A binary biomass-based solid electrolyte film (LSE) was successfully synthesized through the incorporation of lignin nanoparticles (LNP) with sodium alginate (SA). The impact of the mass ratio of SA to LNP on the microstructure, porosity, electrolyte absorption capacity, ionic conductivity, and electrochemical properties of the LSE was thoroughly investigated. The results indicated that as the proportion of SA increased from 5% to 15% LNP, the pore structure of the LSE became increasingly uniform and abundant. Consequently, enhancements were observed in porosity, liquid absorption capacity, ionic conductivity, and overall electrochemical performance. Notably, at a SA amount of 15% LNP, the ionic conductivity of resultant LSE-15 was recorded at 14.10 mS cm1, with porosity and liquid absorption rates reaching 308% and 58.4%, respectively. LSE-15 was employed as a solid electrolyte, while LNP-based carbon aerogel (LCA) served as the two electrodes in the construction of a symmetric all-solid-state supercapacitor (SSC). The SSC device demonstrated exceptional electrochemical storage capacity, achieving a specific capacitance of 197 F g1 at 0.5 A g1, along with maximum energy and power density of 27.33 W h kg1 and 4998 W kg1, respectively. Furthermore, the SSC device exhibited highly stable electrochemical performance under extreme conditions, including compression, bending, and both series and parallel connections. Therefore, the development and application of the binary biomass-based solid electrolyte film in supercapacitors represent a promising strategy for harnessing high-value biomass resources in the field of energy storage.

An approach to estimate the dynamic characteristic of multilayered three-dimensional cubic quasicrystal cylindrical shells, annular plates, and truncated conical shells with different boundary conditions is presented. These investigated structures can be in a vacuum, totally filled with quiescent fluid, and subjected to internal flowing fluid, in which the fluid is incompressible and inviscid. The velocity potential, Bernoulli’s equation, and impermeability condition have been applied to the shell-fluid interface to obtain an explicit expression, from which the fluid pressure can be converted into the coupled differential equations in terms of displacement functions. The state-space method is formulated to quasicrystal linear elastic theory to derive the state equations for three structures along the radial direction. The mixed supported boundary conditions are represented by means of the differential quadrature technique and Fourier series expansions, respectively. A global propagator matrix, which connects the field variables at the internal interface to those at the external interface for the whole structure, is further completed by joint coupling matrices to overcome the numerical instabilities. Numerical examples show the correctness of the proposed method and the influence of the semi-vertical angle, different boundary conditions, and the fluid debit on the natural frequencies and mode shapes for various geometries and boundary conditions.

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb-Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on Xillinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping total memory requirements low due to real-time processing.

Production planning managers need much information within the organization to create the best production plan. This information is scattered throughout the organization. The planning unit must gather data from sales orders and specified priorities, and use information from production capacity assessments, in-line inventory levels, and many other variables to develop a reliable production plan. This research aims to examine the production planning system at a car part manufacturing company, diagnose the current issues in this area, and endeavor to develop a hierarchical production planning model at the AP levels for the company through a mixed-integer linear programming model.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.

Videogames are recognized as significant tools and mediums to be used in civic participation in spatial planning and fostering local communities. As the phenomenon is widely recognized in papers presenting singular case studies and broader analyses in the field, selecting such serious games with certain characteristics remains unclear. The informed process of choosing games with particular properties regarding genesis, graphic style, genre, and complexity as the response for specified needs and process assumptions appears to be supportive in preventing unnecessary costs and data overproduction. Such avoidance is an important part of sustainable digital transformation. Therefore there is a need for the more conscious process of selecting videogames to be used in a participatory process. The following paper aims to propose a decisional instrument that could be useful for specifying the characteristics of games to be utilized in participation. They performed a multicriteria analysis of documented cases of implementing video games in civic engagement, allowing the creation of a set of numeric indicators that help determine the properties of games that will be most appropriate for given process assumptions. Such a tool can prevent overproducing data on the one hand and may cause dissemination presented way of handling the participation process on the other.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

The search for materials with exceptional properties has led to significant advances in the field of non-carbon diamond-like conductive thin films (DLC-CTFs). These films, synthesized by chemical vapor deposition (CVD) and other techniques, are being actively researched for their potential in high-tech applications. This literature review discusses the synthesis methods, material properties, and applications of DLC-CTFs, highlighting the role of non-carbon dopants in enhancing electrical conductivity. This literature review aims to provide a comprehensive overview of the historical development, synthesis techniques, material properties, characterization methods, and applications of DLC-CTFs, with a focus on the impact of non-carbon doping on their conductive properties. By reviewing the literature. The review also addresses the challenges faced and suggests future research directions.

The material properties and structural characteristics of ballistic composites are crucial to their ballistic performance. Investigation for the influence of the material and structural parameters of UHMWPE body armor on anti-fragmentation penetration performance has been made by numerical simulation in this paper, with a numerical modeling simulating the penetration of a 1.1 g FSP into the target plate of UHMWPE body armor, by which the simulation results demonstrate that the failure process of the body armor target plate primarily involves shear damage, interlayer delamination, and tensile damage. Furthermore, the study evaluates the effects of elastic modulus, tensile strength, shear strength, number of layers, and interlayer strength on the ballistic limit velocity of UHMWPE body armor. The findings reveal that the ballistic limit velocity is most sensitive to changes in shear strength, with variation rates ranging from -18.02% to 10.81%, showing an approximate positive correlation, while the elastic modulus has the smallest impact on ballistic limit velocity, with variation rates ranging from -1.8% to 3.6%. Additionally, appropriate interlayer strength can improve the ballistic limit velocity of the body armor to a certain extent. This study provides theoretical methods and recommendations for optimizing anti-penetration performance of UHMWPE body armor.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

Pipes have been utilized since ancient times and have seen significant renewal, modification, and replacement to improve their effectiveness and efficiency in the modern era. As early as 4000 BC, Egyptians used clay pipes for drainage systems. The U.S. has the largest network of energy pipelines in the world. The ASCE 2017 Infrastructure Report Card assigned grades of D and D+ to water and wastewater pipelines, highlighting a substantial need for improvement. The paper investigates the infrastructure required for the sustainability of pipeline systems, as well as the construction of underground systems using both open-trench and trenchless technologies, along with the associated risks in the US. It concludes that trenchless methods are increasingly preferred for pipeline construction due to technological advancements, reduced surface disruptions, shorter construction times, and lower social costs. Tackling this challenge will necessitate strategic investment, decisive leadership, thorough planning, and meticulous preparation for future demands. Enhancing pipeline infrastructure can stimulate the U.S. economy, whereas postponing action will lead to higher costs.

Microwave-induced thermoacoustic tomography (MITAT) holds significant promise in biomedical applications. It creates images using ultrasonic sensors to detect thermoacoustic signals induced by microwaves. The key to generating thermoacoustic signals that accurately reflect the fact is to achieve sufficient and uniform microwave power absorption of the testing target, which is closely tied to the microwave illumination provided by the antenna. In this article, we introduce a novel design and implementation of an ultra-wideband water immersion antenna for an MITAT system. We analyze and compare the advantages of selecting water as the background medium. Simulations are conducted to analyze the ultra-wideband characteristics in impedance matching, axial ratio, and radiation pattern of the proposed antenna. The measured |S11| shows good agreement with the simulated results. We also simulate the microwave power absorption of tumor and brain tissue, and the uniform microwave power absorption and high contrast between the tumor and brain indicate the excellent performance of the proposed antenna in the MITAT system.

A constant effort is made in the physical metallurgy of transformation induced plasticity (TRIP) steels. These are multiphase steels, whose mechanical behavior depends on the different phases present, the intrinsic strength of each phase, the work hardening of each constituent and the austenite to martensite transformation, among others. Hard and soft phases can be distinguished in these steels and both stress and strain are distributed between them. The objective of this work was to simulate the stress-strain behavior for two TRIP steels. Bouquerel’s model was modified to account for the initial martensite, a moderate hard austenite and adjustable strain hardening parameters. The austenite fraction that transforms into martensite was computed through an Olson and Cohen model. The Mecking-Kocks model was used to evaluate the increment in the dislocation density and the Rodriguez-Gutierrez model, to compute the hardening of martensite. Ferrite is considered a soft phase, martensite and bainite, hard phases and austenite, a phase with a moderate hardness. The routine was implemented in MATLAB, and results were compared with those obtained experimentally in a tension test of two TRIP steels of the same composition but different heat treatments, obtaining in both cases a good agreement for the stress-strain-curves.

Umbria is an Italian region with a highly fragmented water supply, with many small water distribution systems fed by local sources. The chlorination in these small water systems faces many difficulties related to the high demand variation, low economic relevance, and access to infrastructures. As a result, there are many noncompliances with the quality of the supplied water, which causes a decrease in the performance indicators and tariff. This research investigates the possibility of using a low-cost chlorination system to consider different environmental conditions (e.g., temperature variations of water and environment, pH, ...) and discontinuous consumption. A device for this specific use is developed and tested by laboratory tests and is ready for use in the field.

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing reference for future planning and design of such structures. The study resulted showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m², a facade width between 4.1 and 6.38 meters, and a building depth from 7.67 to 12.68 meters. In addition, low negative correlation between facade width and total floor area, a high positive correlation between building depth and total floor area, and a moderate negative correlation between facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable room and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable room increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used.

