The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

The state estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore this paper introduces a, on one hand, a new lithium-ion battery data set with dynamic validation data over degradation and on the other hand a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset resulting in a SOC and SOH estimation over battery life. The results show that the dataset can be used to extract parameters, design models based on it and validate with dynamically degraded battery cells.

Plant Factory Transplanter (PFT) is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a DT model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the PFT digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of X, Y, Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Infectious disease like COVID-19 will have a great impact on human society. An effective prevention and control measure is to quickly isolate the source and reduce the population’s exposure to the infection. Therefore, it is very important to quickly trace and identify the virus carrier and its close contacts in order to effectively suppressing transmission. This article provides a method, system and storage medium for locating the source of infection based on big data. We adopt the method of combining Bluetooth and GPS positioning to track the action trajectory of each individual. The method stores the close contact and location information locally and update on the cloud platform for an appropriate period of time. The method and system can provide personal risk levels and regional risk levels through related algorithms. When entering a high-risk area or if the personal risk level within the Bluetooth interconnection range is greater than the set threshold, an alarm function is provided. We can also generate reports on the spread of the epidemic over a period of time. As a result, the system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.

Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high density storage and in-memory computing applications. However, the traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era of big data. Many research groups have demonstrated that RRAM possesses the possibility of multi-level cell to overcome the demand of mass storage. Among numerous semiconductor materials, gallium oxide, as the fourth-generation semiconductor material, is highly-profile due to its excellent material proper-ties, and already be widely used in optoelectronics, high-power, resistive switching device and so on, due to its wide bandgap and transparent properties. In this study, we have successfully demonstrated the Al/GO/Ga2O3/ITO RRAM to achieve the possibility of two-bit storage. Compared to the single-layer counterpart, the bilayer structure has excellent electrical properties and stable reliability. The endurance characteristics could be enhanced above 100 switching cycles with an ON/OFF ratio of over 103. Moreover, the filament models are also described in this thesis to clarify the transport mechanisms.

In terms of obtaining fuels as well as chemicals with or without catalysts at different conditions, the plum seed stands out as an alternative biomass source. Under varying heating rates (10, 50, and 100ºC min-1) and pyrolysis temperatures (400, 450, 500, 550 and 600 °C), the plum seed was pyrolysed at constant sweep gas flow at a constant rate (100 cm3min-1) in a tubular fixed bed reactor. According to the results, an oil yield reaching to a maximum of %45 was procured at the heating rate of 100 ºCmin-1 and the pyrolysis temperature of 550ºC in non-catalytic procedure. The catalytic pyrolysis was carried out in optimum conditions with two selected commercial catalysts, namely ZSM-5 and PURMOL-CTX and clinoptilolite (natural zeolite, NZ) with catalyst ratio of 10 % of raw material. Along with the catalyst addition, the quantity and the quality of bio-oil increased including the calorific value, removal of oxygenated groups, and hydrocarbon distribution. An increase related to the desirable products like phenols, alkene, and alkane and a decrease in undesirable products like acids were observed in the presence of catalysts. When all the results are considered and evaluated, using zeolite materials in the pyrolysis as catalysts is a recommendable option to achieve enhanced chemicals and fuels.

Considering rotation-induced centrifugal stiffening, spin softening, and Coriolis effects, the three-dimensional finite element model of a rotating blade with a dovetail structure is built through the contact dynamics theory. The fixed-interface component mode synthesis method is then adopted to reduce the model for high computational efficiency and adequate model accuracy. The effects of the number of normal modes on the first-six-order modal characteristics of the model varying with the rotating speed are studied between the reduced and full models. Next, the influence of rotating speed and friction factor on the nonlinear dynamic characteristics of the model under the action of aerodynamic force are elaborately discussed. The results show that: (1) tenon-mortise joint contact-induced nonlinearity under low rotating speeds results in the intermittent interference of contact surfaces and frequency multiplications of aerodynamic excitation frequency in the spectrum cascades, while that under high rotating speeds causes the continuous interface contact and asynchronous excitation at the tenon-mortise joint; (2) the increase of friction factor results in a lower contact pressure distribution and the right shift of the resonance peak (i.e., hard nonlinearity).

The management of waste cooking oil (WCO) often poses significant challenges. Improper disposal of WCO results in negative environmental impacts and economic losses. However, from a circular economy perspective, WCO can be recycled and used as a sustainable feedstock for numerous industrial products, replacing virgin vegetable oils. This approach enables the recovery of resources while simultaneously addressing the problem of WCO disposal. By employing a multi-criteria decision analysis (MCDA) approach, the study assesses three alternative recycling pathways for WCO used as a feedstock in the production of A1) biodiesel, A2) biolubricant, and A3) biosurfactant. The aim is to identify the optimal alternative, taking into account environmental, economic, and technical factors. The procedure involved a team of chemical engineers working in the WCO recycling sector who were selected as decision-makers. The 'priority scale' combined with the Paired Comparison Technique was employed as a weighting method to evaluate the selected criteria. The results revealed that the decision-makers considered environmental sustainability as the most crucial evaluation criterion, followed by the economic criterion. In contrast, the aspect of process management was deemed less significant. Among the compared alternatives, utilizing WCO as a feedstock for biosurfactant production was assessed as the optimal WCO recycling solution. This alternative not only demonstrated the lowest coefficient variation but was also deemed the most favourable option. Biolubricant production was determined to be the second-best alternative. The adopted MCDA approach proved to be a reliable and effective tool, enabling the clear identification of the preferred WCO recycling alternative among those assessed. This was achieved through the utilization of the decision-makers' expertise and knowledge.

This work deals with the evaluation of adhesive single-lap joints using ultrasonic guided waves. An adhesive bonded lap joint can be modelled as a three-layer system made up of two adherents and an adhesive. The propagation of guided waves in the joint is developed from matrix formulations. The behavior of the different guided wave modes that propagate in the bonded region is characterized and it is found that their relative amplitudes can be estimated based on the properties of the incoming wave that propagates in the non-bonded region. It was verified that the excitation of these modes is ruled by the degree in which the shapes of both modes match each other. A 3D simulation of two aluminium bonded plates using 500 kHz ultrasonic transducers in a pitch and catch configuration was implemented using the Matlab k-Wave toolbox. Scattering effects due to some produced defects located in the bond line of the lap joints were simulated. Experimental setup with some artificial defects produced in the aluminum joints were used in order to compare with the simulated. Qualitative agreement was observed for the two approaches. The observed deviation can be due to the different characteristics of the experimental and simulated defects.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

The photocatalytic biohydrogen generation using ZnO with the aid of simultaneous deposition of copper from an aqueous biomass-derivative glycerol solution was investigated. The effects of concentration of glycerol solution, Cu ion concentration and reaction temperature on biohydrogen generation were investigated. The photocatalytic biohydrogen production rate increased with increasing the concentration of aqueous glycerol solution, and the observed data could be fitted to the Langmuire-Hinshelwood kinetic models. The photocatalytic H2 production efficiency with ZnO could be significantly improved by simultaneous Cu deposition. The photocatalytic biohydrogen production rate was dependent on temperature, and increased with the increasing temperature. The possible mechanisms for oxidation of glycerol solution and photocatalytic hydrogen generation were proposed.

Molecular modeling studies were carried out at the DFT level of the adsorption of propane and propene on Ag surfaces as a model of the interaction of light hydrocarbons with Ag/ZrO2 catalysts for the catalytic combustion reaction. It was found that the most stable mode of adsorption of propene through its π system on Ag atom has energies consistent with chemisorption and generates an elongation of the C1=C2 bond, which would explain the increase in the activity of the catalysts as function of its metallic charge. The results obtained from the DFT calculations explain the different types of interaction between propene and propane with the metallic surface. The propene is chemisorbed on the Ag surface, distorting its bonds and generating its activation. This would imply that a higher metallic charge in the catalyst would increase the number of active sites in which this activation occurs, generating a higher activity. Besides, with the addition of O, the binding energy between the propene and the metal surface increased. On the other hand, the presence of a metallic surface is not enough for the activation of the propane molecule. This would explain why by increasing the amount of metal in the catalyst, the activity for the combustion of propane is practically not affected

Background: For the treatment of bone defects, biodegradable compressive biomaterials are needed as a substitute, which degrade at the same rate as the bone regenerates. In this study, the compressive strength of CPC ceramics (with a layer thickness of more than 12 layers) should be compared with sintered ceramics. Methods: Round scaffolds with 20, 25, 30 and 45 layers were 3D printed the 3D Bioplotter, solidified in a water-saturated atmosphere for 3 days and then tested for compressive strength together with a sintered ceramic using a Zwick universal testing machine. Results: The 3D printed scaffolds are more compressive at 41.56 ± 7.12 MPa than the sintered ceramic at 24.16 ± 4.44. Conclusions: The round geometry sintered ceramics approach or exceed the upper limit of the compressive strength of cancellous bone in the direction of compacta (depending on the author).

The process of Thermal spray is effective for creating a metal matrix composite (MMC) by embedding zirconium diboride reinforcement into a molybdenum matrix. This allows for the combination of beneficial properties in a new composite, as both materials have a high The materials Mo and ZrB2 possess different characteristics. For example, Mo has a melting point of 2622 ◦C while ZrB2 has a higher melting point of 3246 ◦C. Additionally, Mo has a high thermal conductivity of 139 W/m◦C compared to ZrB2's thermal conductivity of 24 W/m◦C. However, both materials have good thermal shock resistance, and Mo has a low coefficient of thermal expansion of 5.35 µm/m◦C while ZrB2 has a coefficient of thermal expansion of 5.8 × 10−6 K−1., as well as the ability to maintain strength at elevated temperatures and stability in extreme environments. The study focused on creating Mo/Zrb2 composites using a thermal spray coating technique, varying the parameters of spraying distance, number of passes, and gas pressure, and testing the resulting castings to evaluate their hardness and Young's Modulus. The primary objective of the research was to use the Taguchi technique for identifying the optimal parameters for generating the highest Young's Modulus and hardness for the castings. The Taguchi method integrates experimental and analytical principles to identify the most significant parameter affecting the response, which can significantly enhance overall performance. The study found that the optimal parameters were a spraying distance of 20 cm, gas pressure of 6 bar, and the number of passes of 18. The Taguchi method accurately predicted the parameters that produced the highest properties for the composite coatings, which demonstrated good surface formation without hot cracks and fewer pores, with well-formed metallurgical bonding between the coating and the substrate.

