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Gesture detection has recently attracted a lot of attention due to its wide range of applications, notably in human–computer interaction (HCI). However, when it comes to video-based gesture recognition, elements in the background unrelated to gestures slow down the system's classification rate. This paper presents an algorithm designed for the recognition of large-scale gestures. In the training phase, we utilize RGB-D videos, where the depth modality videos are derived from RGB modality videos using UNET and subsequently employed for testing. However, it's worth noting that in real-time applications of the proposed dynamic hand gesture recognition (DHGR) system, only RGB modality videos are needed. The algorithm begins by creating two dynamic images: one from the estimated depth video and the other from the RGB video. Dynamic images generated from RGB video excel in capturing spatial information; while, those derived from depth video excel in encoding temporal aspects. These two dynamic images are merged to form an RGB-D dynamic image (RDDI). The RDDI is then fed into a modified Xception-based CNN model for the purpose of gesture classification and recognition. In order to evaluate the system's performance, we conducted experiments using the EgoGesture and MSR Gesture datasets. The results are highly promising, with a reported classification accuracy of 91.64% for the EgoGesture dataset and an impressive 99.41% for the MSR Gesture dataset. The results demonstrated that the suggested system outperformed some existing techniques. [ABSTRACT FROM AUTHOR]

Power image tampering brings certain security risks to the safe operation of power grids, among which splicing tampering is the most common. Although image tampering detection has received much attention in recent years, relatively little research has been conducted on the practical application to power systems, and the detection results are poor due to the difficulty of learning subtle edge features of tampered regions and the lack of power image tampering dataset for training. In order to effectively detect image tampering regions, this paper proposes a splicing tampering detection model with a Dual-channel Augmented Attentive Dense-convolutional Network (DAAD-Net) structure. The model consists of three main parts: backbone network feature extraction, augmented attention feature extraction, and tampered region detection. Firstly, the backbone network feature extraction module fuses the original tampered image features with the image residual features and transfers them into the backbone network to extract the feature map. Secondly, the augmented attention feature extraction module extracts the tampered region features in the higher and lower layers using hierarchical encoding and decoding operations. Finally, the extracted feature maps of each layer are sent to the tampered region detection module, and by combining the loss of each feature map for optimizing the network parameters. Additionally, we produced a power image tampering dataset containing 552 samples. Experiments demonstrate that the proposed method outperforms current state-of-the-art models, with 1% to 31% improvement in evaluation indicators, and has good robustness to noise and JPEG compression attacks. [ABSTRACT FROM AUTHOR]

Breast cancer, a widespread global disease, represents a significant threat to women's health and lives. Many researchers have proposed computer-aided diagnosis systems for classifying breast cancer. The majority of the approaches primarily utilize deep learning methods, which overlook the crucial necessity of incorporating both local information for precise tumor detection. In addition, available breast cancer datasets are imbalanced in nature. Therefore, this paper presents the hybrid breast network (HBNet) for detection of breast cancer, designed to address two critical challenges: class imbalance and incorporation of both global and local information in achieving precise tumor classification. To overcome the problem of class imbalance, HBNet incorporates the borderline synthetic minority oversampling technique. Simultaneously, it employs a feature fusion approach to combine deep and handcrafted features extracted by utilizing ResNet50 and HOG which incorporates global and local information. Moreover, the proposed method integrates the block-matching and 3D denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The proposed HBNet is evaluated with BUSI and UDIAT datasets and achieved an average accuracy of 95.824% and 90.37%, respectively. [ABSTRACT FROM AUTHOR]

Microscopic emission estimation based on driving states plays a crucial role in controlling the pollution of on-road vehicles. Existing research has evolved from fitting nonlinear models of driving cycles and emission factors to utilizing neural networks to exploit driving patterns and construct a nonlinear mapping between driving states and emission values. However, due to the small percentage of driving-cycle related to high emissions, it is still challenging to capture the vehicle emission peaks, which lead to the most noteworthy high-emission characteristics being regarded as abnormal disturbances instead. To address the issue, this paper proposes a peak-sensitive microscopic emission estimation framework characterized by sequence-to-sequence learning for on-road vehicles. Sequence-to-sequence learning emphasizes serial pattern mapping from driving sequences to emission sequences to provide more statistical constraints and reduce the estimation uncertainty brought by unstable driving behaviors. Specifically, the framework aims at capturing context features related to high emissions from sequences dynamically in an adaptive and self-learning way and is composed of a driving states embedding module and a dynamic aggregation module. Particularly, an incremental tracking loss (ITL) is proposed to adjust the incremental emissions at adjacent time steps by supervising the differences of the generated sequences, enabling the model to track sudden changes in emissions. Extensive experiments are conducted on the on-board diagnostics (OBD) dataset with 12628 sampling records collected from a heavy-duty diesel vehicle. The results show that the estimation accuracy of our proposed method is significantly better than state-of-the-art methods, and it can effectively capture high-emission peaks. [ABSTRACT FROM AUTHOR]

Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud methods face challenges related to low recall rates, inaccurate results, and insufficient causal modeling ability. This paper proposes a credit card fraud detection model based on counterfactual data enhancement of the triplet network. Firstly, we convert the problem of generating optimal counterfactual explanations (CFs) into a policy optimization of agents in the discrete–continuous mixed action space, thereby ensuring the stable generation of optimal CFs. The triplet network then utilizes the feature similarity and label difference of positive example samples and CFs to enhance the learning of the causal relationship between features and labels. Experimental results demonstrate that the proposed method improves the accuracy and robustness of the credit card fraud detection model, outperforming existing methods. The research outcomes are of significant value for both credit card anti-fraud research and practice while providing a novel approach to causal modeling issues across other fields. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent recognition and statistics of hydrogenation catalyst image information, this paper presents a new method to judge the active phase by image recognition, which is different from traditional methods. Firstly, considering that hydrogenation catalyst image targets are small and easy to stack, the feature extraction network in the CenterNet model is optimized by adding the multi-feature fusion module to improve the accuracy of the network in edge positioning. Secondly, according to the linear shape of the hydrogenation catalyst, the mask branch is added to the CenterNet model to train the hydrogenation catalyst stripes with unclear target to reduce the leakage rate of the hydrogenation catalyst. The experimental results show that the detection accuracy of the improved CenterNet network is 91 % , 7 % higher than that of the original one, with a decline in detection rate by 12 % . The method proposed in this paper can accurately identify and segment the hydrogenation catalyst in the electron microscope image, which can provide technical support for the statistics and analysis of the hydrogenation catalyst image. [ABSTRACT FROM AUTHOR]

Due to the growing demand for clean and sustainable energy sources, there has been an increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right design parameters to achieve the most efficient energy output that aligns with the energy system's needs can be quite challenging. This complexity arises from the intricate models and the inherent inaccuracies in the available information. To tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process. In this process, SCA search agents explore the search space, while the PSO search agents leverage the outcomes derived from SCA exploration. This study evaluates the effectiveness of ASCA-PSO in accurately estimating the parameters of single- and double-diode models using data from two commercial solar cells. The findings are compared with those of cutting-edge methods. It is demonstrated that ASCA-PSO can identify global solutions for multifaceted and intricate objective functions. Furthermore, it proves to be a viable option for designing solar cells even in the presence of noise. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions—Levy, Gaussian, and Cauchy—for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation. [ABSTRACT FROM AUTHOR]

Successful deployment of convolutional neural networks on resource-constrained hardware platforms is challenging for ubiquitous AI applications. For latency-sensitive scenarios, real-time inference requires model compression techniques such as network pruning to achieve the purpose of inference acceleration. However, many researches focus on hardware-independent filter pruning methods, which cannot balance the contribution of pruned structure to latency and the accuracy drop. Although some pruning methods have introduced latency constraints into the pruning process, most of them are based on look-up tables, which omits the key step of hardware optimization, resulting in significant deviation in latency estimation. In this paper, we propose a novel latency-constrained pruning method, named hardware-in-loop pruning (HILP). It is based on the fast optimal pruning rate search within the layer and layer-wise hybrid pruning, which can prioritize removing the less important layers with considerable latency contributions. The proposed hardware-in-loop pipeline enables the hardware optimization module to be integrated into the entire framework. During pruning, an intermediate network architecture is automatically transformed to a deployable model for accurate latency measurement. The latency-optimized intermediate architecture is then selected by traversing all layers for next progressive step. HILP is generally applicable to any platform that provides a hardware optimization toolchain, such as NVIDIA GPU and Cambricon NPU. We evaluate HILP on both image classification task using ResNet50 with ImageNet and object detection task using YOLOv3 with COCO, HILP can reduce the inference latency of these two networks to 60% and 75%, respectively, within the range of accuracy variation not exceeding 0.6%. Extensive experiment results have proven that HILP is able to achieve a significant advantage in latency-accuracy performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel incipient fault estimation and fault-tolerant control approach for wireless power transfer (WPT) systems with disturbances and incipient sensor faults. Firstly, the dynamic models of the WPT system and incipient faults are established. Then, a k-step incipient fault estimation observer method is analyzed for estimating the states and incipient faults of the system. Based on it, a nonlinear dynamic output feedback fault-tolerant controller is devised to ensure the stability of the WPT system when considering incipient faults and disturbances. Further, the designed controller can monitor the system in real time without the knowledge of the system states. Besides, the stability analysis approach is rigorous, and the proof method can also apply to other similar systems. At last, the simulation example shows that the proposed fault diagnosis method is correct and effective. [ABSTRACT FROM AUTHOR]

One of the most popular techniques for changing the purpose of an image or resizing a digital image with content awareness is the seam-carving method. The performance of image resizing algorithms based on seam machining shows that these algorithms are highly dependent on the extraction of importance map techniques and the detection of salient objects. So far, various algorithms have been proposed to extract the importance map. In this paper, a new method based on Rényi entropy is proposed to extract the importance map. Also, a deep learning network has been used to detect salient objects. The simulator results showed that combining Rényi's importance map with a deep network of salient object detection performed better than classical seam-carving and other extended seam-carving algorithms based on deep learning. [ABSTRACT FROM AUTHOR]