Today the Vocational Education and Training curriculum is state-regulated, combining school-based and work-based learning it is offered at upper-secondary and post-secondary levels. The main problem of nowadays period for Vocational Education and Training is about preparing a new agenda for technical education in Greece for the new circumstances. Our paper aims to enrich, redefine, and expand the current curricula and educational practices, while also proposing a complete plan for the overall reformation of Vocational Education and Training’s learning conditions from the point of view of teaching and learning analytics. Through the existing changing institutional framework, the updated bibliographic references, and the experiential data of the educational reality research, we can understand the expected needs of technological educators and decide how to address the priorities that must be determined. As a result of expectation and one of the main topics of this paper, we focus on the preparation of the trainers and the overall redefinition of the role of education in preparing graduates. This paper's main conclusions or interpretations for the cognitive orientation of the existing curricula, as well as the directions of the thinking and teaching skills of teachers, and to build confidence in the prospects of technical education in Greece.

In this paper, a new way to obtain photoactive cements was presented. In this method amorphous TiO2 is added to cooler during the cooling of the cement clinker (Górażdże company) during cement production. Amorphous TiO2 was taken from the installation for obtaining titanium dioxide using the sulphate method. During the studies amorphous TiO2 was added to clinker at different temperature 300, 600, 700 and 800 °C. The properties of the obtained cement were tested during the bending and compressive strength and beginning and the end of bending setting time measurements. The adhesion of the obtained materials to concrete block, ceramic brick and plasterboard were also evaluated. The photocatalytic activity of the obtained materials was studied during NO and BTEX (benzene, toluene, ethylbenzene, p-,m-,o-xylenes decomposition) decomposition. Cement with 5 wt.% TiO2 added to clinker at 700 C had the highest photocatalytic activity and the best mechanical properties.

Background/Objectives: Dental implants have become a cornerstone of restorative dentistry, providing a long-lasting method for tooth replacement. The level of osseointegration has a big impact on the biomechanical stability at the bone-implant contact (BIC), which determines how long these implants will work. However, the exact consequences of changing osseointegration levels on different implant designs, especially in bones with variable densities, are not well known. Three short dental implants were tested in this study: BioMet 3iT3, Straumann® Standard Plus Short Regular Neck (SPS-RN), and Straumann® Standard Plus Short-Wide Neck (SPS-WN). 3D finite element analysis (FEA) was used to look at how well they worked biomechanically. This paper tests the implants at four stages of osseointegration: 25%, 50%, 75%, and 100% in both high-density (Bone Type III) and low-density (Bone Type IV) cancellous bone. It also created and examined realistic CAD models under static occlusal loading conditions to assess stress distribution and major strains at the bone-implant contact.; Results: The study discovered that as osteointegration increases, von Mises stress and principal strains go down significantly for all implant types. The SPS-WN implant had the lowest strain values, especially for bone with low density. These reductions demonstrate increased mechanical stability as the bone-implant inter-face becomes more capable of dispersing mechanical stresses, minimizing the potential for localized deformation and bone resorption.; Conclusions: The results highlight the importance of achieving optimum osseointegration to reduce mechanical stress and increase the lifespan of dental implants. The SPS-WN type implant performed better in biomechanical tests than the others, especially when bone conditions were not ideal. This makes it a great choice for clinical applications that need long-term implant success.

Full depth precast deck panel (FDDP) is one element of the prefabricated bridge element and systems (PBES) that allows for quick un-shored assembly of the bridge deck on-site as part of the accelerated bridge construction (ABC) technology. This paper investigates the structural response of full-depth precast deck panels (FDDPs) constructed with new construction materials and connection details. FDDP is cast with normal strength concrete (NSC) and reinforced with high modulus (HM) glass fiber reinforced polymer (GFRP) ribbed bars. The panel-to-girder V-shape connections use the shear pockets to accommodate the clustering of the shear connectors. A novel transverse connection between panels has been developed, featuring three distinct female-to-female joint configurations, each with 175-mm projected GFRP bars extending from the FDDP into the closure strip, complemented by a female vertical shear key and filled with cementitious materials. The ultra-high performance fiber reinforced concrete (UHPFRC) was selectively used to joint-fill the 200-mm transverse joint between adjacent precast panels and the shear pockets connecting the panels to the supporting girders to ensure full shear interaction. Two actual-size FDDP specimens for each type of the three developed joints were erected to perform fatigue tests under the footprint of the Canadian Highway Bridge Design Code (CHBDC) truck wheel loading. The FDDP had a 200-mm thickness, 2500-mm width, and 2400-mm length in traffic direction; the rest was over braced steel twin girders. Two types of fatigue tests were performed; incremental variable amplitude fatigue (VAF) loading and constant amplitude fatigue (CAF) loading, followed by monotonically loading the slab ultimate-to-collapse. It was observed that fatigue test results showed that the ultimate capacity of the slab under VAF loading or after 4 million cycles of CAF exceeds the factored design wheel load specified into the CHBDC. Also, punching shear failure mode was dominant in all the tested FDDP specimens.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

Ballastless track has high smoothness, but the laying requirements are strict. Therefore, the maximum span of cable-stayed bridges laying ballastless tracks is 392m. For laying ballastless track structures over larger spans, the deformation characteristics of long-span cable-stayed Bridges under complex loads are incomplete, and the interaction between long-span bridge-track structures is unclear. The influence of wavelength of cosine wave on the track- bridge mapping of different orbital structures was explored. The wavelength characteristics of vertical deformation under complex loads were investigated. The track-bridge integrated model for the cable-stayed bridge was establish to analyze the mapping relationship between rail and bridge and wavelength characteristics of deformation. Based on mapping relationships and wavelength characteristics of deformation, the Train-Track-Bridge dynamic simulation model was simplified. The results show that when the minimum wavelength of bridge deformation surpasses 6 m, 10 m, and 16 m, the rail deformation in the ballasted track, the longitudinal-connected track, and the unit slab-type ballastless track accurately mirrors the deformation of the bridge. For the span of bridge ranging from 200 m to 600 m, the wavelength of vertical deformation ranges from 21 to 1270 m under complex loads. During local loads, the vertical deformation below the 200 m wavelength constitutes a significant proportion near the pie. Considering the influence of the deformation on train vibration response, the Train-Bridge dynamic coupling model can be employed to treat the track structure as a load to reduce complexity of model and enhance the calculation efficiency.

This study focuses on the design, installation, and performance evaluation of a photovoltaic farm situated in Tulcea County, Romania, connected to a 20 kV distribution network. The farm, with a peak capacity of approximately 2800 kWp, benefits from Romania’s favorable climate conditions and supportive renewable energy policies. The analysis encompasses both the photovoltaic panel array and the electrical energy evacuation system. The substation at the park is equipped with technology that adheres to European standards and incorporates advanced features typical of modern electricity distribution networks. The assessment includes an evaluation of the park’s operational efficiency, with detailed calculations of the performance ratio and various power losses attributed to factors like temperature fluctuations, fouling, inverter efficiency, grid-related challenges, and availability issues. The farm contributes around 3620 MWh of electricity to the grid annually, demonstrating the effectiveness of photovoltaic installations in promoting sustainable energy solutions within regional energy networks.

In this paper, we investigated the formation isolation regulation issue regarding multiple autonomous underwater vehicles (AUVs) characterized by a "leader-follower" framework. Considering the cooperative-competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer was designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme was designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source and the structure converges was achieved, with the absence of zeno behavior also demonstrated.

Small and Medium Enterprises (SMEs) play a crucial role in the European economy, representing the vast majority of businesses and a significant portion of employment. As the global business environment increasingly embraces digital supply chains, coopetition—collaborative competition—has emerged as a vital strategy for sustaining business growth and competitiveness. Despite its recognized importance, the potential of coopetition to enhance firm performance, particularly within SMEs, remains underexplored. This study aims to address this gap by developing and validating a reference model for coopetition networks specifically tailored for SMEs, utilizing the Service-Dominant framework. The research introduces a comprehensive model that guides the formation, management, and dissolution of coopetition networks throughout their lifecycle. The model is empirically validated within the Portuguese ornamental stone sector, providing insights into its practical applicability. Key findings demonstrate that the implementation of coopetition networks can significantly enhance production efficiency and operational effectiveness within SMEs. The study also highlights the critical role of technology as an enabler of service exchange and institutionalization within coopetition networks, facilitating collaborative interactions and value co-creation. While the study makes substantial contributions to the literature, it also identifies areas for further research, including the exploration of human dynamics within coopetition networks and the long-term sustainability of such networks. The findings offer valuable guidance for SMEs seeking to enhance their competitiveness and operational efficiency in an increasingly digital and interconnected business landscape.

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing CCHP system efficiency by identifying optimal design parameters and assisting decision-makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize net power and exergy efficiency while minimizing CO2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved higher net power output, satisfactory exergy efficiency, and lower CO2 emissions compared to similar studies. The study shows that maximum net power and exergy efficiency, with reduced CO2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO2 emissions and improved exergy values.

The current emphasis in the advancement of space-based synthetic aperture radar (SAR) is on lightweight payloads under 100kg with resolutions surpassing 1m. This focus is directed towards meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) Satellite, launched on January 23, 2024 accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, scan 1, scan 2, and others, with a maximum achievable resolution exceeding 1m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.

