errors. This desired feature is illustrated by comparing its performances against those of unmatched Kalman filters. When provided with too high or too low noise variances the Kalman filter is outperformed by the $H_\infty$ filter, which essentially delivers identical error magnitudes.

3D printing has significantly transformed the design and manufacture of robotic prostheses, making these devices more accessible, customized, and functional. This paper examines the historical evolution of prosthetic technology, tracing its development from rudimentary mechanical devices to the integration of advanced technologies, such as 3D printing. This innovation has enabled the production of prostheses at lower costs while enhancing their adaptability and performance. The review highlights how 3D printing has driven a disruptive shift in prosthetic customization, and how emerging technologies—including smart materials and artificial intelligence—have expanded the capabilities of prosthetic devices, offering more adaptive and natural movement. However, challenges persist, particularly regarding the need for standardization and infrastructure expansion to ensure equitable access to these technologies. Future research into novel materials and manufacturing techniques holds the potential to improve further the functionality, affordability, and accessibility of prosthetic devices. In conclusion, while 3D printing has marked a significant milestone in the evolution of robotic prosthetics, overcoming existing challenges is essential to realize its global impact and benefits fully.

This study is focus on the petrographic properties and geo-mechanical properties of the Inzari formation rocks at Tar Khel Village, District Swabi, Khyber Pakhtunkhwa, and whether they are suitable for use as building materials for structures like bridges, buildings, and roads. Five fresh and large samples have been taken from various areas of this formation. These rock samples were identified as siliciclastic mudstone through a careful petrographic analysis. The test result of physical properties (water absorption, specific gravity, and porosity) and mechanical properties (unconfined compression strength, unconfined tensile strength, Schmidt hammer test, and shear strength) of these rock samples shows as follows: their unconfined compressive strength with between 23 to 94 MPa, their specific gravity values being very high (≥2.78), and their porosity and water absorption values being very low (<1%), imply that the Inzari formation rock can be used as an excellent building material. The rock sample strength variations may be mainly caused by the presence and abundance of stylolite and fractures. Furthermore, the petrographic property analysis of these rock samples indicates that they have the significant amount of micrite and calcite, which are suitable to be use as both asphalt and concrete aggregate. According to this study, the Inzari formation rock is appropriate for use as fine aggregate in concrete, asphalt, and other building materials.

In metropolitan regions, which are particularly vulnerable to seismic damage, numerous reinforced concrete (RC) buildings are being constructed. Public safety has been an important consideration to safeguard and protect people, buildings, and infrastructure from the potential effects of earthquakes. The assessment of seismic vulnerability within urban areas encompassed an analysis of both building vulnerability and the scale of seismic hazards prevalent in the locality. This assessment aimed to ascertain the likelihood of building damage resulting from ground motion induced by an earthquake of a specific magnitude. In recent decades, considerable efforts have been made to develop and improve methods for assessing earthquake damage to reinforced concrete (RC) buildings. These efforts included the creation of seismic hazard and seismic risk indices that were used to quantify the potential destruction of individual building components or the entire building. This scholarly review article presents an in-depth analysis and concise summary of the primary techniques (including qualitative or empirical, quantitative, analytical, and experimental test methods) devised for appraising the seismic vulnerability of reinforced concrete frame buildings, pre- and post-earthquake occurrences. It is a valuable reference for policymakers, engineers, researchers, and specialists engaged in earthquake risk mitigation efforts.

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR recorded data allows power quality forensics and event analysis, but this capability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations and without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR recorded data storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency, and Root Means Squared (RMS) histograms, which alleviate current high data storage requirements and provides effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the date storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR recorded event.

This research delves into the water hammer occurrences, in the transmission pipeline of the SMD (Manda Daba) Multi Village Water Supply Schemes in Afar, Ethiopia. It utilizes simulation software, Bentley HAMMER version 8i to assess transient flow issues and the effectiveness of protective measures. The study pinpoints high risk areas in Scenario 1 marked by pressure spikes, negative pressures and frequent cavitation highlighting the importance of efforts. Prior to safeguards significant pressures reaching 288.27 m H₂O and minimum pressures dropping as low as 9.98 m H₂O are observed. In Scenario 2 where protective measures are implemented notable enhancements are seen with pressures ranging from 121.38 m H₂O to 150.49 m H₂O improved safety levels and substantial reductions in minimum pressures at pump stations. Moreover the study delves into Scenario 3 which explores pipe materials and diameters to provide insights, for pipeline design standards. It emphasizes how protective measures effectively mitigate water hammer effects while also highlighting cost saving potential by incorporating 2000 liter hydro tanks at pump stations. This integration leads to decreased pipeline collapses and maintenance expenses underscoring the significance of addressing challenges pinpointing failure causes and offering solutions to enhance system performance and strengthen reliability within the SMD water supply system

Recent increases in the demand for automotive lithium-ion batteries (LIBs) have led to higher needs for critical materials like lithium, cobalt, nickel, and graphite. Consequently, recovering materials from spent batteries has gained importance. This study aimed to (1) develop a mechanical pre-treatment method for separating and concentrating materials from spent NCM622 cells, and (2) evaluate the economic, environmental, and scarcity potentials of these fractions. The pre-treatment involved grinding and granulometric separation, producing six particle-size fractions. Analysis showed that the finest fraction (n < 0.5 mm) had 85% of lithium, 77.4% of cobalt, 75.1% of manganese, and 68.5% of nickel. This fraction represented 40.4% of the processed mass and had the highest economic value (USD 3,669/ton NCM cell). This fraction was also prioritized for recycling due to its superior environmental and scarcity considerations.

Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies enabling desired interaction. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacted objects, and seldom quantify the assistive effects during activities of daily life (ADL), such as extension angle and joint stiffness predicted. The lack of quantification poses challenges to the effective and sustainable advancement of rehabilitation technology. This paper introduces the design, modeling, and testing of pneumatic bioinspired Segmented Composite Proprioceptive Bending Actuators (SCPBAs) for hand rehabilitation in ADL tasks. The actuator’s soft-joint/rigid-bone segmented structure, inspired by the human finger anatomy, provides a superior fit compared to traditional continuous structures. A quasi-static model is established to predict the bending angles based on geometrical parameters. The SCPBAs also include a quantitative evaluation of joint stiffness and extension angle utilizing proprioceptive bending information. Additionally, a soft under-actuated hand exoskeleton equipped with SCPBAs demonstrates its potential in ADL rehabilitation scenarios.

Urban areas are increasingly challenged by the urban heat island effect, resulting in increased energy consumption and reduced efficiency of conventional air source heat pumps (ASHPs). This study explores the integration of ground source heat pump (GSHP) systems with energy piles in urban environments, focusing on small to medium sized residential units. Energy piles combine structural support with geothermal heat exchange capabilities, taking advantage of stable ground temperatures to improve energy efficiency. The research examines key design considerations such as soil thermal properties, heat transfer mechanisms, and the optimal configuration of heat exchangers within the piles. Results indicate that GSHP systems can significantly reduce electricity consumption and operating costs compared to ASHPs, despite higher initial costs. Several factors, including site conditions, pile materials, and heat exchanger designs, are critical to system performance. The study concludes that while GSHP systems offer significant long-term benefits, their widespread adoption in Southeast Asia will require further development of construction methods and design strategies, as well as supportive government policies to mitigate initial costs. This work aims to advance the understanding and implementation of energy piles in urban environments, thereby promoting sustainable energy use in smart city development.

Aluminium matrix composite’s overall quality is impacted by the friction stir process parameters, which have a major impact on the microstructure, soundness, and particle dispersion inside the stir zone. Although it requires a lot of experimentation, optimizing these factors is vital. For the purpose of optimizing the ultimate tensile strength and microhardness of the AA5083/Silicon carbide and AA5083/Coal composites fabricated by FSP, this work used the Taguchi L9 design of experiments. Three process parameters were investigated at three levels each. For the AA5083/Silicon carbide composite, the results of the ANOVA analysis showed rotational speed has a larger impact on ultimate tensile strength while forAA5083/coal tilt angle has a larger impact on ultimate tensile strength. On the other hand, rotating speed had a greater effect on microhardness and percentage elongation of the AA5083/Coal and AA5083/Silicon carbide composite joints, according to ANOVA analysis. The need for specific process optimization for each composite was highlighted by the fact that different combinations of parameters were best for obtaining the necessary attributes in the AA5083/Silicon carbide and AA5083/Coal composites.