This paper presents some aspects of command and control of the new generation of machines-tools with numerical control. A problem faced by numerical control programmers is related to the length (relatively long) of the programs, with implications in the costs and duration of the manufacturing preparation. A method of reducing the main program and other redundant expenses is the introduction and use of processing cycles and subroutines for automatic calculation of the cutting tool trajectory, a fact that also allows the optimization and simplification of numerical control programming on flexible order manufacturing systems numeric. The method involves the use of predefined cycles and appropriate subroutines in order to improve the efficiency of the programming activity in a balanced way. It is a new and rapid method for optimize the structure of program using the numerical subroutine for command the movement of tool, in our case, with application for turning and milling.

There is a fast-growing interest in the use of selective laser melting (SLM) for metal/alloy additive manufacturing. Our current knowledge of SLM printed 316 stainless steel (SS316) is limited and sometimes appears sporadic, presumably due to the complex interdependent effects of a large number of process variables of the SLM processing. This is reflected from the discrepant findings in the crystallographic textures and microstructures in this investigation with those reported in the literatures, which also vary itself. The as-printed material is macroscopically asymmetric in both the structures and crystallographic textures. The <101> and <111> crystallographic directions align parallel with the SLM scanning direction (SD) and build direction (BD), respectively. Like-wise, some characteristic low angle boundary features are reported crystallographic, while this investigation unequivocally proves them non-crystallographic since they always maintain an identical alignment with the SLM laser scanning direction irrespective of the matrix material’s crystal orientation. There is also 500±200 nm columnar or cellular features, depending on the cross-section, generally found all over the sample. These columnar or cellular features are formed with walls made of dense packing of dislocations entangled with Mn, Si and O enriched amorphous inclusions. They remain stable after the ASM solution treatments at 1050 °C temperatures, and therefore, are capable of hindering boundary migration events of recrystallization and grain growth. Thus, the nanoscale structures can be retained at high temperatures. Large 2-4 µm inclusions form during the solution treatment, within which the chemical and phase distribution are heterogeneous.

Dataspaces are decentralized and open ecosystems that guarantee trustworthy and secure sharing of data among their participants. In recent years, dataspaces have gained popularity due to their design for managing and sharing heterogeneous data from various sources and domains; and their capability to incrementally solve data integration issues. Leveraging dataspace and advanced technologies plays a vital role in solving many real-world applications effectively and efficiently in real-time. Drone technology is one type of technology that can be deployed to gather data from different resources in harsh or smart environments. Beyond fifth-generation (B5G) communication networks significantly contribute to drones’ development and widespread use by providing low latency and high throughput. Therefore, data sharing among drones in B5G networks offer significant potential to enhance commercial and civilian applications. However, several security issues for collaboration and data sharing, such as data privacy leakage, because of sensitive data and the lack of trustworthy centralized monitoring. Furthermore, sharing data is one of the essential requirements for drone collaboration to achieve their tasks effectively and efficiently in real-time. This conceptual framework presents a novel dataspace in the sky, focusing on securing drone data sharing in B5G for Industry 4.0 toward Industry 5.0. We present how Federated Learning (FL) assists drones in collaboration effectively and efficiently, sharing models instead of raw data. However, because of the fragility of the central curator, the reliability of contribution recording, and the poor quality of shared local models, there are still significant security and privacy issues for drone-assisted smart environments in B5G. Therefore, we present the conceptual framework for leveraging blockchain and FL to secure and manage data sharing of collaborative drones’ dataspace in space in a decentralized fashion. The decentralisation of dataspaces would significantly expand the drive and market for the development of citizen-friendly mobility services.

This paper deals with the use of low earth orbit (LEO) satellite links in long-term monitoring of water, ice and snow levels across wide geographic areas, where no other communication links can be applied. Unlike geostationary satellites, LEO satellites do not maintain the same position relative to the ground station, and transmissions need to be scheduled for satellite overfly periods. For our application, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our approach is based on online learning, and is applicable to any LEO satellite transmissions.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

This article presents a wide-band antenna designed for the Cube Satellite (CubeSat). Based on a quadrifilar structure, the antenna provides circular polarization radiation, which is suitable for satellite communication. Moreover, the antenna is designed and fabricated using two 1.6 mm thickness FR4-Epoxy boards that connect together by the metal pins. In order to improve the robustness, a ceramic spacer is placed in the centerboard, and four screws at the corners are added to fix the antenna with the CubeSat structure. These additional parts reduce antenna damage caused by vibrations in the launch vehicle lift-off stage. The proposal has a dimension of 77mm x 77mm x 10mm and covers the LoRa frequency bands of 868 MHz, 915 MHz, and 923 MHz. According to the measurements in an anechoic chamber, antenna gains are obtained with the values of 2.3 dBiC and 1.1 dBiC for the 870 MHz and 920 MHz, respectively. Finally, the antenna is integrated into a 3U CubeSat that was launched by a Soyuz launch vehicle. The terrestrial-to-space communication link was measured, and the antenna performance was confirmed in the real-life scenario.

According to architectural standards, the common proportion of a rectangular-shaped classroom is the shallow proportion, which has a longer external room facade versus the perpendicular internal sides. Even in the Kingdom of Saudi Arabia (KSA), the same proportion is used in school projects. Environmentally, this proportion is logical when considering the role of adequate natural lighting for classroom visual functions, which could be more easily achieved if its longest side is the external one. But, the environmental efficiency of any architectural space cannot be only judged according to a single human comfort achievement, especially with the diverse environmental characteristics of places around the world. This diversity can show a defect in other human comfort achievements or other environmental issues in different ways when achieving a certain function in a unified way. A holistic study should be done for any common architectural shape or proportion to ensure their priority among other options in achieving maximum green architectural principles for a specific location before confirming and using it. The manuscript compares a rectangular classroom of different external/internal wall proportions to search for their preference to be used in Taif city schools in KSA. The compared case studies have different window variations and similarities to include their effect too. According to the overall green principles assessment, the results showed that the common proportion of classrooms didn’t give the higher green credits, which alerts us that the traditional rectangular classroom proportion is not always optimum for all regions including Taif city in KSA.

Hyper duplex stainless steels, HDSS, are a new alloy group of duplex stainless steels with the highest corrosion resistance and mechanical properties among the existing modern stainless steel. Due to the addition of extra high content of alloying elements, e.g. Cr, Ni, Mo, etc., the crystallization behavior of the δ-ferrite from the liquid is of vital importance for controlling the steel properties. In this work, the effect of cooling rate (i.e. 4°C/min and 150 °C/min) on the growth process of δ-ferrite in S33207 during the solidification process was investigated using high-temperature confocal scanning laser microscopy (HT-CLSM) in combination with electron microscopes and thermodynamic calculation. The results showed that the solidification mode of S33207 steel was a ferrite-austenite type (FA mode). L→δ transformation occurred at a certain degree of supercooling, and merging occurred during the growth of δ phase dendrites. Similar microstructures were observed after solidification under these two different cooling rates. The variation of area fraction of δ ferrite at the free surface and temperature as well as time during solidification of S33207 steels were calculated at different cooling rates of 4 °C/min and 150 °C/min, respectively. The current HT-CLSM study provides in-situ experimental evidence to confirm that a low cooling rate favored the growth of primary δ-ferrite, whereas a high cooling rate favored the nucleation of primary δ-ferrite during the solidification process. The post-microstructure as well as composition evolution is also briefly investigated. This work shed light on the real time insights for the crystallization behavior of hyper duplex stainless steels during solidification, which aims to contribute to the process control of manufacturing of this steel grade.

Activated sludge process is a well known method to treat municipal and industrial waste water. In this complex process, the oxygen concentration in the reactors plays a critical role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory (LSTM) network to identify an input-output model suitable for the design of an oxygen concentration controller. The model is identified from easy-accessible measures collected from a real plant. This dataset covers almost a month. The performances achieved with the proposed LSTM model are compared with the ones obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models catch the oscillation and the overall behaviour (ARX ρ=0.833 , LSTM ρ=0.921), but, while the ARX model fails in reaching the correct amplitude (FIT=41.20%), the LSTM presents satisfactory performance (FIT=60.56%).

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.

Road infrastructure is a key public asset because it benefits the social and economic development of any country. It plays an important role for the development of the industrial complex, the production sector and the road surfaces of transport roads should be of high quality, have a long service life. Road infrastructure, as well as all infrastructure, requires preservation, maintenance and repair. There are special requirements for the roadway that must be observed during construction or repair. This article is devoted to a detailed study of the use of fiber optics sensors (FOS) based on the fiber Bragg grating (FBG) for road surface monitoring. Such a fiber sensor consisting of a fiber Bragg grid and a pair of grids can offer the possibility of simultaneous measurement of deformation and temperature for monitoring the pavement. Temperature and deformation measurements were carried out by installing a sensor on the surface of a made asphalt sample. The built-in fiber sensor based on FBG provides important information about how the pavement structure can withstand the load, subsidence of soil, timely implement road safety and stability measures evaluate and predict the service life of the pavement. The results of the study showed that the synchronicity, repeatability and linearity of the characteristics of the fiber sensor are excellent. The difference between the experimental and theoretical results is about 7%. Thus, based on the results of the obtained data, the fiber sensor on the FBG can be used for monitoring, designing road surfaces and in general transport infrastructure.

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.

Integrating numerous renewable resources has been extensively researched and studied to create a dependable energy system. This study investigated the best techno-economical hybrid energy system configuration while using hydrogen energy to keep the system's energy balanced. Additionally, this study aims to determine the best hybrid system configuration for the location under investigation. A novel technique of DBM (Deterministic Balanced Method) was introduced to determine the total annual energy output for the project's lifetime. This approach is used to contrast the outcomes of other HOMER software tools. Then, using HOMER, it is possible to confirm the sizing optimization results for the suggested method by comparing the power ratings and yearly energy output. A detailed comparison between our proposed method and HOMER Pro has been conducted for validation and verification. Our method has been rigorously tested in various hybrid off-grid energy systems, ensuring its applicability and reliability across different scenarios and configurations. The results of our study indicate a high degree of similarity between the proposed approach and HOMER pro for computing annual energy power for the studied energy systems. Specifically, our analysis revealed that the difference between the approaches was within a range of 5%, indicating a high level of consistency and reliability in our method. These findings suggest that our approach can be confidently applied to determine the appropriate size of hybrid energy systems accurately. Overall, our study provides valuable insights into the development of effective sizing methodologies for hybrid energy systems. After considering the amount of solar radiation and wind speed gathered by global weather data platforms like NASA and METEONORM, this paper has also included a real case study of the Cairo International Airport's hybrid renewable energy production systems. To guarantee the viability of this study, the system design was founded on an actual load that was researched and simulated. A cluster of distributed networks was finally proposed to use the excess energy and enable energy conversion processes to be used in industry and transportation applications.

