Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in underwater confined environments, researchers have studied different underwater positioning methods and selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms, and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have also developed some bio-inspired autonomous positioning methods. This article introduces the applicable robot positioning methods and sensors in underwater confined environments and discusses the research directions of robot positioning methods in such environments.

Tracked vehicles play a vital role in accessing challenging terrains, offering stability, traction, and versatility for various applications, including deep-sea exploration. However, the seafloor and sediment contain valuable but poorly understood resources, making it a significant challenge for tracked vehicles during exploration. Consequently, it is imperative to conduct comprehensive studies before considering these terrains for off-road purposes. In this study, we developed a rigid, flexible track vehicle equipped with various sensors, focusing on monitoring pressure sinkage. Cohesive soils like Bentonite and Diatom soil, particularly when combined with sand and gravel mixtures, were used for the investigation and assessed for ground pressure, bearing capacity, and sinkage in rigid-flexible tracked vehicles on these characterized terrains under varying moisture conditions. Our findings indicated that increased moisture content in bentonite soil resulted in significantly higher track slippage and sinkage compared to diatomaceous earth soil. Regarding soil behavior, diatom soil exhibited notable compaction and sinkage characteristics compared to bentonite soil. These significant variations between these two soil types under changing moisture conditions highlight the complexity of such terrains. Therefore, understanding the soil and its physical properties is crucial before applying them to natural terrains. Furthermore, this study emphasizes the importance of precision in assessing the physical properties of natural terrains for the development of tracked vehicles for off-road exploration. This knowledge contributes to better vehicle design and a deeper comprehension of terrain mechanics.

The aim of this study is to present a method for developing fragility curves for soil liquefaction that are consistent with the seismic hazard using Monte Carlo simulation. This method can account for all the uncertainties and variabilities of the input parameters. The seismic parameters of earthquake magnitude (M) and the associated peak ground acceleration (PGA) are considered together for the liquefaction assessment. The liquefaction potential and damages obtained by this method are more realistic. A case study is performed using data from a sand-boil site in Yuanlin, Changhua county, where liquefaction occurred during the Chi-Chi earthquake in Taiwan in 1999. The results show that the liquefaction potential index, IL, the post-liquefaction settlement, St, and the liquefaction probability index, PW, are all suitable parameters for evaluating the liquefaction damages. The fragility curves for soil liquefaction developed by this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the ‘Taiwan Earthquake Loss Estimation System’-TELES, and serve as a basis for scenario simulation and an earthquake early warning system for liquefaction damages.

"Chemical absorption is currently the most advanced technique for capturing carbon dioxide after combustion. A fascinating approach within amine-based technology for post-combustion carbon removal is the utilization of aqueous ammonia as a solvent, leading to a method called chilled ammonia capture. This study aims to enhance the performance of chilled ammonia capture through a thermodynamic assessment, focusing on crystalline configuration.""This study explores the development of an improved second generation chilled ammonia capture process, which involves the formation of solids to enhance capturing efficiency. The simulation software ASPEN is employed to model the expanded UNIQUAC Thermodynamics hypothesis. The modeling encompasses both the conventional carbon ammonia capture process and the more advanced carbon ammonia capture system powered by a crystallizerIn this study, the energy costs associated with implementing different technologies for carbon ammonia capture in typical ultra supercritical power plants are evaluated. The introduction of solid formation in the carbon ammonia capture system has yielded promising results. The findings indicate that the energy required for the carbon ammonia capture system, which utilizes a crystallizer, is approximately 10% lower compared to the overall cost associated with the conventional carbon ammonia capture system. These results highlight the potential for significant energy savings and improved efficiency in carbon capture processes

Annotation. The experience of using coal flotation waste (CFW) as a weakly exothermic insulating backfill for insulating the head part of forging dead-melted steel ingots is presented.It has been shown that when using weakly exothermic fills based on single-component fills in the form of CFW, it is possible to reduce the chemical heterogeneity of the ingot by producing a closed shrinkage cavity, which allows halving heat loss and depth of penetration of shrinkage looseness into the body of the ingot, reducing the segregation of impurities, compacting the head part of the ingot and increasing the yield; moreover, the profitable part of the ingot is more dense and less contaminated with nonmetallic inclusions.The studied patterns of the formation of a closed shrinkage cavity with a dense “bridge” in the head part of a dead-melted steel ingot made it possible to develop and implement a technology for casting large forging ingots of sufficiently high quality using weakly exothermic heat-insulating materials based on metallurgical waste (coke screenings, coal flotation waste)

Summary: PURPOSE. Development of a drainless energy-saving technology for the processing of high-moisture iron-containing sludge from metallurgical production. RESEARCH METHOD. The regularities of the combined process of chemical dehydration of high-moisture iron-containing sludge by powdered waste of lime and dolomite, hardening and agglomeration by pressing in a single technological cycle on the developed experimental plant were studied. In the course of laboratory studies, the temperature of the mass, the rate of dehydration, the chemical composition of the mixtures, the appearance of the resulting briquettes, the weight loss during agglomeration-humidity of the mixtures, and mechanical strength of the briquettes were measured. RESULTS. New patterns have been established that have made it possible to develop a nonfiring method for producing iron-containing materials and self-healing briquettes. The essence of his method, which is one of the main provisions of scientific novel provisions, is to combine dehydration and self-hardening of the mixture with the process of shaping by applying external pressure to the hardening mixture in molds to obtain agglomerated material in the form of briquettes in a single technological cycle. The proposed technology does not require drying or firing, and a set of strength properties occurs as the material is cooled in air during the day. A new energy-efficient and drainless method for the production of iron-containing briquettes has been developed, which combines the processes of chemical dehydration and agglomeration in one technological cycle. CONCLUSION. The proposed project and technology will allow organization of production for the processing of high-moisture iron-containing sludge and the production a complex iron-containing material as a secondary metal-containing raw material for metallurgical plants for the production of steel and rolled metal. and dolomite dust from dry gas cleaning as dehydrating and binding materials, as well as screening of coke and coal as a reducing agents. The proposed technology also solves the problems of environmental pollution and land acquisition for the storage of production waste.

This research investigates the fatigue crack propagation on the lower surface of rocket deflector troughs of offshore rocket launch platforms. Initially, a numerical model of the offshore rocket launch platform is established using ABAQUS based on the Extended Finite Element Method (XFEM). Subsequently, two variable parameters—namely, the initial crack length and initial tilt angle—are introduced. This research systematically analyzes the impact of these parameters on fatigue crack propagation patterns in both the maximum stress and maximum deformation regions of the deflector channels under the combined conditions of high temperature and impact. Finally, the research indicates that the propagation length of surface cracks in the deflector trough exhibits a trend of initial increase followed by a subsequent decrease with the increase in the preset inclination angle. Notably, the stable propagation rate of the crack in the region of maximum deformation surpasses that observed in the region of maximum stress. Through meticulous comparative analysis, it is evident that temperature loading significantly fosters the initiation and propagation of cracks, particularly in the upper region of the deflector channel's lower surface.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF tracking loops. Firstly, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Secondly, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other receivers in complex dynamic environments.

The Indorama Eleme Petrochemical Limited (IEPL) is found in the southern part of Nigeria more specifically on the Rivers State southern religion Nigeria. It consists of five distinct units, each of which generates its own unique trash at a rate of around 3115.98 tonnes per year. This study had the goal of concentrating on the management of the processing wastes in order to minimize the negative effects that they have on the environment. It is necessary to have an understanding of the number, nature, and make-up of IEPL's industrial wastes in order to effectively manage and regulate the development of waste there. As a result, we decided to collect data through the use of questionnaires. The classification of industrial wastes was accomplished by doing a comparative study and synthesis of research that were relevant to the management of petrochemical waste. Because an individual coding system is required for the integrated management of industrial waste, we gave each type of trash a code that consists of thirteen digits. The primary components of trash were catalysts (9.58 percent), metallic materials (7.62 percent), plastic barrels (35.77 percent), coke (12.66 percent), wood (4.47%), oil (4.15%), glass (0.028 percent), cooling tower packaging (6.7 percent), and other material (18.83 percent). According to the findings of the investigation into the physical qualities of the wastes, only 11.81% of these residues were liquid, while 88.19% were solid. The current handling of these wastes has been experiencing significant difficulties as of late. garbage management at IEPL was particularly challenging due to the wide variety of wastes produced there and the dangerous nature of the wastes themselves (88.19% of garbage was hazardous waste). Recycling and reuse of garbage may have been the greatest decision in some situations; yet, incineration and disposal of waste are also necessary options.

As the global semiconductor market is growing steadily, the need for optimization of the man-ufacturing process is paramount. For example, in the process of transferring the semiconductor chips to the substrate, each chip needs to be individually separated from the dicing tape via a plunger. In this process, however, the fixed chip is not easily removed from the tape due to strong adhesion, and the chip can become damaged if it is forcibly pulled out. Therefore, the present paper reports the development of functional adhesive tapes that initially adhere strongly to the semi-conductor chip on the dicing tape but can be easily removed after transfer. The fabricated functional tape has an initial adhesion of 350 gf, which enables firm fixing of the semiconductor chip to be transferred, and a final adhesion of 0.8 gf after UV curing, thereby facilitating removal of the chip without damage after transfer. Therefore, the fabricated functional tape is expected to contribute to the optimization of the semiconductor manufacturing process and is expected to be useful in the semiconductor package manufacturing process.

Road pavement monitoring represents the baseline to the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a network of roads. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black box equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of these vehicles. In this study, a statistical method was to analyze the effects of speed on road pavement conditions at different severity levels through the data recorded by taxi vehicles. Likewise introduced was a process that overcomes the speed effect when considering road surface conditions. The methodology proposed has succeeded in predicting the right damage severity level based on only two recorded parameters: speed and pavement deterioration index.

