With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

Laser Powder Bed Fusion (LPBF), as a transformative technology in the field of additive manufacturing, offers unprecedented design flexibility and material efficiency. This review paper delves into the latest advancements in LPBF technology and the various alloys used in the process, including titanium alloys, aluminum alloys, nickel-based superalloys, steel alloys, copper alloys, and high-entropy alloys. For each alloy category, we discuss their specific applications, performance characteristics, and challenges faced in LPBF usage. The paper discusses important technological breakthroughs, including innovations in laser systems, scanning strategies, and in-situ monitoring technologies, which have enhanced the precision and reliability of LPBF.

The present paper proposes an accurate characterization of a NACA 23012 airfoil at near stall conditions at a Reynolds number $Re=3\cdot 10^5$. In light of the unavoidable limits of which experiments suffer near the stall regime both in terms of effective two--dimensionality and data portability across different research groups, the present characterization is performed through high fidelity numerical simulations. Taking advantage of the local discontinuous Galerkin (LDG) LES solver, implemented in the open source library \textit{FEMilaro}, the airfoil behavior is investigated ranging from $\alpha=5^{\circ}$ to $17^{\circ}$, with a peculiar focus for $\alpha \ge 10^{\circ}$. Accuracy of the found results is ensured both from the realistic and accurate reconstructed flow physics and by means of a proper comparison with existing experimental data. Then, a reliable numerical baseline is obtained for any future investigation involving a NACA 23012 airfoil at $Re=3 \cdot 10^5$. The future study of the actual research group will be a parallel BVI analysis, but results are completely extendable as they are to any other research.

Road traffic accidents are associated with different factors, such as non-signalized road networks, inexperienced drivers, lack of communication skills, distraction, and visual or cognitive impairment of road users, which have led to this increase in traffic accidents. Understanding these factors in alignment with road user communication skills can provide solutions that can reduce traffic fatalities. Road users’ communication is predominantly formal in nature, but informal means of communication also exist but are not given proper attention during the driving training and licence testing processes. Therefore, this study investigated communication skills between drivers as a factor contributing to traffic accidents in South African cities. Consequently, 16 traffic scenarios of communication between drivers were simulated using PTV Vissim and Blender software, and a semistructured interview questionnaire was used to collect data from South African road users. The questionnaire was further used to evaluate the confidence level of road users concerning informal communication. The data were analysed via regression analysis to establish the relationship between traffic accidents and miscommunication between drivers. The information gathered and analysed from this study helps to understand the informal communication types between drivers and establish where communication loss exists, which ultimately leads to a traffic accident. The results also show that misinterpretation of communication signals can present the possibility of traffic safety risk.

In recent years, the AHRS has become an important research area. In order to improve the orientation measurement accuracy of AHRS, this paper first expounds the algorithm principle of the AHRS. Then, aiming at the problem of poor stability of the traditional gradient descent method under high dynamics or large magnetic field interference, a gain-scheduled anti-interference AHRS orientation algorithm is designed. By using the switching architecture designed with this algorithm for gain scheduling, it can ensure stable performance while being subjected to apparent acceleration and ferromagnetic interference. Finally, experimental verification is conducted, and the experimental results show that: in the presence of interference, the roll angle error is 0.0350°, the pitch angle error is 0.0277°, and the yaw angle error is 0.9611°, thereby verifying the strong anti-interference performance of the gain-scheduled AHRS orientation algorithm proposed in this paper.

This paper explores strategies for fostering efficient vocal communication and collaboration between human workers and collaborative robots (cobots) in assembly processes. Vocal communication enables division of attention of the worker, as it frees the visual attention, and the worker’s hands, dedicated to the task at hand. Speech generation and speech recognition are pre-requisites for effective vocal communication. The study focuses on cobot assistive tasks, where the human is in charge of the work and performs the main tasks while the cobot assists the worker in various peripheral jobs, such as bringing tools, parts, or materials, and returning them, or disposing them; or screwing or packaging the products. A nuanced understanding is necessary for understanding how human-robot interactions can be optimized to enhance overall productivity and safety. Through a comprehensive review of relevant literature and empirical studies, this manuscript identifies key factors influencing successful vocal communication, and proposes practical strategies for implementation.

The examination of ampacity in overhead transmission lines offers a comprehensive overview, covering its definition, thermal evaluation methodologies, standards, practical applications, and the potential of high-temperature low-sag (HTLS) conductors. By navigating through static and dynamic approaches to thermal evaluation and detailing methodologies prescribed by international standards like those from CIGRE and IEEE, this study provides a solid understanding of how ampacity is determined and optimized. Furthermore, the exploration of HTLS conductors introduces a forward-looking perspective on enhancing transmission capacity whereas mitigating the need for extensive infrastructure modifications. Through an analysis of regional considerations and preferences using Slovak standards and CIGRE Technical Brochure 601, insights are provided on how environmental factors influence transmission line performance. The analysis of the transition from traditional ACSR to advanced ACCC (Aluminium Conductor Composite Core) conductors demonstrates the tangible benefits of adopting advanced conductor technologies in real-world scenarios.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

In this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares. In time-sensitive applications or in embedded systems with limited resources, the runtime of the approximate function is as crucial as its accuracy. The proposed algorithm search to find the Optimal Piecewise Polynomial (OPP) with minimum computational cost while ensuring the error below a specified threshold. This was accomplished by using smooth piecewise polynomials with optimal order and number of intervals. The computational cost only depends on polynomial complexity, i.e., the order and the number of intervals at runtime function call. For optimal approximation, computational costs for all the possible combinations of piecewise polynomials were calculated and tabulated as ascending order for the specific target CPU off-line. Each combination was optimized through constrained least squares and random selection method for given sample points. Afterward, whether the approximation error is below the predetermined value is examined. When the error is permissible, the combination is selected as the optimal approximation or the next combination was examined. To verify the performance, several representative functions were examined and analyzed.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

The paper describes an application of machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long-short term memory networks enabled to obtain reasonable agreement of stress-strain curves for cyclic deformation in low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility to get parameters for a new material without the necessity to conduct any further optimizations. As the power and robustness of the developed method was demonstrated on the very challenging problem (cyclic deformation, crystal plasticity, self-consistent model, isotropic and kinematic hardening), it is directly applicable to other experiments and models.

Failures in hydraulic systems in offshore wind turbines represent an enormous challenge for manufacturers and operators, as the pitch system statistically is one of the subsystems contributing the most to the downtime of the turbines, which is the case for both electrical and hydraulic pitch systems. However, the complex failure mechanisms of the various different hydraulic components mean that, typically, the critical components of hydraulic systems must be tested to better understand the failure mechanisms. Nonetheless, conventional testing procedures are lengthy and costly. Accelerated testing plays a critical role as it can mimic hydraulic system failure mechanisms in a shorter period. However, the lack of standardized test methods and detailed knowledge about the failure-accelerating effects complicates the process. Therefore, this paper offers a comprehensive examination of approaches applicable to conducting accelerated tests on hydraulic systems. It identifies and discusses five primary component types or sub-components related to the acceleration of testing in hydraulic systems being: pumps, cylinders, seals, valves, and hoses. Each section references studies that delve into accelerated testing methodologies for these individual components. Furthermore, within each component, a concise overview of the current techniques is provided, followed by a discussion and summary based on the state of the art.

Beans are the most widely used protein source in the world and their productivity is directly linked to nitrogen (N). The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen prediction using NIR spectroscopy. Increasing doses of nitrogen were applied to the plants and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the VIP. Considering predictions with the whole NIR, the best results were achieved with RF (R2 = 0,84 and RMSE = 2,69 g kg-1) and KNN (R2 = 0,77 and RMSE = 3,86 g kg-1). The intervals of 700-740 nm and 983-995 nm were considered the most important for the study of N. More efficient predictions were verified when only spectral regions screened by VIP were included, increasing the accuracy of the RF, KNN and M5 models by 6%, 4% and 8%, respectively. The efficiency of N prediction based on NIR reflectance combined with machine learning was verified. This approach can optimize the management of nitrogen fertilization, serving as an important tool in precision agriculture.