After an earthquake, assessing the seismic vulnerability of buildings is essential for prioritizing emergency response, guiding reconstruction, and ensuring public safety. Accurate and timely evaluation of structural damage plays a vital role in mitigating further risks and facilitating in-formed decision-making during disaster recovery. This study investigates the performance of various Generative Artificial Intelligence (GAI) models, developed by different companies with diverse model sizes and context windows, in analysing post-earthquake images. The primary objective was to evaluate the models' effectiveness in classifying structural damage according to the EMS-98 scale (with 5 levels of damage), which ranges from minor damage to total destruction. For masonry buildings, the correct classification rates varied widely across models, from 28.6% to 64.3%, with mean damage grade errors ranging from 0.50 to 0.79. In the case of reinforced concrete buildings, correct classification rates ranged from 37.5% to 75.0%, with mean damage grade errors between 0.50 and 0.88. The use of fine-tuning could probably improve the results substantially. Improving the accuracy of GAI models could significantly reduce the time and resources needed to assess post-earthquake damage, namely when comparing with more traditional approaches. The results achieved thus far demonstrate the promise of GAI models for rapid, automated, and ac-curate damage evaluation, which is critical for expediting decision-making in disaster response scenarios.

Cybersecurity in Critical Infrastructures, and in particular Digital Substations is a topic of wide interest for industry and academic community. One of the approaches widely used to support research on this field is the use of datasets (i.e., traffic samples collected during the operation of an infrastructure). However, generating datasets from operational electrical systems presents certain challenges: i) The generation of these datasets generally implies the operation under controlled or ideal conditions, which possible ignores the dynamics of real-world operations within a digital electrical substation; and ii) Captured data often contains sensitive information, posing challenges for publication within the research community. This paper introduces the development of a dataset for cybersecurity research, focusing on the analysis of GOOSE spoofing attacks, given the critical role of GOOSE protocol in executing operational and control actions within Digital Substations. The dataset exposes the real effect of the attack by executing unwanted operations or actions under various operational conditions, including both stable electrical system scenarios and situations where failures are present in the electrical system. The dataset was acquired from a laboratory testbed developed within a physical infrastructure that emulates the actual operation of a digital substation with two bays. Experiments allowed to observe relevant properties of the traffic associated to GOOSE protocol, and the actual vulnerability of a DS infrastructure regarding Spoofing Attacks.

This study aims to investigate the effects of cement kiln dust (CKD) on the hydration reactions and mechanical properties of cement and to evaluate its potential for use as a supplementary cementitious material (SCM). The key variables are the CKD type and incorporation rate. Cement paste and mortar specimens containing CKD were prepared to examine their effects on the cement hydration and mechanical properties. The impact on hydration was assessed using setting time measurements, heat of hydration tests, and thermogravimetric analysis (TG). In addition, compressive strength tests were conducted to evaluate the effect of CKD on the mechanical properties of the cement. The results indicated that CKD promoted early cement hydration and enhanced the early age mechanical properties. However, owing to its lack of pozzolanic reactivity, it did not significantly affect long-term hydration. Given that the effects of CKD vary slightly depending on its chemical composition, careful consideration of CKD's properties suggests that its potential use as a SCM is promising.

The air attenuation of pure tones as a function of temperature and humidity is well known and described in ISO and ANSI standards. However, room acoustic measurements are performed in octave bands, which is the reason for a complicated, nonlinear behavior of the air attenuation. It is known that room acoustic measurement results depend on air temperature and humidity, and these atmospheric data should always be reported in connection with room acoustic measurements. A calculation model for the air attenuation can make it possible to normalize the room acoustic results. For accurate results it is possible to apply a summation method on each time sample in the impulse response. An approximation to the air attenuation at and above 1 kHz is possible by linearization using the pure-tone attenuation of two different frequencies, one frequency valid for the early part of the impulse response, and another frequency valid for the later part of the impulse response. The purpose of the suggested methods is to make it possible to normalize room acoustic measurements to a standard atmosphere.

The paper addresses the challenging issue of trajectory tracking for uncertain quadrotor UAVs, particularly under the constraints of input saturation and external disturbances. It introduces adaptive laws for managing uncertainties in mass and inertia moments without requiring prior knowledge, ensuring effective control even with varying system parameters. To counteract the effects of input saturation, the study incorporates an auxiliary system designed to compensate for these limitations. Additionally, a disturbance observer(DO) is utilized to manage and mitigate the impact of time-varying external disturbances. The proposed control strategy integrates a sliding mode adaptive control approach with an inner-outer loop structure, enhancing robustness and adaptability. Numerical simulations demonstrate the effectiveness of the designed control strategy.

Rapid urbanisation and city expansion worldwide have significantly increased impervious surfaces such as roads, buildings, and pavements. These impervious surfaces impede water infiltration into the ground, resulting in escalated surface runoff during rainfall. This has been exacerbated by climate change and heightened precipitation levels, resulting in recurrent annual flooding. Conventional drainage systems are now insufficient to cope with the amplified surface runoff, contributing to the escalating flood occurrences. Sustainable urban drainage systems such as green roofs, swales, and permeable pavements were developed as modern flood control techniques to replace the traditional drainage system approach by optimising resource utilisation and developing novel and more productive technologies. While most SUD techniques are sustainable in design, construction, and operation, permeable pavements are considered one of the least sustainable SUD techniques. This has garnered considerable attention from researchers who have been studying the design of a more sustainable permeable pavement system by utilising sustainable and recycled materials. This paper reviews the performance of permeable pavements systems using findings from extensive reviews and analyses of scientific publications on permeable pavements. It introduces a future solution and design of permeable pavements using recycled materials. The study will consist of two permeable pavement systems, one incorporating recycled materials in its layers and the other using traditional construction materials. The data collected from both systems will be compared to determine their performance.

Research on high-volume fly ash concrete, which utilizes large quantities of fly ash, has been actively conducted to promote resource recycling and reduce carbon dioxide emissions. However, while cement mixed with fly ash exhibits different temperature sensitivities compared to traditional cement, its hydration and mechanical properties vary depending on curing conditions. Despite this, there is a significant gap in research on this topic. In this study, high-volume fly ash concrete was produced with fly ash replacement rates of 35 % and 55 %. Compression strength tests were performed at various curing ages under three different curing temperature conditions, and the results were analyzed. Additionally, an isothermal calorimeter was used to analyze hydration characteristics under different curing conditions. The results indicated that concrete with high fly ash content was more sensitive to curing temperature compared to ordinary Portland cement.

In terahertz communication systems, lens antennas used in transceivers are basically plano-convex dielectric lenses. As the aperture increases and the focal length decreases, the thickness and mass of the plano-convex lens increase rapidly. Through theory and simulation, we designed a Fresnel lens suitable for the terahertz band to meet the requirements of large aperture and short focal length, and simulated the performance, advantages and disadvantages of the terahertz Fresnel lens. A 300GHz terahertz wireless communication system was built to verify the gain effect of the Fresnel lens antenna. The experimental results show that the Fresnel lens can amplify the signal in the terahertz band. The theoretical gain of a 30 cm Fresnel lens is 48.83dB, while the actual measured gain is approximately 45dB.

Loop-Mediated Isothermal Amplification (LAMP) is a widely used technique for nucleic acid amplification due to its high specificity, sensitivity, and rapid results. Advances in microfluidic lab-on-chip (LOC) technology have enabled the integration of LAMP into miniaturized devices, known as μ-LAMP, which require precise thermal control for optimal DNA amplification. This paper introduces a novel thermal bed design using PCB copper traces and FR−4 dielectric materials, providing a reliable, modular, and repairable heating platform. We present the design, fabrication, and control mechanisms of the thermal bed, demonstrating its ability to maintain accurate temperatures essential for μ-LAMP applications. The thermal bed’s performance is validated through finite element method (FEM) simulations, confirming its effectiveness in diverse applications such as disease diagnostics, biological safety, and forensic analysis.

As a method of lightweight design, multi-material design aims to make targeted use of materials in order to reduce CO2 emissions. In this context, it can be described as one of the product development methods used to meet the challenges of climate change. However, since the design of structures in multi-material design is complex, topology optimization can be used to support the product developer. In this article, a multi-material topology optimization method is developed that combines the Velocity Field Level Set method with the Reconciled Level Set method. Furthermore, the current design is explicitly resolved in each iteration by means of multi-material remeshing. The edge collapse phase in the remeshing process is achieved by applying the producer consumer pattern. The developed method is then validated using known examples from the state of research and the influence of parameters of the method on the result is analyzed by means of studies.

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search, due to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape, complicating UAVs' search efforts and hindering the convergence of the MARL algorithm training. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of Decentralized Partially Observable Markov Decision Process. Based on this model, a Spatio-Temporal Efficient Exploration network and a Global Convolutional Local Ascent mechanism are proposed. Subsequently,we introduce a Multi-UAV Escape Target Search Algorithm Based on MAPPO(ETS-MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS-MAPPO algorithm outperforms five classical MARL algorithms in terms of the number of target searches,the area coverage rate, and other metrics.