With the increasing prominence of renewable energy sources providing flexibility to the power grid, there is a growing recognition of the need to expand virtual Energy Storage Systems (ESS) to replace traditional ESS, which faces challenges in terms of supply and scalability. Grid-connected microgrids can supply power stably and are being built, so they can be used as virtual ESS to provide flexibility to the power system. This study proposes an approach to determine the optimal Power Conversion System (PCS) and battery capacity for a Virtual Energy Storage System(VEES) based on a grid-connected microgrid incorporating Photovoltaic (PV) and ESS. In this paper, it is assumed that microgrids can provide flexibility to the grid by participating in the energy trading market, there are corresponding profits and penalties for order. Numerical examples are performed to demonstrate the effectiveness of the proposed method.

This systematic literature review explores the impact of social media use (SMU) and practices (SMPs) on team feedback (TF) and performance (TP) in construction project management. The study delves into the intricate dynamics between SMU and SMPs and their influence on communication, collaboration, and stakeholder engagement within construction projects. It investigates how these digital practices reshape the traditional methods of team interaction, feedback mechanisms, and overall team performance in the complex and multifaceted environment of construction sites. The review aims to provide a comprehensive understanding of the role of social media in modern construction project management, highlighting its significance in enhancing team communication, coordination, and performance. This research is crucial for understanding the evolving landscape of construction management, where digital tools and platforms are increasingly integral to successful project outcomes. The findings are expected to offer valuable insights for professionals in the construction industry, helping them leverage social media effectively for improved project management and team collaboration.

The utilization of vegetal fabric reinforced cementitious matrix (FRCM) represents an innovative approach to composite materials, offering distinct sustainable advantages when compared to traditional steel-reinforced concrete and conventional FRCM composites employing synthetic fibers. This article introduces a design for sandwich solutions based on a core of extruded polystyrene and composite skins combining mortar as a matrix and diverse vegetal fabrics as fabrics such as hemp and sisal. The structural behavior of the resulting sandwich panel is predominantly driven by the interaction between materials (mortar and polyurethane) and the influence of shear connectors penetrating the insulation layer. This study encompasses an experimental campaign involving double-shear tests, accompanied by heuristic bond-slip models for potential design of sandwich solutions. The analysis extends to the examination of various connector types, including hemp, sisal, and steel, and their impact on the shear performance of the sandwich specimens. The results obtained emphasize the competitiveness of vegetal fabrics in achieving effective composite strength comparable to other synthetic fabrics like glass fiber. Nevertheless, the study reveals that the stiffness of steel connectors outperforms vegetal connectors, contributing to an enhanced improvement in both stiffness and shear strength of the sandwich solutions

In this paper, an ultracompact combined sensor for displacement and angle synchronous measurement is proposed based on self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission respectively within one single structure in meantime. The optical transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that, the optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4nm for linear displacement measurement, and a sensitivity of 0.26mV/arcsec within a range of ±1° for angle measurement. Benefitting from a simple common-path structure without using optical components including reflectors and polarizers, the sensor shows an ultrahigh compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in the fields such as integrated mechanical positioning and semiconductor fabrication.

"Hydrogen is an extremely promising energy carrier, poised to become a commercially viable product in the coming years. However, there are significant hurdles that need to be addressed. One such challenge is the high energy consumption during the electrolysis process, which is used to produce hydrogen. Furthermore, the co-production of CO and CO2 during steam hydrocarbon reforming adds to the complexity. Finding innovative solutions to these issues is crucial for the widespread adoption of hydrogen as a clean and efficient energy source." Simultaneously, various industries have the capability to generate hydrogen as a by-product, each with differing levels of purity. For instance, sodium and chlorine production plants have the potential to produce high-purity hydrogen. On the other hand, digester and sewage gas can yield low-purity hydrogen. In both instances, there is an economic incentive to utilize this hydrogen for energy production or to inject it into existing natural gas pipelines A brief estimation of the technical potential of utilizing hydrogen from various sources in Nigeria, specifically for fuel cell usage, has been conducted. Moreover, resource potential visualization maps have been generated to help visualize the availability and distribution of these hydrogen sources throughout the country.

Keywords: Demographics, Diversity, Gender, Race, Age, Professionals, Built Environment, Construction Industry, South Africa

The research object was the stress-strain state of shear stud bonds in the contact joint of steel-concrete beams. The work diagrams for three types of anchors are given. Based on the analysis of numerical and full-scale experiments, analytical dependencies are proposed that reflect the actual operation of anchor ties. Comparisons of the calculation results by analytical formulas and comparisons with numerical and full-scale experiment data are given. Numerical studies have revealed the pattern of the stress-strain state of the contact joint, the stages of development of cracks in the concrete flange, and the sequence of development of deformations in anchor ties. It was revealed that vertical shear studs bonds have an increased bearing capacity, although displacements are 1.5-2 times greater than those of loop-shaped anchor ties.

Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.

This paper aims to study and analyze a new Peltier cell layout using a PCM in biphasic mode, with the liquid phase encapsulating the p-n junctions and the solid one playing the role of heat sink. The selected PCM is an organic compound that operates within a temperature range corresponding to the melting process. The phase change zone encompasses the working temperature of the Peltier cell, avoiding thermal gradient variation across the cell section and maintaining the operating conditions and the cell efficiency. PCM encapsulates the Peltier cell thermocouples, creating a layer that increases the power generation as the thickness reduces. We simulated the performance of the new configuration for different temperature gaps between both sides of the cell, obtaining an improvement in power generation from 1.65 to 7.93. This power increase ratio depends on the temperature gap and transient time to reach the steady working temperature. The control of the layer thickness, transient time, and working temperatures resides in the external heating power source, which varies the operating conditions regulating the heat power and the airflow. Maximum output power from the cell prototype is 36.5 W, while conventional Peltier cells operating in similar conditions produce 7.3 W, which represents a power gain of 500%.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

The purpose of this study is to analyze the causes of cost variation in natural disaster prevention projects (NDPP) in Gyeongsangbuk-do and to develop effective cost and risk management strategies. The study collected data by conducting an online survey of 244 stakeholders. Binary logistic regression was used to analyze the impact of various variables on cost deviation, in-cluding project management risk (PMR), project costing and execution risk (PCER), project exe-cution strategy risk (PESR), construction project risk (CPR), project cost and schedule risk (PCSR), project management challenges (PMC), and construction project subcontractor and safety man-agement (CPSSM). The results showed that PMR (OR=3.744, 95% C.I. [1.657, 8.457]), PCER (OR=5.068, 95% C.I. [2.236, 11.484]), PESR (OR=3.447, 95% C.I. [1. 853, 6.413]), CPR (OR=2.292, 95% C.I. [1.020, 5.151]), PCSR (OR=4.817, 95% C.I. [2.250, 10.317]), PMC (OR=2.954, 95% C.I. [1.452, 6. 013]), and CPSSM (OR=2.419, 95% C.I. [1.297, 4.515]) had statistically significant effects on cost deviation. In particular, PMR, PCER, and PESR were identified as the main causes, and the effects of additional factors such as CPSSM were also important. The developed predictive model plays an important role in helping stakeholders of NDPP to manage risks in advance and take appro-priate preventive measures to ensure the success of the project. This study empirically supports the theoretical hypotheses of previous studies and provides a clear understanding of the causes of cost deviation in NDPP, which provides important insights for strategic decision-making and provides a new perspective on risk management.

This study aims at the sustainable valorization of eggshells (ES) from different countries of origin in cement concrete as a cement replacement for Hong Kong. For this purpose, sixteen different eggs from different regions across the world were selected from the local market. Firstly, the extent of uniformity in the weight and mineral content of sampled eggshells were assessed by a specific gravity test and thermogravimetric analysis (TGA), respectively. It was found that specific gravity varies between 2.02 and 2.39, with an average value of 2.20 ± 0.01, whereas CaCO₃ varies between 94.65% and 97.23%, with an average value of 96.33%. These values were a bit lower than those of extra-pure limestone (LS) because of the organic part and the porous structure of eggshells. Secondly, three different eggshells were selected for cement replacement in cement concrete, both in the uncalcined and calcined states, based on having the highest, medium, and lowest CaCO₃ content, respectively. To get the calcined eggshells, the eggshells were calcined at 800^oC for three hours. It was found that the strength of eggshells varies with CaCO₃, but the variation was acceptable. The calcined eggshells showed comparatively more compressive strength and were close to the LS because of the free CaO and the absence of an organic part. In general, the variation in the basic properties of eggshells from different regions across the world is negligible and suitable to use as a cement replacement with acceptable variation in strength in Hong Kong.

The advancement of technology has led to a shift in the automotive industry from gasoline vehicles to electric vehicles. One of the key factors in modern vehicles is the communication between control devices and commands. This thesis presents a prototype of communication in the form of CAN BUS Protocol with CAN STANDARD SAE.J1939 for All-Terrain Vehicle (ATV) using MRS Developers Studio software to control the CAN Bus system. This thesis discusses the operation of communication from the assembly of the ATV, the packing of a 72 V 25Ah battery as a power source for the vehicle, the layout of the wiring system for receiving/sending communication signals for the 3 CAN Bus control boxes, and programming with MRS Developers Studio software used in communication. The communication of the vehicle in the ATV consists of receiving/sending signals of switches, headlights, left turn signal, right turn signal, brake light, emergency light, horn, forward-reverse gear, and accelerator. The results of the operation of the CAN Bus Protocol found that the CAN Bus control box has only 6 input and output pins, making the communication that has both receiving and sending signals insufficient. Therefore, starting from the CAN Bus1 control box acts as a receiver of signals from the forward-reverse gear switch sent to the output of the CAN Bus3 control box, the CAN Bus1 control box acts as a receiver of all light signals sent to the output of the CAN Bus2 control box. The signal transmission between devices will send signals through the CAN HUB line to ensure stability and minimal interference. In addition, the communication between devices from testing the operation of various switches on and off up to 1000 times found that this communication does not cause errors, making this communication reliable and stable.