As technology advances, the field of electrical and computer engineering continuously demands the introduction of innovative new tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. This research addresses an identified gap in technology-augmented education capabilities and researches the integration of Virtual Reality (VR) technology with real-time electronic circuit simulation to enable and enhance the visualization of non-observable concepts such as voltage distribution and current flow within these circuits. In this paper, we describe the development of our immersive educational platform, which makes understanding these abstract concepts intuitive and engaging. This research also involved the design and development of a VR-based circuit simulation environment. By leveraging VR’s immersive capabilities, our system enables users to physically interact with electronic components, observe the flow of electrical signals, and manipulate circuit parameters in real-time. Through this immersive experience, learners can gain a deeper understanding of fundamental electronic principles, transcending the limitations of traditional two-dimensional diagrams and equations. Furthermore, this research focuses on the implementation of advanced and novel visualization techniques within the VR environment for non-observable electrical and electromagnetic properties, providing users with a clearer and more intuitive understanding of electrical circuit concepts. Examples include color-coded pathways for current flow and dynamic voltage gradient visualization. Additionally, real-time data representation and graphical overlays are researched and integrated to offer users insights into the dynamic behavior of circuits, allowing for better analysis and troubleshooting.

The only solution to avoid pollution due to sewage is to treat the effluent and reuse water and sludge at the wastewater production site. This makes it necessary to install treatment plants distributed along the sewer system to prevent the movement of effluents with the consequent losses to the aquifer. This new management is called decentralized wastewater treatment. To make this change possible, it is necessary to develop treatment plants that do not represent a health hazard for citizens, eliminating the nuisance caused by traditional plants. These plants need to be robust, operate autonomously, have reduced maintenance and exhaustive control of each process, where each cubic meter of effluent is perfectly controlled, avoiding losses to the aquifer or discharges that may occur due to deficiencies in their purification capacity; constantly producing water and sludge, which are directly reusable with all the health and environmental guarantees regardless of variations in inlet effluent or environmental conditions. This article presents a paradigmatic case of a decentralized solution with conditions of maximum complexity that has been solved efficiently, saving around 98% of the initial investment, completely avoiding wastewater discharges into the environment and reusing 100% of the treated water, in 14 years of operation.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations, reflections, and the anti-symmetry for the phase swapping have been considered to reduce the number of input parameters. The neural network, establishing a correlation between curvature and height function values, can efficiently predict the local interface curvature. We compare the trained neural network to the standard Height Function method to assess its performance and robustness.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

This paper presents the design and analysis of a novel 3-degree-of-freedom (3-DOF) parallel ma-nipulator equipped with self-sensing Ni-Ti (Nitinol) actuators. The manipulator's architecture and mechanical design are elucidated, emphasizing the integration of Nitinol actuators. The self-sensing technique implemented in a previous work was extended to a 20 mm actuator length, and the actuator was used to design the 3 DOF manipulator. Kinematic analyses were conducted to eval-uate the manipulator's performance under various operating conditions. A dynamic model was implemented for the dynamic dimensioning of the actuators, which work synergistically with a bias spring. The manipulator was realized, and a control strategy was implemented. Experimental tests, although documenting some positioning accuracy issues, show the efficacy and potential applica-tions of the proposed manipulator in robotics and automation systems, highlighting the advantages of self-sensing Nitinol actuators in small parallel manipulator designs.

To achieve low-cost and on-demand launches of micro satellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous-oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar-turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of low fuel regression rate; i.e., it achieved a higher fuel-gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up and its total impulse was increased to over 50 kNs for reaching an altitude of 15 km. In addition to the engine, other components were also improved, such as through incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device and data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. The presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments.

Railway transportation is one of the main modes of long-distance coal transportation, and it is inevitable to cause environmental pollution during the transportation process. In order to improve the environment along the railway and increase the utilization rate of coal resources, this paper proposes a detection algorithm for scattered coal solidification layer during transportation based on the YOLO v8 model, and designs an intelligent recognition model suitable for coal solidification layer detection devices by combining fluorescence detection methods. Through experimental analysis, the model meets the requirements of practical detection and plays a crucial role in environmental protection, with strong practical application value.

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform.

This study aims to precisely characterize the use of magnetoelectric nanoparticles (MENPs) for stimulating peripheral nerves. Numerical methods were employed to quantify the interaction between MENPs and nervous tissue. The influence of MENPs orientation, concentration and distance was assessed in terms of the external potential distribution exerted by the MENPs, the amplification of the exerted MENPs stimulation required to excite the neural fibers and the current injected into the intracellular space. The results highlight the significance of MENPs concentration for stimulation accuracy and efficiency, the impact of MENPs orientation on the electric potential distribution sensed by the nervous tissue, as well as the importance of the MENPs distance over the fibers’ recruitment. Given the considerable variability in the interaction between MENPs and nerves, our research provides a crucial step towards understanding this interaction, offering quantitative support for the application of MENPs in nervous tissue stimulation.

The field of construction materials is pivotal in developing sustainable and resilient infrastructure. This demands continuous research to understand the properties and behaviors of these materials under diverse conditions. Although advancements in material science and numerical modeling have been significant, experimental testing remains indispensable for a thorough comprehension of material characteristics, particularly under extreme stresses and environmental factors. This need has forced the development of a wide array of testing techniques, each designed to evaluate specific engineering properties, thus creating a complex and varied landscape of experimental methodologies. This study provides a comprehensive overview of both conventional and innovative experimental testing techniques used to characterize construction materials. It explores the related methodologies and identifies potential gaps in their application within the industry. The spectrum of testing covered includes mechanical, chemical, thermal, microstructural, durability, physical, rheological properties, and non-destructive testing methods. Mechanical tests assess strength, hardness, and fatigue, whereas chemical analyses utilize techniques such as spectroscopy and chromatography. Thermal properties are examined using thermogravimetric analysis, and microstructural characteristics are explored through advanced imaging techniques. Durability testing focuses on materials’ resistance to environmental challenges, while physical properties tests evaluate aspects like porosity and water absorption. Rheological and non-destructive tests further examine materials’ behavior and integrity without causing damage. By discussing these methodologies, the study sheds light on various approaches to material evaluation and aims to enhance the selection, application, and development of testing standards. This overview seeks to inform and inspire future research and innovation in the science of construction materials, contributing to the development of more resilient and sustainable construction practices.

Elbow dislocation and instability present significant clinical challenges, necessitating a thorough understanding of the underlying bio-mechanical mechanisms. In this study, a quasi-static three-dimensional finite element model of the human elbow joint is developed to investigate stress distribution and stages of dislocation in the human elbow under various loading conditions. The model simulates the elbow joint in different degrees of flexion (30°, 45°, 60°, and 90°) and forearm positions (pronation and supination), providing detailed insights into the progression of dislocation. Significant findings include the identification of high stress concentrations on the humerus at 90° flexion and on the radial and coronoid processes at 30°, 45°, and 60° flexion. Three reproducible stages of dislocation were observed, particularly in flexed positions with forearm pronation or supination. These stages align with experimental observations and highlight the initial occurrence of bony failures, such as radial head and ulnar coronoid fractures, preceding soft tissue tears. Clinically, the study underscores that early-stage low-impact posterior elbow dislocations retain enough stability to be managed with closed reduction and early mobilization. However, as dislocations progress, significant damage to the medial and lateral collateral ligaments is expected, necessitating more invasive treatments. This research provides valuable bio-mechanical insights into elbow dislocation, aiding in the development of improved treatment strategies and enhancing patient outcomes through precise and timely clinical interventions. The validated FEM serves as a powerful tool for pre-surgical planning offering a comprehensive understanding of elbow joint mechanics.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

This study examines the recovery efforts at Gran Tarajal Harbor following a significant oil spill, employing innovative technologies. Initial response efforts were hampered by inadequate contingency plans and bureaucratic delays, underscoring the need for improved preparedness. Cleanup operations utilized advanced methods such as enhanced absorbent sponges, absorbent granulates, bioremediation with allochthonous species, and a combination of barriers and sponges. These technologies proved effective in minimizing environmental impact, reducing hydrocarbon concentrations in sediments from nearly 60,000 mg/kg to less than 1,600 mg/kg in seven months. Heavy metal analysis revealed lead and copper concentrations associated as well as with routine port activities, while mercury levels linked to the spill decreased over time. Tissue analysis indicated minimal contamination in local organisms, suggesting limited impact from the spill, and evaluation of the biological communities in sediments suggest an ecological recovery. Study underscores the importance of rapid response, advanced technologies, and detailed contingency plans tailored to each port. Despite the successful cleanup and ecosystem recovery, political and bureaucratic challenges remain significant obstacles to implementing these improved methods. The findings advocate for a re-evaluation of current oil spill response protocols to incorporate more effective, sustainable, and cost-efficient technologies, aligning with the principles of a circular economy.