It aims to utilize more solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. Therefore, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking(MPPT) methods are utilized for the maximum power generation in photovoltaic panels systems under different weather conditions. In this paper, a novel hybrid and clever maximum power point monitoring fuzzy logic controller maximum power point tracking algorithm established on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in MATLAB/SIMULINK environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel were used for the simulation. The hybrid method proposed here was found to reduce the voltage and current ripple compared to the classical incremental conductivity method. The proposed intelligent method has been shown to stabilise the system more quickly in the event of sudden weather changes.

The context of this research is given by a socio-economic situation in which new and more demanding challenges appear. Using wood, a naturally occurring, renewable, and biodegradable material, in construction is becoming more and more necessary due to the awareness of the negative impact on the environment of the construction industry and the energy crisis limitations. The purpose of this study is to analyze what do we really know about the use of wood as a building material. The search for an answer to the research question implies the use of a scientometric literature analysis, followed by an in-depth and expert analysis. The research results indicate a major preoccupation for studying engineering wood structures, like the Cross laminated Timber, the structural behaviour of the structures, the implementation of new IT discoveries in wood construction technology and a more recent approach regarding the environmental impact of wood construction, mostly the possibility of using wood waste to prolong the life of wood as a building material. The results also show a need to create new models of understanding the structural behaviour of wood that consider the fibrous and cellular nature of the material. Wood as a construction material is an extremely complex material and for a better understanding of it, there is a need to have an interdisciplinary approach in which to combine the knowledge of construction with that of forestry, ecology, biology and others.

In-situ production of PC components is the same process as in a factory, with the order of installing processed rebar, pouring concrete, curing, and storage. In-situ production not only reduces environmental impact by more than 14.58% compared to factory production, but also reduces costs by up to 39.4% compared to factory production when increasing the number of in-situ produced PC components. The stock yard area is more than 5 times larger than the production area, which accounts for a significantly higher proportion. Research on in-situ production of PC components is mainly focused on the production area. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the improved dung beetle optimization algorithm optimization improved the adjacent correlation by 22.79% and reduced carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm, which verifies its efficiency. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.

(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the Blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the Blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. For example, Satoshi Nakamoto's (2008) original paper on Bitcoin introduced proof-of-work (PoW) as a method of achieving consensus on the network. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DCDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DCDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DCDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DCDC a promising advancement in the field.

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

The integration of digital technologies in managing technical and design information is trans-forming the fields of architecture and construction. This article presents a website developed to compile detailed information on the construction processes of buildings at Pontificia Universidad Javeriana (PUJ) campus. The site offers comprehensive construction records and interviews with designers, engineers and other professionals who collaborated to align design approaches with technical construction requirements. A chronological record of the construction processes was maintained, from inception to completion of the buildings. Digital tools include interactive modules that facilitate access and data updates, improving coordination and efficiency for man-aging future projects. Gamification elements and social media dissemination boost user engage-ment. The study also assesses the impact of the website on the university community, analyzing usability and user (N=235), among students, teachers, and staff members. Findings indicate that digital technologies optimize construction processes and improve the management of technical and design information, and reveal opportunities for future projects and research, benefiting both internal and external stakeholders.

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is analyzing the changes in the foot-floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implant for deep brain stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months (T1), and 12 months (T2) after the DBS neurosurgery for bilateral high-frequency subthalamic nucleus stimulation. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5-minute walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in the UPDRS-III score from 19.4±1.8 to 10.2±1.0 points (T0 vs. T2) (p < 0.001). In particular, the percentage change of atypical gait cycles of PD more affected side decreased at T1 (Straigh-line: -72%; Turnings: -43%) and at T2 (Straigh-line: -54%; Turnings: -20%). The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.

There is a growing awareness of the detrimental environmental impact of the construction industry, primarily due to the utilization of building materials that necessitate energy-intensive processes for their production. The objective of this paper is to explore whether mixing wood and cement could serve as a viable solution to mitigate this adverse environmental effect. The data was obtained by extensively investigating the Clarivate Web of Science database, adhering to the PRISMA technique guidelines, and subsequently processed using Bibliometrix and VOSviewer software. An in-depth critical analysis of the literature revealed a heightened interest in this subject matter in recent years, with the focal points evolving from initial experimental endeavors aimed at understanding the behavior of the innovative wood cement composites, primarily in terms of their mechanical properties, to assessing their environmental impact and the advantages associated with their use. The researchers suggest that optimal results can be achieved by treating the wood with tetraethyl orthosilicate, incorporating cellulose nanocrystal particles or wollastonite into the mixture, and utilizing wood from the Pinus species. Furthermore, it was discovered that a noticeable improvement in strength is only attainable if a low percentage of wood, up to 30%, when wood ash is employed. Ongoing research concerning wood and cement composites continues to progress, with regular advancements and breakthroughs, thus offering promising prospects for the development of superior and eco-friendly building materials.

The study focused on assessing passenger risks specifically at the platform-train interface (PTI). The primary aim of this investigation is to analyze how various factors, including station design, passenger flow, and weather conditions, impact the frequency and types of incidents occurring at this critical interaction point. To achieve this, the study utilized a comprehensive data analysis approach, examining records from the Valparaiso Metro for the period of 2022-2023. During this timeframe, a total of 361 incidents were documented, providing a substantial dataset for evaluating incident patterns and their correlations with different influencing factors. The analysis revealed that incidents are notably influenced by peak hour conditions and weekdays. Specifically, the study found that crowded conditions inside the train during peak hours, which typically occur in the morning and evening rush periods, contribute significantly to the occurrence of incidents. When examining incidents occurring on the platform, the study identified that these are closely related to the type of station and the presence of stairs for station access. Conversely, stations designed with more accessible features might experience fewer such incidents. Future studies will focus on incorporating additional factors and analysing new data to provide a more comprehensive understanding of incident dynamics.

In this work, we investigate how to obtain the optimal active elements in Hybrid Relay-Reflecting Intelligent Surface (HR-RIS) systems while considering enhancing the sum rate of the communication system in the presence of malevolent eavesdropper. Therefore, when dealing with numerous elements in the HR-RIS, the complicated optimization task of activating and deactivating the active elements of RIS renders typical exhaustive search methods impracticable. Standard techniques, such as alternating optimization, often frequently converge and unfortunately become trapped in local optima, leading to less-than-ideal results. To address these issues, we suggest performing the Jaya algorithm, which modifies its search strategy in response to historical data to find a compromise between investigating novel avenues and taking advantage of well-established answers. Our simulation findings demonstrate that this strategy outperforms the most recent techniques, yielding higher sum rates with the best possible user location and robust security against eavesdropping. It’s interesting to observe that even at lower total power levels, the Jaya algorithm performs incredibly well and efficiently identifies the optimal amount of active elements. This demonstrates how it can decrease interference, increase signal quality, improve Signal-to-Interference-plus-Noise Ratio (SINR), and increase secrecy rates, opening the door for more efficient and secure wireless communication systems.

The dynamic development of robotics is affecting people's lives. Flying robots replace pilots and crews in performing dangerous tasks. There are various designs of the above-mentioned robots such as multi-rotors, fixed-wing aircraft, helicopters, among which wing-in-ground (WIG) units can also be distinguished. WIG vehicles, which utilize ground effect to enhance efficiency and stability, share similarities with UAVs in their control and aerodynamic challenges. The article presented the results of the autopilot test for the NeoX UAV, which have the potential to be utilized in WIG UAVs. The NeoX was developed at the Air Force Institute of Technology as a reconnaissance flying robot and is a tailless mini UAV. It has a characteristic feature which is the flying wing with a small fuselage. The electric engine with the pusher propeller ensures thrust. The tailless airplane has a greater drag-to-lift ratio than a conventional airplane, but this airplane configuration needs a control system that ensures a stable flight. The structure of auto-pilot for the horizontal flight phase (a cruise phase), the climbing, and the descending phase were presented. The authors concentrated on the longitudinal channel of autopilot and showed the recorded results from the flight tests. The research focused on developing autopilot system that could be adapted for use in WIG craft, which operate with similar aerodynamic principles but require unique control adaptations due to their proximity to the water or ground. The findings from the NeoX UAV tests are instru-mental in enhancing the stability and performance of these specialized crafts, ensuring safer and more efficient operation in their respective environments.