In recent years, wireless power transfer (WPT) has progressed rapidly in both theory and commercialization abroad. However, existing research into WPT coil design for low-power devices is limited. This paper investigates coil structure using printed circuit board (PCB) for magnetic resonant coupling WPT to address this gap. Specifically, the impacts of key geometric parameters on the coil quality factor and coupling coefficient are analysed through models and simulations. Equivalent PCB coils were formed for mutual inductance models, and four basic compensation circuits were analysed. The impacts of changes in coil thickness, line width, turn spacing, and number of turns on the quality factor of PCB coils were analysed with a fixed outer diameter of the coil. In this paper, to mitigate the coil offset while retaining PCB manufacturing simplicity for practical uses, a dual layer PCB coil structure is proposed. The structure optimizated PCB coil could offer remarkable improvements in the WPT system for low power electronic devices.

The paper describes and analyzes the performance of a new thermoelectric generation system based on the use of Peltier cells inserted in the ducts of the air conditioning equipment. The hot and cold air streams that flow in the two separated sections of an air conditioning system are used to create a thermal gradient between the two faces of the Peltier cells. Hot and cold air flow inside the A/C unit are driven to a built in thermal chamber where the Peltier cells are placed. Peltier cells are incrusted into an insulation wall that separates the two sections of the chamber through which hot and cold air streams are flowing. The so designed configuration allows the Peltier cells to generate electric current. The study has been run for constant cold air temperature and variable hot air temperature, resulting in a variable performance of the thermoelectric generator. Theoretical study has been completed with experimental tests to determine real performance of the thermoelectric unit. Efficiency of the thermoelectric generator has been found to be linearly dependent on the temperature difference between hot and cold air subsections of the chamber. Maximum efficiency, however, is limited by the hot air stream temperature, since in real conditions ambient temperature cannot overpasses a specific value. For standard operational conditions, 5º C and 45º C for the cold and hot air streams, the thermoelectric generator operates at 44% of the maximum efficiency attainable.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

With the constant growth of Software Defined Radio (SDR) technologies in wireless communications related fields the need for efficient ways to test and evaluate the Physical Layer (PHY) protocols developed for these technologies in real life traffic scenarios become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow feeding GNU Radio applications with test traffic in a simple way and through an interoperable interface. This makes possible the use of any traffic generator, existent one or custom built to evaluate the GNU Radio application. In addition, the paper proposes an efficient way to collect PHY layer specific monitoring data that improves the performance of the critical components of the message delivery path by employing the Protocol Buffers library. The paper considers the entire testing and evaluation ecosystem and demonstrates how PHY layer specific monitoring information is collected, handled, stored, and processed as time series allowing complex visualization and real time monitoring.

The urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

A sintered wick was formed in a heat pipe through the process of sintering a mixture of copper powder with particle sizes of 100㎛ and 200㎛, mixed with a pore-forming agent. On application to the heat pipes. The thermal resistance of heat pipes containing a sintered wick was calculated for different tilting angles (0°, 45°, and 90°) according to the type of powder, thickness of the sintered wick, and the filling ratio of the working fluid. The heat transfer efficiencies were evaluated through temperature tests. The capillary force, which is closely related to heat transfer efficiency, is determined by the effective pore radius resulting from the sintering of the copper powder. Furthermore, it was observed that the heat transfer performance of the heat pipe was at its best when the working fluid filling ratio was 30%. Additionally, the width of the wick was found to have a direct correlation with the heat transfer performance.

Smart home and building systems are popular solution that support maintaining comfort, safety and improving energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things, are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that mix technologies of field-level and ICT networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, along with a systematic review of the literature, case-studies and an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a SWOT analysis of its usability. Future works are proposed as well.

Chromium-nickel austenitic stainless steels are widely used due to their high corrosion resistance, good weldability and deformability. To some extent, their application is limited by their mechanical characteristics. As a result of their austenitic structure, increasing the static and dynamic strength of the components can be achieved by surface cold work. Due to the tendency of these steels to undergo intercrystalline corrosion, another approach to improve their mechanical characteristics is the use of low-temperature thermo-chemical diffusion processes. This article proposes a new combined process based on sequentially applied diamond burnishing (DB) and low-temperature gas nitriding (LTGN) to optimally improve the fatigue strength of 304 steel. The essence of the proposed approach is to combine the advantages of the two processes (DB and LTGN) to create a zone of residual compressive stresses in the surface and subsurface layers–the enormous surface residual stresses (axial and hoop) introduced by LTGN, with the significant depth of the compressive zone characteristic of static surface cold working processes. DB (both smoothing and single-pass hardening), in combination with LTGN, achieves a fatigue limit of 600 MPa, an improvement of 36.4% compared to untreated specimens. Individually, smoothing DB, single-pass DB and LTGN achieve 540 MPa, 580 MPa and 580 MPa, respectively. It was found that as the degree of plastic deformation of the surface layer introduced by DB increases, the content of the S-phase in the nitrogen-rich layer formed by LTGN decreases, with resultant increased content of -phase, and a new (also hard) phase: stabilized nitrogen-bearing martensite.

As most of the current dynamic positioning systems are based on model ships, they cannot accu-rately reflect the motion state, position changes, and mutual influence of each part of the dynamic positioning system of actual ships in complex environments. Other actual ships such as cargo ships cannot add various sensors and auxiliary equipment to verify and analyze the positioning system. This article takes the intelligent research and training dual-use ship of Dalian Maritime University, which integrates scientific research and training, as the object of study. This ship will not be affected by the voyage period and route and can choose a suitable sea area for research. Therefore, in order to improve the accuracy and reliability of the dynamic positioning system, research on the ship’s dynamic positioning system was carried out. Firstly, an accurate mathematical model of ship motion was established, and the hydrodynamic derivatives of ship model motion were obtained by nu-merical calculation, and applied to the three-degree-of-freedom model of the intelligent research and training dual-use ship. Then, the model was used as part of the closed-loop simulation model of the ship’s dynamic positioning system, and the terminal sliding mode controller was used for sim-ulation and emulation, thereby obtaining ideal simulation test results. This research helps to im-prove the accuracy and reliability of the dynamic positioning system and provides new ideas and methods for ship motion control.

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL-magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the generated magnetic field using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

The application of dry lubrication in rolling contacts is challenging due to wear. In previous studies graphene platelets applied as dry lubricants on the surfaces of angular contact ball bearings demonstrated superior properties. These specific bearings, experiencing both rolling and spinning motion, create more severe conditions for dry lubricants. The applied graphene platelets have a thickness of few graphene layers in the nm range and a diameter in the µm range. Such platelets can be produced by mechanical exfoliation from graphite. To delve deeper into the lubrication effects, investigations were carried out using a milli tribometer. In the tests, a fixed steel ball is sliding against an oscillating counterpart under defined load. During the measurements, the applied load and tangential forces on the ball are recorded to calculate the friction. In our case, a steel ball with a diameter of 6 mm was used against graphene-based films on flat bearing surfaces (axial bearing washer). Comparative investigations included nano graphite particles and fullerene as dry lubricants, in addition to graphene platelets of various staple thicknesses. To increase the adhesion of the films to the surfaces, a pre-rolling process was implemented. Afterwards, the friction on the compressed films was measured. The results indicate that the pre-rolling process effectively reduces the friction of the system. After testing, the surfaces underwent analysis using laser scanning microscopy to assess the formed films, wear, and material transfer. It has been demonstrated that the pre-rolling process leads to the formation of a very thin compacted film with surface protective properties. On the ball as counterpart, the graphene platelets generate a transfer film on the contacting surface.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

Surface Plasmon Resonance (SPR) technology has been known as a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the section, we will summarize kinetic data on the interaction of SARS-CoV-2 with different variants with various ligands which were obtained from the results of SPR signal analysis. These affinity-related kinetic data are very useful both in drug discovery for therapeutic purposes and in the detection of SARS-CoV-2.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

In order to study the influence of falling rock shape on its rolling characteristics and realize the optimization of falling rock protection design, the following research was carried out. Design model experiments to explore the rolling characteristics of different shapes of rockfalls. Based on the experimental results, it was found that the slenderness ratio, center of gravity, and rotational inertia of the rockfalls can affect their rolling characteristics, leading to swaying and changing the rolling axis during the rolling process, thereby affecting their rolling speed. On this basis, the formation mechanism of rolling resistance is analyzed, and the main cause of energy loss is the rolling resistance arm formed with the rolling surface during rockfall rolling, and a shape parameter to quantify the rolling resistance is proposed. The parameters are substituted into the kinematic formula, in which the influence of the rockfall shape, slope and slope roughness on the rolling speed is considered. Combined with the offset and initial position of the rockfall, the formula can be used to calculate the rolling speed and impact energy in the rolling region and any position in the region. The calculation formula was validated using model experimental data, and the results showed that the error between the experimental and calculated values was small. The error was corrected based on the experimental data. After on-site testing and verification, it can provide reference for the management of rockfall disasters.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

The use of friction-based Beam-to-Column Connections (BCC)s for earthquake-resistant Moment Resistant Frames (MRFs), aiming at eliminating damage to beam end sections due to the development of plastic hinges, has taken hold since the early eighties. Different technical solutions were proposed for steel structures, some were also designed for timber structures, while few more recent studies concern friction joints employed in reinforced concrete structures. Research aimed at characterizing the behavior of joints has focused on the evaluation of the tribological properties of the friction materials, coefficient of friction, shape and stability of the hysteresis cycles, influence of the temperature, speed of load application, effects of the application method, stability of preload, the influence of seismic excitation characteristics on the structural response, statistical characterization of amplitude and frequency of the slip excursion during seismic excitation. Studies aimed at identifying the design parameters capable of optimizing performance have focused attention mainly on the slip threshold, device stiffness, and deformation capacity. This review compiles the main and most recent solutions developed for MRFs. Furthermore, the pros and cons for each solution are highlighted, focusing on the dissipative capacity, shape and stability of hysteresis loops. In addition, the common issues affecting all friction connections, namely characteristics of friction shims and the role of bolt preload, are discussed. Based on the above considerations, a guideline can be outlined, which can help to choose the appropriate solutions for beam-to-column connections for MRFs.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