The present study drops the line for the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers undergoing the extrusion process during three-dimensional printing. Applying to the Herschel-Bulkley rheological model the research is ready with the sound theoretical basis which is used to understand the flow behavior of these materials under various conditions. A combination of experimental and numerical techniques that are based on the Finite Element Method (FEM) in COMSOL has been used in this study. The results of both the experimental and simulation approaches are then compared and the behavior of the material during extrusion is then examined. The experimental results proved PP fiber content has significant effects on rheological properties. The experiments on mixes which are either devoid of fibers or possess a high content of fiber, encountered problems like a high static yield stress and material segregation. Mixes with fibers of moderate content showed smoother extrusion processes, which means that a suitable range of fiber addition exists for the fibers that optimally balance the mechanical properties and extrudability. Numerical simulations typically agreed with experimental data for fiber contents up until it reached a certain level but not above, suggesting the existence of more complex interactions inside the fibers and the necessity for the subsequent model refinements. The investigation outlines a 0.25% and 0.5% fiber content range that leads to enhanced performance without bringing the extrusion process to greater complications and further making the production of proper, architecturally printed structures possible.

This work investigates the stability conditions for linear systems with time-varying delays via augmented Lyapunov-Krasovskii functional(LKF). Two types of augmented LKFs with cross terms in integral are suggested to improve the stability conditions for interval time-varying linear systems. Through this work, the compositions of the LKFs are considered to enhance the feasible region of stability criterion for linear systems. Mathematical tools such as Wirtinger-based integral inequality(WBII), zero equalities, reciprocally convex approach, and Finsler’s lemma are utilized to solve the problem of stability criteria. Two sufficient conditions are derived to guarantee the asymptotic stability of the systems using linear matrix inequality(LMI). First, asymptotic stability criteria are induced by constructing the new augmented LKFs in Theorem 1. And then simplified LKFs in Corollary 1 are proposed to show the effectiveness of Theorem 1. Second, asymmetric LKFs are shown to reduce the conservatism and the number of decision variables in Theorem 2. Finally, the advantages of the proposed criteria are verified by comparing maximum delay bounds in two examples. Two numerical examples show that the proposed Theorem 1 and 2 obtained less conservative results than existing outcomes. Also, Example 2 shows that the asymmetric LKF methods of Theorem 2 can provide larger delay bounds and fewer decision variables than Theorem 1’s in some specific systems.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

This work focuses on identifying the most effective machine learning techniques and supervised learning models to precisely estimate power output from Photovoltaic (PV) plants. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest, Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Linear Regressor (LR), Gradient Boosting, and k-Nearest Neighbors (KNN). The methodology applied starts with meticulous data preprocessing steps aimed at ensuring dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold, is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2). Among the array of models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048% and an R2 score of 0.968. Furthermore, the best-performing model is integrated into a MATLAB application for real-time predictions, thereby enhancing usability and accessibility for a wide range of applications in renewable energy.

Measuring sound pressure is a complex and tedious task in industrial environments. Classical methods can be costly, inaccurate, and potentially dangerous for workers. Integrating robotics and computer vision into acoustic measurement processes offers an innovative solution to these problems. This paper uses a Doosan Robotic Vision System to investigate an innovative automatic acoustic sound pressure measurement solution. The Doosan robot, equipped with a scanning camera and microphone, captures acoustic data and produces a map of the machine’s acoustic radiation using computer vision algorithms. The measurement technique is accurate, efficient, and safe for workers. It can also be used for continuous monitoring of the industrial acoustic environment. The developed measurement method offers a promising way to automate acoustic measurement, which can be performed faster and with better accuracy to help industries improve their product sound quality and reduce noise levels. The method was compared to analytical predictions. A 3D acoustic mapping was conducted to visualize the spatial distribution of sound sources within the environment. The experimental results show a good correlation with the theory.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

This research presents a novel algorithmic implementation to improve the analysis of visually controlled interception and accompanying motor action through the computational application of harmonic ratios and stimulation invariants. Unlike traditional models that focus mainly on psychological aspects, our approach integrates the relevant constructs into a practical mathematical framework. This allows for dynamic prediction of interception points with improved accuracy and real-time perception-action capabilities, essential for applications in neurorehabilitation and virtual reality. Our methodology uses stimulation invariants as key parameters within a mathematical model to quantitatively predict and improve interception outcomes. The results demonstrate the superior performance of our algorithms over conventional methods, confirming their potential for ad-vancing robotic vision systems and adaptive virtual environments. By translating complex theo-ries of visual perception into algorithmic solutions, this study provides innovative ways to im-prove motion perception and interactive systems. This study aims to articulate the complex inter-play of geometry, perception, and technology in understanding and utilizing cross-ratios at infin-ity, emphasizing their practical applications in virtual and augmented reality settings.

Spray penetration into citrus canopies is critical for adequate coverage and deposition to ensure effective pest control. However, mismatch of air assistance to target canopy characteristics can lead to unintended spray loss through over penetration. To evaluate the effect of air assistance on on-target deposition, two sprayers (surrogates for airflow rates) were used to apply a fluorescent tracer dye solution @ target concentration of 300 ppm to 16 medium foliage density tree blocks in a commercial mandarin orchard. The complete factorial experiment in three replications comprised two ground speeds (1.6 and 4.8 km/h), two disc-core nozzles (TeeJet® D3-25 and D6-45), and either one or two nozzle rows to obtain a wide range of application rates (496 to 9,719 L/ha). Dye deposition significantly decreased with canopy depth (p=<0.001) by nearly 7 times across the 3.4-m-wide canopies but was not significant over 1.2 to 2.2 m sampling height (p=0.867). Deposition obtained with the low airflow rate sprayer was significantly greater (p=<0.001) than that obtained with the high airflow rate sprayer over the dose range likely due to too much air pushing out spray droplets. This study underscores the importance of matching the air assistance of orchard sprayers to the target canopy.

The background of this research is the environmental sustainability assessments in the aviation ecosystem, particularly concerning airports, are getting significant attention from the industry, regulators, and researchers. A standardized and comprehensive approach is essential for uniformly addressing the global impacts of these assessments across the industry. The main objective of this study is to develop a unique framework that encompasses the requirements of aviation regulators as well as industry and academic metrics, aiming for a standardized approach to environmental sustainability assessments at airports. The methodology used the Sum of Ranking Method to evaluate each environmental indicator precisely. It was applied to five airports globally, providing a diverse testing ground to assess the proposed metrics’ applicability and effectiveness and validate the framework. The results showed that applying the framework across these varied airport environments demonstrated its viability and effectiveness, indicating that it can be successfully applied to different airport realities. The conclusion shows that the developed framework can be successfully applied globally, suggesting that it can be a valid method for international adoption in environmental airport sustainability analysis, offering a standardized approach that could be accepted and utilized worldwide.

Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

In a software-defined radar (SDR) system, most of the signal processing usually implemented in hardware is implemented by software, thus allowing for higher flexibility and modularity compared to conventional radar systems. However, the majority of SDR demonstrators and proofs-of-concept reported in the open literature so far have been based on simple antenna systems. As a result, the full potentialities of an SDR approach have not been completely exploited yet. In this work, we propose a flexilbe antenna module to be integrated into an active electronically scanning array (AESA) with controlled sidelobe level over a wide angular range, exhibiting polarization reconfigurability with low cross-polarization level and high isolation. For this purpose, analytical and numerically efficient techniques for the synthesis of the aperture distribution and the correct evaluation of the radiating features (e.g., beamwidth, pointing angle, sidelobe levels, etc.) are presented in order to grant a real-time control of the digital beamforming network. A sub-array module demonstrator is fabricated and measured to corroborate the concept.