A comprehensive 3D Multiphysics model was developed to simulate a plate fin heat exchanger designed for hydrogen liquefaction, incorporating ortho-para- hydrogen conversion catalyst in the hot fin channel. The model encompassed the 3D serrate fin structure, turbulent flow within the cold fin channel, and porous flow through the catalytic hot fin channel. Species transportation within the hot fin channel is coupled with ortho-para hydrogen conversion kinetics, while heat transfer mechanisms between the hot and cold fin channels are rigorously accounted for. Addi-tionally, the state- of- art equation of state is employed to accurately describe the thermodynamic properties of ortho- and para- hydrogen within the model. Numerous operational parameters, including the gas hourly space velocity, cold gas velocity, ortho-para- hydrogen conversion ki-netics, and operating pressure, were systematically varied to identify the kinetic and heat transfer constraints during the heat exchanger operation. The findings revealed that the ortho-para- hy-drogen conversion kinetic parameter predominantly governs operations requiring high gas hourly space velocity, particularly in large- scale hydrogen liquefaction processes. Furthermore, significant pressure drop within the catalytic filled channel was observed, however, operating at higher pressure mitigates this issue while mildly enhancing ortho-para-hydrogen conversion kinetics.

The field of smart materials is attaining global industrial importance rapidly. The ionic polymer metal metaIl composites withheld there importance in this field. They are not only used as biocompatible devices but also in the field of thin and conformal bio coatings. The nafion 117 is unstable at elevated temperatures and its water intake resistance is low, so the nafion 117 is coated with the noble material to increase its thermal stability and water resistant property. The high cost of noble metal make its too costly to use in day to day applications so parylene has been used as substitute for noble metal in order to reduce the cost. The high thermal stability and water intake property if parylene make its suitable for the same. Multiple chemical treatments like plasma ionization and others have been done on the parylene coated nafion to increase its desirable properties considerably. The low adhesion rate between parylene and nafion 117 has been increased upto the certain extend by the use of ionized plasma. This paper reviewed the plasma chemical treatment of paryelene coatings for the biocompatible devices and provides an insight knowledge on classification, structure and thermal stability of the parylene coatings.

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.

This research paper investigates the enhancement of strength and performance of self-compacting concrete (SCC) for integration in Department of Public Works and Highways (DPWH) projects using a value engineering approach. The study explores key elements of SCC's strength and performance, including mix design optimization, construction practices, and durability considerations. Strategies to optimize SCC's strength, durability, and workability are discussed, emphasizing the benefits of SCC in improving construction efficiency and quality control within DPW infrastructure. The study also identifies challenges and gaps in SCC utilization, such as standardized performance verification and cost considerations, and proposes future directions for advancing SCC technology in civil infrastructure development.

The moiré effect is typically observed in regular periodic structures and sometimes in random (aperiodic) structures. However, only regular structures were used in measurements. In this study, we propose structures whose images are not regular but not completely random, and resemble a series of lines, such as a grid of dotted lines or a matrix of dots. A relatively simple image processing technique can make the moiré effect in such structures very similar to regular structures. We demonstrated using five different structures (including text) in measurements, with similar results.

Drag reduction by laminarization, an innovative technology in aerodynamics design, provides the possibility to improve significantly the aerodynamic performance of aircraft and remains to be one of the most promising and effective technologies. In this paper, the design method for transonic natural laminar flow (NLF) wing at high Reynolds number is investigated in details and a new optimization model is established to solve conflicts between the increase of a laminar flow region and the shock wave strength at the trailing edge. In order to numerically handle this competitive situation efficiently, a two-objective hierarchical variable fidelity Pareto evolutionary optimization model based on search space contraction is proposed and implemented. Numerical experiments show that it can capture simultaneously a Pareto front of the two-objectives: a wave drag minimization and a laminar flow region maximization. The results also show that both wave drag and skin friction drag performances of non-dominated Pareto members can be significantly improved via the optimal laminar wing shape and shock wave control device. Nearly 40% of the laminar flow area on the wing surface is achieved at the Reynolds number of 107. It is concluded that the present hierarchical variable-fidelity multi-objective evolutionary optimization method is computationally efficient, and illustrate its potential for solving complex engineering design problems.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

In the context of energy conversion from renewable sources to distribution grids (insulated or not), a converter is often required to transfer energy from a low voltage source, towards three-phase grids. This paper presents the HW design, the simulation results and the conversion performance of a CSI converter intended to interface low voltages renevable sources to three-phase grids. The main focus of this paper is to obtain the best performance in terms of voltage increase towards the output stage while maximizing the conversion efficiency. In comparison with the currently used energy conversion systems for small photovoltaic systems, hereafter some solutions were adopted to level and maximize the energy flow from the source to the DC-link and improve the quality of current supplied in terms of harmonic distortion. The proposed system is composed of two conversion stages: the first, voltage-to-current, the second current-to-current via a three-phase CSI bridge modulated with SVM technique. The stages are not completely decoupled from an electrical point of view, therefore, in order to mitigate the effects of these interactions, synchronization strategies have been adopted.

Wave and tidal energy, recognized as vital renewable resources, harness the ocean's kinetic and potential power. This study aims to provide an in-depth patent analysis of the technological landscape within these sectors. We applied a dual approach: first, a descriptive analysis was conducted to explore patent publication trends, technology lifecycle stages, patent activity by country, top assignees, and IPC classifications. Our analysis provided a detailed overview of the sector's growth and the key players involved. Second, we utilized topic modeling, specifically BERTopic enhanced with large language models (LLMs), to identify and fine-tune key technolog-ical themes within the patent data. In this study, we identified seven distinct clusters each for wave and tidal energy using this approach. This method led to a novel categorization of the pa-tents, revealing latent themes within the patent data. Although our categorization differs from traditional methods, it provides deeper insights into the thematic focus of the patents, highlight-ing emerging trends and areas of innovation within wave and tidal energy technologies to better exploit and optimize ocean energy conversion infrastructure.

This paper employs the Finite Element Method (FEM) to simulate and analyze the effects of corrosion defect parameters on the stress and failure pressure of pipelines. It investigates how the boundary conditions of the pipeline model influence stress and examines the sensitivity of failure pressure to corrosion defect parameters. A nonlinear regression equation has been developed from a dataset obtained through simulation experiments to predict the failure pressure of corroded pipelines. To validate the effects of corrosion defect parameters on failure pressure, a hydrostatic test platform for an API 5L X65 pipeline with corrosion defects was established to measure stress levels and failure pressures across varying corrosion defects. The study reveals that failure pressure is negatively correlated with corrosion length and depth, while positively correlated with corrosion width. Among these parameters, corrosion depth exerts a more significant influence on the pipeline's failure pressure than corrosion length and width. Within the range of corrosion defect parameters examined, the maximum deviation of the prediction equation's results from the simulation results is 8.71%, with an average deviation of 5.81%. The standard deviation of the fitted residuals is 0.01837. Additionally, the maximum deviation between the predicted results and experimental measurements is 8.39%, with an average deviation of 7.77%. The strong agreement between the predicted results from the equation and the actual measured data underscores the effectiveness of the nonlinear regression equation.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

The optimal combination of five components to develop a concrete for high-temperature applications was studied through a response surface methodology known as mixture design of experiment. The selected components were water, an alkali-activated binder based on ferronickel slag activated with potassium silicate and potassium hydroxide, and three aggregate sizes corresponding to 0-4 mm, 4-8 mm, and 8-16 mm. Selected response parameters were slump and compressive strength before and after heat exposure. Six cubes of side 150 mm were cast for each concrete mix, three for ambient and three for residual (post-heat exposure) compressive strength. All 20 concrete mixes were exposed to a thermal load of 600 °C for two hours. The optimal concrete mix was adapted for large-scale applications, by increasing slump and replacing limestone with olivine aggregates for enhanced residual compressive strength. The adapted mix resulted in concrete with 76 MPa and 32 MPa compressive strength before and after high-temperature exposure. The CO2 emissions were found to be 77 % lower when compared to traditional OPC concrete.

Soil stabilization using Portland cement is a widely adopted technique. Previous research has demonstrated that molasses, which contains sugars, enhances the reaction between cement and aggregates. This study investigates the impact of adding molasses to soil stabilized with Ordinary Portland Cement (OPC) on geotechnical properties. Expansive clay soil samples from Taru Jabba, District Nowshera, Pakistan, were treated with various combinations of molasses and cement. The concentrations of each stabilizer were varied at 0%, 4%, 8%, and 12% by dry weight of the soil. Additionally, the soil was treated with constant molasses contents of 4%, 8%, and 12%, while varying the cement content at 4%, 8%, and 12% by dry weight. Geotechnical tests, including Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and swelling potential, were conducted to assess the effects of the stabilizers. The results indicated that the addition of molasses improved soil strength, mitigated shrinkage cracks, and reduced brittleness. Specifically, the CBR value increased from 3.2% in the native soil to 12.3% with 12% molasses and 12% cement. The Plasticity Index (PI) decreased from 14.23% to 8.12%, and the CBR swell value reduced from 9.66% to 3.82%. Furthermore, the UCS of the stabilized soil increased by 64.7% compared to the untreated soil after a 7-day curing period.