In this study, the hybrid biological ion exchange (BIEX) resin and gravity-driven membrane (GDM) process was employed for the treatment of coloured and turbid river water. The primary objective was to investigate the impact of both physical and chemical cleaning methods on ceramic and polymeric membranes in terms of stabilised flux, flux recovery after physical/chemical cleaning, and permeate quality. To address these objectives, two types of MF and UF membranes were utilised (M1 = polymeric MF, M2 = polymeric UF, M3 = ceramic UF, and M4 = lab-made ceramic MF).Throughout extended operation, the resin functioned initially in the primary ion exchange (IEX) region (NOM displacement with pre-charged chloride) and progressed to a secondary IEX stage (NOM displacement with bicarbonate and sulphate), while membrane flux remained stable. Subsequently, physical cleaning involved air/water backwash with two different flows and pressures, and chemical cleaning utilised NaOH at concentrations of 20 and 40 mM, as well as NaOCl at concentrations of 250 and 500 mg Cl2/L. These processes were carried out to assess flux recovery and identify fouling reversibility. The results indicated an endpoint of 1,728 bed volumes (BV) for the primary IEX region, while the secondary IEX continued up to 6,528 BV. At the end of the operation, DOC and UVA254 removal in the effluent of the BIEX columns were 68% and 81%, respectively, compared to influent water. This was followed by 30% and 57% DOC and UVA254 removal by M4 (ceramic MF). The stabilised flux remained approximately 3.8 – 5.2 LMH both before and after the cleaning process, suggesting that membrane materials do not play a pivotal role. The mean stabilised flux of polymeric membranes increased after cleaning, whereas that of ceramics decreased. Enhanced air–water backwash flow and pressure resulted in increased removal of hydraulic reversible fouling, which was identified as the dominant fouling type. Ceramic membranes exhibited higher removal of reversible hydraulic fouling than polymeric membranes. Chemical cleaning had a low impact on flux recovery; therefore, we recommend solely employing physical cleaning.

Fastener holes are among the most common natural stress concentrators in metal structures. The life cycles of various structural elements, such as those in aircraft structures, automobiles, and rail-end bolt joints are limited by fatigue damage around the holes. An effective approach to delay the formation and growth of fatigue macrocracks is to introduce residual hoop compressive stresses around the holes. Two methods have become established in the prestressing of fastener holes in aircraft components: split sleeve and split mandrel, which implement one-sided processes. The common disadvantage of both methods is the complex procedure due to the need for high accuracy of the initial holes. This article presents a new modified split mandrel method providing the same tightness (interference fit) with a wide tolerance of the pre-drilled hole diameters reducing the number of technological cycle steps and production costs. То implement the new method, a functionally connected tool and a device with a hydraulic drive were developed. An extensive experimental study of 2024-T3 AA specimens was carried out to evaluate the effectiveness of the method under a high scattering of the pre-drilled holes. The new method provided a deep zone of residual hoop compressive stresses on both faces of the specimens after cold working and after hole final reaming. The removal of a plastically deformed layer around the hole of suitable thickness during the final reaming decreased the axial gradient of residual hoop stress distribution. Fatigue tests on a tensile pulsating cycle verified the effectiveness of the modified split mandrel method to significantly increase the fatigue life by 6.6 times based on cycle fatigue strength compared to the conventional case of machining the holes. The obtained S-N curves for three groups of samples with initial hole diameters of 8.0, 8.1, and 8.2 mm which were cold worked with the same tightness of 0.32 mm and final reamed, aligned well indicating that the new method can provide constant fatigue strength for a given stress amplitude.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

In the construction business, which is known for its intricate and strategic nature, it is crucial to have efficient risk management in place. This is because various internal and external elements can cause disruptions to projects. Although the industry has had a historically negative reputation for risk analysis, the methodical process of identifying, analyzing, and managing risks has been more widely recognized and respected. This paper examines the historical progression of construction risk management, highlighting the need to identify and classify risks promptly. The study examines various risk factors related to finance, law, environment, and logistics. The study explores various risk management strategies, such as contract management, risk distribution, evaluation, mitigation, and sharing. The article discusses the significant impact of risk management software, such as Procore, PlanGrid, e-Builder, Autodesk BIM 360, and Oracle Primavera, on improving project results. The study finishes by highlighting the importance of proactive risk management in protecting against financial losses and maintaining a reputation in the construction business.

This study investigates the specific factors affecting the usage intention of distributed ledger technology (DLT), such as availability, diversity and economic value from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Availability and economic value affect performance expectancy, while availability, diversity, and profitability have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, financial, medial, and logistics sectors.

The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒18.29 m, GP: 0.12‒0.15, and HC: 18.64‒26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22‒2.54, BS: 68.47‒82.34, and GP: 0.1‒0.14.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application in both brain and breast imaging. This paper discusses the current trends in NIRS for brain and breast imaging, exploring advances in multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, and the impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts to enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited spatial resolution and tissue heterogeneity, are discussed. The discussion extends to diffuse optical tomography and ongoing instrumentation development, clinical trials and studies validating NIRS diagnostic efficacy in breast imaging. The paper emphasizes the need for standardization, integration into routine clinical practice, and motivates for future work.

In the last ten years, plasma waste treatment has gained increasing prominence as a technology in response to growing challenges in waste disposal and the recognition of opportunities for generating valuable by-products. The efficiency and outcomes of this process are intricately linked to the characteristics of the plasma involved, such as plasma enthalpy and heat flux. We employed an experimental radio frequency inductively coupled plasma (RF-ICP) torch operating at atmospheric pressure to generate plasma. Utilizing optical emission spectroscopy (OES), we measured plasma parameters, specifically the effective electron temperature (Teff) and plasma density (ne), while varying argon gas flow rates and RF powers. Our investigations revealed that both flow rate and RF power exert significant control over plasma parameters, and we observed an unusual phenomenon within this range of discharge pressures. Consequently, both the plasma's enthalpy and the heat flux it generates are influenced by variations in both flow rate and RF power.

Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy

Genetic engineering and directed evolution are effective methods to address the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions, improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase, as well as high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

In Thailand, there are several bus fleets—predominantly diesel buses—which is an internal combustion engine (ICEs) in circulation. These fleets are consumed a lot of energy and cause pollution, especially by emitting GHGs and PM2.5. Battery electric buses (BEBs) have been proposed to address these issues. BEB fleets still have a high initial cost, which is why most fleet governance agencies, such as the Bangkok Mass Transit Authority (BMTA), are facing investment decisions. This study aims to develop an energy model for the BMTA BEB fleet by using their real operating data. The methodology for the development of the investment model with two steps is described; there is a BMTA bus route analysis step and a fleet management and charging design step. The output is illustrated in terms of the maximum number of BEBs that can be operated on a sample route with the minimum number of chargers. Interesting results were obtained with the first step: Two of the five BMTA bus routes can be changed into BEBs in phases with a limit of 200 kWh for the energy requirements for every two rounds. The results from the second step demonstrated that the maximum number of BEBs for the two routes was 13, with four charger plugs of charger, thus requiring 150 kW per plug. The charging profile peaked at 600 kW from 4:00 to 8:00 p.m. These results show the potential of the model for fleet design and investment decisions. The sample results from the models illustrate energy savings and cost evaluations for fleet management and design. Compared with diesel buses, THB 10.44 million per year can be saved in terms of energy costs by using a BEB fleet. An optimization model was used to assess the savings incurred due to the investment cost. More than 37% of the costs were saved. A full economic investigation should be carried out in the future. These results show only the energy used when the fleet has already been transformed into a BEB fleet in phases. The emphasis of battery size investigation and energy used were illustrated. They only depict an economic validation of the model, but do not refer to such projects’ feasibility, and the model has not yet been fully validated.

For underwater vehicles navigating in Arctic Sea ice-covered waters, fatigue damage is a crucial issue. Many scholars have conducted low-temperature fatigue analysis on low-temperature ma-terials and substrate platforms for ships, and the results show that low-temperature is beneficial for improving the mechanical properties of materials. However, they mainly focus on low-temperature performance experiments and simulation analysis of standard components of materials, and there are very few fatigue life experimental studies on underwater vehicle me-chanical structures. This paper conducts experimental investigations on a scaled model of an underwater vehicle rudder mechanism in a polar environment laboratory. Using a comparative analysis method involving simulations and experiments, the low-temperature fatigue test data of the scaled underwater vehicle rudder mechanism are analyzed, and the quantified analysis be-tween fatigue and different low-temperature loading cycles, as well as the influence of icing on the fatigue life, is explored. It is indicated that the arctic environment can decrease the fatigue life of the underwater vehicle rudder mechanism by deteriorating its material properties. The paper builds the foundation for the fatigue life of an underwater vehicle rudder mechanism in arctic low-temperature environments.

Hydrogen storage in high pressure tanks can occur with different filling strategies. Many studies have been carried out on supplies with increasing pressure rates. However, hydrogen filling with constant mass inflow rate have not received the same attention. The present work aims to carry out numerical simulations to study hydrogen flow, in a type 3 tank of 70 MPa NWP and constant inflow rate condition. From the analysis of the results, it was possible to verify that there is a linear relationship between the increase in the total equilibrium temperature and the final hydrogen temperature. There is also a linear increasing relationship between Inlet and final temperatures when the equilibrium temperature is fixed. Considering fully adiabatic walls results in significant increases in temperature. The difference between total and static temperatures found in the Inlet tube decreases with the decrease in mass flow rate. The choice of a polymer for the tank lining led to higher temperatures when compared to an aluminum lining.

In this paper, a swarm trajectory planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real-time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. Simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory planning method for a network of ASVs.