In this study, the literature on varnish contamination is investigated among the various con-taminations of lubricant. As the period of use of lubricant increases, the lubricant deteriorates and becomes contaminated. The varnish causes filter plugging, sticking of hydraulic valves and fuel injection pumps, obstruction of flow, reduction of clearance, poor heater and cooler perfor-mance, and increase of friction and wear in various lubrication systems. These problems also cause mechanical system failure, performance degradation, and increased maintenance and re-pair cost. To improve the problems causes by the varnish, an adequate understanding of varnish is required. Therefore, in this literature review, definitions and characteristics, generating ma-chinery, generating mechanisms, causes, measurement methods, and prevention or removal methods are summarized. Most of the data are reports of companies related to lubricant and machine maintenance, and are included in some papers. We hope that this summary of varnish will be helpful to those who are engaged in reducing or preventing varnish problems.

To improve the wear resistance of polyimide (PI), surface modification was developed. In this study, the tribological properties of graphene (GN), graphene oxide (GO), and KH550-grafted graphene oxide (K5-GO) modified PI were evaluated by molecular dynamics (MD) at the atomic level. The findings indicated that the addition of nanomaterials can significantly enhance the friction performance of PI. The friction coefficient of PI composites decreased from 0.253 to 0.232, 0.136, and 0.079 after coating GN, GO and K5-GO, respectively. Among them, the K5-GO/PI exhibited the best surface wear resistance. Importantly, the mechanism behind the modification of PI was thoroughly revealed by observing the wear state, analyzing the changes of interfacial interactions, interfacial temperature, and relative concentration.

The digital twin (DT) research field is experiencing rapid expansion, yet the research in industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. The study findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the break-even point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. The study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

Lately Silicon Carbide (SiC) and Galium Nitride (GaN) based high frequency power conversion has gained immense popularity which stems from relatively low switching energy, robustness to high temperature operation, wide-band gap and superior thermal limits. While this aspect inherently results in a high-density power stage design, the design of adjoining passive components, particularly magnetics, needs careful attention. This is especially true if the compatibility of core and winding options and their performance is limited with the use of higher switching frequency. In this regard, this paper presents a brief review of existent research conducted in materials, optimization and integration of magnetics for single and three phase applications, ranging from filters, resonant inductors to isolation transformers. Design knobs pertaining to medium voltage (MV) applications, low-voltage (LV) applications, e-mobility sector such as more electric aircrafts (MEAs) and electric vehicles (EVs) are discussed. Guidelines for magnetics design are summarized for each application with a projected roadmap on future trend for magnetics.

The methylene blue (MB) adsorption was performed on a natural material powder of Cynarascolymus as identified by Cs. To analyze the Cs material, FTIR, SEM, isoelectric point (pHpzc) analysis, TGA, and DRX were used. The maximum experimental adsorption capacity of the Cs material was 203.333, 192.187, and 179,380 mg•g-1 at 298, 303, and 313 K, respectively. The correlation coefficients (R2) and average percentage errors APE(%) values for the kinetic and isotherms models indicated that the adsorption kinetics fol-lowed a pseudo-nth order model and that the Traditional isotherm model Red-lich-Peterson (R-P) correctly described the experimental data obtained at 298, 303, and 313 K, respectively. The steric, energetic, and thermodynamic characteristics of the most rele-vant advanced model (double-energy single-layer model (AM 2)) were analyzed in detail. The number of active sites for the first receptors (n1) was determined to be 0.129, 0.610, and 6.833; whereas the number of second active sites (n2) was determined to be 1.444, 1.675, and 2.036 at 298, 303, and 313 K, respectively. This indicated the presence of both multi–docking and multimolecular modes for the first style of MB ions (n1); while only a multi-molecular mode for the second style of MB ions (n2). Thermodynamic characteristics demonstrated that MB adsorption onto the Cs adsorbent is spontaneous and feasible.

A flexible structure is applied to improve the lubrication performance of a misaligned journal bearing. The journal bearing is representative sliding bearing and there are damages due to metal-to-metal contact by misalignment. Since misalignment is an unavoidable phenomenon, a journal bearing with a flexible structure was proposed as a way to improve it. The lubrication characteristics of the bearing are evaluated numerically under steady-state condition. EHL (elastohydrodynamic lubrication) analysis considering elastic deformation was performed. The lubrication performance is compared in accordance with variation of the geometry of the flexible structure and evaluated based on the minimum film thickness. Moreover, the results of the journal bearing with flexible structure are compared with those of the journal bearing without the flexible structure. The flexible structure is then applied in the form of a groove to the area supporting high load on the journal bearing; it is elastically deformed by the generated oil-film pressure, which helps to obscure a larger oil film. Through numerical analysis, it is found that the journal bearing with flexible structure can improve the lubrication performance in the misaligned condition.

Abstract: Interactions in multicomponent Ni-Cr-Mo-Al-Re model alloy were determined by ab initio calculations in order to investigate the Re doping effect on Haynes 282 alloy. Simulation results provided an understanding of short-range interactions in the alloy and successfully pre-dicted the formation of a Cr and Re-rich phase. The Haynes 282 + 3 % wt. Re alloy was manu-factured using the additive manufacturing Direct Metal Laser Sintering (DMLS) technique, in which the presence of the (Cr17Re6)C6 carbide was confirmed by an XRD study. The results ob-tained provide useful information about the interactions between Ni, Cr, Mo, Al, and Re as a function of temperature. The 5-element model designed can lead to a better understanding of phenomena that occur during the manufacturing or heat treatment of modern, complex, multi-component Ni-based superalloys.

The paper investigates the influence of ion nitriding on the microstructure, corrosion resistance, and tensile strength at elevated temperatures of Haynes 282 nickel superalloy specimens produced by the Direct Metal Laser Sintering-DMLS technique. Analysis of the specimens’ surface morphology revealed that the ion nitriding process significantly affects the physical and chemical phenomena occurring on the surface of the specimens. Phase composition and stress analysis showed that Haynes 282 nickel alloy specimens produced by the DMLS technique and a chromium nitride layer produced in the ion nitriding process have tensile stress. The layer is also characterized by high nanohardness compared to specimens produced using the classical method (wrought) and DMLS technique. The conducted corrosion resistance tests confirmed the positive effect of the nitrided layer on the corrosion resistance of Haynes 282 nickel alloy produced by the DMLS technique. The ion nitriding process did not significantly affect the elevated-temperature tensile strength of Haynes 282 nickel alloy specimens produced by the DMLS technique.

Estimation of the potential consequences from events occurring downstream of a dam are part of the risk assessment needed during the installation phase of a new dam. In case of specific Natural or Man Made ongoing or prospected events, it may be also important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study is aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool.

To study the strengthening effect on recycled concrete columns by carbon fiber composite materials (CFRP) under different levels of seismic damage, four column specimens were designed for pseudo-static tests. The four specimens were respectively non-destructive without strengthening (prototype), non-destructive strengthening, medium seismic damage strengthening and severe seismic damage strengthening, according to the replacement rate of recycled aggregates and the level of seismic damage. The characteristics of the deformation damage and seismic performance indicators of each specimen were compared and analyzed. A decrease was observed in the initial stiffness of the seismically damaged recycled concrete column specimens strengthened with CFRP, while the ductility, peak bearing capacity and energy dissipation capacity of the specimens were improved. On the other hand, with the reduction of seismic damage and the increase of recycled aggregate replacement rate, the ductility and energy dissipation capacity of the reinforced seismic damaged recycled concrete column specimens were enhanced to different levels. In particular, the cumulative energy consumption of the strengthened specimens under medium seismic damage increased most significantly by 32.5%. In general, the hysteretic curves of the strengthened specimens were full, and the average ductility coefficients were 4.1–6.8. CFRP strengthening was more effective in restoring and enhancing the performance of the recycled concrete column specimens with medium and lower seismic damage levels (displacement ratio ≤ 3%).

This study employed a TSMC 0.18-µm 50 V process to establish a high-voltage n-LDMOS structure. In the reference device, the number of floating-poly (poly-2) turns was 7, and the width and spacing of each turn was 1 µm. Although increasing the number of turns reduces the peak field, directly reducing or increasing the number of poly-2 turns is a common practice. The present experiment was realized in three steps, namely fixing the number of occupied area adjustment turns, adjusting the width of each turn, and adjusting the distance between turns. The first step was fixing the number of occupied area adjustment turns. The number of turns was increased from 7 to 9, and the number of turns was reduced to 5 and 3 in the reference group. A reduction in the number of turns led to a greater decrease in the peak field and increase in the breakdown voltage despite the area remaining unchanged. A comparison of the use of three turns with that of seven turns revealed that the maximum electric field decreased by 42%, from 4.17e+3 to 2.46e+3 V/cm, and that the breakdown voltage (VBK) increased from 30.2 to 35.84 V. The second step was adjusting the width of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. This increase in width reduced the maximum electric field and led to a greater increase in VBK. When the width was increased to 1.4 µm, the maximum electric field decreased by 29% from 1.95e+3 to 1.4e+3 V/cm, and the VBK increased by 183% from 30.2 to 85.6 V. The third step was adjusting the spacing of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. The results revealed that a reduction in the spacing led to a greater reduction in the maximum electric field and an in-crease in the VBK. When the pitch was reduced to 0.6 µm, the maximum electric field decreased by 33%, from 1.95e+3 to 1.32e+3 V/cm, and the VBK increased by 345%, from 30.2 to 134.4 V.