In the provinces of the region, empirical constructions reached maximum points with these practices. The effects and pathologies appear as physical, chemical and mechanical failures in pillars, walls and roofs, generating uncertainty for the user. The current powerful design can detect the interior deterioration of coatings in adobe, brick and concrete constructions, and prevent collapses, material and economic losses. Experimental methods performed affirm the serious validity. The accuracy of the simulation and design provide a pre-test reliability of 95%. Five piles constructed of various materials participate in the prototype performance test, there is a correlation (R=0.9997) between the magnetic field range "B" (cm) and density "ρ" (g/cm³), also by increasing the current (i) in transit Q1 and coil L1, the induction of the "B" increases proportionally until penetrating the density of the wall of the building, having 5% reading error.

With an increasing adoption of Battery Electric Bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a Simulated Annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm denoted as the "quick" and "heuristic" implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

The development of devices for In-Situ Resource Utilization (ISRU) of the lunar surface powder (regolith) by means of microwaves needs regolith simulants with electromagnetic properties similar to the lunar regolith. This document deals with the measurement of complex permittivity and dielectric loss tangent of the aforementioned simulants at ambient temperature from 400 MHz to 20 GHz. In addition, further studies are carried out taking into account humidity of the samples and their densities. The obtained results will be applicable for comparing the measured values of EAC-1A and JSC-2A among them and with other previously measured simulants and real samples. The measurements are carried out applying two different nonresonant techniques: Open- Ended Coaxial Probe (OECP) and Transmission Line. For this purpose, the DAK and EpsiMu commercial kits were respectively used.

The research was aimed at comparing the fire characteristics of different types of pepper in the context of designing explosion prevention. The following characteristics were studied: explosion pressure pmax and Kst at selected concentrations, ignition temperature of the deposited dust layer from the hot surface and minimum initiation energy. The comparison of the chemical properties of the used types of pepper was performed using TG/DSC. Results of the measurements suggest that different types of peppers exhibit different explosion characteristics. Each of the samples reached the maximum value of the explosion pressure, and their rate of pressure rise increased at different concentrations. The volume of the explosion chamber used also influenced the explosion characteristics. It is a consequence of the fact that the explosion characteristics strongly depend on the mechanism of action of a particular igniter. The minimum effect on the safety characteristics was observed when measuring the minimum initiation energy and the minimum ignition temperature of dust layer from the hot surface. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. This information should then be considered in explosion prevention.

This review explores the potential of Intelligent Reflecting Surfaces (IRS) to address limitations of traditional antennas and empower next-generation wireless networks. IRS technology offers a paradigm shift by passively manipulating radio waves, enhancing signal coverage, network capacity, and energy efficiency. IRS technology utilizes flat surfaces equipped with tiny, adjustable reflectors. Unlike traditional antennas that actively send and receive radio waves, IRS passively manipulates incoming signals by reflecting them in specific directions with controlled adjustments. This approach significantly improves signal coverage and strength, particularly in areas with poor reception. The review reveals the IRS technology offers significant advantages over traditional antennas. It can enhance signal coverage by reflecting radio waves, boost network capacity which allows networks to handle a larger number of users simultaneously, promote lower energy consumption compare, and unlock potential of 6G Networks and beyond. Additionally, IRS enables functionalities such as enhanced localization and ultra-fast data transfer. Integrating IRS with next-generation antennas unlocks new possibilities. This includes reconfigurable antennas that dynamically adjust radiation patterns, compact and low-profile designs, and multi-functional antennas that support various services simultaneously. In conclusion, this paves the way for a new generation of robust and sustainable wireless communication systems.

Underwater communication for Underwater Wireless Sensor Networks (UWSNs) faces challenges due to the unique underwater environment. This review explores these challenges (attenuation, multipath propagation, limited bandwidth) across acoustic, optical, and radio frequency (RF) communication channels. It highlights how innovative antenna designs are crucial to mitigate these limitations and enable efficient data transmission. The review analyzes monopole, dipole, and helical antennas, discussing their trade-offs in radiation pattern, efficiency, and mitigating multipath effects. Advancements in materials, metamaterials, and reconfigurable antennas offer promising solutions for improved signal propagation, reduced attenuation, and better environmental adaptability. Research gaps include material exploration for low-conductivity antennas and optimizing antenna designs. Rigorous performance evaluation remains essential. Overall, advancements in underwater antenna technology are key to unlocking the full potential of UWSNs and enabling breakthroughs in marine applications.

This study provides an in-depth review of the available literature on metal composite material machining, focusing on traditional machining operations such as turning, milling, shaping, drilling, and grinding. The motivation for the study is that this composite type has emerged as a vital material in applications requiring functionality, resilience, cost-effective operation, low consumption of energy and lightweight. Despite its superior characteristics, the manufacturing complexity and poor machinability of metal matrix composites have been the primary barriers to its adoption. Nevertheless, there is a reduction of exceptional efficiency, increase in surface roughness, tool wear and so on in metal matrix composites throughout machining. As a result, this review gives a comprehensive investigation of metal matrix composite machining and effect on process parameters and procedures. Furthermore, this study distinguishes metal matrix composites towards sustainable manufacturing and the metals employed by the researchers, as well as the gaps discovered for future recommendations in metal-based composites.

In this study is considered the design and implementation of pilots in desalination plants, in a systematic way, to evaluate energy improvements in water desalination processes. Pilot tests with the latest generation of reverse osmosis membranes in large desalination plants are becoming increasingly common, as small-scale experiments are carried out on the operation of new generation membranes with better salt rejection and lower energy consumption, to achieve the best water quality required for each case at the lowest possible cost. In this way, pilot tests are being carried out before the decision is made whether or not to change the reverse osmosis membranes, as this represents a significant investment and we minimize the risk of making a mistake in order to ensure the best results in terms of energy consumption, operating costs and reducing the environmental impact while complying with the required water quality. In this case, the Pareto analysis is used to highlight the 2 or 3 most significant causes whose treatment affects more than 80% of the possible energy improvement to be implemented. In this sense, figure 3 below shows the Pareto diagram for the energy improvement of N plants in a given territory. A series of factors that can affect this energy improvement have been numbered from 1 to 7, such as the seawater intake, the decision on the membranes, the energy recuperators, the high-pressure pump, the decisions on the pre-treatment, the feed pump or the post-treatment. The Pareto diagram has been studied at territorial level with a total of 180 seawater desalination plants. The novelty of this article is to apply Pareto diagram to a study of a total of 180 seawater desali-nation plants at territorial level in Canary Islands.

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, large numbers of Individuals have always required mechanical ventilation per year. The Covid-19 pandemic elevated the significance of the mechanical ventilation which have played a significant role in sustaining Covid 19 infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally.This research work presents the formulation of a detailed Port Hamiltonian model of a mechanical ventilator integrated to the human respiratory system. The interconnection and coupling conditions for the various subsystem within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system is also presented.A structure preservation discretization is provided along side numerical simulations and results. The obtained results are found to be comparable to results presented in literature.The future work will include the design of suitable controllers for system.

Starting from the model of a slow-rolling missile with six degrees of freedom (6 DOF) in the body frame, a 6 DOF model in the Resal frame is obtained, which is used to linearize the coupled commanded motion. Based on the linearized model, we obtain the structural scheme of the commanded object and define the quality parameters. The obtained parameters have a complex representation (with real and imaginary parts) due to the coupling between longitudinal channels. Then, the kinematic guidance equations, the seeker equations and the actuator specific to the slow-rolling single-channel missile are defined using a switching function. The guidance kinematic equations, the seeker equations and the actuator model are linearized in the Resal frame, and the structural diagram of the homing missile is constructed. Starting from this, the characteristic polynomial having complex coefficients is determined and then analyzed with the Frank-Wall stability criterion. Based on the analysis, a stability range is determined for the navigation constant and a minimum and possibly a maximum limit for the time to hit the target is obtained. The stability range defined for the navigation constant on the linear model is finally validated on the nonlinear model in the body frame.

Bicycle transport stands at the forefront of sustainable mobility initiatives, a cornerstone of European Union policy. While support for cycling is crucial, its advancement hinges upon the presence of high-quality, secure infrastructure for cyclists. Safety emerges as a paramount concern, influencing the viability of cycling as a mode of transportation, yet its perception varies among user demographics. In light of this, this article endeavors to examine and contrast cycling infrastructure across various European nations, alongside analyzing statistics pertaining to fatal traffic accidents involving cyclists. The findings underscore a substantial correlation between the quality of cycling infrastructure and the incidence of accidents. This correlation underscores the pressing need for robust infrastructure development and safety measures to bolster cycling as a safe and viable transportation option. By understanding the intricate interplay between infrastructure quality and safety outcomes, policymakers can better prioritize resources and enact measures to foster safer cycling environments, thus furthering the objectives of sustainable mobility on a continental scale.