The energy supply for isolated systems remains a current challenge in Brazil and worldwide, particularly when known solutions are compared regarding cost, the ability to supply energy on demand, and sustainability. In this context, green hydrogen, which has been considered one of the main paths for the decarbonization of the energy chain, is also seen as a potential solution for energy supply in isolated systems. Based on a literature review of articles addressing the topics of green hydrogen and isolated systems, an analysis is conducted on the application of green hydrogen as an energy supply solution for isolated systems. The review identified and collected data on important parameters for comparing solutions, such as CAPEX, OPEX, energy cost, and the price per kilogram of hydrogen. Using these data, analyses were performed to provide an overview of the application of green hydrogen in isolated systems. One of the objectives of this work is to present a comprehensive analysis of the green hydrogen chain and its application in the context of electricity generation, particularly for isolated systems in Brazil.

Small wind turbines placed at City locations are affected by variable speed winds that frequently also change the direction. Architectural constructions, buildings of different height and abrupt orography of Cities make the winds that occur at City locations, more variable than in flat lands or at sea. However, performance of Small-wind turbines under this type of variable winds is not deeply studied in specialized literature. Therefore, this article analyses this important issue, also considering three types of power conversion configurations utilized in small-wind turbines; the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called Pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e. the Ayanz wind turbine with screw blades, which presents very adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and birds’ casualties avoidance. Thus, a wide simulation and experimental tests-based analysis is carried out, identifying which are the main factors affecting to the maximization of energy production of small-wind turbines in general and the Ayanz turbine in particular. It is concluded that depending how the wind turbine is designed in terms of these identified main factors, the simple Pseudo-MPPT power conversion configuration could even extract more energy than more complex MPPT configurations, especially in quickly varying wind situations.

This study examines stress distributions in adhesive joints under various loading and temperature conditions. Finite element analysis (FEA) was employed to compute the peel and shear stresses at the adhesive interface and bondline mid-section. Dependency analysis shows that mid-section peel stress significantly impacts the experimental shear strength of SLJs more than shear stress. This insight highlights the need to carefully analyze peel stress and bending moment factors. The analytical solutions proposed by Goland and Reissner were analyzed with modifications by Hart-Smith and Zhao. Hart-Smith’s approach performed more effectively, especially when the adhesive layer thickness (t_a) is 0.5 mm and the overlap length to thickness ratio (c/t_a) is ≥20. FEA revealed stress distributions at the adhesive/adherend interface and bondline mid-section. DP490 adhesive joints exhibited lower stresses than EA9696. Temperature variations significantly affected joint behavior, particularly above the adhesive’s glass transition temperature (T_g). Both EA9696 and DP490 adhesive joints displayed distinct responses to stress and temperature changes. The parabolic and biquadratic solutions for functionally graded adhesive (FGA) joints were compared. The biquadratic solution consistently yielded higher shear and peel stress values, with an increase ranging from 15% to 71% compared to the parabolic solution at various temperatures because of larger gradient of the Young's modulus distribution near the overlap ends. The ratio of peak peel stress to peak shear stress suggests selecting an adhesive with a superior peel strength or primarily reducing the peak peel stress by functionally grading is advisable, particularly if the adhesive is brittle. Comparison of stress distributions emphasize the importance of selecting adhesives based on stress type, temperature, and solution methods in optimizing adhesive bonding applications. These findings provide valuable insights for thermomechanical applications where thermal stimuli may be used for controlled debonding.

The purpose of this study was to investigate the effects of cognitive dual-tasking on gait initiation and sit-to-walk. Twenty-eight healthy young adults performed gait initiation and sit-to-walk under two conditions: while engaging in serial subtraction (dual-task) and without any additional task (single-task). Motion data were collected using a 10-camera optoelectronic motion capture system, synchronized with force plates and surface electromyography. We analyzed spatiotemporal parameters, center of mass displacement, center of pressure trajectory, and lower limb muscle activation patterns. We found that dual-task conditions significantly affected both gait initiation and sit-to-walk patterns, increasing the duration of transitions and mediolateral center of mass displacements, while reducing vertical center of mass displacements and forward propulsion. We also observed a more constrained and less efficient center of pressure path, with reduced posterior displacement during the weight shift phase. Muscle activation, particularly in the tibialis anterior and biceps femoris, decreased during dual-task conditions, indicating altered neuromuscular strategies. These findings suggest a shift in postural control demands and motor performance during dual-task transitions.

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. Calculations 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 polyurethane 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.

As the demand for long-distance flights continues to increase, supersonic transport (SST) has once again become a hot research topic. However, due to its uniqueness, there was few corresponding evaluation methods to compare the supersonic civil aircraft with common subsonic aircraft. This paper proposed an evaluation method for the competitiveness of supersonic civil aircraft based on the Analytic Hierarchy Process (AHP). Key indicators were selected to form a competitiveness evaluation model from the perspective of four criterion factors of economy, comfort, adaptability, and environment. The evaluation method proposed in this paper could reflect characteristics of supersonic civil aircraft and other subsonic aircraft, and effectively compare their competitiveness. This work can be used to evaluate the social acceptance of supersonic civil aircraft and provide reliable decision-making support for airlines' design work.

The minimum ignition energy (MIE) has been extensively studied via experiments and simulations. However, our literature review reveals little quantitative consistency, with results varying for f=1.0 from 0.324 to 1.349 mJ, and for f=0.9 from 0.22 to 0.944 mJ. Therefore, there is a need to resolve these discrepancies. This RANS study aims to partially address this knowledge gap. Additionally, it presents other flame evolution parameters essential for robust combustion design. Using the reactingFOAM solver we predict the threshold energy required to ignite the fuel mixture. For this, the single step using the Arrhenius law is selected to model ignition in the flame kernel of stochiometric and lean CH₄/air mixtures, allowing it to develop into a self-sustained flame. The ignition power density, an energy quantity normalised with volume is incrementally varied keeping the kernel critical radius r_s constant 0.5 mm in the quiescent mixture of two equivalence ratios fs 0.9, and 1.0, for varied operating pressures 1, 5 and 10 bar at constant initial temperature 300 K. The minimum ignition energy is validated with twelve independent 1 bar data sets both numerical and experiments. Finally, a mathematical formulation of MIE is devised, a function of pressure and equivalence ratio shows a slightly curved relationship.

To analyze the impact of weather factors on the resilience of airport flight operations, particularly on flight operation, economic performance, and transportation capacity, Kolmogorov-Arnold Network (KAN) model was used to identify key weather features and establish the mapping relationship between weather factors and airport operational resilience. Taking Xi'an Xianyang International Airport as an example, the weights of each route were determined using the Grey Relational Analysis method considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to calculate the importance of key weather factors on the resilience of critical flight operation indicators. The results indicate significant differences in average daily flight volume, flight distance, and airport flow stability across different routes, with routes in economically developed areas having a greater impact on airport flow stability. Temperature and wind speed changes are the key factors affecting flight operation and economic indicators, with importance values of 0.35 and 0.32 respectively. Wind direction change and wind speed change have the greatest impact on flight transportation capacity, with importance values of 0.7 and 0.65 respectively. Therefore, when conducting flight scheduling and risk assessment, special attention should be paid to key weather factors such as temperature and wind speed, while also considering the weight of each route to ensure flight safety, efficiency, and economic benefits.

This study presents an advanced data augmentation framework to enhance diagnostics in centrifugal pumps using vibration data, with a focus on practical industrial applications. Initially, Gaussian noise and signal stretching were employed to generate synthetic data, addressing the common issue of insufficient defect data in industrial settings. These methods simulate varied operating conditions and different rotating speeds. Recognizing the limitations of traditional approaches, we further integrated advanced models—Long Short-Term Memory (LSTM), Autoencoder (AE), and Generative Adversarial Networks (GANs) to augment the dataset comprehensively. This enhanced approach increases the robustness and accuracy of machine-learning models for fault detection and classification. Experimental results show significant improvements in diagnostic algorithm performance, reducing false positives and increasing fault detection rates. The study offers a complete framework for incorporating both traditional and advanced data augmentation techniques into predictive maintenance protocols, ensuring reliable operation of centrifugal pumps in diverse industrial environments.

This paper presents three approaches to estimating battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted with the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels; whereas both in the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

The implementation of Emissions Trading System (ETS) poses significant challenges for the Indian maritime sector. This paper investigates the regulatory implications of ETS, focusing on how these regulations impact compliance and operational practices within the Indian shipping sector. It also analyzes the economic effects, including the financial burden and competitive dynamics faced by the Indian shipping companies in a global context. Additionally, the paper explores various adaptation strategies, emphasizing technological innovations and operational adjustments essential for navigating ETS requirements. By integrating recent industry data and case studies, this study offers a comprehensive overview of how the Indian shipping industry can effectively address the evolving regulatory landscape of ETS.