Portland cement emits large amounts of CO2 in production. Given proposals for "carbon peaking and carbon neutralization" studying alternative low-carbon cementitious materials to reduce emissions is extremely important. Alkali-activated slag (AAS) cement, a new green cementitious material, has high application potential. The chemical corrosion resistance of AAS concrete is important for ensuring durability and prolonging service life. Studying the chemical corrosion mechanism of AAS concrete and understanding its influence on performance provides important theoretical foundations for application in different environments.

The effectiveness of a hybrid technique for identifying seismic damage in planar, multistory, steel X or V-braced frames is demonstrated here through an example of a six-story frame. This proposed technique, referred to as “M and P”, combines the instrumental Monitoring (M) with Pushover analysis (P). According to the methodology, the diagram of stepping eigenfrequencies of the frame in the inelastic region is initially plotted against seismic roof displacement. The fundamental natural frequency detected via the monitoring process is then utilized in this key diagram to reveal the inelastic roof displacement that corresponds to the damage state of the steel braced frame. This displacement is subsequently used as the target in the pushover analysis, facilitating the identification of seismic damage in the existing steel braced frame. Finally, the damage image is correlated with the damage stiffness matrix of the frame at the same inelastic roof displacement. The investigation results indicate that combining instrumental monitoring with pushover analysis enables accurate identification of seismic damage potential in existing steel braced frames.

A medium scale household in South Africa consumes about 1000 kWh on average, and ae household spend about 3000 ZAR per month on energy supply using grid electricity. Grid electricity. South Africa’s energy is supplied by a huge governmental organization. Statistical analyses indicate that projected increase of the cost of grid electricity in South Africa is about 30% per annum for the next five years. Our research show that Photo-Voltaic (PV) technology can offer a viable alternative, provided a large initial capital outlay can be made. The technology is user-friendly and easy to operate. It is calculated that the cost of electricity, capitalized over 20 years, considering an 80 % supply of needs and assuming 20 sunny days per month, is about 2.80 ZAR. The study also shows that utilizing a hybrid combination of renewable energies, such as combining PV supply with solar thermal energy absorbers, and converting thermal energy to electricity, as energy supply systems, could reduce the current cost of electricity per household in South Africa to about half of national grid supply prize, R1,50 per kWhr. The study also shows that water provides as a most viable medium to store energy, instead of using expensive battery technology to perform such functions.

Dispatching goods is becoming more difficult to manage in the field of logistics due to the high demand for order shipments. This is relative to the increasing popularity of the use of e-commerce platforms among consumers, where delivery of products is required as compared to buying in physical stores. Dispatch management is one of the critical components in a supply chain since it covers the coordination tasks among stakeholders from warehouse up to the consumer’s doorstep. In this study, the authors intend to propose a framework leveraging geographic information to sustain the logistics operations, specifically in terms of managing last-mile delivery and return trip orders. This includes scheduling, communications, and inventory of goods shipment. A mobile application built on this framework was integrated with a waypoint order optimization algorithm considering the entire route traversing all the required pick-up and delivery points. It was pilot tested with an actual dispatch operation of a logistics company yielding a decrease of 87% in total shipment time, 79% in operations cost, and 3% in carbon footprint per vehicle. With the adoption of this framework, the study aims to contribute to the overall efficiency and sustainability of logistics operations in a wider geographic range.

The Gaussian signaling strategy with power control for the Gaussian Z-interference channel with weak interference is reviewed in this paper. In particular, we study the various communication strategies that may arise at various points of the capacity region and identify the locations of the phase transitions between the various strategies. The Gaussian Z-interference channel with weak interference is known to have two critical points in its capacity region, where the slope of the region shows a sudden change. They occur at the points of the unconditional maximum rate for one of the users and the maximum rate that can be accommodated by the other user. In this paper, we discuss two additional critical points (locations of phase transitions) in the achievable region of this channel. At these points, we do not observe a discontinuous slope in the achievable rate region, but there is a discontinuity in the second derivative of the rate contour of the achievable region. This review paper is mainly based on some of our ITA (Information Theory and Applications Workshop, UCSD, San Diego, CA, USA) papers since 2011.

The integration of Augmented Reality (AR) into Human-Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved situational awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human-Computer Interaction (HCI) centered around user experience and design.

In order to solve the problem of structure damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization one, was first introduced to identify structure damage. The genetic algorithm (GA) are improved to obtain a refined genetic algorithm (RGA). An objective function is constructed by combining the dynamic response transfer-ratio without modal analysis. INFO and RGA algorithms are used to optimize the objective function for damage identification. Though simulations, the INFO, RGA, GA algorithms were used to identify the damage on a simply supported beam and cantilevered plate with different levels of noise and different frequency spacing of pickup points. The simulations verify the effectiveness of the three damage identification methods, and the INFO algorithm has the highest damage identification accuracy, the RGA is second and the GA is the worst.

An efficient cooling system is necessary for the reliability and safety of the modern microchips for longer life. As microchips become smaller and more powerful, the heat flux generated by these chips per unit area also rises sharply. Traditional cooling techniques are inadequate to meet the recent cooling requirements of microchips. To meet the current cooling demand of Microelectromechanical devices (MEMS) devices and microchips, microchannel heat sinks (MCHS) technology is the latest invention that can dissipate a significant amount of heat because of its high surface area to volume ratio. This study provides a concise summary of design, material selection, and performance parameters of MCHS that are developed over the last few decades. The limitations and challenges associated with different techniques employed by researchers over time to enhance the thermal efficiency of microchannel heat sinks are discussed. The effects on the thermal enhancement factor, Nusselt number, and pressure drop at different Reynold numbers, in passive techniques (flow obstruction) i.e. ribs, grooves, dimples, and cavities change in curvature of MCHS are discussed. This study also discusses the increase in heat transfer using nanofluids and how change in coolant type also significantly affects the thermal performance of MCHS by obstructing flow. This study provides trends and useful guidelines for researchers to design more effective MCHS to keep up with the cooling demands of power electronics.

The European Parliament had adopted legislative resolution of 10 April 2024 on the proposal for a directive of the European Parliament and of the Council concerning urban wastewater treatment. The reduction of pollution in discharged treated wastewater in the parameters of BOD5, total ni-trogen and total phosphorus was emphasized. Based on these results, it stated that the impacts on the quality of lakes, rivers and seas in the EU are visible and tangible. At the same time, it was emphasized that the sector of urban wastewater removal and treatment is responsible for 0.8% of the total electricity consumption and about 0.86% of all greenhouse gas emissions in the entire EU. Almost a third of these emissions could be prevented by improving the treatment process, better use of sewage sludge and increasing energy efficiency, as well as a higher rate of use of renewable resource technologies. It is also necessary to better integrate treatment processes into the circular economy. Sludge management and water reuse is sub-optimal as too many valuable resources are still being wasted. This review article is focused on sustainable municipal wastewater treatment, innovative and new wastewater treatment processes and technologies (combined and hybrid processes, Anammox, etc. and their use in practice with the aim of increasing environmental and energy efficiency and reducing the carbon footprint. The research is focused on the possibilities of increasing the efficiency of energy processing of sludge, reuse of nitrogen and phosphorus, sludge and reuse of treated wastewater.

The construction of tall buildings generates a high spatial and temporal concentration of greenhouse gas (GHG) emissions. Research studies have shown that as building height increases, more resources per floor area are required to withstand the increasing effects of lateral loads (wind and earthquake). This has major implications for the environmental performance of tall buildings since the Embodied GHG Emissions (EGHGE) of structural systems tend to represent the greatest portion of the life cycle GHG emissions of tall buildings. This study presents a data driven-based approach for decarbonization of tall buildings and evaluates the significant impact of material types (concrete, steel, and timber), concrete strength, and structural systems relative to a building's height on the EGHGE associated with tall buildings. In mitigating the effects of climate change, this research implements machine learning (ML) algorithms as an early-stage design tool to facilitate the choice of materials, and structural systems for tall buildings. This work considers a wide range of high-rise buildings with four different types of lateral structural systems (Braced-Frame system, Outrigger-Belt system, Shear Wall system, and Tubular system), four different types of construction materials (concrete, steel, hybrid, and timber), varying heights (ranging from 10 to 100 stories), and various concrete materials with different compressive strength ranging from 32 to 90 MPa. Data gathered from more than 100 existing tall building projects along with the data obtained from finite element analysis of 120 high-rise buildings models were utilized to train various ML regression algorithms: Decision Tree, Support Vector Machine, Polynomial Regression, and Elastic-Net Regularized Regression. The performance of ML models was carefully assessed, and the best prediction model was selected to estimate the total amount of CO2 emissions for high-rise buildings. Results indicate that hybrid structures with the Out-Rigger-Belt system exhibit the lowest carbon emission (110 kg/m2) compared to other structural types and systems.

Agrivoltaic agrotunnels are currently designed for high density grow walls that are not amenable to bush berries or root crops. Commercial grow bins provide deeper substrates for produce with more root systems but have high costs per unit growing area. To overcome the economic limitations of grow bins, this study applies the distributed manufacturing open-source design paradigm to develop four designs for low-cost open source structures. The designs target root vegetables and bush fruit specifically to be adopted by remote communities with limited or no outdoor growing environment to offset the market price for imported fresh produce. The indoor growing designs provide the necessary structure for supporting grow light and grow bins and enabled the transplanted berry plants to flower and produced fruits. They provide a comparable amount (110L) or more of grow volume from 106 to 192 L. The water reservoir volume for the commercial system (62 L) and grow area (0.5 m³) is surpassed by all new designs that range from 64 to 192 L and 0.51 to 0.76 m³, respectively. These superior properties are possible with material costs for all four designs that save more than 90% of the economic cost of the commercial systems.