Complex precision components are integral to many sectors, straddling both military and civilian applications. These include aerospace telescopes, infrared thermal imaging systems, artificial intelligence, semiconductor chip lithography, medical imaging apparatus, and avant-garde communication technologies. These intricate precision components have become vital elements of the aforementioned optical systems, characterized by a wide range of extensive requirements totaling in the tens of millions. Within the realm of computer controlled optical surfacing (CCOS), high-efficiency bonnet polishing (BP) and high-precision magnetorheological finishing (MRF) are two compliant polishing methods with distinct advantages, extensively applied to ultra-precision machining of complex curved surface components. However, the bonnet polishing tool is prone to wear, the tool influence function is unstable, and the control process is complicated. The material removal efficiency of MRF is low; it easily introduces mid-spatial frequency (MSF) errors, and improving the performance of the magnetorheological fluid (MR fluid) is challenging. Therefore, summarizing these two techniques is essential to enhance the application of compliant polishing methods. The paper begins by examining the unique strengths of both technologies and then explores the potential for their integrated application. The paper then provides a detailed introduction to the origin, principles, equipment, and applications of BP. Next, the paper outlines the research progress of key technologies, including modeling of the tool influence function (TIF), management of MSF errors, and the wear of the bonnet tool within the realm of BP technology. Following that, the development history, technical principles, equipment, types, and compound methods of MRF are presented. Then, the research progress of several key technologies, such as modeling of TIF, controlling MSF error, and the preparation of MR fluid in the field of MRF technology, are reviewed. Lastly, the paper provides a summary and outlook for the two technologies, such as further in-depth study of the material removal mechanism and the suppression method of the edge effect in BP, a further in-depth study of methods to improve the material removal rate, and MSF error suppression methods in MRF. [ABSTRACT FROM AUTHOR]

Within the context of reducing the environmental impact generated by the current generation, a constant challenge for the automotive industry is to produce vehicles with the lowest possible environmental impact. In this context, in order to reduce vehicle weight and, consequently, reduce fuel consumption and emissions of gases harmful to the environment, the automotive industries have been using advanced high-strength steels. These steels make it possible to reduce thickness without losing mechanical strength and, therefore, without compromising passenger safety. Among these steels, 22MnB5 steel stands out. Among the welding processes used to weld steel sheets, electrical resistance spot welding is the most widely used joining technique in the automotive industry. The main reasons for its wide application are that it is a process in which no material addition is required and it has a high processing speed and low processing costs. In this context, this study proposes a bibliometric and systematic review of electrical resistance spot welding of 22MnB5 steel. The main objective of this study was to identify current research trends and impacts, along with a comprehensive revision. The research was carried out using the Proknow-C method. A bibliographic portfolio of 50 articles was compiled. The bibliometric analysis revealed that articles related to this topic increased substantially between 2016 and 2023 compared to previous years. The systematic evaluation of the papers in the bibliographic portfolio made it possible to identify aspects such as the research problems addressed, the materials and methodologies used, the results evaluated, and the research gaps. It was found that most of the studies focused on the weldability and mechanical performance characteristics of similar two-plate joints with an average thickness of 1.5 mm and Al-Si coatings. Finally, this article provides a comprehensive overview of the research on electric resistance spot welding of 22MnB5 steel and can provide guidance for future studies to improve the process. [ABSTRACT FROM AUTHOR]

Ultrasonic-assisted grinding (UAG) can effectively reduce the grinding force and improve the machining quality of carbon fiber-reinforced ceramic matrix composites (C/SiC) due to its excellent machining performance. Many researches have shown that the machining performance of UAG is closely related to the kinematic trajectory characteristics of the abrasive grains, so the surface morphology of diamond grinding wheels with various process preparations has an important influence on the grinding quality. In this paper, to investigate the performance of UAG of C/SiC composites with diamond grinding wheels prepared by different processes, UAG experiments were carried out on C/SiC composites using diamond grinding wheels prepared by brazing and electroplating processes. The trajectories of the abrasive grains and the maximum undeformed chip thicknesses with different abrasive grain protrusion heights were analyzed and the impact of the two diamond grinding wheels and the grain mesh size on the grinding force, surface roughness, and surface morphology were comparatively investigated. The results indicated that the grinding force and surface roughness obtained by brazed grinding wheels were reduced by 13 ~ 36.05% and 5.95 ~ 19.3% compared with electroplated diamond wheels at 240#, respectively. The brazed grinding wheels could improve the grinding surface morphology to a certain extent. However, the surface roughness value fluctuated with the increase of abrasive grain size. Additionally, the protrusion height of the abrasive grain of the brazed diamond wheels is more favorable for the UAG effect and the grinding wheels are less prone to clogging. [ABSTRACT FROM AUTHOR]

This paper introduces an approximate grinding method (AGM) and tooth flank deviation correction strategies for helical face gears (HFGs) generated by grinding worm, addressing the issue of singularities on the grinding worm thread surface that hinders HFGs grinding properly. Firstly, the tooth flanks of the face gear and grinding worm are derived, and singularities on the grinding worm are determined. Then, based on the grinding generation theory of HFGs, the AGM is proposed to solve the challenge of grinding HFGs with a large helix angle. Next, the tooth flank deviation of HFG generated by the grinding worm with the AGM is investigated; meanwhile, the tooth contact pattern and transmission error analysis are conducted. In addition, the correction method of the HFG tooth flank deviation is researched. Finally, a grinding experiment is performed and demonstrates the validity of the theoretical analysis of the AGM. [ABSTRACT FROM AUTHOR]