Diabetes is one of the diseases that has spread widely in today's societies. Diabetics should constantly measure their blood sugar. There are also several common methods used to measure blood sugar levels, Diabetes is one of the diseases that has spread widely in today's societies. Diabetics should constantly measure their blood sugar. There are also many common methods used to measure blood sugar levels, and this project significantly accelerates diabetes monitoring, academic research and maintenance. Therefore, a comprehensive examination of several intrinsic properties is of fundamental importance because it contains effects that affect the behavior of the aqueous solution and the safety of the diabetic patient. The dielectric constant of the aqueous solution of different samples of raw sugar is also are make able property in this context. The electrical properties of any material vary according to the properties of the insulating material. Accurate measurement so these properties will be useful for many electronics applications, and these measurements of dielectric materials affect the data values of basic design parameters. For example, both the frequency and impedance of microwaves are affected by the properties of the dielectric material. Additionally, this knowledge can be applied to improve the basic design. Through this research, we will discuss the electrical insulation properties in terms of permittivity and permeability. But it is necessary to address microwave resistance. Permittivity and permeability are not fixed values, as they are affected by many factors such as frequency, temperature, direction, mixing, pressure, and different samples of sugar in the aqueous solution

In wheeled vehicles, there exists a challenge concerning the alignment of the front wheels during turns. When both wheels are turned to the same extent, the inner wheel experiences lateral slippage on the road surface, resulting in reduced steering efficiency. Such problems can be solved by using the Ackermann principle. There are two approaches: through the steering mechanism or by employing autonomous wheel control. From a technical standpoint, it is achievable to synthesize a steering mechanism that fulfills the Ackermann condition, ensuring as accurately as possible for any given turning radius. This research paper focuses on synthesizing a steering mechanism, specifically a two-circuit linkage that links the left and right wheels. The structure and kinematics of this mechanism are being investigated. Following the principles of Ackermann, an objective function for the kinematic synthesis of the mechanism has been formulated. The synthesis process of the mechanism is conducted using the analytical method of interpolation in four parameters. A dedicated method has been developed, and kinematic analysis of the mechanism using the synthesized parameters has been performed. The simulation results have demonstrated high accuracy in achieving the desired implementation of the Ackermann condition.

Purpose: this paper evaluates the effect of thermo-formed aligners with complementary biomechanical attachments (CBA) on periodontal ligaments (PDL) during the expansion process of the maxillary arch using simulations based on the finite element method (FEM). Methods: four types of teeth (canine 3, premolar 4, premolar 5 and molar 6) were modeled using 3D CAD. In addition, aligners, CBAs and PDL were also 3D modeled. With this information, a FEM model was generated, and a clinical rehabilitation scenario was simulated. For the validation of the results, a comparison with medical records was performed. Results: for canine 3, the displacement was 0.134 mm with a maximum stress of 4.822 x10-3MPa in the amelocemental junction. For premolar 4, the displacement was 0.132 mm at a maximum stress of 3.273x10-3MPa in the amelocemental junction. Premolar 5 had a displacement of 0.129 mm and a stress of 1.358 x10-3MPa at 1 mm from the amelocemental junction. Molar 6 had a displacement of 0.124 mm and a maximum stress of 2.440 x10-3MPa. Conclusions: The simulations showed that the use of CBA reduces tooth tipping. The model with Vestibular CBA presented the best results in terms of the dental movement of expansion of the maxillary arch with the use of thermoformed aligners. Significance: Considering the difficulty of performing in vivo tests that evaluate the configuration effectiveness of the different CBA alternatives in thermoformed aligners, FEM techniques were used to reduce time and costs, and get accurate results with minimal errors.

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models.

The role of coalesced bainite in hydrogen embrittlement of tempered martensitic steels was investigated. Results showed that the coalesced bainite significantly enhanced the hydrogen embrittlement resistance by restricting hydrogen accumulation at the prior austenite grain boundaries, thus shifting the hydrogen-assisted failure mode from intergranular to transgranular fracture.

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths; each pair having negligible spin-orbit fine structure splitting; each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions; the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations, and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs, principal among are 1) foundry fabrication of arrays of isolated quantum dots, 2) generation of localized sub-surface shear strain in a semiconductor stack, 3) a cryogenic anvil cell, 4) channeling entangled photons into single mode optical fibers, 5) unconditionally secure decryption key distribution over the fiber network, 6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and 7) conversion of a quantum nondemolition measurements of a qubit states into a classical bit.

Glass cleaning is one of the major cleaning tasks in malls, offices, and modern households where the human workforce is still used for cleaning. Manual cleaning on façade glasses is a less safe, tedious, ineffective, and complex process. With the advancement of robot technology, it is possible to use robots for glass cleaning to facilitate mankind and improve cleaning efficiency. This work presents the development of a vacuum-type glass cleaning robot for façade cleaning and its performance analysis. The developed square-shaped robot is controlled by an Arduino Mega microcontroller, equipped with vacuuming and cleaning technology. An adhesion mechanism on the wheels makes the robot move forward and backward on the glass. The robot runs on a 24-volt DC with a power rating of 75 watts. There are ultrasonic sensors to detect obstacles and help the robot navigate. The robot uses a random-walk mechanism and works continuously for 90 minutes effectively. The performance of the robot has been analyzed in terms of coverage area, repeatability, and dust collection efficiency.

There are considerable benefits in applying Artificial Intelligence to automation in modern industries including Oil and Gas Industries. Oil and Gas Industries have many complex systems in their production and flow line. The failure of gas equipment will have serious consequences including financial and environmental issues. Condition monitoring of oil and gas equipment provides the condition of components, machines, systems, equipment, data hardware, and even software. The predictive maintenance and industry 4.0 applications have more advantages in petrochemical industries to make a safer economic environment and this paper addresses the Artificial Intelligence (AI) application of condition monitoring for Oil and Gas Industries. To understand the effectiveness of AI in condition monitoring for oil and gas industries, a case study related to the condition monitoring of drilling machine is conducted and applied Artificial Neural Network (ANN) algorithm to analyze and predict the potential failures. The novelty of this work is the proposal of an approach for tool wear monitoring in drilling using acoustic emission sensors for feature extraction and considering wavelet packet decomposition for further analysis. The extracted features from WPD are given as input for ANN to identify the healthiness of the drill bit and machine. This work aims to find the effectiveness of AI-based condition monitoring in enhancing effectiveness of monitoring and the safety of equipment in Oil and Gas Industries.

Currently, the use of recycled waste plastic in road construction is not just considered ecofriendly but also considered advantageous for improving road performance over time. This research explores using recycled plastic bags, made of low-density polyethylene (LDPE), in asphalt mixtures to improve road performance and manage waste. The study aims to assess the effects of incorporating different amounts of shredded waste plastic into asphalt mixes and determine the optimal percentage. The aggregate used was obtained from Sargodha quarry Pakistan and asphalt used was 60/70 grade obtained from national refinery limited Pakistan. Using the Marshall mix design, the optimum bitumen content (OBC) was found to be 4.5% of the asphalt mix weight. Thirty samples were tested with waste plastic proportions of 6%, 8%, 10%, 12%, and 14% of OBC weight, along with a standard mix. Tests on stability, bulk density, flow, and air voids showed that a 10% plastic content significantly enhances asphalt performance, increasing stability by around 25%. The study recommends using 10% plastic by OBC weight in asphalt mixes for better performance and suggests further research on optimal incorporation techniques of waste materials in asphalt mixtures.

The last wave of Artificial Intelligence is dedicating particular emphasis to the line of explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided-diagnosis systems and its usage for knowledge discovery are collecting interest in the Medical Imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI were focused on interpreting the predictions returned by deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This raises the attention in proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process in learning and identifying representative prototypical parts from the input images, and they are collecting increasing interest and results in MI applications. This narrative review provides a summary of existing PP networks, their application in MI analysis and current challenges.

The organic material present at the same depth in the reservoir has the potential for conversion, as indicated by analyses conducted before and after combustion in the development of heavy oil. This paper examines the oxidation and pyrolysis reaction pathways of hydrocarbons, specifically benzaldehyde (C₇H₆O) and naphthalene (C₁₀H₈), before and after combustion using molecular dynamics simulations. The primary products formed under various temperature conditions include H₂O, HO₂, CO, and CO₂. The discussion includes the number of molecules, such as HO and H, as well as their temperature variations. The activating group functions as an electron donor, while the inactivating group serves as an electron acceptor. The oxidation and pyrolysis reactions of naphthalene and the synthesis pathway of benzaldehyde are also explored. The C-C dissociation pathway in the early stages of combustion and the process of C-C bond synthesis in the later stages of the reactions are investigated through dynamic simulations at different temperatures: 3000 K, 3500 K, and 4000 K, with a particular focus on the reaction network at 4000 K. The application of the molecular reaction dynamics method to heavy oil combustion research is the primary objective of this work. This study aims to provide a novel approach to investigating hydrocarbon conversion at high temperatures and offers recommendations for enhanced oil recovery.