Free-edge effect is one of the factors affecting the mechanical properties of three-dimensional woven composites under tensile load. However, current research is relatively poorly understood regarding the effect of free-edge on the stiffness and strength of the material. This paper aims at examining the influence of free-edge effect on the mechanical properties of 3D woven composites under tension through experimental and simulation mathods. The three-dimensional DIC technique is used to collect the full-field strains on the specimen surface during the test, and the stress-strain differences in different regions in the width direction are analyzed, and the overlap of the curves in each region is found to be high. Experimental studies are conducted on specimens of different widths (within the range of 15-20 mm), and the results indicate that the differences in mechanical properties of 3D woven composites under tension loading in this width range are not significant. A progressive damage finite element model is developed for calculation and compared with experimental results.

The goal of this paper is to investigate the aerodynamic and aerothermodynamic behaviour of the Schiaparelli capsule after the deployment of the supersonic disk-gap-band (DGB) parachute during its re-entry phase into the Martian atmosphere. Large-Eddy Simulations of compressible flows have been performed over the capsule and the flexible DGB parachute to analyse the behaviour of the turbulent wake behind the capsule and its interaction with the parachute flow-field. The simulations are performed at an altitude of 10 km and a Mach number around 2, i.e., a regime in which large canopy-area oscillations are observed. These simulations have shown a strong interaction between the bow shock, the recompression and expansion waves, high pressure, density and temperature gradients, heat flux towards the airstream and the body implying turbulence generation, ingestion, and amplification through the shock waves. This nonlinear turbulence flowfield explains the non-axisymmetric behaviour around and behind the parachute that caused the uncontrolled capsule oscillation and the mission failure.

Bending of solid wood from European oak is one of the most demanding technological processes due to its specific structural and physical properties and variability. We investigated the influence of wood moisture content (MC) and stiffness, determined by NDT, as well as previous drying methods on the bending ability of the wood. The best quality was obtained with bending specimens bent at a moisture content of at least 16 % and quarter or semi-quartersawn. The number of rejected specimens increased slightly when HF bending was used. Single-stage predrying of oak to a final MC of 8 % resulted in a high rejection rate (&gt; 70 %) regardless of drying technique. The acceptance rate was higher for less stiff specimens where the ratio of ultrasonic velocity in the straight (vS) and bent region (vB) was less than 0.5 (vB/vS).

The communication link between the base station and the mobile sensor networks, such as multi-agent systems for collaborative tasks which include ground mobile robots and drones, is crucial for localization and success of tasks in indoor environments. The Power-domain Non-Orthogonal Multiple Access (P-NOMA) is an emerging multiplexing technique that enables the base station to accumulate signals for different agents using the same time-frequency channel. The environment information such as distance from the base station is required at the base station to calculate communication channel gains and allocate suitable signal power to each agent. The accurate estimate of the position for power allocation of P-NOMA in a dynamic environment is challenging due to changing location of the end-agent and shadowing. In this paper, we take advantage of the two-way Visible Light Communication (VLC) link to: (1) estimate the position of the end-agent in a real-time indoor environment based on the signal power received at the base station and (2) allocate resources using the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with the look-up table method. In addition, we use the Euclidean Distance Matrix (EDM) to estimate the location of the end-agent whose signal was lost due to shadowing.

This paper proposes a novel biologically inspired design for an insect-scale flapping robot. The proposed scheme employs two motors to drive four wings, mimicking the flapping motion of nat-ural four-winged insects. The vehicle incorporates two pairs of flapping wings to generate lift force, with a transmission mechanism featuring flexure hinges instead of pivot joints, resulting in reduced weight and volume. The flexure hinges also exhibit nonlinear stiffness to facilitate smooth wing motion. The vehicle is designed to be symmetrical in structure, with the wings of each section generating greater lift through the clap-and-fling mechanism. The final product weighs 6.2g, pos-sesses a wingspan of 17cm, a one-sided flapping wing angle of 103 degrees, and generates lift that exceeds its weight, attesting to its successful design.

Rising temperatures due to the climate change can significantly impact the frost/thaw condition of airport pavements in cold regions. This case study investigates the implications of the the warming temperatures on frost/thaw penetration and frost-heave of pavements for critical airports across Canada. To this end, different methods were used in the quantification process through climate change simulations considering the emission scenario RCP8.5 in a 20- and 40-year time horizon. The results show that climate change would have different design implications for airport pavements depending on their location. The predictions suggest a shallower frost penetration depth and possibly less frost-heave for the airports not underlain by permafrost, while airports over permafrost areas might experience an increase in thickness of the active layer ranging from 41 to 57 percent by 2061. Among the different methods used in this study, it was observed that some methods performed the best in predicting frost depth of fine soils, while others worked better with the frost depth prediction of coarse soils. The results indicate the need for more mechanistic models to provide a more realistic prediction of frost/thaw penetration as compared to the existing empirical models.

Multiwalled Carbon Nanotubes (MWCNTs) is more efficient for fabrication modern design of silicone rubber (SR) that used in industrial engineering applications; it has been embedded in room temperature vulcanization silicone rubber (RTV-SR) matrix for controlling in dielectric characterization. Frequency dielectric measurements were performed for multiwalled carbon nanotubes SR/MWCNT nanocomposites. Hence, the dielectric properties of modern design SR/MWCNT nanocomposites has been deduced as experimental work done to clarify the benefit of filling MWCNT with different patterns inside dielectrics. In variant thermal conditions (up to 40oC), it has been investigated that the new behaviors of SR/MWCNT nanocomposites dealing with nanotechnology techniques for designing new dielectric behaviors of insulation materials. Finally, this research succeeded to characterize dielectric constant, electric modulus and dielectric loss of SR/MWCNT nanocomposites with respect to traditional silicon rubber under variant thermal conditions and frequency response analysis (FRA) according to using certain types and concentrations of MWCNTs.

Extensive research has been conducted on printable antenna technologies, believed to be fast-growing due to their significant impact on wireless devices. It is necessary to study the progress of printable antenna technology as they are core devices for wireless technology today. This review work offers a comprehensive account of the previous and recent research achievements based on the principle of printable antenna, and parametric studies of several key design parameters, for future wireless network applications. The systems considered for evaluation include broadband techniques, large Bandwidth, high gain, dual/multi-band, or reconfigurable structure, low-profile, size-reduction, compact, linear, or circular polarization applications. It also highlights the fabrication procedures, and the numerous material characteristics affecting the antenna performance for various wireless applications with design considerations. Fully printed antennas on different substrates and conductive ink were investigated, including polyethylene terephthalate (PET), synthetic paper (Teslin), resin-coated photo paper, and Kapton polyimide substrates, among many others. The findings show that the fully inkjet-printed antenna made fabrication easy, improving the accuracy and conductivity of the printed patterns by concentrating on the inherently difficult problems and opportunities. Inkjet-printed antennas are believed to be the futuristic demand for sustainability and wireless solution in several ways.

The successful handling of large semiconductor wafers is crucial for scaling up their production. Early-stage warpage control allows to prevent undesirable asymmetric warpage, known as wafer bifurcation or buckling. Indeed, even in a gravity-free environment, thinning an 8" or 12" semiconductor wafer can result in warpage and bifurcation. To mitigate this issue, the taiko method, which involves creating a thicker ring region around the rim of the wafer, has been widely used. Previous research has focused on the theoretical factors affecting the warpage of a backside metalized taiko wafer. This work extends the case to a front-side metalized taiko wafer and introduces the concept of the equivalent thickness of a taiko wafer. The equivalent thickness of a taiko wafer, influenced by the ring region, lies in between the thickness of the central region and that of the annular region. Because of the limited number of taiko wafers that can be produced on a production line, modelling can be beneficial. In this work we compared the results of a developed analytical model with those obtained from a finite element analysis (FEA) approach with ANSYSY® software to model the equivalent thickness of a taiko wafer. We investigated the curvature as a function of the stress of the metal layer, considering key design factors such as the substrate region thickness, the thickness of the thin metal film, the step height, and the width of the ring region. By systematically varying the thickness of the central region of the taiko wafer, we explored the curvature as a function of stress induced by thermal load in the linear regime and determined the slopes in the linear region of the curvature vs. stress curves. The aim of this study is to identify regularities and similarities with the Stoney equation and investigate the validity of the analytical approach for the case of a taiko substrate. It results that there is a good agreement between the analytical model of a taiko wafer and the numerical analysis gained by the FEA methods.

3D printing with cement-based materials is a promising manufacturing technique for civil engineering applications that already allows for the design and the construction of complex and highly customized structures using a layer-by-layer deposition approach. Usually, in this type of additive manufacturing, a mixture of cement, water, aggregate and additives is extruded through a specific print head and deposited in successive layers to form the desired shape. The extrusion mechanism is one of the most expensive parts of the 3D printer. Also, for low scale 3D printers, based on the shape of the extruder and the geometry limitation of the mixing blade, the 3D mixture is often limited to a narrow range of materials due to the risk of layer splitting or blockage. Therefore, there is a need to develop affordable and feasible alternatives to the current design-fabrication-application approach of 3D printers. In this paper, various new designed mixtures of fiber reinforced mortars that can be 3D printed using only a commercially available screw pump are analyzed based on their fresh properties and mechanical characteristics. The reduced facility requirements in this approach allow 3D-printing to be more available for civil engineering applications. With further innovation in the future, this method and mixtures can be extended for sustainable and economically feasible printing of single-family housing units.

Safety is very important in aviation since a loss of safety frequently results in both fatalities and financially damaging situations that are typically unrecoverable. Thus, achieving safety as much as possible is the primary goal of practically all aviation technology work. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based classifier is created to estimate the fault risk factor of airplanes. Five categories of real fleet data belonging to structure, electrical, avionic, motor systems, and incident statistics of the planes have been used for classifier development. A risk factor determination for each plane is the output of the developed intelligent classifier, and it can be used to identify general overhaul candidate planes and stop defects and crashes before they happen. The obtained results show that using ANFIS provides a great capability in processing many inputs and outputs depending on different types and classes in the aviation industry and thus predicting the failure risk of the airplane efficiently.