Hydrogen mobility embodies a promising solution to address the challenges posed by traditional fossil-fuel-based vehicles. The use of hydrogen in small heavy duty road vehicles based on internal combustion engines (ICEs) may be appealing for two fundamental reasons: direct electrification seems less promising in heavy duty transport systems and fuel cell-based hydrogen vehicles implementation may not proceed at the expected pace due to higher investment costs compared to ICEs. On the other hand, hydrogen combustion is gaining attractiveness and relies on robust and cheap technologies, but it is not the only renewable solution. In this framework, this work presents a methodology to assess Weel-to-Whell primary energy consumption and CO2 emissions of small heavy-duty vehicles. The methodology is applied in a real case-study, namely a passenger coach travelling on a 100 km-mission in non-optimized conditions. Therefore, the suitability of hydrogen is compared with standard diesel and other alternative diesel-type fuels (biodiesel and synthetic diesel types). Hydrogen shows competitivity with standard diesel from the point of view of CO2 emission reduction (-29%) while it hides a higher primary energy consumption (+40%). Nonetheless, HVO brings the highest benefits both from the point of view of primary energy consumption and emission reduction, namely -35% and 464-634 kgCO2/100km avoided compared to hydrogen.

The thermomechanical and viscoelastic properties of a glass fiber polyethylene terephthalate glycol (GF/PETG) continuous unidirectional (UD) tape were investigated using differential scanning calorimetry (DSC), thermo-mechanical analysis (TMA), and dynamic mechanical analysis (DMA). The study identified five operational conditions based on Army Regulation 70-38 Standard. The DSC results revealed a glass transition temperature of 78.0 ± 0.3°C, guiding the selection of temperatures for TMA and DMA tests. TMA provided the coefficient of thermal expansion in three principal directions, consistent with known values for PETG and GF materials. DMA tests, including strain sweep, temperature ramp, frequency sweep, creep, and stress relaxation, defined the linear viscoelastic region and temperature-dependent properties of the material. The frequency sweep indicated an increase in modulus with rising frequency, identifying several natural frequency modes. Creep and stress relaxation tests showed time-dependent behavior, with strain increasing under higher loads and stress decreasing over time for all tested input values. Viscoelastic models fitted to the data yielded R² values of 0.99, demonstrating good agreement. The study successfully measured thermo-mechanical and viscoelastic properties across various conditions, providing insights into how temperature influences the material's mechanical response under extreme conditions.

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out Of Distribution (OOD) recognition. The term "Out of Model Scope" (OMS) detection provides a clearer perspective, although the original problem remains unsolved. The ability to detect OMS inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting OMS situations, we not only enhance the system's robustness but also prevent it from operating in unknown and unsafe scenarios. In this paper, we introduce a novel approach for OMS detection that combines three sources of information: 1) the original input, 2) the latent feature space extracted by an encoder, and 3) the synthetic data generated from the encoder's latent feature space. We demonstrate the effectiveness of combining original images and synthetically generated images against adversary attacks in the computer vision domain. We achieve results comparable to other state-of-the-art methods and demonstrate that any Encoder can be integrated into our pipeline in a plug-and-train fashion. Our experiments further evaluate which combination of the Encoder's features works best in order to discover OMS samples and highlight the importance of a compact feature space for the training of the Generator.

Microplastics, pervasive in both terrestrial and aquatic ecosystems, have emerged as significant contributors to greenhouse gas (GHG) emissions, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). This review synthesizes recent studies from 2019 to 2024, revealing the complex mechanisms through which microplastics influence GHG production. These mechanisms include the adsorption of nutrients and pollutants onto microplastic surfaces and their role as substrates for microbial colonization. The impacts of microplastics vary significantly across different environments, such as agricultural soils and marine sediments. The review underscores the urgent need for standardized methodologies and long-term field studies to accurately assess the ecological consequences of microplastics. Notably, the role of nitrous oxide (N2O), a potent GHG with a global warming potential nearly 300 times that of CO2, remains underexplored in the context of microplastic interactions, highlighting a critical research gap. Additionally, the synergistic effects of microplastics with other pollutants require further investigation to fully understand their cumulative impact on GHG emissions. This review calls for a coordinated effort among researchers and policymakers to advance our understanding of microplastics' role in global GHG budgets and to develop effective strategies for mitigating their environmental and climatic impacts.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.

A complementary characterisation of the Al-alloy EN AW-5454 was carried out, intended for obtaining the laser hybrid welding parameters of sub-assemblies in the automotive industry. The investigation included a microstructural examination, and determination of the alloy's properties using several analytical methods (HV5 hardness measurement, tensile test, Charpy impact toughness, fracture mechanics analysis). Samples were prepared in the longitudinal and transverse directions of a cold-rolled sheet of EN AW-5454 with thicknesses of 3.5 mm and 4 mm. The measured hardness on the thinner sheet was 5% higher than on the thicker sheet. The tensile and yield strength were nominal, while the elongations were smaller by 2.2 - 3.2% for the longitudinal samples and by 2.7 - 13.7% for the transverse samples. The smaller deviations from the nominal values are for the thinner sheet metal. A precise topographical analysis showed brittle fractures of the samples. The Charpy impact toughness results on the thicker plate showed a 20% greater work needed to break it in the longitudinal direction than in the transverse direction. With the thinner sheet metal, 40% greater work was needed. SEM (Scanning Electron Microscope) analysis has shown that the intermetallic Al6(Mn,Fe) particles in the longitudinal samples were mostly intact, with evidence of tough areas on the upper part of the fracture, indicating a better toughness than the specimens in the transverse direction. More crushed intermetallic particles were observed at the fractures of the transverse samples, and their distribution appeared to be more oriented in the direction of rolling. Fracture mechanics SENB (single edge notch bending) tests and their analysis showed that the resistance of the material to crack propagation in the longitudinal sample was about 50% greater than that in the transverse sample. SEM analysis of the fractures showed that the state of the intermetallic particles in the fracture mechanics testing, and the fracture mechanism differed from the one in the Charpy fractures.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper-resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP-ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data-sharing mechanisms. Furthermore, the paper delves into implementing hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments.

One of the efficient data mining tools is density-based clustering, including the density grid-based clustering. However, a common drawback always existing in clusters made by the density grid-based method is the existence of weakly connected grids deriving mainly from noise. Appearing such an unwanted connection with a high frequency reduces the accuracy of the obtained cluster data space (CDS) and its application efficiency. Here, we present an essential improvement to overcome this problem. First, we describe a concept of the weak-connected grid cell (WCG) and present a fuzzy-type approximation to depict the density-based distribution of data points at grid nodes. Then, we propose a strategy of searching WCG for density grid-based clustering (SWCG-DGB) to set up a CDS, filter noise, and tune the created CDS. A buffer is deployed during this phase to collect border points and filter noise, which improves the computational time significantly, especially for noisy datasets. Results from numerical surveys reflected the compared efficiency of this method in clustering validity, including the accuracy of the number of clusters.

Lean practice is recognised for having a great potential in promoting safety risk management in offsite construction (OSC). This paper presents results of a study conducted to assess the impact of lean practice in safety risk management in OSC in a developing country. A quantitative approach using survey-based questionnaire was adopted. Lean management practices (LMPs) identified from literature review were empirically tested using a sample survey of 103 OSC contractors. The survey responses were subjected to descriptive and inferential statistics. The top ranked LMPs for safety risk management in OSC included two mistake proofing practices, i.e., use of personal protective equipment (PPE) and use of hazard warning equipment; two last planner system (LPS) practices, i.e., involvement of workers in safety planning and providing necessary working equipment and one first run studies (FRS) practice, i.e., critical analysis of work methods. These LMPs are useful in controlling high consequence safety risks in OSC. Based on evidence found in this study, the paper argues that lean practice can bring great value in the safety risk management in OSC in countries where OSC is transitioning.

The state of the grinding wheel in the grinding process directly affects the surface quality of the workpiece. The monitoring of the wear state of the grinding wheel can effectively identify the wear information of the grinding wheel and timely and effectively repair the grinding wheel. In this paper, a multi-sensor fusion precision grinding wheel wear intelligent monitoring system is established for typical difficult-to-machine aerospace material titanium alloy, and a multi-eigenvalue fusion algorithm based on improved support vector machine (SVM) is proposed. The extracted sound emission signal wavelet packet decomposition coefficient mean square value, force signal grinding force ratio, and vibration signal effective value are used as the input of the improved support vector machine, and the identification strategy is adjusted by the entropy weight evaluation method. The results show that the monitoring system established in this paper can effectively identify the wear state of the grinding wheel, and the monitoring accuracy can reach more than 92%.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

With the development of the energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels such as CH4 and CH3OH. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic one, for storage through various other solutions besides hydrogen, has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less mentioned aspect in current works but of great importance.