Acoustic emissions (AE) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNR(signal-to-noise ratios) generated in rocks. The basic idea comes from that the variances of the crack wave and noise have different characteristics depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The results using the proposed algorithm to detect crack wave source localization suggest use for evaluating and real-time monitoring of damage in tunnels or other underground facilities.

Dental implantology has evolved greatly as a result of the application of biomechanical engineering concepts, notably the utilization of additive manufacturing technologies that recreate the intricate architecture of real bone. This technique intends to improve functional compatibility with bone tissue while also addressing continuing problems of stress distribution inside dental implants. The research examines two types of dental implants: fully gyroid latticed and hybrid gyroid latticed with a solid neck, comprising three different cell sizes—FI-111, FI-222, FI-333, and their hybrid equivalents HI-111, HI-222, and HI-333. Traditional solid implants often cause stress shielding, which reduces long-term osseointegration and implant durability. Developed using nTopology for lattice configuration and meshing, this design undergoes a comprehensive stress analysis using finite element analysis (FEA). The study seeks to determine the most efficient implant design by maximizing stress distribution and mechanical stiffness. Analyses suggest that hybrid latticed implants, notably the HI-222 type, possess superior mechanical characteristics. These implants successfully balance stiffness and flexibility, reducing stress concentrations and improving stress distribution throughout the implant structure. This balance is critical for decreasing micromotions at the bone-implant interface, boosting osseointegration, and, ultimately, increasing implant longevity and success rates in clinical settings. The paper describes a substantial redesign of dental implants by combining biomechanical engineering and modern additive manufacturing. The novel proposed hybrid structure not only addresses typical challenges such as stress shielding, but it also establishes new standards in implantology, potentially leading to better clinical results and patient quality of life. This complete approach emphasizes the ability of personalized implant designs to fulfill individual clinical demands, indicating a significant improvement in dental treatment technology.

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two-parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

Hydrogen has the potential to decarbonize the energy and industrial sector in the future, mainly if it is generated by water electrolysis. Proton Exchange Membrane water electrolysis (PEMWE) system is regarded as a propitious technology to produce green hydrogen from water using power supplied by renewable energy sources. It offers many benefits such as high performance, high proton conductibility, quick response, compact size, and low working temperature. Many conceptual and functional parameters influence the effectiveness of PEM, including temperature, pressure of anode and cathode regions, water content and wideness of layer, and cathode and anode exchange current density. In addition, the anodic half reaction (known as oxygen evolution reaction (OER)), and cathodic half reaction (known as the hydrogen evolution reaction (HER)) perform an important function in the development of PEMWE. The current paper aims at presenting these parameters and discussing their impacts on the performance of PEM. Also, the PEM efficiency is presented. The different methods used to enhance the scattering of OER electrocatalyst and minimizing catalyst loading to minimize the price of PEMWE are also highlighted. Moreover, the alternative of noble metals that could be used as electrocatalyst in HER and OER to minimize the cost of PEM are reviewed and presented.

A new type of laminated flooring decorated by resin impregnated paper was designed, with the advantages of high stability, low deformation, natural and beautiful surface texture, comfortable foot feeling, low formaldehyde emission, improved flame retardant, high surface hardness and high wear resistance. The structure of this engineered wood flooring is based on the ordinary laminated flooring, followed by a decorative layer of thin wood pieces, and then the transparent improved flame retardant, wear-resistant paper is added to the top. After main influencing factors analysis, the surface unevenness was seriously affect the flame-retardant performance. The wear-resistant paper pressing technology commonly affects the decorative effect of laminated flooring. The hot-pressing temperature is the most important factor affecting the adhesion of resin impregnated. The optimal hot-pressing parameters are selected as the hot-pressing pressure of 3.5 MPa, hot-pressing temperature of 180 ℃, and hot-pressing time of 40 s. The new laminated flooring was improved with high flame retardant, high wear-resistant, combined with the conventional advantages of both solid wood composite flooring and reinforced wood flooring. The new laminated flooring decorated by resin impregnated paper has broad application prospects.

The main goal of optimization is to find factors values that bring minimum feedstock consumption or maximum final product or services of the production process. In a production process, the factors that make up the model that describes it are usually the properties of the final product. In the case of biodiesel, which is the final product of the process that is the object of study, among the various properties, the glycerol content is among the most important. The present study aims to maximize the model yield that defines glycerol content on the biodiesel composition. This optimization was executed by a hybrid metaheuristic algorithm (SAGAC),and its experimental results are compared with other optimization techniques named by RSM (Response Surface Methodology). After run the experimental assays, SAGAC algorithm has find the follow values of model factors: molar ratio = 3(mol/mol), catalyst content = 0.52 (wt%), reaction temperature = 49(^oC), Time = 45 (min) generating model yield of 14.46 that, match with EN 14105 standard value of 0.33 (wt%). Comparing results between SAGAC and RSM methodologies, SAGAC has presented an improvement of 31%.

Distributed Denial of Service (DDoS) is among the commonly used type of network attacks, particularly aimed at the IoT devices in the emerging future wireless services specially for strictly latency-limited applications. DDoS attacks pose serious threats for many entrepreneurial businesses as they can prevent legitimate customers from accessing network resources of corporate websites, demanding intelligent analytics prior to entertaining a service request. DDoS involves exploiting the vulnerability of IoT devices by launching distributed multi-point attacks to generate high traffic by flooding the network of victims. The consequences of DDoS attacks could be worst in any Software-Defined Networking (SDN) than in traditional networks because SDN is configured as centralised architecture, failing to address the weaknesses current paradigm. However, SDN can play a crucial role in mitigating the effects of DDoS attacks as it bears the features of easy programmability, manageability, flexibility, and scalability. In this manuscript preliminary framework to detect DDoS attacks by mitigating their threats in an SDN architecture is presented. The authors propose a defense mechanism that uses OpenFlow and sFlow to mitigate the effects of DDoS attacks. The initial results show that the proposed model can minimize efficiently the consequences of DDoS attacks in an SDN type of architectural paradigm.

In the face of increasing environmental protection requirements, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology for developing emission models for older vehicles by employing advanced AI and machine learning techniques. The study utilized algorithms, including gradient boosting, to analyze data from road tests and the OBDII diagnostic interface, allowing the creation of precise models for CO₂, CO, THC and NOx emissions. A key achievement is the implementation of input data clustering, which significantly enhances the accuracy of emission forecasts. Model validation demonstrated high precision, with notable R² coefficients and mean squared errors. The developed models serve as a crucial tool for analyzing and managing emissions in the context of an aging vehicle fleet, providing new opportunities to shape transportation policy and strategies for pollution reduction.

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor based converters, preceding the control evaluation phase. The proposed methods enable thorough evaluation and evaluation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilize the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT is further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test is introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements are conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

In light of the accelerated pace of economic growth and urbanisation, the transformation of green buildings has emerged as a prominent area of re-search. A considerable number of existing buildings have been constructed and are still in use. However, the energy consumption and environmental impact throughout the entire life cycle of these buildings are not adequately considered in the renovation design. This results in high energy consumption and poor indoor comfort. In this work, we include improving the thermal insulation of buildings, such as thickening the insulation of external walls and replacing energy-efficient windows to reduce energy losses caused by temperature differences between in-door and outdoor buildings; Adopt energy-efficient HVAC systems to reduce en-ergy consumption and improve indoor air quality by upgrading equipment effi-ciency, optimizing duct design, and intelligent control systems; At the same time, we will introduce renewable energy sources such as solar energy, and we will not only install solar photovoltaic panels to generate electricity, but also use solar thermal collector systems to provide hot water and heating, so as to achieve di-versified and sustainable use of energy. Combined, these measures can effec-tively reduce the overall energy consumption of the building, reduce carbon emissions, and improve the comfort and health of the indoor environment. During the experimental analysis, the energy consumption and indoor comfort before and after the renovation were evaluated, and the results showed that the energy consumption was significantly reduced and living comfort was improved.