Abstract: Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

The Gulong shale oil reservoir is formed in freshwater to slightly saline lacustrine basins, mainly consisting of pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of fluid occurrence space and the variation of fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores, and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, the porosity measured by saturation method was calibrated using NMR T2 spectra. Combined with the identification of fractures in shale cores using micro-CT and the analysis of porosity and permeability parameters, it was found that the permeability of shale cores was related to the development of fractures in shale cores. Through the testing and analysis of NMR T1-T2 two-dimensional spectra of the shale cores before and after saturation with oil, it was found that the shale mainly contains heavy oil, light oil, and clay-bound water, and they were distributed in different positions in the T1-T2 spectrum. Finally, the T1-T2 two-dimensional spectra of the shale core at different imbibition stages were analyzed, and it was found that the saltwater mainly entered the minuscule inorganic pores of clay minerals during the imbibition process, and squeezed the larger-sized inorganic pores containing light oil through the hydration expansion effect, thus expelling the light oil from the shale core and achieving the purpose of enhanced oil recovery.

Palpation is a simple but effective method to differentiate tumors from healthy tissues. Development of miniaturized tactile sensors embedded on endoscopic or robotic devices are the key toward precise palpation diagnosis and the subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. Utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration combining pneumatic sensing and hydraulic actuating also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibility in early detection of solid tumor as well as in the development of all in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.

The global context of research for new sustainable energy storage technologies makes it a very active sector with significant scientific and economic challenges. Indeed, due to the irregular development of renewable energies and the shutdown of traditional power facilities, it is difficult to maintain a stable balance in terms of supply and demand: energy storage can help in particular for substantial changes in the latter. Metal air batteries have a higher energy density and are safer than other available energy storage devices. Based on the existing and proven lead-acid battery technology, this paper proposed an open cell foam manufactured by the Excess Salt Replication process for use as an anode for lead-air battery cellsies with sulphuric acid as the electrolyte. This will save lead and reduce the battery weight. A 25% antimonial lead alloy was used to produce open cell foams with a cell diameter between 2 mm and 5 mm for the antimonial lead-air battery. Preliminary results of the effective electrical conductivity of self-discharged primary battery cells, measured experimentally, showed that all antimonial lead foam-air battery cellsies performed better than that made from the same dense non porous antimonial lead alloy. This is generally due to their important specific surface area where oxidation-reduction reactions took place. A correlation between the effective electrical conductivity and the cell diameter has been established and the highest conductivity was obtained with a cell diameter of 5mm. The feasibility of such an electrical system has been demonstrated.

This paper aims to investigate the feasibility of strengthening the web opening in the shear zone of RC beams by using iron-based shape memory alloy (Fe-SMA) bars. To conduct this study, numerical analysis with ABAQUS software was employed. The research was divided into six groups of beams with web openings of different lengths (150, 300, and 450 mm), prestressing levels (0%, 30% and 60%), and reinforcement diameters (14, 18, and 22 mm) of Fe-SMA bars. The results show that the presence of web openings can cause a significant reduction in the cracking and ultimate loads of the beams, with reductions ranging from 11 to 50% and 36 to 48%, respectively. However, by adding pre-stressed Fe-SMA bars around small web openings (100×150 mm), the shear capacity of the beam is restored, and the beam exhibits behavior similar to solid beams. Additionally, activating the Fe-SMA bars by 30 and 60% resulted in almost similar cracking loads but improved load-carrying capacity of the beam with small openings by 12% and 9%, respectively, compared to the solid beam. The technique proposed for enhancing shear strength is most effective for beams with small (100×150 mm) and medium (100×300 mm) web openings as it can restore both the beam’s shear strength and stiffness. However, for beams with larger web openings (100×450 mm), the use of activated Fe-SMA beams can recover almost 90% of the solid beam's shear capacity. Furthermore, reinforcing small openings with Fe-SMA bars of different diameters enhances beam shear capacity and stiffness, while for larger openings, higher Fe-SMA reinforcement ratios could potentially restore the beam's full strength and stiffness. This study emphasizes the importance of strengthening web openings in RC beams, particularly in shear zones, and provides significant insights into how to strengthen beams with web openings, thereby contributing to developing safer structures. However, further laboratory experiments are recommended to validate, complement and extend the findings of this numerical study.

Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators, and non-linear loads are used for different industrial processes. As a result of this, the need to classify and analyze Power quality disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents A Novel Mul-titasking Deep Neural Network (MDL) for the Classification and Analysis of Multiple Electrical Disturbances. The characteristics are extracted with a specialized and adaptive methodology for non-stationary signals, Empirical Mode Decomposition (EMD). The methodology’s design, devel-opment, and various performance tests are carried out with 28 different difficulty levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was devel-oped with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as crest factor, Per Unit voltage analysis, Short Term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.

A study was conducted to predict failure stresses, failure locations, and their paths of initiation of perforated polymethyl methacrylate (PMMA) specimens as they were loaded using pins inserted into the holes. First, a series of experiments were conducted for perforated PMMA coupons. There were six different types of coupons. The test specimens had three different widths with a circular hole of a diameter of 8 mm, which was placed at two different locations along the specimen length. All the specimens were loaded using a pin inserted into the hole with three different pins of nominal diameters of 8 mm, 6 mm, and 5 mm, respectively. To predict the failure and compare the results against the experimental data, the recently proposed universal failure criterion was used in the study using finite element analyses. The failure criteria gave acceptable results as compared to the experimental data.

This study investigates the stress corrosion cracking (SCC) behavior of type 316L stainless steel (SS316L) produced with sinter-based material extrusion additive manufacturing (AM). This technology has been shown to produce SS316L with microstructures and mechanical properties comparable to its wrought counterpart in the annealed condition. However, despite plenty of research on SCC of SS316L, little is known about SCC in sinter-based AM SS316L. This study focuses on the influence of sintered microstructures on susceptibility to SCC initiation and crack-branching. Custom-made C-rings were exposed to different stress levels in acidic chloride solutions at various temperatures. To better understand the SCC behavior of SS316L, solution-annealed (SA) and cold-drawn (CD) wrought SS316L were tested for comparison. Results showed that sinter-based AM SS316L was more susceptible to SCC initiation than SA wrought SS316L but more resistant than CD wrought SS316L. Moreover, sinter-based AM SS316L showed noticeably better resistance to crack-branching than both wrought SS316L counterparts. The investigation was supported by comprehensive pre- and post-test microanalysis using light optical microscopy (LOM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and micro-computed tomography (micro-CT)

A new three-dimensional numerical model of a polymer electrolyte fuel cell (PEFC) with a single straight channel has been developed to primarily investigate the important impact of the double-sided microporous layer (MPL) coating on the overall performance of the fuel cell and the distribution of the current and the oxygen concentration within the cathode gas diffusion layers (GDLs). Realistic experimentally estimated interfacial contact resistance values between the gas diffusion layer and each of the bipolar plates and the catalyst layer values were incorporated into the model, and parametric studies were performed. The results showed that the double-sided MPL coating could significantly improve the fuel cell performance by up to 30%. Also, it was shown that the neglect of the contact resistance between the MPL and the catalyst layer could overestimate the fuel cell performance by up to 6%. In addition, the results showed that the fuel cell performance and the distribution of the current and oxygen are more sensitive to the porosity of the MPL facing the bipolar plate than the porosity of the MPL facing the catalyst layer. All the above results are presented and critically discussed in detail.

Accurate evaluation of health status of lithium-ion batteries must be deemed as of great significance, insofar as utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as fast or non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap, by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum, to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked to establish a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

The local reactive power control in distribution grids with a high penetration of distributed energy resources (DERs) is essential in future power system operation. Appropriate control characteristic curves for DERs support stable and efficient distribution grid operation. However, the current practice is to configure local controllers collectively with constant characteristic curves that may not be efficient for volatile grid conditions or the desired targets of grid operators. To address this issue, this paper proposes a time series optimization-based method to calculate control parameters, which enables each DER to be independently controlled by an exclusive characteristic curve for optimizing its reactive power provision. To realize time series optimizations, the open-source tools pandapower and PowerModels are interconnected functionally. Based on the optimization results, Q(V)- and Q(P)-characteristic curves can be individually calculated using the linear decision tree regression to support voltage stability and provide reactive power flexibility and potentially reduce grid losses and component loadings. In this paper, the newly calculated characteristic curves are applied in two representative case studies, and the results demonstrate that the proposed method outperforms the reference ones suggested by grid codes.

This paper provides a comprehensive overview of recent advancements in autonomous electric vehicle (AEV) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle motor mechanisms, and braking systems. Furthermore, this paper showcases several prototype autonomous electric vehicles as conclusive study findings.

Zirconium nitride (Zr-N) is a candidate material for inert matric fuel elements in breeder reactors. They have shown exceptional resistance to radiative corrosive environments. They have shown excellent resistance to radiation stability at operational conditions in current generation reactors. But being a candidate nuclear fuel material in generation reactors, their radiation stability at higher temperature conditions observed has not been reported. We have employed molecular dynamics simulations to study the damage cascades and radiation stability of ZrN at 600K. The process of primary damage evolution by considering varying overlapping cascades with 10, 20 keV incident PKA’s reported here. The defects interface interaction and diffusion mechanism at the interface are reported.

Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally， a NARX is used to obtain preliminary PV power components and residual vector features， and the PV power is predicted by combining three models of LightGBM， LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.

Recent empirical work has revealed that drivers have different lane change strategies. These strategies help to understand driver heterogeneity and decision stochasticity, which are both often lacking in microscopic models. In this paper we generalize the lane change strategies in a theory for social interactions. The theory introduces a mechanism by which two new driver traits, ego-speed sensitivity and socio-speed sensitivity, endogenously influence other driver traits that govern lane change behaviour such as desired speed, desired headway and lane change desire. A key concept in this mechanism is that drivers are not just affected by what happens in front of them, but also by what they observe in their rear-view mirrors. The idea is that drivers reduce their headways to exert (social) pressure onto leading drivers (on the same or adjacent lanes), which may force them to increase speed, change lanes, or conversely, to refrain from changing lanes. We refer to this reduction of the headway as tailgating. Through an example implementation of the theory, we demonstrate that these social interactions may have a profound impact on several key (mesoscopic) traffic flow characteristics. Our results show that these new driver interactions influence the number of lane changes, platoon lengths, and headway variance. As such, the theory improves our description and understanding of traffic flow and consequently it paves the path for more realistic traffic models.