Titanium alloy milling is prone to burrs at the edges of the workpiece, which can negatively affect surface integrity and dimensional accuracy, and even lead to part scrap. Ultrasonic vibration–assisted milling technology can effectively inhibit burr generation and improve machining quality. However, the research of ultrasonic vibration–assisted milling on burr inhibition is not clear, so this paper establishes a mathematical model of ultrasonic vibration vertical milling titanium alloy top burr size based on the chip deformation process and specifically analyses the effect of ultrasonic machining parameters on burr through experiments. The experimental results show that the depth of cut has the greatest influence on the burr size, and the ultrasonic vibration has the second greatest influence on the burr. The cutting force and the burr size on both sides of the groove show a trend of "decrease and then increase" with the increase of ultrasonic amplitude. When the ultrasonic amplitude was 3 µm, the cutting forces Fx and Fy were reduced by 34.42% and 31.36%, respectively, and the heights and widths of the burrs on the up milling side and on the down milling side were reduced by 75.49%, 44.33% and 89.16%, 47.82%, respectively, when comparing with no ultrasonic machining. The longitudinal-torsional ultrasonic vibration converted the large piled-up, rolled-up, and serrated burrs into intermittent, small-sized flocculent burrs, which significantly improved the burr morphology and weakened the serrated characteristics of the chips. [ABSTRACT FROM AUTHOR]

Rivet connection is one of the most important connection technologies. The main factors affecting the rivet-forming quality of riveted structures are squeezing force, squeezing velocity, and materials of rivets and plates, which significantly impact the damage and failure of riveted holes in riveted structures. This paper provides a detailed study of the rivet-forming quality of riveted structures under different dynamic loads. A theoretical analysis method for the nonlinear dynamic plastic behavior of riveted structures is proposed to predict and investigate the rivet-forming quality of riveted structures under different squeezing forces, velocities, and materials. The proposed theoretical analysis method for the rivet-forming quality of riveted structures is conducted using the explicit dynamics method. Furthermore, the nonlinear dynamic plasticity theory method and nonlinear explicit dynamics model calculation results of riveted structures are compared with measured and Ultra Plus field scanning electron microscope scanning values. The results confirm the accuracy of the nonlinear dynamic plasticity theory method for riveted structures. In addition, the effects of different squeezing forces, velocities, rivet material, and plate material properties on the rivet-forming quality of riveted structures are also studied. This paper proposes a theoretical analysis method for the rivet-forming quality of riveted structures, providing an optimal squeezing force and velocity for rivet hole damage and rivet failure under different rivet and plate materials. [ABSTRACT FROM AUTHOR]

The cutting subsurface plastic deformation layer of titanium alloys has a serious influence on fatigue performance. Hence, it is necessary to establish a predicting model of the depth of subsurface plastic deformation. However, empirical models are difficult to be applied for different cutting methods and are controlled by various cutting parameters. This paper establishes an analytical model for predicting the depth of subsurface plastic deformation based on cutting force. In this case, as long as the cutting force is known, the analytical model can be used to predict the depth of subsurface plastic deformation layer for various cutting conditions. In experiments, the depth of subsurface plastic deformation was measured by using a scanning electron microscope (SEM) and electron back-scatter diffraction (EBSD). The measured and predicted values are closed, and the average prediction error is only 16.01%. Therefore, the analytical model is reliable and useful to predict the depth of subsurface plastic deformation during cutting titanium alloys. This study will have an important application value to control the depth of subsurface plastic deformation to improve fatigue performance. [ABSTRACT FROM AUTHOR]

Laser-based powder bed fusion of metals (PBF-LB/M) is an additive manufacturing process enabling the fabrication of parts with highly complex and customizable geometries, enhanced strength-to-weight properties, and minimized material waste. Despite its unique capabilities, PBF-LB/M needs research and innovation efforts to enhance process dynamics and product quality, as well as to broaden its adoption in high-value industrial sectors, such as aerospace and biomedical. In this context, in-process monitoring solutions and post-process part quality evaluations are fundamental to improving the process towards sustainable, first-time-right, and zero-defect production. This paper describes a novel building plate concept for metal laser powder fusion, whose characteristics were specifically designed to enable and improve the performances of in-process monitoring and high-resolution X-ray computed tomography (CT) measurements. In particular, the plate features markers for perspective correction in off-axis optical monitoring and dismountable inserts with machined geometrical elements to be used for the precise alignment between high-resolution CT reconstructions, in-process gathered data, and building volume geometry. The plate capabilities were demonstrated through examples related to in-process monitoring and post-process X-ray CT measurements. [ABSTRACT FROM AUTHOR]

The cooling process of a hot rolled strip on the run-out table (ROT) mainly determines the microstructure and mechanical properties of the final product; therefore, a method of investigation that helps companies follow the optimum cooling scenario is a profound issue. This paper develops a 2D finite element model based on industrial data that predicts the cooling pattern of hot rolled strips, with the potential to be formulated for steel grades dealing with complex boundary conditions, flexible to be applied for any cooling table. Meanwhile, this model investigates the thermal behavior of strips facing different heat transfer mechanisms in the full-scale ROT of Mobarakeh Steel Company (MSC). Moreover, coiling temperature (CT) and cooling pattern are validated through experimental data obtained from the Evraz hot rolling mill. Regarding the simulation of various header configurations, each four-header bottom bank, upper laminar, and water curtain headers deliver 10 ∘ C / s , 10.66 ∘ C / s , and 7.85 ∘ C / s of cooling rate, respectively. The simulations also predict the heat flux in the impingement, parallel, and air-cooling zones to be in the range of 4000–12,000, 500–2500, and 80–400 (w / m 2 K) on the top surface, and 21,000–5400, 700–4200, and 380–170 (w / m 2 K) on the bottom surface, respectively. According to the temperature-dependent attitude of steel properties, the effect of strip's thermo-physical properties on the heat transfer along ROT was examined, illustrating the significant impact of specific heat on cooling, which leads to the endorsement of the functionality of early cooling compared to delayed and distributed strategies for the investigated HSLA (high-strength low-alloy) steel. [ABSTRACT FROM AUTHOR]