The present study analyses the influence of scratches and fatigue on the mechanical characteristics of aluminium alloys commonly used in the aviation industry: 2024-T4, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6. 120 specimens were used for the study. Out of these, 60 samples were scratched, and the other 60 samples were also scratched under the same circumstances and then subjected to low-cycle fatigue (LCF) before undergoing the tensile test. Based on the statistical examination of the measured data, mathematical prediction models have been established. Compared to their unscratched counterparts, the results indicate a significant decrease in the mechanical properties of both scratched and scratched-fatigued sample sets. The LCF pre-treatment contributes to the negative impacts of scratches, resulting in a reduced value for the ultimate tensile strength and elongation at break. The results conclude that surface integrity plays an essential role in preserving aircraft components' structural strength.

High-frequency circuit performance is significantly impacted by impedance variations, particularly within the low-resistance regime. Traditional Ohm’s Law based modeling approaches often fail to accurately predict circuit behavior in these conditions, leading to design inaccuracies and potential system failures. The Standard Ohm’s Law based model’s prediction of infinite current as resistance approaches zero is physically unrealistic and hinders its application in practical scenarios. Despite the recognition of these limitations, existing models have not comprehensively addressed the complex impedance behavior observed in high-frequency circuits. To overcome these challenges, this paper introduces a modified version of the Ohm’s Law incorporating an exponential correction term. The Modified Ohm’s Law’s accuracy was evaluated through simulated experiments across a wide frequency range (1kHz to 1GHz) using various electronic components. The findings demonstrate the superior performance of the modified model in predicting currents under low-resistance and high-current conditions compared to the Standard Ohm’s Law model. By providing finite and accurate current values, the proposed model effectively mitigates the unrealistic infinite current predictions of the standard approach. The enhanced predictive capabilities of the Modified Ohm’s Law hold significant implications for high-frequency circuit design and analysis. Its application can lead to improved performance and reliability in power electronics, telecommunications, and other high-frequency systems. By incorporating non-linear impedance behavior, the model offers a more accurate representation of real-world circuit conditions. Future research should focus on refining the exponential term’s parameters to optimize the model's accuracy across a broader range of applications. Additionally, real-time implementation and hardware validation are essential to assess the model's practical utility in complex circuit environments.

As well known, the torque produced by a rotating motor generates an opposite and equal reaction torque in the machine casing that must be transmitted to the base. In many applications, especially when the reaction moment has high values, it is necessary to apply some constructive solutions, which in certain cases are difficult to implement. In this context, the need to reduce the reaction moment from drive motors is a challenging topic, which has not been completely exhausted. In this paper, the authors present an original concept of a device which uses the centrifugal force generated by some equidistantly placed weights on a chain drive, for reducing the reaction torque of the motor used for driving a rotating tool. The proposed system is capable to produce a supplementary torque which can be added to driving moment. Due to this fact, by using this system, the power of the driving motor can be decreased, with the consequence of reducing the reaction moment, that must be absorbed by the base.

This study harnesses the potential of artificial intelligence (AI) focusing on machine learning (ML) to improve decision-making in advanced engineering education. The onset of the COVID-19 pandemic compelled a swift transformation in higher education methodologies, particularly in the domain of course modality. This study aims to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches, particularly learning styles, theoretical, practical, and theoretical-practical, prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and E-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees (DT), support vector machines (SVM), and random forest (RF)—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

Due to the convenience of implementing without additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means of monitoring unmanned aerial vehicles and other aircraft. However, the non-ideal radar signal form causes the strong signal to cover the weak signal, and the clutter suppression is required to expose the target. As the target signal is extremely low, and the performance of conventional clutter suppression methods is limited, the residual clutter remains after the time-varying clutter is suppressed, which leads to many false alarm points on the processed RD spectrum. The two-dimensional distribution of these false alarms in range-Doppler is very similar to the target and difficult to be distinguished, which seriously affects the target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference of spatial-temporal stationarity between target signal and clutter in a short time is discussed in this paper, a radar false alarm suppression method based on target spatial-temporal stationarity is proposed by using the difference of stationarity between target signal and false alarm points in range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD spectrum of sub-frames with short time interval is obtained by using the sliding matching filtering method and extract the peak points. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate false alarm points. Finally, false alarm points are further eliminated through the target tracking and retain the real target information, so as to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% false alarms points while maintaining the detection performance of the target. The processing of actual data in the field experiment shows that the after suppression false alarm by this algorithm can obtain more ideal target detection results, and realize the tracking of the UAV and other aircraft track.

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Known Inc. A comprehensive characterization was performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements were fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic, with additional bespoke modifications assessed. These models, typically formulated as coupled algebraic-differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, were optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder-Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensued, accompanied by an examination of sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. The high material costs of the gas diffusion layer(GDL) and bipolar plate(BP) creates a barrier for large scale commercial application. This study aims towards addressing this challenge by optimizing the material and design of the cathode, GDL and BP. While Deterministic design optimization(DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing Reliability based design optimization(RBDO) to optimize four key PEMFC design variables i.e., δ_gdl, d_ch, w_ch, and w_land. The objective is to maximize cell voltage(V_cell) considering material cost of cathode gas diffusion layer(Cost_cGDL), and cathode bipolar plate(Cost_cBP) as reliability constraints. Results of the DDO show an increment in V_cell of 31mV, with reliability of around 50% for Cost_cGDL and Cost_cBP. In contrast RBDO method provides a reliability of 95% for both Cost_cGDL and Cost_cBP.

This paper evaluates the open and closed loop DC-DC converter operation within a DC coupling multilevel inverter architecture to obtain an infinite level stepped sinusoidal voltage. Adding a cascade controller to the DC-DC converter should reduce the settling time and increase the number of levels in the output voltage waveform, it could decrease the speed error and phase shift concerning the sinusoidal reference signal. The proposed methodology consists of implementing an experimental multilevel inverter with DC coupling, through a single-phase bridge inverter energized from a BUCK converter, trigger signals for the two converters are obtained from a control circuit based in an ATMEGA644P microcontroller, and a digital controller is also implemented to evaluate the operation of the BUCK converter in open and closed loop and observe its influence in the stepped sinusoidal output voltage. The evaluation is performed to energize a resistive load with common output voltage in multilevel inverters, that is 3, 5, 7, 11, and infinity levels. Results show that during the design stage, fast dynamic elements, like the storage capacitor, can be used to obtain a minimum THD because the settling time is sufficiently fast and the speed error remains small and there isn’t a need for a controller. A digital controller requires processing time and although in theory, it can reduce the settling time to a minimum, the processor introduces latency in the control signals generation, producing the opposite effect. Controller complexity of the digital controller must be considered because it increases processing time and influences the efficiency of the closed-loop operation.

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and ice-breaking capabilities. We propose a path planning method for icebreaking that leverages the synergistic capabilities of the Safe Particle Swarm Optimization and Genetic Algorithm (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The underwater unmanned vehicle (UUV)-assisted icebreaking approach developed here utilizes the UUV's flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional Safe Particle Swarm Optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions.

In the era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation existing infrastructure through health monitoring has emerged as an important challenge facing structural engineers. In recent years, intelligent approaches, such as data driven machine and deep learning crack detection, gradually dominate over traditional methods. Among them, the semantic segmentation using deep learning models is a process of characterization of accurate location and portrait of cracks using pixel level classification. Most available studies rely on single model knowledge to perform this task. However, it is well-known that the single model might suffer from low variance and low ability to generalize in case of data alteration. By leveraging the ensemble deep learning philosophy, a novel corporative semantic segmentation of concrete cracks method called Co-CrackSegment is proposed. Firstly, five models, namely the U-net, SegNet, DeepCrack19, DeepLabV3-ResNet50, and DeepLabV3-ResNet101 are trained to serve as core models for the ensemble model Co-CrackSegment. To build the ensemble model Co-CrackSegment, a new iterative approach based on the best evaluation metrics, namely the dice score, IoU, pixel accuracy, precision, and recall metrics is developed. Results show that the Co-CrackSegment exhibits a prominent performance compared to core models and weighted average ensemble by means of the considered best statistical metrics.

The importance of bio robotics has been increasing day by day. Researchers are trying to mimic nature in a more creative way so that, the system can easily adopt the complex nature and its environment. Hence, bio robotic gripper plays a role in physical connection between the environment and bio robotics system. While handling the physical world in biorobots gripper, the complexity occurs in feedback system where sensor plays a vital role. Therefore, human centered gripper sensor has a good impact on bio robotics field. But categorical classification and their selection process is not very systematic. This review paper follows PRISMA methodology to summarize the previous works on bio robotics gripper sensor and its selection process.. This paper discusses challenges in soft robotics systems, and the importance of sensing systems to facilitate critical control mechanisms along with their selection considerations. Furthermore, a classification of soft actuation based on gripper has been introduced. Moreover, some unique characteristics for soft robotics sensor are explored namely compliance, flexibility, multifunctionality, sensor nature, surface properties, and material requirements. In addition, a categorization of sensors for soft robotic gripper in terms of modalities has been established ranging from the tactile and force sensor to slippage sensor. Various tactile sensors ranging from piezoelectric sensing to optical sensing are explored as it has utmost importance in soft gripper to effectively address the increasing requirements for intelligence and automation. Finally, taking everything into consideration, a flow diagram has been suggested for selecting sensors specific to soft robotics applications.