DNS is a necessary infrastructure for accessing the Internet. Until now, privacy protection in domain name resolution has mainly focused on end user privacy (communication encryption between clients and DNS full-service resolver). For this reason, communication between DNS full-service resolver and authoritative DNS servers is still done in plaintext. A DNS request from a DNS full-service resolver to an authoritative DNS server does not pose a privacy issue because the source IP address that comes from is the DNS full-service resolver. However, in recent years, there have been reports of specific techniques for identifying the privacy of previously unknown institutions by analyzing the logs of authoritative DNS servers. In order to further strengthen privacy in DNS communication, we proposed an architecture to encrypt all DNS communication in DoH, created a prototype environment, and investigated performance evaluation. The main contributions of this paper are threefold. First, we proposed the Full-DoH DNS architecture. This is a domain name resolution framework designed specifically for institutional privacy. Second, we evaluated the proposed architecture through a prototype implementation. Finally, we discussed related issues to the proposed architecture.

Smart mechatronics system in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, as results, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of the smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules, and various networks applied in agriculture processing were examined. Finally, we were explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strong related keywords for different journals. The virtually limited use of sophisticated mechatronics in the agricultural industry, and at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics system in agricultural sectors would be taken into consideration in order to overcome these issues.

The use of fiber optics as a tool for different kinds of geotechnical monitoring can become highly attractive and cost effective when compared to conventional instruments such as piezometers, inclinometers, among others. A single fiber optic cable may cover a larger monitoring area compared to conventional instrumentation, and the possibility of monitoring more than one physical quantity with the same fiber optic cable. Consulting the literature, it is possible to find several different examples where distributed fiber optic systems are being used. For the use of any sensor, a calibration curve is required. In the case of strain sensors, calibration is required to derive strain values from the frequency measurement quantity. However, fiber optic sensor cable manufacturers often do not provide their own calibration parameters and consult the values in specialized literature, which can result in monitoring errors. In this context, this article presents a bench adjusted for tests on single mode fiber optic cables, as well as results of tensile tests aimed at defining the function of strain variations of two different optical fiber cables, manufactured by different companies, using two different distributed interrogators. This paper also proposes a methodology for calibrating of the fiber optic cables deformation since the calibration parameters are provided by the cable manufacturers, which can result in errors depending on the application. It should be noted that there are few manufacturers of fiber optic cables aimed at application in civil engineering. Therefore, with the calibration methodology proposed in this paper, it is intended to transmit to the academic community the possibility of obtaining calibration parameters of any fiber optic cable, even those manufactured for telecommunications purposes and not only for cables manufactured with the intended use in civil engineering. Because of this fact, researchers will not be restricted to the acquisition of special cables for their applications. Based on the results, it was possible to conclude that the application of calibrated fiber optic sensors in experimental piles foundations is viable, in order to evaluate the load-displacement behavior of these elements, under different loading conditions.

Catamaran hulls have always been associated with smaller vessels in naval engineering and construction. However, recent research results have shown that by considering specific criteria based on materiality, structure and functionality, they can be designed for a more complex industry, such as hydrofoil design. Among the challenges in this type of vessel, one of the most difficult variables is estimating the resistance to the advance in the function of the degree of uncertainty generated. This research focuses on optimising the drag of a sport sailing catamaran by implementing hydrofoils, through the application of computational fluid dynamics (CFD), at different speeds to demonstrate that it is possible to modify and improve the current appendages of this vessel.

In recent years, artificial intelligence technology has widely influenced the field of design, bringing new ideas to efficiently and systematically solve urban renewal design problems. The purpose of this study is to create a stylized generation technology for building facade decoration in historic districts, which will aid in the design and control of district style and form. The goal is to use the technical advantages of conditional generative adversarial network (CGAN) in image generation and style transfer to create a method for independently designing a specific facade decoration style by interpreting image data of historical district facades. The research in this paper is based on the historical district of Putian in Fujian Province, through an experiment of image data acquisition, image processing and screening, model training, image generation, and style matching of the target area. The research found that: (1) CGAN technology can better identify and generate the decorative style of historical districts. It can realize the overall or partial scheme design of the facade; (2) in terms of adaptability, this method can provide a better scheme reference for historical district reconstruction, facade renovation, and renovation design projects. Especially for districts with obvious decorative styles, the visualization effect is better. In addition, it also has certain reference significance for the determination and design of the facade decoration style of a specific historical building; (3) This method can better learn the internal laws of the complex district style and form so as to generate a new design with a clear decoration style attribute. It can be extended to other fields of historical heritage protection to enhance practitioners' stylized control of the heritage environment and improve the efficiency and ability of professional design.

Samples of mortar mixtures were prepared substituting the cement Portland (CPC-30R) by 0 (standard), 10 and 15% of fly ash and the structural evolution and compressive strength at 3, 7, 14 and 28 days were determined. The results for standard mortar samples showed the mineralogi-cal species portlandite, calcite, ettringite, iron oxide, silicon oxide and sillimanite. Magnetite was identified in the mixtures of mortars with portland cement substitution by 10% and 15% fly ash. The peaks corresponding to the portlandite, and ettringite showed an increase in their intensities with increasing curing time attributed to the consolidation of mineral species. The SEM tech-nique results showed that the mortar samples without fly ash addition contained mainly port-landite and ettringite while the samples with cement substitution by 10 and 15% of fly ash at 28 days further contained particles of fly ash coated with portlandite and ettringite, particles with a smooth surface and particles of fly ash with signs of attack on its surface. An increase inc was observed when the age of the mortar and the substitution of Portland cement by fly ash were increased from 3 to 28 days and from 0 to 15% respectively. The maximum value of c was reg-istered for the mortar sample with Portland cement substitution by 15% fly ash at 28 days of curing with 17.38 Mpa.

Prosthetics have come a long way since their inception, and recent advancements in materials science have enabled the development of prosthetic devices with improved functionality and comfort. One promising area of research is the use of auxetic metamaterials in prosthetics. Auxetic materials have a negative Poisson's ratio, which means that they expand laterally when stretched, unlike conventional materials, which contract laterally. This unique property allows for the creation of prosthetic devices that can better conform to the contours of the human body and provide a more natural feel. In this review article, we provide an overview of the current state of the art in the development of prosthetics using auxetic metamaterials. We discuss the mechanical properties of these materials, including their negative Poisson's ratio and other properties that make them suitable for use in prosthetic devices. We also explore the limitations that currently exist in implementing these materials in prosthetic devices, including challenges in manufacturing and cost. Despite these challenges, the future prospects for the development of prosthetic devices using auxetic metamaterials are promising. Continued research and development in this field could lead to the creation of more comfortable, functional, and natural-feeling prosthetic devices. Overall, the use of auxetic metamaterials in prosthetics represents a promising area of research with the potential to improve the lives of millions of people around the world who rely on prosthetic devices.

This paper presents designing an attitude motion control strategy for a half-car model using an anti-jerk predictive controller. Anti-jerk predictive controller based on active aerodynamics surfaces is employed to deal with the anticipated future road maneuvers and improve vehicle ride performance by canceling out external jerks acting on the vehicle body. The body jerks are produced during vehicle traversing on a double lane-change maneuver, acceleration, or braking. The control strategy helps the vehicle to achieve a realistic operation of the active aerodynamic surface to improve the vehicle’s ride performance, i.e., ride comfort and road holding during cornering, acceleration, or braking. The anti-jerk predictive controller is developed based on the predictive control strategy, which predicts future road inputs and uses them to compensate for the vehicle’s attitude motion. The simulation results show that the proposed control strategy effectively reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling. The anti-jerk predictive controller successfully tracks the desired attitude position by canceling the external body jerks.

The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI=R532nm / R641nm). Rust sources and products in steel girders can be identified by the unique spectral signatures and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the corrosion rust indicators. The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the absorption at 8000 cm-1 and 5850 cm-1.

Quartz Crystal Microbalance (QCM) is a well-known method to measure the mass of ligands bound to receptor-covered surfaces in various fields, ranging from materials science to biology. Actin molecule is essential in various cellular processes through its function of monomer-to-filament polymerization, forming a double-stranded filament with a 75 nm pitch. In this study, QCM was applied to measure the physical properties of actin, finding that the frequency shifted negatively when monomeric actin bound to the QCM surface, whereas the frequency shifted positively when filaments bound. Using fluorescence light microscopic observations, we have examined whether negative and positive frequency shifts originated from their length. In order to control this process, a severing protein, fragmin was used to change the length of actin filament. When the average length of filaments shortened, the magnitude of positive shift decreased in a concentration-dependent manner. However, shorter filaments showed a negative frequency shift depending on their concentration, showing that the weight derived from the loaded concentration and the function derived from actin polymerization dynamics are detectable. Interestingly, the transition from positive to negative occurred when the average length of actin was ~33 nm, or approximately equal to the half-pitch of filaments. Thus, actin filaments can serve as an excellent standard to measure the mechanical properties of biopolymers. Our results show that the QCM sensor could be both a "weight" and a "function" sensor of biomolecules in vitro.

This paper discusses a passive control issue for Nonlinear Time-Varying (NTV) systems subject to stability and attenuation performance. Based on the modeling approaches of Takagi-Sugeno (T-S) fuzzy model and the Linear Parameter-Varying (LPV) model, a Parameter-Dependent Polynomial Fuzzy (PDPF) model is constructed to represent the NTV systems. According to Parallel Distributed Compensation (PDC) concept, a parameter-dependent polynomial fuzzy controller is built to achieve robust stability and passivity of PDPF model. Furthermore, the passive theory is applied to achieve the performance constraining the disturbance effect on the PDPF systems. To develop the stability criteria, by introducing a parameter-dependent polynomial Lyapunov function, one can derive some stability conditions which belong to the term of Sum-Of-Squares (SOS) form. Based on the Lyapunov function, two stability criteria are proposed to design the corresponding PDPF controller such that the NTV system is robustly stable and passive. Finally, a numerical example is applied to demonstrate the effectiveness of the proposed stability criterion.