Recent experiments have shown that improper encoding in MR user interfaces exacerbates the individual cognitive symbol effect, leading to issues of excessive cognitive workload and elevated psychological stress levels in individual cognitive task methods. In light of this, this paper proposes a novel MR user interface information encoding method. By assessing the improvement level of object clues and action clues on information encoding quality, it identifies the optimal encoding forms for combining these two types of cues, thereby establishing a more intuitive and natural communication channel for information. The paper presents several human–machine interface usability test results and compares the proposed encoding forms with the MR interface encoding information currently used in significant factories like AVIC (Aviation Industry Corporation of China) to analyze the advantages and disadvantages of the proposed encoding forms. [ABSTRACT FROM AUTHOR]

Gear shafts are commonly manufactured by machining of gear teeth and shaft separately. This kind of approach entails quite some problems, such as long processing time, large die, and low production efficiency. To tackle these problems, this paper innovatively proposes a new process of cross wedge rolling (CWR) that is specifically tailored to the manufacturing of gear shafts, with which the shaft and teeth are formed at one go. The finite element model of CWR forming of gear shaft is established to simulate and analyze the CWR forming process. A three-factor and three-level response surface test is designed to optimize the processing parameters, where the friction coefficient, rolling temperature, and die movement speed are chosen as three factors, and inhomogeneous deformation is set as the response. The optimal parameters are obtained as follows: the rolling temperature is 1240 ℃, the speed of the die movement is 336 mm/s, and the friction coefficient is 0.8. This result is subsequently verified by experiments. Further on, the microstructure evolution of the characteristic points of the CWR forming process of gear shaft is analyzed. It is found that the grains at the gear teeth section are continuously refined and grown from the boundary at the early section of deformation, and the final grains grown are much smaller than the original ones. In addition, the grains appear to grow in a short range of time in the process of deformation, but the overall trend is towards refinement. In the shaft section, the evolutions of the grains in the two stepped shaft segments are almost the same; the grain deformation shows cyclic changes; the degree of grain refinement in the shaft is higher than that in the teeth; and the degree of grain refinement in the shaft center is higher than that in the shaft edge. The results of the study provide a theoretical basis for short-process forming of high-performance gear shafts by cross wedge rolling. [ABSTRACT FROM AUTHOR]

Liquid composite molding (LCM) is a common technology for manufacturing the polymer composites. The LCM process involves a non-isothermal flow of resin which influences the curing stage. The gradient of curing degree generated before gel point in curing stage seriously affects the quality of composites. The optimized design of cure cycle plays a crucial role in reducing gradient of curing degree. In this paper, the influence of non-isothermal flow on curing stage is considered. Based on ANSYS FLUENT software, the 3D non-isothermal resin filling-curing co-simulation is applied. The flow parameters, temperature parameters, and curing reaction parameters are fully considered in the co-simulation simulation, which can realize the multi-field coupling of thermal-flow-chemical field in the LCM process. The effectiveness of the co-simulation is verified by non-isothermal experiment of LCM process. An optimization method of cure cycle which the gradient of curing degree decreases significantly before the gel point is proposed. Through this method, the data obtained from the cyclic co-simulation to determine the temperature and heating rate at different curing times, and finally the optimal cure cycle, is obtained. Furthermore, these results have industrial significance for improving the LCM manufacturing of the key composite parts in aircraft, automobile, and ship technology. [ABSTRACT FROM AUTHOR]

Laser-based powder bed fusion (LPBF) is a widely utilized technique for additive manufacturing (AM) of metal components. The size of LPBF-produced parts is restricted by machine dimensions. Welding emerges as a feasible solution for connecting smaller parts generated via PBF. Nevertheless, the weldability of PBF parts differs from conventional ones due to the unique thermal history of PBF components. This paper investigated the weldability of stainless steel 316L and tensile behavior of welded samples, comparing samples produced through LPBF and the conventional wrought method. Tensile testing, grain size analysis, microhardness test, and assessment of secondary dendrite arm spacing were conducted on every welded combination of materials (AM-AM, wrought-wrought, and AM-wrought) using gas tungsten arc welding (GTAW). The findings indicate that the wrought material demonstrates a higher maximum strength (577.5 MPa) and a lower yield strength (231.7 MPa) compared to the AM material (with a maximum strength of 548.2 MPa and a yield strength of 399.9 MPa). This difference may be attributed to the annealing process applied to the wrought material, which has not been applied on the AM material. Additionally, welded wrought samples have a significantly lower tensile strength compared to non-welded samples. The wrought samples exhibit a larger average grain size (11.14 to 31.12 μ m ) in the heat affected zone (HAZ) compared to non-heat affected regions (8.13 μ m ). In contrast, it is found that grain coarsening does not occur in PBF samples, which exhibit a similar grain size in the HAZ (15.04 μ m ) vs 13.97 μ m outside of the HAZ. Larger grain sizes within the HAZ correspond to a larger reduction in tensile strength for the wrought samples, compared to PBF samples. Similar secondary dendrite arm spacing has been observed in the weld regions of both PBF and wrought materials. Microhardness test results indicated that the AM base material has the highest microhardness and gradually decreases as transitioning to the HAZ and weld region. In summary, the PBF samples exhibit less reduction of strength and ductility after welding than wrought samples. Therefore, it is determined that additively manufactured 316L stainless steel has a higher weldability than traditionally manufactured wrought 316 stainless steel. [ABSTRACT FROM AUTHOR]

Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg (DH) convention and used the Stewart platform "Tiger 66.1" developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per least-square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform's center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The target poses and actual poses are then compared, and the error compensations are estimated using the least-square methods to calculate the predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements. Given that "Tiger 66.1" is based on the general Stewart platform structure, the proposed calibration method holds promise for extension to machines operating on similar principles where non-invasive calibration is desirable. This study contributes to advancing the field of Stewart platform calibration, paving the way for more precise and efficient applications in various domains. [ABSTRACT FROM AUTHOR]

Energy efficiency is a challenging and relevant research field in modern manufacturing industries, where robotic systems play an essential role in the automation of several industrial operations. In this paper, we present an approach for the energy-efficiency optimization of a 3-DOF parallel robot. The proposed strategy leverages the task placement, the execution time, and the length of the robot lower arms to minimize the energy consumption for the execution of a predefined high-speed pick-and-place operation. To evaluate the actuators energy consumption, the kinematic, dynamic and electro-mechanic mathematical models, as well as an equivalent multibody model, of the parallel robot are implemented. The results of extensive numerical simulations show that the proposed strategy provides notable improvements in the energy efficiency of the parallel robot, with respect to alternative approaches. Starting from a pick-and-place task with optimal task placement with a consumption of 38.2 J (with a cycle time of 0.4 s), the energy expenditure can be reduced to 3.75 J (with a cycle time of 1.86 s), with a reduction percentage of 90.2%, by additionally optimizing the execution time, and the length of the robot lower arms. These results lead to a reduction from 5733 J/min (for 150 cycles/min) to 121 J/min (for 32 cycles/min), allowing to choose the best trade-off between robot productivity and consumed energy. [ABSTRACT FROM AUTHOR]

Human errors are prevalent in manual assembly line statistics, but humans are still a necessary and numerous working force for assembly tasks. Since faster and shorter production cycles lead to higher mental efforts, an alternative to decrease the cognitive load and errors of manual assembly lines is using augmented reality (AR). AR increases system interaction and assists operators in the step-by-step of any tasks performed. Its use allows granular production data, enabling an increase in quality control and timely detection of assembly problems. However, AR alone is not enough to avoid demotivation and lack of engagement in the task, which is repetitive. Gamification can become an auxiliary resource to increase operators' motivation and engagement. This paper presents a system model to integrate AR and gamification for a manual assembly line of printed circuit boards (PCBs) called GRAAL, used to develop an actual system for PCB assembly, which can guide operators using AR but also tracks down their efficiency and efficacy which is then used for gamifying their performance. A prototype system was implemented and assessed by 34 specialist respondents. TAM 3 (Technology Acceptance Model 3) Questionnaire was used to assess the system, which allowed us to find that GRAAL was well accepted (all scores were above the center of a Likert scale from 1 to 7 values), suggesting that GRAAL may be useful as an auxiliary resource to manual assembly lines. We conclude that modeling a system that merges AR and gamification is feasible but a non-trivial task in industry. The system's features are dependent on conditioning factors such as Application Environment, Application Context, Application Audience, AR hardware, and gamification elements. [ABSTRACT FROM AUTHOR]

Bearings represent crucial components within rotating machinery, and unexpected failures can lead to significant damage and unplanned breakdowns. This paper introduces a novel approach to diagnose bearing faults under variable working conditions, leveraging the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sequential backward selection (SBS). CEEMDAN automatically selects intrinsic mode functions (IMFs) from vibration and current signals to establish a comprehensive set of health indicators. Subsequently, the SBS algorithm identifies the most pertinent indicators for different bearing failure modes. The accuracy of the proposed method is evaluated on both vibration and electrical signals using data from a dedicated test bench at the Signal and Industrial Process Analysis Laboratory (LASPI). Results demonstrate the effectiveness of the proposed method in accurately identifying and classifying bearing faults across various working conditions, utilizing both types of signals. This approach holds promise for real-world industrial applications, offering a reliable method for condition monitoring and diagnostics in bearing systems. [ABSTRACT FROM AUTHOR]

Polyetheretherketone is a thermoelastic biopolymer that has good properties for use in medical implants. The paper presents the results of experimental research on the influence of some machining conditions (feed per tooth and cutting strategy) on the size of the temperature in the milling area, the size of the components of the cutting force, the roughness of the machined surface, the hardness of the surface layer resulting from the milling application. The working feeds had values in two ranges (0.0167–0.025 mm/tooth and, respectively, 0.05–0.15 mm/tooth). Different zig, zig-zag, and follow periphery machining strategies were applied to evaluate their effects on the values of some milling parameters of interest and, respectively, on the quality and integrity of the surface resulting from milling. The experimental research was carried out using modern research equipment, such as the confocal microscope and an interferometer, for the observation and analysis of the surface texture. It was found that a movement of the tool along a zig-zag type strategy leads to smoother surfaces compared to those obtained by zig-zag and follow periphery strategies. Higher cutting feeds (0.05–0.15 mm/tooth) determine lower values of the roughness parameter Sa. When feed values are in the range of 0.05–0.15 mm/tooth, the cutting temperature reaches values of 160–183 °C. In accordance with the higher temperature values, an increase in the hardness of the superficial layer resulting from milling is also observed. A consequence of the temperature increase in the cutting zone was a reduction in the sizes of the components of the cutting force, the material becoming more ductile and easier to cut. [ABSTRACT FROM AUTHOR]