In recent years, extensive research has focused on investigating rubberized concrete as a structural material due to its enhanced properties, including increased ductility, improved energy dissipation, and higher damping ratios. Additionally, rubberized concrete contributes to sustainable development by recycling non-biodegradable waste and reducing the use of natural aggregates in concrete mixtures. However, its performance in retrofitting existing structures remains unclear and requires thorough investigation before it can be widely implemented in construction activities. The main objective of this research is to evaluate the seismic performance of reinforced concrete buildings strengthened with rubberized concrete jackets under severe earthquake excitations. To achieve this, laboratory tests were conducted to assess the properties of high-performance, self-compacting rubberized concrete mixes with various rubber content levels. Additionally, finite element models of reinforced concrete retrofitted with these mixes were analyzed using nonlinear response history analysis to compare their performance against control models. The results of this experimental work indicate a significant reduction in the mechanical properties of rubberized concrete. However, there is a considerable improvement in the damping ratio, which enhances the energy dissipation capacity of the structures. This improvement contributes to an increase in damping energy and a reduction in hysteretic energy, suggesting that rubberized concrete jackets can enhance the seismic resilience of reinforced concrete buildings.

The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. Since direct measurements of Vs are generally not available at all foundation locations in a wind farm, CPT-based correlations are often used to determine location-specific stiffness parameters for foundation design. Existing correlations have mostly been calibrated to onshore datasets which may not contain the same soil types and stress conditions found in the North Sea. The distinct geological history of the North Sea necessitates a critical review of these existing CPT-based correlations. They are evaluated against an extensive database of in-situ Vs measurements in the Southern North Sea. The importance of modelling the stress-dependent nature of Vs is highlighted and a novel stress-dependent model for Vs from CPT data is presented. As the small-strain stiffness is used as an input to foundation response calculations, the model uncertainty of the correlation can introduce significant uncertainty in the resulting foundation response. This transformation uncertainty is quantified for each of the correlations evaluated in this study and shows important variations.

Soft robotic manipulators consisting of serially-stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for applications such as minimally invasive surgery and for inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with a closed-loop control to compensate the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with 6 inputs and 4 outputs.

In the context of the Korean building structure standard KDS2022, wind loads can be readily calculated in terms of the along-wind, across-wind, and torsional components, based on a single wind direction. However, this approach falls short in accurately and safely evaluating the impact of winds from various directions. Therefore, in this study, we developed a Wind Load Calculation Program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. The building shape considered was a square, and the wind force coefficients and power spectral density were measured by combining 4 ground roughness values, 11 side ratios (), 4 aspect ratios (), and wind directions ranging from 0° to 90°. Subsequently, a generalized formulation was devised to facilitate wind load calculations across diverse conditions and a method for calculating load combination coefficients, considering response correlation and load simultaneity, was presented. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model.

This study presents a comprehensive comparative analysis of retrofitting techniques aimed at enhancing the seismic resilience of seismically vulnerable historical buildings. Recognizing the dual objectives of structural safety and architectural preservation, this research evaluates and compares several advanced retrofitting methods, including base isolation, strengthening of masonry, steel bracing systems, Fiber-Reinforced Polymer (FRP) wrapping, and damping devices. Each technique is critically assessed based on its effectiveness in mitigating seismic risks, its compatibility with historical architectural features, and its applicability to various building types. Base isolation is highlighted for its ability to decouple buildings from ground motion, thereby significantly reducing seismic forces and protecting architectural integrity. Strengthening of masonry through the use of fiber-reinforced polymers and steel elements enhances the ductility and strength of masonry walls, making them more resilient to seismic forces. Steel bracing systems are shown to provide essential lateral support, particularly in timber-framed structures, improving overall stability and reducing deformation during earthquakes. FRP wrapping is identified as a versatile technique that strengthens structural elements without compromising aesthetic value, making it suitable for reinforcing concrete and masonry components. Damping devices, including viscous dampers and tuned mass dampers, are discussed for their ability to dissipate seismic energy and control structural vibrations, offering a minimally invasive solution that preserves historical aesthetics. The study also addresses several challenges and research gaps associated with these retrofitting techniques, such as ensuring architectural compatibility, assessing long-term material performance, optimizing cost-effectiveness, and navigating complex regulatory frameworks. By advancing knowledge in these areas, the research aims to foster interdisciplinary collaboration and innovation in the development of retrofitting solutions. Ultimately, this study contributes to the ongoing efforts to safeguard historical buildings against seismic hazards. By leveraging cutting-edge technologies and best practices in structural engineering and architectural conservation, stakeholders can develop sustainable, cost-effective, and culturally sensitive retrofitting strategies that protect valuable cultural heritage for future generations.

Securing the structural safety of blades has become crucial, owing to the increasing size and weight of blades resulting from the recent development of large wind turbines. Composites are primarily used for blade manufacturing because of their high specific strength and specific stiff-ness. However, in composite blades, joints may experience fractures from the loads generated during wind turbine operation, leading to deformation caused by changes in the structural stiff-ness. In this study, 7,132 debonding damage data, classified by damage type, position, and size, are selected to predict debonding damage based on natural frequency. The change in the natural frequency caused by debonding damage is acquired through finite element (FE) modeling and modal analysis. Synchronization between the FE analysis model and manufactured blades is achieved through modal testing and data analysis. Finally, the relationship between the debond-ing damage and the change in natural frequency is examined using artificial neural network techniques

Facility Management (FM) has increasingly focused on using Building Information Modeling (BIM) in large-scale development projects. To successfully integrate BIM in the FM sector, cooperation among different participants involved in various project stages must be improved. The objective of this digital revolution in FM is to enhance the efficacy of planning, constructing, and managing assets, benefiting all parties involved. However, the utilization of BIM in FM is limited due to factors such as incomplete owner understanding, insufficient data accessibility, and stakeholders' unfamiliarity with BIM procedures and standardization. Although the FM industry acknowledges BIM's significance, achieving successful implementation remains challenging. Facility managers lack a clear understanding of BIM's benefits in expediting operations and cost efficiency and the necessary skills for efficient utilization. To address this constraint, it is essential to develop an Employee’s Information Requirement (EIR) at the outset of a project, serving as a strategic plan and providing a comprehensive vision for all parties involved, particularly large-scale projects. Two essential technologies that facilitate the transition towards building operations are BIM and the Internet of Things (IoT). Incorporating these technologies is crucial to reducing time, expenses, and operational challenges throughout any project. The aim of this research is to create a Digital-Trio workflow, as a new expression, by providing a methodology for using facility management with BIM through the integration of EIR and IoT, maximizes the benefit of stakeholders strategically and operationally. It also explores how the preparation of the EIR by communicating with all parties will definitely improve the design process, sustainability, efficiency, cost, and time for any project, especially megaprojects.

Abstract. This paper proposes a Second Order Terminal Sliding Mode (2TSM) approach to the trajectory tracking of Differential Drive Mobile Robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the inner most loop, tracks the robot’s velocity quantities; while a kinematic controller, at the outer most loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated and finite time convergence is guaranteed by the second order TSM manifold which involves higher order derivatives of the state variables, resulting in inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

The strengthening of existing columns using additional Reinforced Concrete (RC) jackets is one of the most popular techniques for the enhancement of column’s stiffness, load bearing capacity and ductility. Important parameters affecting the effectiveness of this method are the strength of the additional concrete, the concrete shrinkage and the connection between the old and the new con-crete. In this study the application of Ultra High Performance Fibre Reinforced Concrete (UHPFRC) jackets for the structural upgrade of RC columns has been examined. Extensive numerical study has been conducted to evaluate the effect of parameters such as the thickness of the jacket, the concrete shrinkage and the addition of steel bars and comparisons have been made with conventional RC jackets. The results of this study indicate that the use of UHPFRC can considerably im-prove the strength and the stiffness of existing RC columns. The combination of UHPFRC and steel bars in the jacket leads to the most effective strengthening technique as a significant enhancement of the stiffness and the ultimate load capacity has been achieved.