A block-diagonal fuzzy neural network for short-term load forecasting is proposed. DBD-FELF consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed by use of Fuzzy C-Means clustering algorithm and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The electric load time-series of the Greek power system is examined, and hourly-based forecasting for the whole year is performed. The performance of DBD-FELF is tested via extensive experimental analysis and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

In this paper, feasibility of the initial alignment of the gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained as the conventional INS since centripetal acceleration is very small. Relation between initial heading and accelerometers’ outputs of the gyro-free inertial measurement unit (GF-IMU) is derived. It is determined from the relation whether initial heading can be obtained in 15 GF-IMUs which have been presented in research literatures. The relationship between the accelerometer errors of GF-IMU and the initial heading error is derived. When gyros are additionally attached in one axis or two axes of the body frame, it is investigated that initial heading can be obtained. The initial heading error is also calculated from the accelerometer errors and the gyroscope errors. The results show that the initial heading from only accelerometer outputs of the GF-IMU cannot be used in the GF-INS since the error is too large. It can also be seen from the results that the initial heading can be used in the GF-INS if two gyroscopes are additionally attached to the roll and pitch axes of the body frame.

Unmanned Aerial Vehicles (UAV) are increasingly being used in a variety of domains and precision agriculture is no exception. Precision agriculture is the future of agriculture and will play a key role in long-term sustainability of agricultural practices. This paper presents a survey of how image data collected using UAVs has been used in conjunction with ma-chine learning techniques to support precision agriculture. Numerous agricultural applications including classification of crop types and trees, crops detection, weed detection, cropland cover, and segmentation of farming fields are discussed. A variety of supervised, semi-supervised and unsupervised machine learning techniques for image-based preci-sion agriculture are compared. The survey showed that for traditional machine learning approaches, Random Forests performed better than Support Vector Machines (SVM) and K-Nearest Neighbor Algorithm (KNN) for crop/weed classification. And, while Convolutional Neural Networks (CNN) have been used extensively, the U-Net-based models out-performed conventional CNN models for classification and segmentation tasks. Among the Single Stage Detectors (SSD), YOLO series performed relatively well. Two-Stage Detectors like R-CNN, FPN, and Mask R-CNN generally tended to outperform SSDs. Vision Trans-formers (ViT) showed promising results amongst transformer-based models which did not generally perform better than CNNs. Finally, Generative Adversarial Networks (GANs) have been used to address the problem of smaller datasets and unbalanced data

This paper develops a chemical model for a closed constant volume combustion of gaseous mixtures of methane and hydrogen. Since the combustion is strongly depending on temperature, pressure and fuel composition, they were chosen the actual corresponding thermodynamic systems using this kind of combustion, respectively the spark ignition reciprocating engines in order to evaluate the combustion parameters and exhaust flue gases composition. The actual cycles impose extra restrictive operational conditions through the engine volumetric compression ratio, the geometry of combustion volume, the mode to prepare the mixture of methane and hydrogen, the cooling system and the delivered power. The chemical model avoided the unknown influences in order to explain accurately the influence of hydrogen upon the constant volume combustion and flue gases composition. The model adopted simplifying hypotheses, respectively isentropic compression and expansion processes, closed constant volume combustion developed by two successive steps obeying to the energy and mass conservation laws and, flue gases exhaust described also by two steps, i.e. an isentropic expansion through the flow section of exhaust valves followed by a constant pressure stagnation (this succession is corresponding in fact to a throttling direct process). The chemical model supposed the homogeneous mixtures of gases with variable heat capacities function of temperatures, the Mendeleev - Clapeyron ideal gas state equation and, the variable chemical equilibrium constants for chosen chemical reactions. It was assumed that the flue gases chemistry is prevailing during the isentropic expansion and flue gases exhaust and has mainly a thermal ground. The chemical model allowed evaluation of flue gasses composition and of noxious emissions.

Smart flexible materials with piezoresistive property are increasingly used in sensors field. When embedded in structures, they would allow an in-situ structural health monitoring and damage assessment of impact loading such as crash, bird strikes and ballistic impacts. However, this could not be achieved without a deep characterization of the relation between piezoresistivity and mechanical behavior. The aim of this paper is to study the potential use of the piezoresistivity effect of a conductive foam made of a flexible polyurethane matrix filled with active carbon for integrated Structural Health Monitoring (SHM) and damage assessment applications. To do so, PolyUrethane Foam filled with Active Carbon, namely PUF-AC, are manufactured and tested under quasi-static compression tests and under Dynamic Mechanical Analyser (DMA) with in-situ measurement of its resistivity during tests. A relation is proposed for describing the evolution of the resistivity versus strain and stress. In addition, a demonstrative experiment of a PUF-AC sample subjected to low velocity impact assesses the interest of this type of materials for damage assessments.

Speckle Plethysmography (SPG) and Photoplethysmography (PPG) are different biophotonics technologies that allow for measurement of hemodynamics. As the difference between SPG and PPG under low perfusion conditions is not fully understood, a Cold Pressor Test (CPT) was used to modulate blood pressure and peripheral circulation. A custom-built setup simultaneously derived SPG and PPG from the same video streams at two wavelengths (639 nm and 850 nm). SPG and PPG were measured at right index finger location before and during CPT using finger Arterial Pressure (fiAP) as reference. The effect of CPT on Alternating Component amplitude (AC) and Signal-to-Noise-Ratio (SNR) of dual-wavelength SPG and PPG signals was analysed across participants. Furthermore, waveform differences between SPG, PPG, and fiAP based on frequency harmonic ratios, were analysed for each subject (n=10). Both PPG and SPG at 850 nm show a significant reduction during CPT for both AC and SNR. However, SPG showed significantly higher and more stable SNR than PPG in both study phases. Harmonic ratios were found substantially higher in SPG than PPG. Therefore, in low perfusion conditions, SPG seems to offer a more robust pulse wave monitoring with higher harmonic ratios than PPG.

In the increasingly complex environment of downhole working conditions such as frequent opening and closing of oil and gas wells, acidification/multi-stage fracturing, steam huff and puff, and strong injection and production, the traditional casing string design method standards based on strength design have gradually highlighted their limitations. It is necessary to formulate string design method standards that consider the full life cycle of wells such as drilling and completion, fracturing, and production operations. Starting from the analysis of the advantages and disadvantages of traditional casing string design methods, this paper presents the characteristics of the strain design methods and seal design methods that have been formulated for the life cycle of the string. The strain design method breaks through the traditional design concept and allows the design concept of controllable deformation of the pipe string. The sealing design method is currently the only standard method for the design of tubing strings. At the same time, it further proposes to consider the trend of pipe strength deterioration and establish a time dimension-based life cycle pipe string design method standard, which effectively solves the safety problem of pipe string design in production and operation.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

Blood testing is a crucial medical application. In this study, we applied Raman spectroscopy to test blood samples and obtained complete biological information, including the main components and compositions in these samples. Short-wavelength (532-nm green light) Raman scattering spectroscopy was conducted on samples of normal blood, abnormal blood in 3 types of preparation including whole blood, plasma, and serum, and the underlying information reflected by the biological characteristics detected in each sample type were analyzed. Raman spectroscopy results indicated that normal blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. Hemoglobin affects blood oxygen level. The characteristic peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. In addition, surface-enhanced Raman spectroscopy was used to collect biological signals that are difficult to obtain in conventional Raman spectroscopy, including those of small molecules such as hormones, antibodies, and enzymes. Changes in biological information collected in this manner can be used as a basis for potentially diagnosing clinical diseases.

This research proposes a roof-mounted auxiliary power supply (APS) system for 600VDC low-floor light rail vehicle (LRV). The proposed APS system consists of five parallel-connected dc-ac inverter modules (modules 1-5). The inverter modules 1 and 2 are three-phase dc-ac inverters for compressor motors of the cooling system, and the inverter modules 3 and 4 are three-phase dc-ac inverters for air pump motors of the braking system. The inverter module 5 is single-phase dc-ac inverter for 220VAC power supply for onboard electric loads. Simulations and experiments were carried out under variable load torque and output frequency for modules 1 – 4; and under full and no resistive loads for the inverter module 5. The measured total input current and total input power of the proposed APS system under full load condition are 118.76A and 71.25kW. Essentially, the proposed APS system is operationally applicable to the 600VDC low-floor RLV. Besides, the novelty of this research lies in the use of five parallel-connected inverter modules, unlike in the conventional APS systems which require three-phase output transformer or isolated dc-dc converter. Specifically, the proposed APS system requires neither three-phase output transformer nor isolated dc-dc converter.

The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

The purpose of this work was to achieve non-destructive detection of the internal defects of in-shell walnuts using X-ray radiography technology based on improved Faster R-CNN network model. First, the FPN structure was added to the feature extraction layer to extract richer image information. Then ROI Align was used to instead of ROI Pooling for eliminating the localization bias problem caused by quantization operation. Finally, the Softer-NMS module was introduced to the final regression layer with the predicted bounding box for improving the localization accuracy of the candidate boxes. The results indicated that the internal defects in intact walnuts with shells could be identified effectively using the proposed improved network model in this study. Specifically, the discrimination accuracy of the in-shell sound, shriveled and empty-shell walnuts were 96.14%, 91.72% and 94.80% respectively, and the highest overall accuracy can reach 94.22 %. Contrasted with original Faster R-CNN network model, the mAP and F1-value of the improved Faster R-CNN model increased by 5.86% and 5.65%, respectively. Consequently, the proposed method can be applied for the in-shell walnuts with shriveled and empty-shell defects.

Compared with the traditional robots on earth, space robotics present additional difficulties including complicated, multi–body dynamics (including anti–resonances absent with earthly robotic systems), space environmental forces and torques, communication delays, and high expense. Pointing accuracy requirements necessitate control algorithms that can accommodate flexible, multi–body dynamics particularly. The high cost of placing systems in space drives necessarily lightweight systems lacking structural stiffness. Natural frequencies of space robot vibration are often so low, the act of implementing control torques causes structural resonance. Seeking improved performance, this manuscript introduces and compares a dozen options, revealing seventy–percent reduction in tracking error may be achieved with only ten percent increase in control effort. Prequel work recommended tracking sinusoidal shaped trajectories while structurally filtering the first mode’s resonance and anti–resonance. The future research recommended in that prequel is manifest in this present work which recommends a simpler system of lower order, eliminating the notch compensation of the first resonance while no longer compensating the first anti–resonance.