Lower urinary tract dysfunction, such as incontinence or urinary retention, is one of the leading consequences of neurological diseases. This significantly impacts the quality of life for those affected, with implications extending not only to humans but also to clinical veterinary care. Having motor and sensory fibers, the pudendal nerve is an optimal candidate for neuromodulation therapies using bidirectional intraneural prostheses, paving the way towards the restoration of a more physiological urination cycle: bladder state can be detected from recorded neural signals, then an electrical current can be injected to the nerve based on the bladder real-time need. To develop such prostheses and investigate this novel approach, animal studies are still required since the morphology of the target nerve is fundamental to optimize the prosthesis design. This study aims to describe the porcine pudendal nerve as a model for neuromodulation studies aiming at restoring lower urinary tract dysfunction. Five male farm pigs were involved in the study. First, a surgical procedure to access the porcine pudendal nerve without muscle resection was developed. Then, an intraneural interface was implanted to confirm the presence of fibers innervating the external urethral sphincter by measuring its electromyographic activity. Finally, the morpho-physiology of the porcine pudendal nerve at the level of surgical exposure was described by using histological and immunohistochemical characterization. This analysis confirmed the nerve fasciculate nature and the presence of mixed fibers with a spatial and functional organization. These achievements pave the way for further pudendal neuromodulation studies by using a clinically relevant animal model with the potential for translating the findings into clinical applications.

This paper presents a thorough review of Electrical Muscle Stimulation (EMS) in the context of Augmented Reality (AR) and Virtual Reality (VR), specifically focusing on its application in providing kinesthetic feedback. Our systematic review of 17 studies reveals the growing interest and potential of EMS in this domain, as evidenced by the growing body of literature and citations. Key elements presented in our review encompass a catalog of the applications developed to date, specifics of the stimulation parameters used, the participant demographics of the studies, and the types of measures used in these research efforts. We discovered that EMS offers a versatile range of applications in AR/VR, from simulating physical interactions like touching virtual walls or objects, to replicating the sensation of weight and impact. Notably, EMS has shown effectiveness in areas such as object handling and musical rhythm learning, indicating its broader potential beyond conventional haptic feedback mechanisms. However, our review also highlights major challenges in the research, such as inconsistent reporting of EMS parameters and a lack of diversity in study participants. These issues underscore the need for improved reporting standards and more inclusive research approaches to ensure wider applicability and reproducibility of results.

This study aims to tackle the challenge of high noise levels on balconies while preserving natural ventilation. Eight innovative balcony designs, incorporating elements like diffuser edges, undulating ceilings, Helmholtz resonators, grooves, or sound traps, were evaluated via finite element (FE) modeling. The extensive FE studies involved about 10 million elements per analysis case. Results showed that a standard balcony with a front parapet wall could achieve a noise reduction of around 10 dBA, with the jagged ledge design providing an additional 2 dBA improvement. Adding protruded ledge designs led to higher insertion loss (IL) by up to 9.4 dBA, especially at higher stories. Having wall-embedded Helmholtz resonators improved IL by up to 1.5 dBA. The jagged ledge design consistently exhibited positive IL across a broad frequency range from 200 Hz to 2 kHz. Combining beneficial balcony ceilings and wall ledges significantly reduced noise across lower and higher frequency ranges. Front jagged and full wavy ceilings offered positive IL throughout different storeys, proving their effectiveness against human vocal range. This study found that jagged ledge and front jagged ceiling designs, effective across various balcony heights, are viable options for improving balcony noise mitigation, providing valuable insights for architects and designers seeking practical solutions for outdoor noise reduction.

An integrated approach to active flow control is proposed by finding both the drooping leading edge and the morphing trailing edge for flow management. This strategy aims to manage flow separation control by utilizing the synergistic effects of both control mechanisms, which we call the Combined Morphing Leading Edge and Trailing Edge (CoMpLETE) technique. This design is inspired by a bionic porpoise nose and the flap movements of the cetacean species. The motion of this mechanism achieves a continuous, wave-like, variable airfoil camber. The dynamic motion of the airfoil's upper and lower surface coordinates in response to unsteady conditions is achieved by combining the thickness-to-chord (t/c) distribution with the time-dependent camber line equation. A parameterization model was constructed to mimic the motion around the morphing airfoil at various deflection amplitudes at stall angle of attack and morphing actuation start times. The mean properties and qualitative trends of the flow phenomena are captured by the transition-SST (Shear Stress Transport) model. The effectiveness of the dynamically morphing airfoil as a flow control approach is evaluated by obtaining the flow field data, such as velocity streamlines, vorticity contours, and aerodynamic forces. Different cases are investigated for the CoMpLETE morphing airfoil, which evaluates the airfoil’s parameters, such as its morphing location, deflection amplitude, and morphing starting time. The morphing airfoil’s performance is analyzed to provide further insights into the dynamic lift and drag force variations at pre-defined deflection frequencies of 0.5 Hz, 1 Hz, and 2 Hz. The findings demonstrate that adjusting the airfoil camber reduces streamwise adverse pressure gradients, thus preventing significant flow separation. Although the trailing edge deflection and its location along the chord influence the generation and separation of the Leading-Edge Vortex (LEV), these results show that the combined effect of the morphing leading edge and trailing edge has the potential to mitigate flow separation. The morphing airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (c_(l,max))). This work also broadens its focus to investigate the aerodynamic effects of a dynamically morphing leading and trailing edge, which seamlessly transitions along the side edges. The aerodynamic performance analysis is investigated across varying morphing frequencies, amplitudes, and actuation times.

The construction of reinforced concrete (RC) structures inevitably consumes an excessive number of rebars, leading to significant cutting waste and carbon emissions. Extensive research has been conducted to minimize this issue and its consequences; however, these methods consistently consume a substantial number of rebars. This includes a previous study that utilizes the lap splice position optimization and special-length rebar concept without considering the lapping zone regulation. Moreover, conventional lap splices pose inherent drawbacks that could jeopardize the structural integrity of RC members. In contrast, mechanical couplers eliminate the need for rebar lapping, effectively reducing rebar consumption. This research aims to evaluate the impact of an integrated mechanical coupler and special-length-priority minimization algorithm on the reduction in rebar consumption and cutting waste in RC columns, achieving near-zero cutting waste. To validate the effectiveness of the proposed algorithm, it was applied to the column rebars of an RC building. The results revealed a significant reduction in ordered rebar consumption by 18.25%, accompanied by substantial reductions in cutting waste (8.93%), carbon emissions (12.99%), and total costs (10.81%) compared with a previous study. The outcomes provide the industry with insights into further reducing rebar consumption and its related consequences. Applying the proposed algorithm to various construction projects will further amplify the corresponding benefits.

This paper proposes the application of force-projecting bilateral control to a master-follower teleoperation system with a pneumatic manipulator on the follower side, and evaluates its effectiveness. The proposed method directly projects the operating force on the master side to the driving force on the follower side, eliminating the need for both position control and external force detection on the follower side, thereby solving the problem of low rigidity and response delay of pneumatic manipulators and providing highly stable sensor-less force presentation against variable environments. In this study, dynamic response analyses of a 1-DOF master-follower system were performed by numerical simulation using a linear system model, followed by experimental verification by implementing an actual system with an external force estimator. The results showed that the proposed force-projecting bilateral control has significantly higher positioning rigidity and better force control stability than the conventional force-reflecting bilateral control. A theoretical consideration was also given using equivalent transformation of force transfer functions to provide evidence for the high stability.

Well deliverability optimization is essential for oil and gas production and nodal analysis is a common tool used for selecting suitable production parameters. To accurately calculate upstream and downstream pressures at various production rates at a certain selected node, proper modeling of multiphase flow in wellbores is necessary. However, this process is complex due to the inconsistent velocities of the fluids (i.e., oil, gas, and water) and the interaction between the liquids under high temperature and pressure. Empirical models, such as Hagedorn and Brown (1965), Beggs and Brill (1973), and Mukherjee and Brill (1983 and 1985), and the drift-flux model (Zuber 1965) are commonly used for multiphase flow calculations in wellbores. In this paper, we programmed and evaluated three multiphase flow models (Hagedorn-Brown, Mukherjee-Brill, and drift-flux) using Matlab. The programming includes holdup calculation, pressure gradient calculation, pressure traverse calculation, inflow curve determination, inflow performance relationship (IPR) curve determination, and well deliverability determination. Additionally, we conducted a case study to examine the sensitivity of parameters and compare and analyze the results of pressure traverse, tubing intake curve, and deliverability determination between the drift-flux and Mukherjee-Brill models. Sensitivity analysis was conducted on two of the seven parameters in the drift-flux model, and the effects of production and reservoir parameters on well deliverability were analyzed. The case study results indicated that the Mukherjee-Brill model predicted slightly higher bottomhole pressure compared to the drift-flux model at a fixed oil flow rate, but its well deliverability determination was lower. Furthermore, the drift-flux model showed that the pressure increased with an increase in the distribution coefficient for bubble flow (C0_bubble), while the beta parameter had little impact on the calculated pressure in the studied range. The well deliverability increased when the tubing size and reservoir pressure increased, or the tubing head pressure decreased.

This paper is the continuation of a previous study, which highlighted some inconsistencies in the RILEM guidelines for the interpretation of the diagonal compression test. Although improved compared to the ASTM guidelines, in fact, the RILEM guidelines underestimate the state of hydrostatic stress induced by the diagonal compression test at the center of the wallette. The new interpretation of the diagonal compression test proposed in this article shows that the RILEM guidelines actually underestimate both the hydrostatic and the deviatoric stress states at the center of the wallette. The new formulation complies with the linear elastic theory and allows us to use the the diagonal compression test to identify the three elastic coefficients of masonry. In particular, it allows the identification of the Poisson ratio, which instead takes on a conventional value in the RILEM and ASTM guidelines. The difference of one order of magnitude between the conventional and proposed Poisson’s ratio is in agreement with the experimental results on another brittle material, namely concrete. Finally, the new proposal fills the gap between the results provided by the two tests usually performed to identify the shear behavior of masonry: the diagonal compression test and the shear-compression test.