The distribution of oil and gas phases around ball-raceway local regions is an important basis and foundation for determining whether a bearing is sufficiently lubricated. To obtain the oil phase distribution law in the inner raceway-ball contact local region (IBCR) and outer raceway-ball contact local region (OBCR) of the under-race lubrication ball bearing, the numerical simulation method is used. The effects of bearing rotation speed, oil flow rate, oil viscosity and oil density on these two regions are studied. The results indicate that the oil phase exhibited significant periodic changes in both time and space. Compared with that in the IBCR, the oil phase distribution in the OBCR is more uniform. Increasing the bearing rotation speed and reducing the oil flow rate made the IBCR and OBCR more uniform. Changing the oil viscosity only alters the distribution pattern of the OBCR. The oil density may not affect the fluid flow state or the oil phase distribution in the bearing.

Floods in various regions cause irreparable damage to infrastructure, agricultural lands, and industrial centers. In recent years, due to the effects of climate change and, in particular, the increased activity of the monsoon system in the Balochistan region located in southern Sistan and Baluchestan province, local rivers have overflowed, causing widespread flooding and significant damage. Accurate flood routing not only provides decision-makers with precise information about flood volume and flow behavior but also facilitates planning for effective preventive measures. In this study, the Muskingum-Cunge model was used to simulate floods in eight major rivers of Balochistan, including Kajou, Sarbaz, Nik Shahr, Kehir, Bahu, Sia Jangal, Rapch, and Kamb. The model parameters (K and X) were optimized using two machine learning algorithms: Particle Swarm Optimization (PSO) and Harmony Search (HS). The results showed that in most stations, the PSO algorithm outperformed HS. The algorithms were evaluated using the coefficient of residual mass (CRM), normalized root mean square error (NRMSE), efficiency (EF), and agreement index (d). For instance, at the Kajou station, PSO improved CRM by 0.017, Ef by 0.921, d by 0.982, and NRMSE by 0.099 compared to HS. At the Kamb station, both algorithms yielded comparable results. Considering the overall better performance of the PSO algorithm in most evaluation indices, it can be concluded that this algorithm is a more suitable option for optimizing the parameters of the Muskingum-Cunge model and simulating flood hydrographs with higher accuracy at most of the studied stations.

Finding sustainable renewable energy sources for energy conversion and management is still an obscure topic. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy harvester (PEH) technology. A dynamic mathematical model of a PEH device consists of a piezoelectric transducer, a controlled PWM rectifier, and a closed-loop buck boost converter is developed. A piezoelectric transducer converts mechanical vibrations from a wind force into an oscillating train of electrical charges. The sinewave peak-to-peak level relationship describes the changeable values of these charges, which depend on the severity of the wind vibration. The controlled PWM rectifier, in conjunction with a closed-loop buck-boost converter, converts this train of sinewave cycles into DC voltages. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load and improves the PEHS output voltage stability. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrate that the proposed dynamic PEHS model effectively feeds low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives.

this paper focuses on a scientific study of the restoration, rehabilitation and reuse of the great throne hall, the most important hall in al-jawhara palace. it also aims to study the most important factors and forces of damage that affected the great throne hall, natural, biological, and human, and the resulting damage. field tests were performed, laboratory tests to determine fracture stress, liquidity and spandex limits tests, chemical analysis on soil samples and geoelectrical testing of the soil, and the results did not show caves or voids in the ground until a depth of 10.00 m, construc-tion materials were studied and tested using x-ray, (xrd), mechanical tests (tensile, shear, com-pression), and a study of the effect of seismic loads on the walls; the results showed horizontal and vertical cracks; based on the study and analysis of these cracks, it was possible to present modern techniques in restoration and reinforcement methods, and in conclusion, the researcher's recommendations.

The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning and lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for obtaining the full field of strains and deformations of full-scale real structures. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

The No. 3 coal seam in the Yuxi Coal Mine has a measured maximum gas content of 25.59 m3/t, along with a maximum gas pressure of 2.9 MPa, indicating its high risk to gas and outbursts. To mitigate outburst risks of the coal seam, the 1301 working face has been implemented gas pre-drainage measures by grid boreholes from underlying roadways. After one year of extraction, it was confirmed that the gas content at all 33 test sites was below 8 m3/t, meeting the outburst prevention standards. However, during subsequent coal tunnel excavation, the gas desorption index K1 value frequently exceeded the standard, resulting in numerous occurrences of abnormal gas emission or small-scale outbursts. To tackle the challenges associated with safe excavation following the first round regional outburst prevention measures, a research and industrial trial of CO2 gas fracturing(CO2-Frac) technology for secondary outburst prevention and rapid excavation was completed. Results show that the dual-hole and high-pressure (185MPa) CO2-Frac considerably contribute to outburst prevention. K1 exceedances per hundred meters of tunnel excavations were from average 2.54 without CO2-Frac to average 0.28 after the new technology being implemented, leading to a eight fold reduction. Additionally, the monthly excavation footage increased from average 81.64 m without CO2-Frac to average 162.42 m with CO2-Frac, resulting in a 2-fold improvement. The dual-hole and high-pressure CO2-Frac is an advanced technology for safe and efficient excavation for secondary outburst elimination in highly outburst-prone coal seams in the Yuxi Coal Mine, with potential for widespread application in similar coal seam conditions.

Coupling faults that occur simultaneously widely exist in the operation of mechanical equipment, and coupling fault features are formed by nonlinear coupling of multiple base fault features. In this paper, hypergraph neural network is used to obtain the characteristics of coupling faults, and two coupling fault diagnosis frameworks based on hypergraph are provided. 1. Coupling fault diagnosis framework based on feature generation: The hypergraph neural network is used as a generator to provide negative samples for discriminator training. The generator outputs hyperedge vectors as fake samples, and each hyperedge connects a base fault containing a fault type. 2. Coupling fault diagnosis framework based on feature extraction: Each node is regarded as a fault type, and the fault characteristics of each type of fault are extracted through the aggregation operation of hypergraph. The attention mechanism is introduced to realize the dynamic adjustment of the hyperedge, and the dynamic vertex is used to classify the unknown faults. The results show that the diagnostic accuracy of coupling faults under the two frameworks reaches 88.6% and 86.76%, respectively.

Continuous casting is the most common method for producing steel into semi-finished shapes like billets or slabs. Throughout this process, steel experiences mechanical and thermal stresses, which influence its mechanical properties. During continuous casting, decreased formability in steel components leads to crack formation and failure. One reason for this phenomenon is the appearance of the soft ferrite phase during cooling. However, it is unclear under which conditions this ferrite is detrimental to the formability. In the present research, we investigated what microstructural changes decrease the formability of microalloyed steels during continuous casting. We studied the hot compression behaviour of microalloyed steel over temperatures ranging from 650 °C to 1100 °C and strain rates of 0.1 s-1 to 0.001 s-1 using a Gleeble 3800® device. We examined microstructural changes at various deformation conditions using microscopy. Furthermore, we implemented a physically-based model to describe the deformation of austenite and ferrite. The model describes the work hardening and dynamic restoration mechanisms, i.e., discontinuous dynamic recrystallisation (dDRX) in austenite and dynamic recovery (DRV) in ferrite and austenite. The model considers the stress, strain, and strain rate distribution between phases by describing the dynamic phase transformation during the deformation in iso-work conditions. Increasing the strain rate below the transformation temperature improves hot ductility by reducing DRV and strain concentration in ferrite. Due to limited grain boundary sliding, the hot ductility improves at lower temperatures (<750 °C). In the single-phase domain, DRX improves the hot ductility provided that fracture occurs at strains in which DRX advances. However, at very low strain rates, the ductility decreases due to prolonged time for grain boundary sliding and crack propagation.

Currently, many health workers have died of being infected by dangerous infectious diseases. One solution is to prevent the spread of transmissions by minimizing contact between patients and staff or others using a mobile robot for delivering logistics services. We propose an approach by applies a navigation system using a position driver and obstacle avoidance using a fuzzy controller and particle swarm optimization for path planning in an unknown environment for a holonomic mobile robot. A Fuzzy Controller was used for obstacle avoidance so that the mobile robot could avoid obstacles using existing proximity sensors as the perception of the robot. In the path planning section, Particle Swarm Optimization is used to find intermediate points that must be reached by the mobile robot to reach their target. First, we performed simulation under the condition that the simulation resolution was similar to that of real conditions. It was found that the Trigonometric Function Adaptive PSO (TFAPSO) was better for finding the optimal solution based on the safest, shortest path, and lowest time. Finally, the holonomic mobile robot is tested directly in an unknown environment with the trajectory path generated by TFAPSO in real condition.

Trenchless technologies are utilized for installing new pipes or repairing and replacing old ones, categorized into construction and rehabilitation/replacement methods. With rapid urbanization, new pipe networks must be established in proportion to population growth. Additionally, due to issues like corrosion and aging, a significant portion of storm sewers, waste sewers, gas, and water pipes need annual replacement or rehabilitation. Traditional open-cut methods are no longer viable due to traffic disruption, safety concerns, environmental impacts, and high costs. Trenchless construction methods address these issues effectively. Construction trenchless methods include horizontal earth boring (no worker entry), pipe jacking, and utility tunneling (worker entry). Horizontal earth boring is further divided into horizontal auger boring, horizontal directional drilling, pipe ramming, microtunneling, pilot tube microtunneling, and compaction methods. The first part of the current study reviews and discusses the design criteria for horizontal auger boring, horizontal directional drilling, pipe jacking, pipe ramming, and microtunneling. Based on these unique project requirements, the optimal trenchless technology can be proposed. In the second part of the research, a hierarchical algorithm is proposed, based on literature concepts, to serve as a decision-making tool for selecting the optimal trenchless construction method. Sometimes, multiple methods may be proposed, with the best one chosen based on equipment availability, skilled teams, budget, and sustainability principles. Although trenchless renewal methods are not the focus of this study, their design criteria are provided in the appendices to offer a comprehensive collection of design requirements for all trenchless technologies.