This study explores the potential of a deep-water hydroponic system integrated with Internet of Things (IoT) technology to enhance the production of Chinese celery (Apium graveolens), a popular leafy vegetable with culinary and medicinal properties. Traditional soil cultivation of Chinese celery can take 75-90 days and is susceptible to fungal diseases. To address these challenges, a 2 x 6 meter hydroponic greenhouse was designed and constructed to accommodate 1,000 plants. The study investigated the effects of two key factors on celery growth and yield: daily light exposure (660 nm wavelength, 6:00-11:00 p.m.) and internal temperature regulation using a fog sprayer when temperatures surpassed 35°C. The experiment was conducted in four separate hydroponic houses, each with different factor controls: House 1 (light and temperature), House 2 (temperature only), House 3 (light only), and House 4 (natural conditions). The IoT-based system maintained optimal water conductivity and pH levels, monitored through the Blynk application and regulated by an ESP32 microcontroller. Sensor data, including water conductivity, pH, internal temperature, humidity, and water temperature, were collected throughout the study. Results revealed significant differences in weight and final yield among the houses, with House 1 achieving the highest yield, 13.91% higher than House 4. A comparison between Houses 2 and 3 indicated that temperature played a more critical role than light in celery cultivation under similar conditions. An economic analysis demonstrated the cost-effectiveness of investing in the automated control system, with House 1 being 14% more profitable than House 4 and a breakeven point achieved at 27 months. This research highlights the benefits of IoT-controlled hydroponic systems in elevating Chinese celery production towards sustainable and efficient practices, offering valuable insights for modern agriculture.

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., Look-up tables (LUTs), flip-flops (FFs), etc. using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs are created that fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across chip routing. Data is collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

As screen sizes are becoming larger and larger, which are beyond human’s physical limitation for direct touching interaction, remote controls are inevitable. However, as techniques applied so far, inertial gyroscopes are susceptible to position inaccuracies, and gesture recognition is limited by cameras’ focus depth and viewing angle. Grating antenna light trapping technology will be an ideal candidate providing the ghost point problem for multipoint inputs can be solved. Therefore, we propose a differential amplitude modulation scheme for grating antenna based multi beam optical touch, which can recognize different incidence points. Where, the amplitude of the incident beams was coded with different pulse width at first. Then, after been trapped by the grating antenna and been transferred into currents by the lined detector arrays, the incident points of the individual beams were recognized and differentiated. The scheme was successfully verified on an 18-inch screen, where two-point optical touch with position accuracy error under 3mm and response time less than 7ms under modulation frequency of 10kHz on both incident beams are obtained. This work paves a practical way for remote multi-point touch, which can make digital mouse in consistent to the users pointing direction obeying natural three-point one ling aiming rule instantaneously.

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance and overcoming anisotropic mechanical properties. This paper analyzes and compare the machinability of 3D printed PLA, PETG and carbon fiber reinforced PETG focusing on surface roughness and burr formation. A design of experiments (DoE) with a full factorial design was used, considering three factors: rotation speed, feed rate and depth of cut. Each factor had different levels: rotational speed at 3000, 5500 and 8000 rpm; feed rate at 400, 600 and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D printed components made of three different materials PLA, PETG and carbon fiber reinforced PETG, an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements.

Heritage bridges constitute an integral feature of the urban landscape in numerous cities. However, it is common for these structures to surpass their life cycle, rendering them ill-equipped to withstand the dynamic demands of users and extreme events, particularly hydrological occurrences. This research presents a methodology for the assessment of heritage riverine bridges, with a focus on the Grau Bridge in Peru as a case study. The investigation commences with an exhaustive literature review, complemented by a historical examination, followed by a preliminary diagnosis. Subsequently, hydrological and hydraulic studies are conducted, encompassing drone surveys of the riverbed and the bridge, soil analyses, and the application of 1D and 2D models in HEC-RAS. The outcomes of this comprehensive analysis reveal the high vulnerability of the Grau Bridge. Finally, strategic recommendations for its conservation are recommended.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

This study aimed to solve the problems of sealing and tearing failure of packer’s rubber cylinders during CO2 downhole injection. Using the simulation software, Comsol, a rubber cylinder model in a supercritical CO2 (SC-CO2) environment was established. The thermal analogy method was used to simulate the CO2 diffusion and rubber cylinder swelling process, thus we analyzed the deformation and stress of the rubber cylinder caused by temperature, pressure and CO2 as swelling agent. Results showed that, in the SC-CO2 environment, under the influence of CO2 diffusion and the consequent swelling, the rubber cylinder body is prone to large deformation, and the maximum shear stress is significantly increased, leading to shear failure of the rubber cylinder. The reduction of the initial seating pressure can alleviate the impact of deformation, whereas the reduction of the maximum contact pressure could cause the rubber cylinder to lose its seal. We also analyzed the change rules between various factors and the maximum contact pressure of the rubber cylinder, which provides a theoretical basis and technical support for improving the sealing performance of the packer’s rubber cylinder in the SC-CO2 environment.

Modern building automation systems implement plenty of advanced control and monitoring functions considering various parameters like users’ activity, lighting, temperature changes etc. Moreover, new solutions based on Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a new approach in building automation systems design to provide effective way of their implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. In this paper, the authors discuss development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with Internet of Things considering organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements from EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator.

Wireless Sensor Network (WSN) and the sensing devices are considered part of the core components of the Internet of Things (IoT). Hence, the modeling of IoT-WSNs is fundamental to having a better understanding, management, and deployment of this kind of technology. A fundamental issue in WSN is energy consumption due to the inherent limitations of this resource in the sensor devices. On the other hand, several issues arise in heterogeneous scenarios due to the coexistence of different sensor devices. Therefore, the modeling process becomes challenging and must consider the different transmission and operation cycles and modes that the transmission protocol implements to assure successful functioning in terms of transmission, synchronization, and energy savings. A duty-cycled MAC WSN protocol (PSA-MAC) has been modeled based on a pair of two-dimensional Discrete-Time Markov Chain (2D-DTMC), whose solution in terms of stationary probability distribution is used by expressions that have been derived to evaluate the energy consumption of sensor devices that could be used for WSN-based IoT applications. Analytical results of average energy consumption are obtained, where the synchronization, data, and sleep periods of a full transmission cycle are considered in a heterogeneous scenario with different node classes and priority assignments. Also, the normal and awake operation cycles are considered to assure synchronization and energy savings. Moreover, the transmission schemes of SPT (transmission of single packets) and APT (transmission of packets in aggregation) are also considered in the model. These results are validated through discrete event-based simulations, and the obtained results are accurate.

The paper provides and comments on the results of studies of the effect of sandstone-based abrasives and quartz sand alone on the wear of martensitic surfaces of wear-resistant steels. The wear process was examined on a ring-on-ring test rig seeking to determine the mass decrement parameter which characterised wear. The tests were conducted for three sandstone varieties: Carboniferous, Permian, and Cretaceous, and they made it possible to determine that the most intense process of deterioration of wear-resistant steels took place in the presence of quartz sand grain, while less intense wear was observed in the case of sandstone-based abrasives. The mass decrement values established in the presence of the sandstones in question did not differ significantly between individual sandstone varieties. Based on a surface damage analysis, the basic damage mechanism was found to be micro-scratching, however, with regard to the sandstones examined, it was also determined that individual grains could be pressed into surface irregularities, as well as that films of soft hematite cement developed in the Permian sandstone and that inclusions of carbonaceous matter were formed in the Carboniferous sandstone. With reference to the wear process observations, a wear model was described for the surface of the steels examined in the presence of sandstone-based abrasives.

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester's total demand from the community even while maintaining the constraint of reducing discharge-charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.