A suitable texture location is a significant factor in avoiding derivative cutting and improving friction conditions. This paper aims to explore the impact of texture in the sticking and sliding friction regions on cutting performance. The chip distribution model was established and validated to assess the distribution of friction regions. Turning experiments with and without MoS2 solid lubricants were conducted on AISI 304 using textured tools with various texture locations determined by the chip distribution model. The results derived from the prediction model aligned favorably with the experimental results, and the location near the boundary of the sticking and sliding friction regions was the suitable initial location of tool surface textures. Under dry cutting conditions, the texture at the termination of the sticking region increased the cutting force and temperature by only 0.5% and 4.8%, while the presence of MoS2 caused a decrease in cutting force and temperature by 2.9% and 3.5%, respectively. [ABSTRACT FROM AUTHOR]

The mitigation of undesired welding deformation during laser welding of metal bipolar plates (BPs), which are crucial components in proton exchange membrane fuel cells (PEMFCs), represents a significant challenge that has yet to be effectively addressed. The low weight requirements of PEMFC endow the BP with ultra-low thickness and sensitivity to processing technology. This paper introduces a study that reduces the deformation of BPs from 4 to 1 mm by implementing a novel multi-path welding process as an alternative to traditional single path welding. The research primarily investigates the weld seam space (WSS) and seven series of experiments with dissimilar WSS are conducted with the objective of minimizing deformation using specially designed welding fixtures. Following this, the weld seam stiffness and residual stress distribution are analyzed in further detail to elucidate the effects of WSS on welding deformation. Furthermore, a stress distribution indicator is proposed in this study to characterize the stress distribution. The conclusion of this study is that multi-pass welding techniques are highly effective in reducing welding deformation, particularly when the WSS is set to 0.75 mm. In principle, the stiffness of the weld seam and the distribution of residual welding stress jointly determine the welding deformation. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0 and the impending shift towards Industry 5.0, the integration of human–robot collaboration (HRC) into production systems has become increasingly widespread. This paradigm shift leverages collaborative robots, or cobots, to mitigate physical and mental strain on human workers, thereby increasing productivity and improving overall quality performance. This paper investigates the interplay of productivity and quality factors with assembly complexity in both manual and collaborative assembly systems. The focus is placed on a product family of electronic boards, with varying levels of assembly complexity, to provide a comprehensive comparison between manual assembly and two different collaborative assembly scenarios. Key performance metrics such as assembly time and total defects are evaluated. This case study, rooted in the electronics industry, seeks to provide a valuable perspective on how assembly complexity influences productivity and quality in product family assembly systems. The results of this study aim to contribute to the growing body of knowledge on the implementation of HRC in manufacturing, facilitate informed decision-making and encourage further advances in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

This work focuses on porosity formation during the welding of dissimilar aluminium alloys (cast and sheet) by pulsed gas metal arc welding (GMAW) with different travel speeds (12–14 mm/s) and by friction stir welding (FSW). The case study concerns the assembling of a battery-pack enclosure prototype. The welded specimens were scanned by 3D X-ray computed tomography. The cast base material (BM) shows a porosity percentage of 1.45%, and it is characterized by pores with a strong hyperbolic relationship between equivalent diameter and sphericity. Considering the GMAW beads, porosity rises with the travel speed (from 1.80 to 5.12%), due to the reduction of the opening window in which pores can escape. Pores with volume higher than 0.10 mm3 rise with the travel speed, representing from 9.75 to 32.98% of the total porosity. These pores are responsible for the weaker hyperbolic connection for sphericity found for the GMAW beads. On the other hand, FSW mixes and homogenizes the pores in the cast BM. The novelty of the paper lays in proving the strong potentialities of FSW for weld porosity reduction. A re-designing of the battery-pack enclosures is necessary to limit arc welding in marginal areas, which are not crucial for sealing but necessary to create a stable platform to be subsequently sealed with FSW. [ABSTRACT FROM AUTHOR]

In comparison to coordinate measuring machine (CMM), on-machine measurement (OMM) offers the benefit of avoiding re-clamping of workpieces and has become a crucial technology for adaptive machining. In OMM, the positioning errors of machine tool shift the ideal position and orientation between the probe and the workpiece. This introduces an error in the measurement result, causing workers to question OMM's precision. Therefore, the clarification of the relationship between positioning error and OMM error is helpful to make this technique more reliable. For this purpose, a five-axis OMM error model considering positioning errors is proposed. First, this paper gives the definition of feasible measurement region (FMR) about five-axis machine tool and obtains the FMR under specified measurement feature. Then, in the interest of identifying the influence of machine tool positioning error on OMM accuracy, an OMM error prediction model considering positioning error is established, which can be used to predict the error distribution in FMR. Next, the validity of the prediction model is verified by OMM experiments on the standard block. Finally, several applications of the OMM error prediction model are proposed. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence significantly affects laser transmission and echo. The size of the echo cross section and the distribution of energy are important parameters to obtain target information. In this paper, the speckle characteristics of laser echo are analyzed using field experiments with different detection distances. The energy attenuation law is observed, and the statistical law governing the cross-sectional area and energy of the echo light spot is determined based on the time variability of atmospheric turbulence. The research shows that in order to obtain stable return laser beam interference information, the current weather situation and the strength of atmospheric turbulence should be considered, and the corresponding interference region should be selected according to the detection distance. Although the size and energy of the echo cross section are directly affected by the detection distance, the variation trend remains consistent and indicates a clear statistical law that provides a strong basis for laser interferometry. [ABSTRACT FROM AUTHOR]