This paper explores various methods for inspecting gravity pipes. It emphasizes that understanding the initial process of pipe prioritization is crucial before discussing inspection methods. Given the extensive gravity pipeline systems in use today, inspecting all suspected pipelines without prioritization would be extremely costly and time-consuming, especially in a large country like India. Therefore, the paper highlights the importance of using algorithms and analysis tools to prioritize which pipelines need inspection. It also describes innovative inspection techniques, such as wave and sound analysis, which can improve inspection efficiency. The research provides an overview of the pipe inspection process and discusses cost-saving algorithms that can enhance field efficiency.

Will human societies accommodate social robots, given the current development rate? Are we under a novelty effect? Can (and should) social robots compete with intelligent agents (e.g., software robots) for the attention of humans? Will the current development of machine learning be the motor of social robotics? What kind of robotics products and culture can we expect that will emerge? These are relevant questions to assess the sustainability of social robots and how they will change business practices and lifestyle. Speculative/exploratory arguments on these topics are possible and have been mostly left out of research papers. This, however, results in an unconventional structure where the novelty comes from the questioning of the role of Social Robotics that has not been duly addressed in the literature. The paper raises questions and (i) presents arguments supporting sustainability grounded on experiments in real-world scenarios, and (ii) identifies challenges according to multiple viewpoints. These are questions for which assertive answers are not found in the literature. Besides the answers to the above questions, the conclusions point to pathways we believe social robotics can evolve, e.g., incorporating maximal knowledge on how humans work.

This case study originates as a design experiment for a sustainable housing system built on-site. The context is Niamey, the capital of Niger. The study takes into account the environmental issues in the construction sector and aims to find a solution capable of meeting housing, environmental, and economic needs. In the field of earthen construction, the most important developments have been achieved in manufacturing methods. In particular, the use of an additive digital manufacturing system, such as large-scale 3D-printing, allows the construction of complex shapes derived from structural and thermal studies, maintaining a high degree of automation in the construction process, reducing construction times and labor costs. This paper investigates the possibility of responding to housing and environmental needs with a settlement system made almost entirely of printed earth, maintaining the highest possible degree of automation. Starting from a study on the state of the art of 3D-printing in architecture and printable earthen compounds, the design choices of similar cases are analyzed to understand the construction techniques, potentials, and limitations of the medium. Finally, a design proposal is developed based on the definition of a fully printable functional module, which, upon aggregation, determines the characteristics of the final settlement. This implies a radical change of approach compared to previous prototyping of 3D-printed earthen buildings, as the design of the single functional module is not an exercise that finds completion in itself, but is oriented to the scale of the settlement right from the definition of its basic geometric characteristics. In other words, the settlement is no longer the result of the serial aggregation of independent basic units, but arises spontaneously from the juxtaposition of functional modules designed to interact with each other and merge into a single residential complex. The settlement is therefore the large-scale replication of the alternation between full and empty spaces that characterizes the single functional module and, even more importantly, the replication can take multiple forms. In fact, the full and empty spaces of the functional module are planned to allow multiple combinations of aggregation. This introduces a certain degree of customization into the growth dynamics of the settlement, a factor that is entirely new compared to previous proposals by repeatable modules. No less important are the environmental implications, as designing for the scale of the settlement allows the low carbon footprint typical of earth-based construction to be extended from the single building to the entire settlement.

Object detection often struggles with accurately identifying visually similar parts, a challenge commonly faced in industrial applications. To address this issue, we propose a clustering methodology based on the visual similarity of 3D object models. This approach is particularly effective when integrated with synthetic image generation, as both processes rely on 3D models. In a case study, we observed a 22 % increase in classification performance on a validation dataset when training an object detector on visually similar groups rather than on all classes, suggesting the potential of our method as a baseline for a multi-stage classification scheme.

Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools’ pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configura-tion in an automated manufacturing environment. All research papers mainly put efforts into developing control and observation architectures in various scenarios, but none of them has dis-cussed the importance of the camera’s location in eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation loca-tion to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. In this paper, we propose an algorithm for the camera’s moving policy so that it explores the camera workspace and searches for the optimal location where the images’ noise level is minimized. Also, this algorithm ensures the camera ends up at a subop-timal (if the optimal one is unreachable) location among the locations already searched, with limited energy available for moving the camera. An automated manufacturing application has been sim-ulated and the results show the success of this algorithm’s improvement of observation precision with limited energy.

The tea leaf paradox (TLP) describes unsteady fluid motions which help entrain and deposit suspended particles at the center of rotation. Various applications depend on the TLP for particle separations spanning across orders of magnitude in length scales and is an important problem in fluid mechanics. Despite papers describing the phenomena, the efficacy of particle separation using the TLP remains unclear as to the relative importance of for example hydrostatics, particle-fluid density ratio, wall friction, liquid bath aspect ratio and the rotation speed. The dynamics involved are notably complex and require a careful tuning of each variable. In this study, we have investigated the role of the limit of the aggregation dynamics in rotational flows within 3D-printed vessels of various sizes in tandem with particle imaging to probe the dissipation effects on the particle motions. We have found that the liquid bath aspect ratio limits how much aggregation may occur for a particle-fluid density ratio greater than unity (e.g., $\rho_p/\rho_f >1$, where $\rho_p$ is the density of the particle and $\rho_f$ is the ambient fluid density.

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective op-eration and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operation status of the charging station and users' charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data is established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging station is conducted using the lack of power supply (ENC), evaluating the risk level of each station, and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliable goals, is constructed to determine the op-eration and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the relia-bility of the system and the overall benefits of operation and maintenance while meeting the re-quired standards.

Tropical regions face significant energy challenges due to climatic conditions. This study investigates the interplay between sustainable architecture and building energy regulations in mitigating these challenges. The study aims to assess the effectiveness of existing building energy regulations in promoting sustainable architecture in tropical climates and identify key architectural strategies and regulatory frameworks for optimizing energy efficiency in tropical buildings. A comparative analysis of building regulations and case studies of sustainable buildings in selected tropical regions is conducted. The findings reveal that current regulations often must be revised to address tropical climates' unique energy demands. Incorporating passive design principles, renewable energy systems, and material selection is crucial for energy efficiency. It is recommended that building energy regulations be strengthened to mandate sustainable design strategies and provide incentives to promote the adoption of energy-efficient technologies.

The SOC (State of Charge) in Lithium-ion battery packs refers to determining a battery's current energy level or capacity at a given time. SOC is important for many battery-operated systems since it shows how much energy is available. In the earlier stage SOC is calculated by different techniques such as Coulomb counting, open-circuit voltage, Kalman filters, Data-Driven Approaches, and Real-Times Updates so these all methods are used to calculate the SOC Estimation in battery packs. In recent years, these old methods have had some errors and accuracy malfunctions in SOC estimation in battery packs. The SOC estimation errors occur on Two-Wheeler, Three-Wheeler, and four-wheeler of Electric Vehicles (EV). To Overcome this problem in current years the AI&ML Algorithms Methods are applied everywhere related to Accuracy, Solutions & All. To increase the accuracy of SOC Estimations we are going to use Machine Learning Algorithms to improve the SOC in Machine Learning there are various methods such as supervised, unsupervised, and reinforcement. Of these three methods, one of the best methods is to select the Algorithms is apply to the Battery packs to get accurate SOC Estimations. In Machine Learning, we select the Supervised Learning Algorithms. Currently, the project will focus on the use of a Supervised Machine Learning Algorithm which includes an Extra Trees Regressor, Bagging Regressor, K-Neighbors Regressor, Decision Tree Regressor, and Random Forest Regressor for SOC Estimation. In this, we will develop algorithms applied to Lithium-ion Battery Packs to accurately the SOC estimates. In conclusion, the algorithms we will develop by using Machine Learning are compared with the algorithms and analytical Models.