In the previous work, the pure fatigue behavior of AM60 magnesium alloy (PF-AM60) was compared with the corrosion fatigue behavior of these specimens (CF-AM60). In this research, in continuation of the previous job, the pure fatigue behaviors of AM60 with polylactic acid (PLA) coating (PF-AM60-PLA) and also, corrosion fatigue behaviors of magnesium alloy with PLA coating (CF-AM60-PLA) were evaluated. Polymer coating was made by fused deposition modeling (FDM) with a 3D printer and attached to standard fatigue test specimens with a glue. Then, they were immersed in the simulated body fluid (SBF) for 27 days. In the end, a high-cycle bending fatigue test was performed on samples. The fracture surface of the samples was also observed using the field emission scanning electron microscopy (FESEM). Due to corrosion, the weight of the specimens redcued by an average of 35%. The corrosion rate decreased in the first 7 days and then increased. PF samples with coating had an average of 49% increase in fatigue lifetime. Regarding the CF samples, despite the use of a 10 times stronger solution, the fatigue lifetime of these samples decreased by only 35%. FESEM results also showed cleavage plates and striations. In addition, the separation of the glue from the coating and Mg was observed. Corrosion products including microcracks and holes were seen on the fracture surface of CF specimens, which caused stress concentration and crack growth. Holes caused by the release of gases were also observed in polymer coatings.

In this paper the possibility of harvesting energy from the vibrations of a plate is analyzed. The harvester takes the form of a cantilever dynamic vibration absorber equipped with a piezoelectric layer and tuned by means of a tip mass to the first mode of vibration of the plate. A mathematical model of the coupled system composed of the plate and the harvester is presented. The validity of the proposed harvester is proved by means of simulations carried out with the modal expansions approach. Simulation results highlighting the effect of harvester tuning and location are presented as well. Then the validity of the harvester is confirmed by experimental tests carried out both with a concentrated impulsive load and with a distributed pressure load. Simulations and experimental tests show that the cantilever piezoelectric dynamic vibration absorber generates open circuit voltage an order of magnitude larger than the one generated by the same piezoelectric layer when directly bonded to the plate surface.

Aiming at the classification identification problem of aeroengines, this paper adopts telemetry Fourier Transform Infrared Spectrometer to collect aeroengine hot jet infrared spectrum data, and proposes an aeroengine classification identification method based on spectral feature vectors. First, aeroengine hot jet infrared spectrum data are acquired and measured, meanwhile, the spectral feature vectors based on CO2 are constructed. Then, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FTIR, were used to telemeter the hot jet of three aeroengines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was approximately 98%. This paper has great significance for the fault diagnosis of aeroengines and classification recognition of aircrafts.

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at BS. The expression of the uplink achievable rate is derived and analyzed. Besides, genetic algorithm (GA) is used to optimize the phase shifts of RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS’s reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs, and reduce the demand for the number of BS antennas, which can significantly reduce the hardware costs at the BS.

This paper introduces a fuzzy logic-based autonomous ship-deck landing system for fixed-wing Unmanned Aerial Vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship-deck landing model is based on a fuzzy system comprised of three interconnected subsystems. Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship-deck landings. Specifically, the Fuzzy Logic System (FLS) takes inputs such as the airspeed, the relative wind direction and speed, the range from the deck and the UAV's flight altitude. The FLS outputs provide data on the Speed of the UAV relative to the ship’s velocity, the Bank Angle, and the Angle of Descent of the UAV. The performance of the designed intelligent ship-deck landing system is evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

A non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; and ash content) for a variety of chipped and ground biomasses was investigated through a combination of thermogravimetric analysis (TGA) and near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). In this study, the thermogravimetric method was employed as a tool to determine the proximate analysis data of biomass samples through TG and DTG curves, which were generated by tracking changes in biomass mass loss over time or temperature. NIRS non−destructively scanned the chipped biomass in diffuse reflectance while ground biomass using transflectance mode, covering the wavenumber range of 3594.87 to 12,489.48 cm−1. The PLSR−based models: Full−PLSR, genetic algorithm (GA)−PLSR, successive projection algorithm (SPA)−PLSR, the spectral multi−preprocessing (MP) PLSR− 5 range method, and the MP PLSR−3 range method were developed. Model performance was assessed, compared, and selected based on the coefficient of determination in the prediction set (R2P), root mean square error in the validation set (RMSEP), and the ratio of prediction to deviation (RPD). Based on the comprehensive analysis of model performance, MC and FC models of chip biomass have demonstrated satisfactory performance which makes it applicable cautiously in any application, including research. The optimum models for MC and FC in chip biomass were constructed using GA−PLSR with 2nd derivative and Full−PLSR with a constant offset, resulting in R2P values of 0.8654 and 0.8773, RMSEP values of 0.85% and 2.12%, and RPD values of 2.9 and 3.0, respectively. Other parameters such as MC and FC in ground biomass as well as VM and ash content in both chip and ground biomass were found suitable for rough screening. The sensitivity of the selected model was assessed by calculating the limit of quantification (LOQ), and the results indicated that the calculated LOQ values for VM in both chip and ground biomass and FC in chip biomass are lower than the minimum reference values used during model development, signifying a high level of sensitivity. However, for the remaining parameters, the LOQ values surpass the established minimum reference value, suggesting the model could not predict the future samples with reference value lower than the calibration range. Nevertheless, enhancing the models by incorporation an ample number of representative biomass samples and consistently validating the models with unknown samples is imperative to ensure accurate predictions.

Internet of Things (IoT) is what we have as a great breakthrough in 5G network. Although 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6 addressing which is the foundation of the Network Slicing for the 5G core network. Comparing to other related papers, we provide in depth information on how the Internet of Things (IoT) is going to affect our lifes and how this technology is handled as Internet of Everything (IoE) in 6G network. Finally, a full reference is made to the 6G network with its challenges comparing to the 5G network.

Stochastic modelling of geometrically imperfect steel frame structures requires inputs of statistical values of the imperfection parameters, in some cases also with suitably selected mutual correlations between these input parameters. The paper presents a verification study of two stochastic methods to directly model the initial global geometrical imperfections of the steel frame structures. The stochastic input values of geometrical imperfections are derived from the tolerance criteria of the corresponding European Standard. Two advanced stochastic methods have been developed, referred as #RSS (random story sway) and #RSP (random story position). These methods are verified using random sampling simulations. This study provides useful provisions for the advanced numerical analyses of multi-story steel frames of various geometries. The proposed methods are verified for equidistant story heights and for structures of up to 24 stories, hence might be used for the most types of the standard steel frame structures.

Smelting used to be less efficient in the past; therefore, wastes obtained from historical processing at smelter plants usually contain certain quantities of valuable metals. Upon the extraction of useful metal elements, metallurgical slag can be repurposed as an alternative mineral raw material in the building sector. A case study was conducted, which included an investigation of the physico-chemical, mineralogical, and microstructural properties of Pb-Zn slag found at the historic landfill near the Topilnica Veles smelter in North Macedonia. The slag was sampled using drill holes. The mineralogical and microstructural analysis revealed that the Pb-Zn slag is a very complex and inhomogeneous alternative raw material with utilizable levels of metals, specifically Pb (2.3 wt%), Zn (7.1 wt%), and Ag (27.5 ppm). Crystalline mineral phases wurtzite, sphalerite, galena, cerussite, akermanite, wüstite, monticellite, franklinite, and zincite were identified in the analyzed samples. The slag’s matrix consisted of alumino-silicates, amorphous silicates, and mixtures of spinel and silicates. Because the extraction of Pb, Zn, and Ag has economic potential, the first reutilization stage will be to convert metal concentrates into their collective concentrate, from which the maximum amount of these valuable elements may be taken out. This will include combination of gravity concentration and separation techniques. The next step is to assess the Pb-Zn slag’s potential applications in civil engineering based on its mineralogical and physico-mechanical properties. Alumino-silicates, amorphous silicates, and mixtures of spinel and silicates present in Pb-Zn slag, which contain high concentrations of SiO2, Al2O3, CaO, and Fe2O3, are suitable for use in cementitious building composites. The goal of this research is to suggest a solution for closing the circle of slag's reutilization in terms of Zero Waste principles, so it is critical to thoroughly investigate the material and establish methods and preparation processes, as well as ways of concentrating useful components into commercial products.

The accumulation of waste polymer in ecosystems is a problem that affects the environment on a global scale. This paper presents the design of Pwave+, a microwave pyrolysis system that has been optimized to recycle waste polymer into valuable hydrocarbon fuels and chemicals. Heating in a microwave oven is both volumetric and rapid, which improves the effectiveness of pyrolysis and the quality of the product. The production of waste polymer oil and gas products was boosted to its full potential by a continuous microwave vortex reactor. The product gases allowed for complete independence in terms of energy production. Modeling, simulation, and experimentation with advanced technology are used to optimize the pyrolysis parameters and the performance of the reactor. As a result, simulation and programming are solutions to problems. The results of this study demonstrated both the technical viability and the long-term viability of continuous microwave pyrolysis as a method for recycling mixed polymer waste streams. The Pwave+ continuous microwave vortex reactor is described in this study. It is capable of converting various polymer waste streams into hydrocarbon products as well as energy. The use of microwaves to assist in the pyrolysis process has the potential to cut down on plastic pollution and the amount of waste that is dumped in landfills. This could provide valuable insights for the design and commercialization of similar systems.

The development of U.S. Army and NATO data link systems is introduced first, and then the development trend of future intelligent data link is summarized into integration, generalization, multifunctionality and high security. A unit-level combat system architecture based on the global combat cloud, which is capable of realizing the flexible scheduling of global combat resources and maximizing the overall combat effectiveness, is proposed. Intelligent data link is an important part of this solution, providing strong information support for future urban unit-level warfare.