While researchers have made notable progress in bio-inspired swimming robot development, a persistent challenge lies in creating propulsive gaits tailored to these robotic systems. The California sea lion achieves its robust swimming abilities through a careful coordination of foreflippers and body segments. In this paper, reinforcement learning (RL) was used to develop a novel sea lion foreflipper gait for a bio-robotic swimmer using a numerically modelled computational representation of the robot. This model integration enabled reinforcement learning to develop desired swimming gaits in the challenging underwater domain. The novel RL gait outperformed the characteristic sea lion foreflipper gait in the simulated underwater domain. When applied to the real-world robot, the RL constructed novel gait performed as well or better than the characteristic sea lion gait in many factors. This work shows the potential for using complimentary bio-robotic and numerical models with reinforcement learning to enable the development of effective gaits and maneuvers for underwater swimming vehicles.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. In the industrial sector, the greatest need is the decarbonisation of energy-intensive in-dustries, such as producers of iron and steel, aluminum, cement, petrochemicals, paper, and more. The steel industry, in particular, is a hard-to-abate high-emissivity industry that, due to chemical processes and high thermal demands (often above 500 °C), contributed to 7.4% of global CO2 emissions in 2021. This paper explores the potential applications of hydrogen in these hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts, especially as a me-dium to facilitate the transition to green electric energy uses. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing that while there is potential for its inclusion as an element for decarbonization, it is necessary to carefully evaluate the energy balances of the various processes and the methods of hydrogen production, in order to avoid achieving only marginal effects.

In view of the fact that the field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method 3DEC, which is suitable for rocky slopes, is selected as the numerical calculation tool, and the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper's method are very close to those of the finite unit stress method and the limit equilibrium method, which verifies the reliability of this paper's method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data, through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope's coefficient of safety, the calculation results show that: the slope's coefficient of safety in natural working conditions is 1.086, basically stable, suffered from continuous rainfall, the slope body is gradually saturated, the slope's coefficient of safety is reduced to 0.987, the slope's safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreased to 0.987, destabilizing and destroying, which is consistent with the site conditions.

A smart water quality monitoring system based on the Internet of Things (IoT) paradigm was designed to protect water resources from pollution in real-time. Long-range (LoRa) application of the low-power, wide-area networking (LPWAN) concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), in the designated water station. It operates at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi with three different spread factors (SF) of 7, 9, and 12. The water stations were located at Sungai Pantai and Sungai Anak Air Batu, both rivers on the Universiti Malaya campus. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the Received Strength Signal Indicator (RSSI), Signal Noise Ratio (SNR), loss packet, and path loss at best were -83 dBm, 7 dB, < 0%, and 64.41 dB, respectively, with a minimum received sensitivity of -129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

In this work, the structural dynamics of a commercial quadrotor was investigated, which presents problems of lateral stability or sliding instead of remaining self-leveling through its autonomous control system. The possible existence of vibrations motivated the characterization of the frequency response of the arms and propellers by means of the frequency response function (FRF) using the impact hammer technique. The experimental results provided sufficient information for the future implementation of structural solutions or through reprogramming in the autonomous control algorithm that compensates or mitigates the negative effect of vibration levels due to resonance phenomenon and improves the stability in self-leveling condition of the quadrotor.

Pulsating high frequency voltage injection can be used for sensorless control of SPMSM at zero and low speed ranges. However, the sensorless capability still faces challenges to the requirements of industrial application, especially at heavy load status. Aiming at this issue, this article proposes a sensorless capability expansion method for SPMSM based on inductance parameter identification. First, incremental inductances at d-q-axis and cross-coupling inductance are identified by three steps combining the rotating high frequency voltage injection and pulsating high frequency voltage injection, then, using polynomial curve fitting algorithm, apparent inductances are calculated. Second, positive DC current injection at d-axis is proposed to enhance the saliency ratio based on the analysis of parameter identification results. Compared with the conventional =0 or < 0 method, when positive DC current is injected at d-axis, saliency ratio is enhanced. Third, convergence region is expanded at heavy load status and accuracy of rotor position estimation is improved using the proposed method. Finally, the experiment results validate that the sensorless capability of SPMSM is expanded.

Based on a constant volume combustion chamber, the initial ambient pressure effect on combustion performance and flame propagation of the active and passive pre-chamber ignition systems were studied. Compared with the passive pre-chamber, the active pre-chamber can significantly expand the lean combustion limit, enhance the ignition and combustion performance, and speed up the combustion of the main combustion chamber. At slightly lean combustion conditions, the heat release performance has a positive correlation with the initial ambient pressure. As the premixed λ increases, the heat release performance and the initial ambient pressure begin to become negatively correlated. Ignition delay can be minimized by controlling the excess air coefficient in the pre-chamber close to the stoichiometric ratio. The high ambient pressure in the constant volume chamber greatly reduces the mixture entrainment ability of the jet flame at lean combustion conditions, thereby reducing the combustion speed and peak heat release rate.

The article presents an accessible route into the field of neuroscience through ardEEG device. This device allows convert Arduino UNO R4 WIFI into a brain-computer interface, making it easy to measure EEG, EMG and ECG signals with 8 channels. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. ardEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks. Moreover, it successfully detects alpha brain waves, showcasing its effectiveness in capturing essential neurological signals

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

One important goal of research on heterogeneous catalysis for Fischer Tropsch synthesis (FTS) is to understand the connection linking catalyst morphology and its activity. Development of these effective catalysts highlights the potential for understanding the effectiveness of promoted supported catalysts and revealing their complex and dynamic effects in FTS. The metal-support interaction (MSI) is influenced by the physicochemical and textural features provided by the modified support and can directly influence the sizes of active metal crystallites, reactants and/or products mass transfer, dispersion, mechanical strength, stability and consequently the adsorption-desorption characteristics of the catalyst. Herein, this article explores the general strategies for modifying silica supports (achieved through doping, coating, or promoting the support or support precursor) and how these strategies can be manipulated to further alter catalyst performance and product selectivity by enhancing the amount of active metal species and improving the intrinsic turnover frequency (TOF). Furthermore, the review emphasises a new strategy (the inverse model of the silica support modification) aiming at overcoming the challenges posed by active metal coating or coverage, as well as pore blockages by promoters that occur during conventional preparation of promoted silica supported FT catalysts.

Rotorcrafts operate HUMS for fault diagnosis, but the capabilities of HUMS analysts are important due to the complex mechanical configuration of rotorcraft and the difficulty of analysis. Accordingly, an advanced algorithm is needed to identify faults, and flight data can be used to develop the algorithm. However, flight data contains abnormal or missing data due to problems such as sensor defects, which can be a major problem in algorithm development. Therefore, in this paper, we study a gap filling method using MLP to obtain normal data when there are problems such as abnormal or missing data in rotorcraft flight data. Existing methods typically fill the gap through interpolation or curve fitting using the before and after values of missing data. However, in this study, normal values were estimated using the values of other parameters at the moment of missing or abnormal data occurred. As a result of evaluating the performance of the trained MLP using normal data, it was confirmed that the output of the model well estimates the changing trend of the target. And when abnormal data was applied to the trained model, it was confirmed that point outliers were eliminated and appropriate values were found in the case of missing data.

Bi1-xSbx crystal is one of the best n-type thermoelectric materials below 200 K, but its weak mechanical strength hinders practical applications for deep refrigeration. Herein, we adopted the mechanical enhancement method of hot extrusion to investigate the comprehensive mechanical and thermoelectric properties of Bi0.905Sb0.095. It revealed that reducing the grain size of the matrix and increasing the extrusion ratio can improve the gain size uniformity and mechanical properties. While the thermoelectric performance depends on the texture, grain size, and local composition. The extruded sample prepared by ingot with the high extrusion ratio of 9:1 generated a high texture degree of (001) plane and small grain sizes between ~4-40 mm, which resulted in the high bending strength of Bi1-xSbx ~130 Mpa and a high-power factor of ~ 68 μW·cm-1·K-2@173 K, as well as the relative high figure of merit of 0.25@173K. This work highlights the importance of the uniform distribution of the grain size and the compositions for Bi1-xSbx, as well as the required universal key parameter for the hot-extrusion method.

Visible light communication (VLC) has attracted much attention in recent years among industrial and academic researchers. The large scale use of energy efficient light emitting diodes (LEDs) in lighting applications offers a unique opportunity to use license free visible light as the carrier in wireless communications. It has many potentials to complement radio frequency (RF) based communication systems. Researchers working on VLC have mainly concentrated on improving the communication speeds to match with Wi-Fi though the latest generation Wi-Fi standards offer significantly higher capacity at longer communication distances. Thus, market penetration is challenging for VLC as Wi-Fi is already quite mature and ubiquitous. Commercialization of VLC can only be successful if VLC can offer applications where VLC is more advantageous over the existing RF-based solutions. Once VLC enters the market through specific applications and becomes more mature in the process, the ecosystem may gradually adopt VLC in broader applications. In this article, the state of the art of the three promising VLC applications, their issues, and possible solutions have been discussed.