A low-profile broadband dual-polarized antenna is investigated for base station applications. It consists of two orthogonal dipoles, fork-shaped feeding lines, an artificial magnetic conductor (AMC), and parasitic strips. By utilizing the dispersion Brillouin diagram, the AMC is designed as the antenna reflector. It has a wide in-phase reflection bandwidth of 54.7% (1.54-2.70 GHz) and a surface wave bound range of 0-2.65 GHz. This design effectively reduces the antenna profile by over 50% compared to traditional antennas without an AMC. For demonstration, a prototype is fabricated for 2G/3G/LTE base station applications. Good agreement between simulations and measurements is observed. The measured -10-dB impedance bandwidth of our antenna is 55.4% (1.58 –2.79 GHz), with a stable gain of 9.5 dBi and high isolation of more than 30 dB across the impedance passband. As a result, this antenna is an excellent candidate for miniaturized base station antenna applications.

With the improvement in medical quality, cold mechanical rehabilitation equipment can no longer meet the psychological needs of the physically handicapped. Therefore, design for behaviour change should be viewed as a strategy for enabling lifestyle changes as soon as possible. Although how to use equipment to assist physically disabled people in hydrotherapy rehabilitation is the primary design goal, the psychological feelings of users are also one of the focuses of this study. Besides, coupled with the large-scale spread of the Coronavirus Disease (COVID-19) in the past two years. People's living habits are also forced to change, many activities that people are used to doing outdoors are being slowly completed at home. During the epidemic, it is very dangerous and inconvenient for users who need to go to institutional rehabilitation regularly. Hence, it is urgent to change past usage habits and places. In this study, a personal multi-functional hydrotherapy bucket is designed. In addition to not having to use a public spa with other people to reduce the risk of contracting the Coronavirus Disease, it can be easily placed at home by reducing its size. According to market research, it is known that the current domestic hydrotherapy buckets on the market usually only have a single function. The large medical hydrotherapy pools are not only rare in Taiwan, but this kind of equipment also takes up a lot of space. Various factors have resulted in a relatively low market share of hydrotherapy pools compared to other rehabilitation equipment. In order to take into account, the physical and psychological needs of the physically disabled, this research focus on the design of a multi-functional but small-sized hydrotherapy bucket. Taking the convenience of entering and leaving the bucket as the starting point to improve the shortcomings of the existing products, on the other hand, because most users are inconvenient to move, safety is also the focus of the design to reduce the occurrence of accidents and injuries. This study is based on the systematic design methodology of concurrent design to obtain product design results. Firstly, conduct market research and select products from different manufacturers for analysis and comparison. Through functional analysis and sorting out the problem points, the TRIZ theory and the morphological diagram method are used to find the solution to the problem. Finally, the best design scheme is obtained by the PUGH method. Besides, construct a 3D model to present the design concept that meets the design goals. The research results are hoped to be used as a reference for designing and developing related products, thereby increasing the home's multifunctional hydrotherapy buckets' market share.

This article presents a review of the distribution system state estimation (DSSE) algorithms from the model-based approach to the recent data-driven methodologies. The insufficient measurements which result in low observability have motivated the need to shift from the conventional algorithm to data-driven approaches that can successfully estimate states despite the DSSE challenges. This article discusses the nonlinearity in the DSSE problem formulation and how different model-based methods have been proposed to mitigate the problems of robustness, ill-conditioning, and complexity. Moreover, approximate DSSE to obviate nonlinearity were discussed: complex linearization, small angle approximation, convexification and compressed sensing. Furthermore, probabilistic DSSE methods were also discussed in the need to quantify the uncertainty associated with the state estimation results. Also, data-driven methods applicable to DSSE, pseudo measurement generations, and topology identification were also discussed using machine and deep learning methods. Lastly, a recent approach that employs a hybrid of model-based and data-driven methods using matrix and tensor completion is surfacing because they work in a low observable condition of the network and can estimate the states satisfactorily. With this review, researchers can look further into developing and improving on the model-based data-driven methods less susceptible to the barriers in conventional DSSE algorithms.

The production of SOX by ship engines is a serious environmental problem and has been addressed by international standards. The restrictions limit the sulphur content of the fuel to 0.5% by mass to reduce SOX emissions; however, using low sulphur fuels such as LNG causes logistical and operational problems and a higher costs. For these reasons, there has been renewed interest in developing devices such as the Seawater Scrubber (SWS). This paper describes a simplified numerical model to simulate the absorption of SO2 in a drop of water during a dynamic "wet scrubbing" process. The dependence on the initial conditions is demonstrated by running multiple simulations as the initial droplet diameter, temperature and SO2 concentration in the flue gas vary. The work shows how the amount of sulphur dioxide absorbed is strongly related to the concentration in the exhaust gases and the initial diameter of the droplet, and highlights how distributions of droplets smaller than an upper limit value optimize the absorption process.

This paper presents a proposal for a non-isolated bidirectional converter (NIBC) controlled by a deep neural network (DNN) to enable vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) charging, which contributes to the development of more efficient and sustainable transportation and energy systems. The DNN controller manages power flow in both directions, making it possible to charge electric vehicles (EVs) and discharge power from EVs to the grid with improved efficiency and performance compared to traditional control methods. The non-isolated topology used in this proposal offers several benefits, including reduced cost, smaller size, and higher efficiency. To train the DNN controller, a large dataset of simulations was used, and the results were validated with a hardware setup. The real-time performance of the DNN controller was compared to a proportional-integral (PI) based controller through simulated results. The findings of the study show that the DNN controller outperforms traditional PI controllers.

The purpose of this research is to present a thorough evaluation of the energy management system, which consists of hybrid energy storage systems, and its control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, analysis of the hybrid energy storage system architecture, converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. The study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. Non-isolate DC-DC converter converter connected to SC is a suitable configuration for the hybrid converter because it is simple to build, reliable, and has minimal loss/weight/cost, which improves vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy rule-based control techniques are successfully assessed using real-scale vehicle experiments and selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.

Sulfate attack is one of the main factors affecting the durability of concrete structures. In recent years, multi-walled carbon nanotubes (MWCNTs) have attracted the attention of scholars for their excellent mechanical properties and durability performance. In this paper, the influence of sulfate attack and dry-wet cycles on the performance of multi-walled carbon nanotube-lithium slag concrete (MWCNTs-LSC) with varied MWCNTs content (0wt%, 0.05wt%, 0.10wt% and 0.15wt%) and varied water-cement ratios (0.35, 0.40 and 0.45) were investigated. Besides, scanning electron microscopy (SEM) and X-ray computed tomography (CT) tests were conducted to analyze the microstructure and pore structure of the concrete. The results show that concrete incorporated with MWCNTs could effectively mitigate sulfate attack The resistance to sulfate attack of concrete is negatively related to the water-cement ratio when the dry-wet cycle is fixed. The MWCNTs-LSC showed the best compressive strength at the water-cement ratio of 0.35 and 0.10 wt% MWCNTs. The SEM test results showed that the MWCNTs both filled the pores and cracks within the specimen and formed bridges between the cracks, enhancing the resistance to sulfate attack. The CT test results also showed that the addition of MWCNTs could reduce the porosity of concrete, refine the pore size and inhibit the generation and development of cracks, thus optimizing the internal structure of concrete and improving its resistance to sulfate attack.

In this paper, we investigate the effects of mean stress, multiaxial loading, and residual stresses on the fatigue life of springs. We study the fatigue life of a homogeneously stressed material subjected to cyclic loading with nonzero mean stress. Traditional methods for estimating fatigue life are based on Goodman and HAIGH diagrams. The formal analytical descriptions, namely "stress-life" and "strain-life" approaches, are found to be more suitable for the numerical methods. The fatigue analysis method described above, which describes crack growth per cycle, is extended. The extensions allow consideration of the mean stress of a load cycle. Closed-form expressions for crack length versus number of cycles are derived. The complementary effects on the fatigue life of springs, which ultimately have a significant influence on the fatigue life of springs, are summarized. For the proposed unified fatigue life functions, the ranges for the stress loading factor are extended by introducing the effective stress intensity. The solution leads to the factor range for the effective stress intensity, the effective mean value of the stress intensity factor and the effective stress ratio. The developed method assumes the homogeneous stress state in the whole spring element. For the non-homogeneously loaded structural elements, the weak-link concept is applied to account for fatigue.

Once on Mars, maintenance and repair will be crucial for humans as supply chains including Earth andMars will be very complex. Consequently, the raw material available on mars must be processed and used. Factors such as the energy available for material production play just as important a role as the quality of the material that can be produced and the quality of its surface. With the aim of developing and technically implementing a process chain that meets the challenge of producing spare parts from oxygen-reduced Mars regolith, this paper addresses the issue of low-energy handling. Expected statistically distributed high roughnesses of sintered regolith analogs are approximated in this work by parameter variation in the PBF-LB/M process. For low-energy handling, a dry-adhesive microstructure is used. Investigations are carried out to determine the extent to which the rough surface resulting from the manufacturing process can be smoothed by deep rolling in such a way that the microstructure adheres and enables samples to be transported. For the investigated AlSi10Mg samples (12 mm x 12 mm x 10 mm), the surface roughness varies in a wide range from Sa 7.7 µm to Sa 64 µm after the additive manufacturing process, pull-off stresses of up to 6.99 N/cm² could be realized after deep rolling. This represents an increase in pull-off stresses by a factor of 392.94 compared to the pull-off stresses before deep rolling, enabling handling of even larger specimens. It is noteworthy that specimens with roughness values that were previously difficult to handle can be treated post-deep rolling, indicating a potential influence of additional variables that describe roughness or ripples and are associated with the adhesion effect of the microstructure of the dry adhesive.

In the last two decades, the continuous ever growing demand for energy has determined a significant development in the production of photovoltaic (PV) modules. Anyway, a critical and very important issue in the module design process is the adoption of suitable encapsulant materials and technologies for cell embedding. Therefore, adopted encapsulants have a significant impact on modules efficiency, stability and reliability, and to ensure the unchanged performance of PV modules in time, the encapsulant materials must be selected properly. The selection of encapsulant materials must maintain a good balance between the encapsulant performance in time and costs, related to materials production and technologies for cells embedding. However, the encapsulants must ensure excellent isolation of active photovoltaic elements by the environment, preserving accurately the PV cells against humidity, oxygen and accidental causes that may compromise the PV modules function. This review provides an overview of different encapsulant materials, their main advantages and disadvantages in adoption for PV production, and also in relation to used encapsulant technologies for cell embedding, additives and the interaction of these materials with other PV components.