Manual handling tasks, both in daily activities and at work, require high dexterity and the ability to move objects of different shapes and sizes. However, musculoskeletal disorders that can arise due to aging, disabilities, overloading, or strenuous work can impact the natural capabilities of the hand with serious repercussions both in working and daily activities. To address this, researchers have been developing and proving the benefits of wrist exoskeleton., This paper, which is the part II of a study on wrist exoskeletons, presents and summarizes wearable wrist exoskeleton devices conceived for use in rehabilitation, assistance, and occupational fields. Exoskeletons considered within the study are those available either in a prototyping phase or on the market. They can support the human wrist by relieving pain or mitigating fatigue while allowing at least one movement. According to the requirements to be met, the majority have been designed active (80%) for higher force/torque transmission, and soft for better kinematic compliance, ergonomics, and safety (13 devices out of 24, more than 50%). Electric motors (11 devices out of 24, almost 50%) and cable transmission (9 devices out of 24, almost 40%) are the most common due to their simplicity, controllability, safety, power-to-weight ratio, and the possibility of remote actuation. As sensing technologies, position and force sensors are widely used in all devices (almost 90%). The control strategy depends mainly on the application domain: for rehabilitation, CPM (Control Passive Motion) is preferred (35% of the devices), while for assistance and occupational purposes AAN (Assistance-As-Needed) is more suitable (38% of the devices). What emerges from this analysis is that while rehabilitation and training are fields in which exoskeletons have been grown more easily and gained some user acceptance (almost 18 devices of which 4 are available on the market), relatively few devices have been designed for occupational aims (6 of which only 2 are available on the market) due to difficulties in meeting the acceptance and needs of users. In this perspective, as a result of the state-of-the-art analysis, the authors propose a conceptual idea of a portable soft wrist exoskeleton for occupational assistance.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

The extreme environmental issues and the resulting needs to save energy have turned the attention to electrification of energy applications. One of the key components involved in energy efficiency improvements is the appropriate conception and manufacturing of electric machines. This paper overviews the electromagnetic analysis governing the behavior of permanent magnets that enabled substantial efficiency gains in recent electric machine developments. Particular emphasis is given to model the properties and losses developed in permanent magnets in the emerging high speed applications. In addition, the investigation of properties and harmonic losses related to ferromagnetic materials constituting the machine magnetic circuits are equally analyzed and discussed. The experimental validation of the implemented methodologies and developed models with respect to the obtained precision is reported. The introduction of mixed numerical techniques based on the finite element method intended to appropriately represent the different physical phenomena encountered is outlined and discussed. Finally, fast and accurate simulation techniques including aggregated lumped parameter models considering harmonic losses associated with inverter supplies are commented.

Commercial buildings situated in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This research paper aims to address this gap by investigating the application of innovative deep learning models in day-ahead hourly cooling load prediction for commercial buildings situated in tropical climates. A range of deep learning techniques, including Deep Neural Network, Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. An extensive comparative analysis is conducted to identify the most effective hybrid model. Sequence-to-sequence model provided better performance compared to the other single and hybrid models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. Further, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies.

The poor solvent crystallization of carbapenem CS-023 has been performed at 25 °C. The following results have been obtained: 1) A solvate crystal Form A (5/2 Ethanol·1/2 H2O) is recovered from 80 v/v% ethanol solution, 2) Form A transforms to Form H (4H2O) by the solid-phase transition through the solvate-free polymorph, Form A-2, and Form A also transformed to Form C (1Ethanol·3H2O) by the solvent-mediated transformation. In the present study, we found that Form C also transforms to Form H by the solid-phase transition through the solvate-free polymorph, Form C-2. The three polymorphs, Form A, H, and C, were composed of the helical-chain structures, respectively. However, there was an incomprehensible affair in the solid phase transition among the three polymorphs. Namely, Form A comprised a left-handed helix.On the other hand, Form C and Form H’s helix chains were in a left- and right-handed helix complex. The solid-phase transition of Form A into Form H suggested the switch of helicity in the solid. We attempted to explain the helicity change in the solid phase transition. As a result, it was suggested that the over-absorption of water of Form A-2 at high humidity plays a vital role in the helicity change.

An event-based dynamic output feedback control (DOFC) strategy for unmanned marine vehicle (UMV) systems is concerned. The whole UMV systems are composed of an UMV closed-loop system, a land-based control unit and the communication network. To increase the effectiveness of data transmission in the network channel and better enable the control unit against attack, an adaptive event-triggered mechanism (AETM) is applied. Moreover, a quantizer is installed between the sampler and the control unit. The quantizer further reduces the communication burden. The occurrence of aperiodic denial-of-service (DoS) attack is considered in the channel from the control unit to the UMV system. Sufficient criterion for ensuring global exponential stability of system with an expected H∞ disturbance attenuation index is obtained. Co-design of the dynamic output feedback controller and the AETM is derived. The effectiveness of the proposed approach is verified in the given illustrative simulation. The simulation results indicate that the reduction percentages of yaw angle amplitudes and yaw velocity accumulative error of the UMV system with the control strategy proposed in this paper are 43.2%and 45.9%, respectively, which are 0.3% and 5.8% improvement in both metrics compared to the previously published work.

Cavitation resulting from underwater explosions in compressible multiphase or multicomp-onent flows presents significant challenges due to the dynamic nature of shock-cavitation-structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock-cavitation-structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces; free surface and a rigid surface. The simulations are conducted using the Unstructured Compressible Navier-Stokes (UCNS3D) finite volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilising the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing.

The automotive industry has witnessed a significant push towards increased fuel efficiency and reduced carbon emissions. Among many factors influencing overall vehicle efficiency, aerodynamic drag is a critical consideration, accounting for a substantial portion of fuel consumption. Our investigation focused on the drag coefficients of three prominent 2023 passenger car models – the Toyota Corolla, Hyundai Elantra, and Kia Soul. Using ANSYS Fluent to perform computational fluid dynamics, we demonstrated an inverse correlation between drag force and gas mileage, underlining the significance of aerodynamic design in fuel efficiency. Notably, the Hyundai Elantra emerged as the most aerodynamically efficient of the three vehicles, followed closely by the Toyota Corolla. In contrast, the boxier-designed Kia Soul exhibited significantly higher drag coefficients and drag forces, rendering it the least efficient of the trio. These findings emphasize the importance of environmental factors in car design. Shaping aerodynamic profiles directly impacts fuel efficiency, stability, and overall performance.

In addressing the challenges of data scarcity in biometrics, this study explores the generation of synthetic palmprint images as an efficient, cost effective, and privacy preserving alternative to real-world data reliance. Traditional methods for synthetic biometric image creation primarily involve orientation modifications and filter applications, with no established method specific to palmprints. We introduced the utilization of the “Style-based generator”, StyleGAN2-ADA, from the StyleGAN series, renowned for generating high-quality images. Furthermore, we explore the capabilities of its successor, StyleGAN3, which boasts enhanced image generation, facilitating smooth and realistic transitions. By comparing the performance of StyleGAN3 on public dataset, we aim to establish the most efficient generative model for this purpose. Evaluations were conducted using the SIFT (Scale-Invariant Feature Transform) algorithm into our evaluation framework. Preliminary findings suggest that StyleGAN3 offers superior generative capabilities, enhancing equivariance in synthetic palmprint generation.

Butadiene-Styrene copolymer (SBS) to improve the mechanical and deformation properties of the binder used in its manufacture. However, the high cost and variability of processing limit its performance. A secondary modifier to solve these problems is nano and micro materials that allow the generation of unique properties in polymeric systems. Based on this, this study experimented with fly ash micro filler ((FA) in low proportions as a binder modifier with SBS for use in PA mixes. The FA residue is considered in 3% and 5% dosages on a base binder with 5% SBS. Rheological results show that FA improves classical, linear viscoelastic (LVE), and progressive damage properties compared to the modified binder. The PA blends with FA reduced binder runout, resulting in a thicker film, showing better abrasion resistance in dry and wet conditions. Samples with FA increase the post-cracking energy in indirect tension due to higher ductility. However, they decrease the fracture energy due to higher cracking before failure. In addition, the FA manages to decrease the difference between dry and wet ITS.

Domestic heating systems in the UK and across northern Europe are responsible for a substantial fraction of their countries’ carbon footprints. In the UK the vast majority of home space heating is via natural gas boilers with ’wet’ hydronic radiator systems. Most of those use TRVs (Thermostatic Radiator Valves) to avoid overheating, improve comfort and save energy. To meet Net Zero targets 20 million such UK gas systems may be retrofitted with heat pumps. Heat pump system designers and installers are cautious about retaining TRVs in such retrofitted systems in part because of worries that TRV temperature setbacks that lower heat demand may actually raise heat pump electricity demand in a "bad setback effect", thus wasting energy. This paper presents a new view of heat pump control and provides the first exploration of this issue, modelling one such industry claim, and finds that though real it should not apply to typical UK retrofits with weather compensation. The energy efficiency benefits of TRVs for older and partly-occupied homes, and to keep bedrooms cooler, remain valid. Comfort-seeking householders and installers should know that setting ’stiff’ temperature regulation may invoke the bad setback effect and cost dearly in energy and carbon footprint.

3D printing with earth-based mortar is still under development and faces challenges. Optimizing the mortar mixture, improving structural strength, determining the relationship between printing speed and the amount of extruded material, and ensuring long-term durability are areas that are still being refined. Additionally, regulatory and certification issues must also be considered to ensure the safety and compliance of 3D printed structures. This paper presents for discussion the records, analyses, studies, and considerations regarding the initial initiatives involving 3D printing with the extrusion of earth-based mortar developed at the Faculty of Engineering of the University of Porto - FEUP. Through this work, it is possible to strengthen and reaffirm that 3D printing with earth-based mortar has significant potential in the construction industry and that the incorporation of dispersed kraft paper fibres from the recycling of cement bags is an excellent resource to achieve good constructability in 3D printing with earth-based mortar.