Recent developments in flexible printed heaters (FPH) for wearable thermal applications, driven by the advancement of printed electronics, show great promise in revolutionizing patient care through the development of wearable flexible heaters for medical applications. Wearable heaters with high thermal stability, heat uniformity, safety, flexibility, comfort, biocompatibility, and power efficiency are desirable for stand-alone medical thermotherapy applications. This paper reviews recent advancements in the design of FPHs for wearable thermal applications. Materials for FPHs, fabrication methods, designs, temperature control mechanisms, medical applications, and performance analysis of specific FPHs are all thoroughly discussed. Materials for FPHs, such as conductive and substrate materials, have received special attention, along with the heater design parameters. Finally, the challenges and future directions for developing FPHs for wearable medical applications are addressed.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly advancing. These technologies have significantly impacted the consumer goods market, including the smart footwear industry. AI and ML are extensively utilized in the design and production of smart and agile footwear. There are various methods for manufacturing smart footwear, which can be categorized based on their applications. These include medical and healthcare smart footwear, assistive footwear for individuals with impairments, footwear designed for navigation in unfamiliar areas, foot-wear for studying foot movements and gait analysis, safety footwear, anti-skid footwear, foot-wear for the elderly, army shoes, and energy-harvesting footwear, among others. These smart shoes enable real-time patient monitoring and provide appropriate suggestions for their care. Additionally, smart footwear can be classified based on the tools and techniques used in their production, as well as the sensors and Internet of Things (IoT) devices integrated into them. This paper reviews the various tools and techniques used in the creation of smart footwear for dif-ferent applications.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

When a circular body is immersed in a fluid, its interaction with the surrounding water leads to the formation of waves and ripples. This fascinating phenomenon is primarily driven by the fluid encountering a crucial point on the body, known as the complex centre of pressure (iCoP). This point acts as a threshold that instigates disturbances in the fluid, illustrating why the body affects the fluid dynamics even before it is fully submerged. The concept of the iCoP introduces a new dimension to our understanding of fluid dynamics defined by the equation as . The iCoP is symmetric and dependent solely on the diameter of the body, which enables it to pinpoint where fluid disturbances originate in the water. The immersion depth and oscillatory behaviour of the circular body are described by . Here, signifies that the body should be half-immersed, with its centre positioned halfway below the waterline, while the term indicates that the body’s oscillatory movement, with an amplitude of , causes the body to move up and down from this equilibrium position which makes the body not to be totally half immersed as indicated by . Consequently, the immersion depth varies and reflecting both partial immersion and dynamic oscillation. The real part of the iCoP reveals critical locations for fluid displacement, which leads to wave formation, while the imaginary part uncovers the phase shifts that describe the body’s dynamic interaction with fluid motion.

A review discusses recent research findings spanning the last two decades concerning ultrasonic technologies applicable to the oil, gas, and coal sectors. Various experiments conducted in laboratories have demonstrated the efficacy, cost-effectiveness, and environmental friendliness of ultrasound in recovering and processing oil, bitumen, coal, and oil shale. Ultrasound enhances formation permeability, coal gas permeability, and oil viscosity, particularly when delivered in short, powerful pulses at medium frequencies. Combining ultrasound with traditional recovery methods has shown promising results, boosting recovery efficiency by up to 100%. At the same time, ultrasonic treatment reduces the use of traditional reagents, thereby reducing environmental pollution. Moreover, ultrasound treatment shows potential in tasks such as separating oil-water emulsions, desulfurization, dewaxing oil, coal enrichment, and extracting valuable metals from metal-bearing shales through hydrometallurgical leaching. However, widespread industrial implementation of ultrasonic technology necessitates further field and mathematical research.

TETRA (Terrestrial Trunked Radio, formerly known as Trans-European Trunked Radio) is used worldwide for Radio Frequency (RF) communications by emergency services, militaries, transportation and for commercial use. TETRA systems have been in use since the 1990’s in critical infrastructure worldwide and are still being deployed in the US for use in new projects. One example is the deployment of TETRA in new Offshore Wind Farm installations which are new to the US and still mimic European standards and installations. Even though the TETRA standard has been around since the nineties, it is still relevant and important to secure. Many of these systems remain in service or are still being deployed without a solution to a recently discovered Air Interface Encryption (AIE) vulnerability. Voice and data transmitted by TETRA radio systems is Air Interface Encrypted to protect its confidentiality and integrity and prevent eavesdropping and man-in-the-middle type attacks. This paper presents a recently discovered vulnerability in one of TETRA’s Air Interface Encryption Algorithms, examines existing solutions, and explores the use of Friendly Jamming (FJ) techniques to solve this problem.

Products made from petroleum-derived plastic materials are linked to many environmental problems such as greenhouse gas emissions and plastic pollution. It is desirable to manufacture products from environmentally friendly materials instead of petroleum-based plastic materials. Products made from biomass-fungi composite materials are biodegradable and can be utilized for packaging, construction, and furniture. In biomass-fungi composite materials, biomass particles (derived from agricultural wastes) serve as the substrate and the fungal hyphae network binds the biomass particles together. There are many reported studies on 3D printing of biomass-fungi composite materials. However, there are no reported studies on biodegradation of 3D printed samples from biomass-fungi composite materials. In this study, two types of biomass materials were used to prepare printable mixture hemp hurd and beechwood sawdust. The fungi strain used was Trametes versicolor. Extrusion based 3D printing was used to print samples. 3D printed samples were left for five days to allow fungi to grow. The samples were then dried in an oven for 4 hours at 120ºC to kill all the fungi in the samples. The samples were buried in the soil using a mesh bag and kept in an environmental chamber at 25℃ with a relative humidity of 48%. The weight of these samples was measured every week over the period of three months. Both types of samples (prepared from beechwood sawdust and hemp hurd, respectively) showed weight change after three months. Furthermore, the samples made from hemp hurd had more weight change than the samples made from beechwood sawdust. The SEM (scanning electron microscope) micrographs and FTIR (Fourier transform infrared) spectroscopy showed evidence of biodegradation of these samples.

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

In today's technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.

The coal mining process generates wastes such as overburden and tailings, the disposal of which is a significant environmental challenge. The circular economy model of eliminat-ing waste and regenerating the economy and ecosystems can minimise the impact of coal mining wastes. Another resource-intensive industry is the construction industry, which is responsible for significant resource consumption, waste generation, and emissions. Reus-ing and recycling coal mining wastes as secondary or value-added building materials in the construction industry per circular economy principles will help minimise the impact and have environmental and economic benefits. Several studies have demonstrated the use and reuse of coal mining wastes in the construction industry. This article conducts a systematic literature review to understand the state-of-the-art utilisation of coal mining waste in the construction industry. After a series of refinements using the PRISMA approach, 54 articles were included in the review. The articles were further divided based on using coal mining wastes in the construction industry as end products/application areas. The review results show that coal mining waste has been successfully used to make bricks and aggregates (fine and coarse), replace cement, and develop geopolymers, concrete, roads, and embankments. The studies suggest the utilisation of coal mining waste primarily in non-structural components, with very few studies suggesting using coal mining waste in structural applications. The systematic literature review helped in understanding the knowledge development related to the possible application of coal mine wastes in the construction industry, identifying the gap in the field, and suggesting future research directions.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

Apartment buildings, which make up 83% of all structures in Jordan, play a crucial role in the country's construction sector. This study examines the main barriers that are preventing the implementation of low-carbon emission techniques in these buildings. It uses the Fuzzy Delphi Method to collect ideas from 14 housing investors. The results suggest that a major obstacle is the insufficient knowledge among end-users regarding environmental concerns, along with financial limitations, resulting in a lack of enthusiasm for low-carbon construction initiatives. Moreover, insufficient cooperation between consultants and contractors leads to subpar constructability, which is worsened by the prevailing conventional procurement method that prioritizes cost and schedule above environmental consequences. To further investigate, it is advisable to examine the utilization of contemporary procurement methods, such as Design-Build and Construction Management, in the housing industry of Jordan. These alternative methods have the potential to solve the current difficulties by promoting more effective and environmentally friendly building practices. This will help increase the use of low-carbon emission solutions and improve the overall sustainability of Jordan's construction sector.

Satellite clock bias (SCB) prediction is a crucial technology for satellite navigation systems, holding significant importance for Global Navigation Satellite System. This paper proposes a deep learning model for SCB prediction based on the fusion of the Beluga Whale Optimization (BWO), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism. The CNN is utilized to extract the spatiotemporal characteristic information from the clock bias sequence, while the BiGRU fully extracts relevant features through forward and backward propagation. The introduction of an attention mechanism aims to preserve essential features within the clock bias sequence to enhance feature extraction by both CNN and BiGRU networks. Additionally, the BWO is employed to optimize parameter selection in order to improve model accuracy. Experimental verification demonstrates that for the BeiDou Navigation Satellite System's (BDS) hydrogen-maser atomic clocks, the predicted clock bias for 6 hours, 3 days, and 15 days are 0.078 ns, 0.475 ns, and 2.130 ns respectively - representing improvements of 31%, 45%, and 66% over CNN-BiGRU-Attention; 6%, 51%, and 56% over CNN-BiGRU; and 32%, 35%, and 73% over BiGRU, respectively.

The risk classification of barrier lake is the key to conducting emergency treatment in a scientific manner. Such difficulties are faced as short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an 8-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lake and intelligent identification of particles on the surface of barrier body were developed, which realized the rapid acquisition of 8-factor evaluation index information in emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of 8 factors. The risk assessment on15 barrier lakes such as Tangjiashan barrier lake show that the conclusion drawn with risk classification method proposed in this paper is basically consistent with that of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss’s impacts on the risk level of barrier lakes, and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

The water exit problem, which is ubiquitous in ocean engineering, is one of the significant research topics in the interaction between navigators and water. The study of the water exit problem can help to improve the structure design for marine ships and underwater weapons for better strength and movement status. However, the water exit problem involves complex processes such as gas-liquid-solid three-phase coupling, cavitation, water separation, liquid surface deformation and fragmentation, making it challenging to study. According to the works carried out by many researchers towards this issue, we summarize the recent developments from three aspects, i.e. theoretical research, numerical simulation and experimental results. In theoretical research, the improved von Kaman model and linearized water exit model are introduced. Several classical experimental devices, data acquisition means and cavitation are introduced in experimental development. Three methods, namely, BEM(boundary element method), FVM(finite volume method) and LES(large eddy simulation) are concluded for numerical simulation. The limitations and shortcomings of these three aspects are then analyzed. Finally, an outlook on the future research improvements and developments of the water exit problem is provided.