The application of adaptive antennas in wireless communication systems is examined in this review of the literature, with particular attention to how well they can transmit and receive signals by dynamically adjusting radiation patterns. The review covers a range of artificial intelligence (AI) approaches that have been used to enhance adaptive antenna performance in beamforming, channel estimation, interference mitigation, and spectrum utilization. Although adaptive antennas have the potential to improve wireless communication systems, the paper also discusses their limitations, including computational complexity, interference sensitivity, and the significance of precise channel state information (CSI) estimates.

In this work, a GaN-on-Si quasi-vertical Schottky diode was demonstrated on a locally grown n-GaN drift layer. The diode achieves a high current density of 2.5 kA/cm², a specific on-resistance RON,sp of 1.9 mΩ.cm² despite the crowding effect in quasi-vertical device and on/off current ratio (Ion/Ioff) of 1010. Temperature-dependent current-voltage characteristics have been measured in the range of 300-433 K to investigate the mechanisms of leakage conduction in the device. At near zero bias, thermionic emission (TE) was found to dominate. At voltage range from -1 to -10 V, electrons gain enough energy to excite into trap states, leading to the dominance of Frenkel-Poole emission (FPE). For a higher voltage range (-10V to -40V), the increased electric field promotes electron hopping along the threading dislocations in the “bulk” GaN layers, and thus, Variable Range Hopping becomes the main mechanism for the whole temperature range. This work provides an in-depth insight into the leakage conduction mechanisms on vertical GaN-on-Si Schottky barrier Diodes (SBDs) grown by localized epitaxy.

This paper is showing the results of a study focused on the occurrence of failures in heterogeneous structural materials, especially on the issue of modelling the formation and propagation of cracks. Attention is paid to the application of the finite element method to various types of materials in order to find critical parameters determining the behaviour of materials during the damage process. Application of damage mechanisms and approaches to modelling the origin of crack propagation by modifications in FEM systems are presented. Furthermore, some practical applications are tested. The main effort is devoted to composites and searching for procedures when using cohesion models for their more accurate modelling, especially near the stress concentrators, or before the crack tip. The use of the cohesive approach of elements that represent the physical nature of energy release in front of the crack front has proven to be promising not only in the direct use of cohesive elements, but also in combination with modified methods of standard finite elements. The authors of this article use their long-term experience in the given issue, which they consistently confront with developments in this area in recent years.

The object of study in the article is the auxiliary variable-frequency electric drive onboard of an AC electric locomotive, and more precisely, the structure and parameters of the control system of the active corrector of power factor (ACPF), which is part of the electric drive, as a unit that ensures electromagnetic compatibility of the electric drive with other equipment on board the electric locomotive and catenary and its energy efficiency. The topic of the article's research is the substantiation of the structure of the ACPF control system, the choice of types (transfer functions) of controllers and the calculation of their parameters. The research method chosen was the calculation of the transfer functions of the controllers of the cascaded loops control system, followed by computer simulation using the OrCAD software. The results of simulating a cascaded two loops control system of an ACPF with different types of controllers are presented and analytically compared. It is concluded that it is advisable to use a cascaded two loops control system, containing a voltage control loop and a current control loop. The best results show the simulation when using an aperiodic current controller (A-controller) and a proportional-integral voltage controller (PI-controller). The data obtained serve as the basis for further improvement of the operation of the ACPF by enhancing and optimizing the settings, structure and algorithms of the control system, which, with the selected hardware of power converter unit, will allow the ACPF to perform its functions with a high degree of reliability in an entire range of disturbing and control influences.

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers' experience, often using single indices such as remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize differences in reservoir heterogeneity, the scale of recoverable remaining oil reserves, water flooding conditions, and oil-water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of remaining oil potential and infill well location optimization. Results demonstrate that the assessment of remaining oil potential comprehensively characterizes the influence of reservoir physical properties and oil-water diversion capacity on remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, further optimization of infill well locations can effectively enhance reservoir development outcomes.

As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints through exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids such as methyl-cellulose dissolved in phosphate-buffered saline is significantly influential to the resulting cellular deformation. We conducted CFD simulations that are conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e. HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.

Multi-stage hydraulic fracturing with horizontal well technology has been widely used in shale oil reservoir. After hydraulic fracturing, hydraulic fractures and opened beddings are intertwined, which result in a complex fracture network. As well as the migration of multi-phase fluids during fracturing and soaking processes, which lead to complex flowback performance, and brings dif-ficulty to productivity evaluation and flowback strategies optimization. In this paper, taking the Daqing Gulong shale reservoir as an example, a numerical model for flowback period, which considered oil-water-gas three-phase flow and orthogonal fracture network has been established. The characteristics and influencing factors of flowback performance have been deeply studied, and the flowback modes of shale oil are reasonably optimized. Factors such as PTPG (pseu-do-threshold pressure gradient), opened bedding stress sensitivity, opened bedding permeability and matrix permeability have obvious effects on the flowback performance, resulting in signifi-cant variations in production peaks, high yield periods, and decline rates. In addition, three flowback modes are distinguished based on the rate at which the BHP (bottom hole pressure) drops. These correspond to three types of choke modes. Based on the result of numerical model, three flowback modes are optimized and the impacts of different flowback modes on shale oil flowback are clarified. This study is particularly important for understanding the performance of shale oil flowback and guiding the efficient exploitation of shale oil.

Cirina forda caterpillars are a significant protein source in Central Africa, yet their preservation and consumption pose challenges. This study investigates the drying of Cirina forda caterpillars to enhance preservation methods and product quality. Caterpillars were subjected to drying experiments at temperatures ranging from 40°C to 70°C, using tunnel and oven dryers. The drying kinetics was analyzed using mathematical models, revealing a water diffusion-limited mechanism in all the tested conditions. The Newton model provided adequate estimations of the drying behavior, facilitating further practical dryer design. Shrinkage analysis indicated a two-step process independent of the drying conditions. Achieving a moisture content suitable for prolonged storage resulted in substantial volume loss, highlighting the challenges of preserving dried caterpillars. These findings shed light on how to optimize drying processes for Cirina forda caterpillars, crucial for sustaining protein sources in Central Africa.

The detection accuracy of bridge superficial defect using the deep neural network approach decreases significantly under light variation and weak texture condition. To address these issues, an enhanced intelligent detection method based on the YOLOv8 deep neural network is proposed in this study. Firstly, multi-branch coordinate attention (MBCA) is proposed to improve the accuracy of coordinate positioning by introducing a global perception module in coordinate attention mechanism. Furthermore, a deformable convolution based on MBCA is developed to improve the adaptability for complex feature shapes. Lastly, the deformable convolutional network-attention-YOLO (DAC-YOLO) detection algorithm is formed by replacing the deep C2F structure in the YOLOv8 architecture with deformable convolution. A supervised dataset consisting of 4794 bridge surface damage images is employed to verify the proposed method, and results show that it achieves improvements of 2.0% and 3.4% in mAP and R. Meanwhile, the model complexity decreases by 1.2G, increasing the detection speed by 3.5/f·s-1.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

The mechanical properties of the materials stored inside agricultural silos influence the loads generated inside them. The different silo calculation methodologies allow predicting these loads under static and dynamic conditions if the mechanical properties of the stored materials are known. It has been known for many years that these properties are highly dependent on the moisture content of the material. However, to date there are not many studies focused on its determination. The goal of this research is the determination of the angle of internal friction, apparent cohesion, dilatancy angle and apparent specific weight of maize subjected to different moisture contents. Direct shear and oedometer assay devices were used for this purpose. In addition, the maize samples used were moistened using a climatic chamber. Moisture contents applied to maize sample ranged from 9.3% to 17.4%. The results obtained for the angle of internal friction and apparent cohesion, as well as those obtained for the apparent specific weight, were similar to those reported in the literature. However, the values obtained for the dilatancy angle of maize as a function of moisture content could not be compared because nothing has been published so far. The values obtained for this parameter overlap with those published for this material under ambient conditions. In addition, no direct relationship between the values obtained for this parameter and the moisture content of the sample could be found. Finally, the apparent specific weight increased with increasing moisture content of the samples, which differed from that published in the literature. The values provided here can be used for the optimization of storage and handling structures for granular agricultural materials using numerical methods.