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

This paper focuses on leveraging a cutting-edge 3D state-space model for a holistic eco-friendly assessment in petrochemical operations. - By applying the ecological carrying capacity theory and the three-dimensional state-space model, this research aims to unleash the next frontier of sustainability in the petrochemical industry. - The proposed methodology will enable advanced and comprehensive eco efficiency evaluation in petrochemical operations. - Through the integration of the ecological carrying capacity theory and advanced modeling techniques, this research seeks to revolutionize sustainability assessments in the petrochemical sector. - The utilization of the cutting-edge 3D state-space model will provide a more advanced and accurate understanding of eco-friendly practices in petrochemical operations. This research presents a unique approach distinct from existing literature, which primarily examines business eco efficiency from behavioral motivation and strategic perspectives. In contrast, this paper introduces a novel three-dimensional state-space model to evaluate the ecological efficiency of petrochemical operations. By combining the ecological carrying index and state space, this model represents the ecological impact of petrochemical operations in a three-dimensional geometric space. It considers petrochemical operations as a significant factor exerting pressure on natural resources and the natural environment. In this three-dimensional state-space model, the three axes represent different dimensions of petrochemical operations. The first axis represents the economic status, which quantitatively describes the financial aspects of the operation. The second axis represents the utilization of resources, capturing the extent to which resources are consumed in the operation. The third axis focuses on the impact of the operation on the environment, allowing for a quantitative assessment of its ecological footprint. By incorporating these dimensions, the model aims to provide a comprehensive and quantitative description of the economic activities of petrochemical operations and their interactions with resources and the environment.The primary focus of this research is to apply the three-dimensional state-space model to calculate the ecological carrying capacity of petrochemical operations. By doing so, it aims to verify the feasibility and applicability of the model in determining the ecological efficiency of these operations. The research also aims to provide theoretical tools that can assist operations in making more informed ecological efficiency judgments. By utilizing this model, petrochemical operations will have a quantitative framework to assess and improve their environmental impact, thus fostering more sustainable practices. The findings of this research demonstrate that the implementation of this method can effectively identify issues related to resources, economy, and environment throughout the development of petrochemical operations. By identifying these problems, it provides managers with a solid foundation for making strategic decisions that promote sustainable development. With this valuable information at their disposal, managers can proactively address challenges, optimize resource utilization, and minimize environmental impacts, thus steering the petrochemical operations towards a more sustainable and responsible path.

This work proposes a physics-based identification technique based on genetic algorithms. The main objective is to obtain a parametric matrix A that describes the time-invariant linear model of the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests the genetic algorithm provided with the adequate fitness function, could be an appealing alternative for aircraft model identification, even when only a limited amount of data is available. Furthermore, in some cases, linearization using genetic algorithm can be more efficient than classical methods.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

For both conventional and renewable energy conversion processes, computational fluid dynamics (CFD) has been used to address more energy-related challenges in recent decades. Using CFD to investigate vertical-axis wind turbines has become more common in recent years. The main goals of this application have been to more accurately predict the turbine's performance and to comprehend the complicated nature of the complex turbulent flow. The Vertical Axis Wind Turbine (VAWT) simulation for energy-generating applications has several intricate components. One of them is being aware of the chaotic flow that occurs during the first stages of the starting process, which influences overall effectiveness. In this article, the performance of the wind turbine was increased by using a passive flow control approach. The numerical research was carried out using Large Eddy Simulation for four alternative tip speed ratios in both cases, the classic and the optimized, equipped with a vortex trap on the exterior of the blades. The power and torque coefficient variations, as well as the velocity magnitude contours, show that the starting process may begin with a significant improvement in efficiency when flow control is used.

Construction Design Management (DM) involves the coordination of design process and activity to ensure quality project design, yet it involves many challenges. This work reports on a collaborative Knowledge Transfer Project (KTP) that tackled several challenges faced by construction design management practitioners. Employing a design science research methodology (DSRM), qualitative analysis of semi-structured interviews with purposively identified design managers first uncovered real world concerns around design coordination, and designer performance monitoring. Then to address these concerns, a web-based design management prototype dashboard was developed which uses typical project data to aid the management of design coordination activity and facilitate design management task prioritization and reporting functionalities. By digitalizing selected design management processes to improve efficiency, and productivity, the visual reporting capability of the dashboard enables design production to be monitored at project and portfolio levels; trends in designer performance to be assessed and focus to be retained on technical queries (TQs) and requests for information (RFIs). By reporting on the development of this web-based Design Management prototype dashboard and illustrating how it can enhance design management productivity in construction firms, this work also provides further evidence of how employing a design science research methodology in construction can produce practical solutions that address real world construction concerns.

The use of aerobic granular sludge is a promising and future-proof solution for wastewater treatment. The implementation of this technology requires the development of efficient and cost-effective methods for the management of excess sludge. The aim of the research was to evaluate the effects of hydrodynamic cavitation on the efficiency of aerobic granular sludge digestion. Respirometric measurements were performed at a temperature of 38°C and an initial organic load of 5.0 gVS/L. The changes in the properties of the pre-treated biomass, the kinetics of methane fermentation, the amount and composition of the biogas produced and an energetic evaluation of the process were carried out. A significant influence of hydrodynamic cavitation on the transfer of organic compounds into the dissolved phase was demonstrated. The degree of solubilisation was 37% for COD and 42% and for TOC. The efficiency of CH4 production from the pre-treated sludge reached a value of 496±12 mL/gVS, which corresponds to an increase of 19.6% compared to the raw biomass. The influence of cavitation on the CH4 content of the biogas was not observed. Strong correlations were found between the efficiency of anaerobic digestion and the concentration of dissolved organic compounds and the hydrodynamic cavitation time used. The highest net energy production of 2.89 Wh/gTS was achieved after 15 minutes of pre-treatment.

Atrial Fibrillation (AF) is known as one of the dominant cardiac disease that is associated with severe consequences such as risk of strokes. This cardiac arrhythmia can be efficiently diagnosed using EEG signals. However, AF episodes in EEG records are not always permanent for all subjects. In addition, lengthy subject preparation and ECG recording sessions makes this practice time consuming and not attractive for efficient diagnosis. One attractive solution for this problem will be the use of wearable Internet of Medical Things IoMT devices for EEG recording and AF detection. From another side the use machine learning for efficient diagnosis could provide high accurate diagnosis. However, the deployment of these wireless wearable devices faces the problems of limited processing capabilities and of energy scarcity. In this context, the design of energy efficient accurate on-sensor AF detection scheme using machine learning algorithms still requires a focused research effort. This paper proposes a wearable solution for ECG-based processing scheme for embedded AF detection. The proposed method achieved 100% sensitivity and 94.5% of specificity and accuracy of around 97.4 % for wide range of subjects while showing low energy-consumption. The obtained results show the efficiency of using Logistic Regression algorithm for accurate AF detection and attest about practical feasibility of the proposed approach in wearable IoMT devices.

Recent studies have shown that the mechanical properties of bioinspired periodic composite materials can be strongly influenced by finite deformation effects, leading to highly nonlinear static and dynamic behaviors at multiple length scales. For instance, in porous periodic nacre-like microstructures, microscopic and macroscopic instabilities may occur for a given uniaxial loading process and, as a consequence, wave attenuation properties may evolve as a function of the microstructural evolution, giving it the designation of metamaterials. The numerical outcomes provide new opportunities to design bioinspired soft composite metamaterials characterized by high deformability and enhanced elastic wave attenuation capabilities given by the insertion of voids and lead cores.

The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. Using observed crash history as a reference, the performance of the proposed ap-proach was compared to two of the well-established methods in practice, namely: the Empirical Bayes (EB) and the Potential for Safety Improvement (PSI) methods. The study results suggest that, using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the per-formance of the proposed method was found comparable to the well-established EB and PSI methods.

ABSTRACT"In the realm of the industrial complex, a pressing concern lies in optimizing resource utilization and enhancing energy efficiency. This critical issue finds strong support from a variety of government programs, strategies, and regulatory documents. To effectively tackle this challenge and revolutionize petrochemical production, it is imperative to establish and develop innovative mechanisms. One such mechanism involves harnessing the power of automation and advanced Industry 4.0 technologies to streamline production processes. By doing so, we can unlock the full potential of these systems, driving operational efficiency, conserving valuable reserves, and propelling innovation within the petrochemical industry."The article delves into an in-depth analysis of the prominent trends in the application of the "Industry 4.0" concept within the petrochemical industry. It sheds light on the various factors that impact the efficiency of organizing production systems. Additionally, the article identifies the primary avenues that drive the advancement and enhancement of production organization practices. Moreover, the article explores the fundamental tools employed for process improvements and highlights the significance of process automation in petrochemical plants. By examining these key aspects, it aims to provide valuable insights and recommendations for leveraging Industry 4.0 technologies to optimize production processes in the petrochemical industry..The study focuses on examining and evaluating various systems and technologies that contribute to the optimization of production processes within the petrochemical industry. Specifically, it delves into the implementation and benefits of the just-in-time system, the 5S system for workplace rationalization, as well as different information systems including ERP systems and CALS-technologies. By exploring these systems and technologies, the article aims to identify the advantages of automating production processes within the petrochemical complex. This analysis will unveil the potential benefits such as improved efficiency, reduced waste, enhanced productivity, streamlined operations, and increased overall competitiveness. Through this research, we aim to shed light on the value of these automation solutions and their impact on driving the petrochemical industry towards greater success.

Electroencephalography (EEG) enabled earbuds represent a promising frontier in evaluating brain activity beyond traditional laboratory settings. However, these devices need more comprehensive characterization before widespread health related usage. A toolbox was developed to facilitate and expand the assessment of ear-EEG devices. The first component of this toolbox is a desktop application (“EaR-P Lab”) that integrates EEG validation paradigms. This application is designed to be used with EEG amplifiers compatible with the Lab Streaming Layer (LSL) protocol. The second element of the toolbox introduces an adaptation of the phantom testing concept, to the domain of ear-EEG by using 3D ear scans of the tested individuals, allowing controlled assessment of ear-EEG devices. The EEG paradigms were validated for scalp acquisitions and applied on ear-EEG measurements, under wet ear electrode conditions. The ear-EEG phantom was successful in measuring useful metrics in EEG system characterization, highlighting performance differences within the electrodes on the tested earpieces. This knowledge was factored in to select an optimal electrode reference which resulted in increased response power on the auditory steady-state response (ASSR). Through this work, an ear-EEG evaluation toolkit is made available with the intention to enable future and systematic assessment of novel ear-EEG devices from the hardware to the neural signals.