Dry Fish is a rich source of proteins and nutrients and can be eaten in many forms. Oman is one of the important fish producers in the world and having high demand in neighboring countries for Oman’s fish. Fishery sector is considered as one of the important economic development sector in sultanate of Oman. In the fishery sector, use of technology is not up to the mark because of less awareness of technology application among Omani fishermen. Also, there is a demand for a greater number of cold storages in different regions to store fresh fishes which is not materialized yet. Similarly, there is a demand of perfect and cheap technology in Oman to dry fishes to improve the profit to the fishermen. Solar energy is abundantly available in Oman and there are high possibilities in Oman to use solar energy in domestic and industrial applications. Since the sunlight is available throughout the year, it is possible to convert in to thermal energy or electrical energy. Electricity can be produced from photovoltaic principle (PV) or indirectly with concentrated solar power. Similarly, we can use solar concentrators for many thermal applications. The solar dryer is a device which is used to dry fruits, vegetables, leaves, clothes etc. If any of the items are dried with the help of solar energy, then we can save lot of non-renewable source of energy and can help to improve economy of the country. The sun can, at least seasonally at Oman’s latitudes, provide a substantial part of the energy needed for various applications. In this work, we used solar dryer to make dry fish with high quality. Use of parabolic type concentrator to dry the fish is an effective solution which is an innovative idea to the fishermen. The temperature variation in dryer is directly proportional to the intensity of solar. The tracking mechanism is a combination of electronic and mechanical design which tracks the sun automatically. In this work, a parabolic concentrator with automatic tracking mechanism was designed and the performance analysis have been done. In the design, copper tubes are used for heat transfer and then the hot fluid is circulated in the box which is used to dry the fishes. As per the experiment, fish drying efficiency obtained from solar dryer is 77.9% and it took 3 days to dry king fish of 20kg. There are many advantages in parabolic type solar concentrators relate to the quality of dryness in the fishes and time taken to dry the fishes which are elaborately discussed in this paper. This solar dryer technology will improve green environment and also will save non-renewable sources of sultanate of Oman for longer period of time.

In this study, we developed a lightweight shoe sensor system equipped with four high-capacity, compact triaxial force sensors and an inertial measurement unit. Remarkably, this system enabled measurements of localized three-directional ground reaction forces (GRFs) at each sensor position (heel, first and fifth metatarsal heads, and toe) and estimations of stride length and toe clearance during walking. Compared to conventional optical motion analysis systems, the developed sensor system provided relatively accurate results for stride length and minimum toe clearance. To test the performance of the system, 15 older and eight young adults were instructed to walk along a straight-line while wearing the system. The results revealed that compared to the young adults, older adults exhibited lower localized GRF contributions from the heel and greater localized GRF contribution from the toe and fifth metatarsal locations. Furthermore, the older adults exhibited greater variability in their stride length and smaller toe clearance with greater variability compared to the young adults. These results underscore the effectiveness of the proposed gait analysis system in distinguishing the gait characteristics of young and older adults, potentially replacing traditional motion capture systems and force plates in gait analysis.

This work presents a novel compact CMOS potentiostat designed circuit for electrochemical cell. The proposed topology functions as a circuit interface, controlling the polarization of voltage signals at the sensor electrodes and facilitating current measurement during the oxidation-reduction process of a analyzed solution. The potentiostat, designed for CMOS technology, comprises a two-stage amplifier, two current mirror blocks coupled to this amplifier, and a CMOS push-pull output stage. It employs the electrochemical method of cyclic voltammetry, operating within voltage range of ±0.8 V and scan rates of 10mV/s, 25mV/s, 100mV/s and 250 mV/s. The circuit is capable of reading currents ranging from 10 µA to 500 µA. Experimental results were obtained using a potassium ferrocyanide (K3(FeCN)6) redox solution with concentrations of 10, 15 and 20 mmol/L, and their corresponding voltammograms were evaluated. The experimental results from a discrete circuit demonstrate that the proposed potentiostat topology produces outcomes consistent with those of classical topologies presented in the literature and industrial equipment.

The small dimensions of microfluidic channels allow for fast diffusive or passive mixing, which is beneficial for time-sensitive applications such as chemical reactions, biological assays, and the transport of to-be-detected species to sensors. In microfluidics, the need for fast mixing within milliseconds arises primarily because these devices are often used in fields where rapid and efficient mixing significantly impacts the performance and outcome of the processes. Active mixing with acoustics in microfluidic devices involves using acoustic waves to enhance the mixing of fluids within microchannels. Using sharp corners and wall patterns in acoustofluidic devices significantly enhances mixing by acoustic streaming around these features. The streaming patterns around sharp edges are particularly effective for mixing because they can produce strong lateral flows that rapidly homogenize liquids. This work presents extensive characterizations of the effect of sharp-edged structures on acoustic mixing in bulk acoustic wave (BAW) mode in a silicon microdevice. The effect of side wall patterns in different angles and shapes, their position, the type of piezoelectric transducer, and its amplitude and frequency have been studied. Following the patterning of the channel walls, a mixing time of 25 times faster was reached, compared to channels with smooth sidewalls exhibiting conventional BAW behavior. The average locally determined acoustic streaming velocity inside the channel becomes 14 times faster if sharp corners of 10° are added to the wall.

The objective of this research project was to evaluate the effects of the addition of inorganic fillers into PEBA at various loading levels on the processing, mechanical, thermal and physical properties of the polymer. The composites investigated in this study were Pebax® 6333 SA01 MED (PEBA100) with 10 wt.%, 20 wt.% and 30 wt.% Barium Sulphate (PEBA/BaSO4) and Bismuth Oxychloride (PEBA/BiOCl) respectively. Composites were prepared using twin screw extrusion and injection moulding. Post-processing, composite samples were analysed for their mechanical properties (via tensile, impact and flexural analysis), thermal properties (via differential scanning calorimetry), rheological performance (via melt flow index) and physical properties (via density analysis and ash content testing). The results of which reveal how filler type and concentration influence PEBA composites. The results of the study suggest that BiOCl filler enhances the aesthetic properties, increases the stiffness, while maintaining some level of flexibility with minimal adverse effects on the tensile and impact strength. When comparing BiOCl to BaSO4 filled composites, it was clear that depending on the application of the polymer composite, BiOCl may provide more desirable properties. The study highlights the importance of optimising filler concentration and processing conditions to achieve desired composite properties for specific medical applications.

Hyper-spectral imaging provides the possibility of describing specific characteristics of materials in air, land and water depending on reflections of each material in a specific zone of the electromagnetic spectrum. Hyper-spectral sensors capture images in continuous and narrow spectral bands in the visible, near-infrared, and mid-infrared spectral regions. These sensors look at objects using a large part of the electromagnetic spectrum. Some objects have unique characteristics in certain electromagnetic spectrums. These spectral signatures, known as "spectral signatures," help to identify materials in images. But the main problem with these sensors is that the images they get in bad weather conditions and in the presence of dust or rain or snow are of very poor quality. Therefore, we go to SAR images, which are able to form a high-resolution image even in bad weather conditions. The method proposed in this paper is to obtain the final elements of hyper-spectral images using the decomposition method into a multilayer negative matrix. The reason for using this method is that, unlike the high-spectral images that form the image in a number of high-traffic zones, we can create the image in 8 different frequencies, so the most important thing is to get all the hidden information at those frequencies. And also, we compare between the RDA, SENMAV, OMP, ISMA and ADMM algorithms.

As the penetration of renewable energies increases, their acceptance of the power grid system is becoming an important issue. In the case of South Korea, a 22.9kV transformer is frequently utilized to reduce the power loss of the generated renewable energy transmission, so the power quality at the point of common connection (PCC) is inevitably influenced by the nonlinearity of the transformer and the uncertainty of the inductance and resistance of the transmission line. In this paper, we present a point of common connection voltage-modulated direct power control (PCCVM-DPC) system that enhances power quality at the PCC. First, we present the mathematical dynamics of active and reactive power at the PCC side, which contains the step-up transformer’s nonlinearity, the grid voltage harmonics, and the parameter uncertainties. By analyzing the disturbance terms of the PCCVM-DPC system, we present the disturbance observer (DOB) for the PCCVM-DPC system. To assess the efficacy of our approach, we perform comparative studies of the PCCVM-DPC without DOB and PCCVM-DPC with DOB by constructing the simulation environment based on the commercial step-up transformer and ESS inverter datasheet. We have validated that the active and reactive power control performance of the PCCVM-DPC with DOB outperforms the PCCVM-DPC without DOB from the observation that the current total harmonic distortion reduced by more than 40% compared to the PCCVM-DPC without the DOB.

Recent advancements in semantic communication (SC) have shown the potential to surpass the limitations of the Shannon theorem, by leveraging an understanding of the meaning of data instead of the data itself. In networks, data-aided sensing (DAS) presents a powerful tool that can significantly reduce communication overhead, aligning perfectly with the goals of SC by providing more powerful semantic messages. This survey explores the synergy between DAS and SC and investigates how their convergence can lead to more efficient and effective communication.