To bypass the difficulty of solving the one-electron time-dependent Schrödinger equation in momentum space with the interacting nonlocal Coulomb potential, we have recently formulated an alternative accurate and efficient ab initio treatment (Ongonwou et al., Ann Phys 375:471, 2016; Ekogo et al., Phys Scr 97:115402, 2022), which relies on the expansion of the atomic wave function and the interacting nonlocal Coulomb potential on a discrete basis set of Coulomb Sturmian functions in momentum space. To further illustrate the validation and credibility of our proposed theoretical and numerical approaches, we study the above-threshold ionization of the hydrogen atom exposed to ultrashort infrared, ultraviolet, and low-and-high-frequency laser pulses. The energy spectra, momentum, angular distributions of the photoelectrons, and bound-state populations at the end of the laser pulse have been numerically evaluated, analyzed, and compared against predictions of other well-known time-dependent calculations in the literature. They are obtained either by using the ionized wave function or by projecting the total electron wave packets at the end of the laser pulse onto the continuum states constructed using the incoming Coulomb wave function. We explore the physical observables' dependence on the carrier-envelope phase of the laser pulses. Our theoretical model captures the left–right dependence and breaks backward–forward symmetry in the emitted photoelectron momentum and angular distributions. [ABSTRACT FROM AUTHOR]

This paper investigates the properties of 100-nm ZnO thin films prepared by radio frequency (RF) sputtering technique on polyethylene terephthalate (PET) and polyimide (PI) plastic substrates using X-ray diffraction (XRD) patterns to show the proper formation of hexagonal ZnO (002) structure, with ZnO on PET plastic substrate recording higher peak and FWHM compared to ZnO on PI plastic substrate. AFM reveals smooth surface morphologies on both substrates with a root-mean-square (RMS) roughness of below 10 nm. Optical transmittance measures values exceeding 80% in the visible and infrared (IR) regions on each substrate. ZnO on PET records 3.33 eV, and ZnO on PI measures 3.35 eV of optical band gap (Eg) from the Tauc plot. The photoelectrochemical cell (PEC) parameter measurement study shows that ZnO thin films on PET and PI plastic substrates have efficiencies (η = 0.93) and (η = 0.88), respectively. [ABSTRACT FROM AUTHOR]

This article reports the photoluminescence studies of CaMgB2O5: RE3+ (RE = Dy, Eu, Sm) phosphors. The reliable, low cost combustion method is used to prepare a series of phosphors for Solid state lighting. In this work, environmental friendly Eu3+ doped CaMgB2O5 phosphor was first time synthesized by combustion method as the aim of our research is to produce and study the environment friendly phosphors for solid state lighting. The crystal structure of the prepared phosphor was studied by X-ray diffraction method and it almost closely matches with the standard JCPDS file No. 730618 showing the phase purity. The photoluminescence (PL) properties of prepared phosphors were broadly studied by excitation and emission spectra in this work. For the excitation wavelength of 352 nm, the emission spectra of CaMgB2O5: Dy3+ phosphor showed two peaks observed at 483 nm and 576 nm corresponds to the well known 4F9/2 → 6H15/2 and 4F9/2 → 6H13/2 energy transitions of Dy3+ ion respectively. The photoluminescence spectra of CaMgB2O5: Eu3+ phosphor revealed strong red emission at 614 nm (5D0 → 7F2) at n-UV excitation of 394 nm wavelength. The photoluminescence spectra of the CaMgB2O5: Sm3+ phosphor exhibit strong orange-red emission at 600 nm wavelength (4G5/2→6H9/2) under the UV excitation of 405 nm wavelength. As a result of observed photoluminescence characteristics, the produced phosphors may be a revolutionary material for the solid state lighting applications point of view. [ABSTRACT FROM AUTHOR]

The polynomial approach has previously been proposed for modelling the optical response of electromagnetic waves propagating across lossless multilayer thin films when a voltage is applied. This study presents a comprehensive investigation of the propagation of electromagnetic plane waves in lossless, nonmagnetic multilayer thin films under the influence of external transverse voltage. The main objectives of this research are to analyse the optical response of thin films subjected to varying numbers of layers. The model involves introducing an external transverse potential to the multilayer thin films to evaluate the impact on the optical response. External voltage can provide a means to actively tune and control the optical properties of multilayer thin films. By applying a transverse voltage, it becomes possible to modify the reflectance, transmittance, and absorption spectra of the films. This ability to dynamically manipulate the optical behaviour opens up new avenues for designing reconfigurable and adaptive optical devices with versatile functionalities. In this present article, the optical responses of electromagnetic waves propagating through multilayer thin films fabricated from calcium fluoride and silicon dioxide are being modelled and generated using the WT code. Both CaF2 and SiO2 are lossless materials. CaF2 and SiO2, being largely different in resistivity, are expected to display an enhanced effect in the accumulation of charge at the interfaces of adjacent layers. Simulations are performed on the optical response of multilayer CaF2/SiO2 thin films subjected to a tuneable transverse potential. The model is computationally implemented in a MATLAB program written by the author, dubbed the Wave Tensor (WT) code. The study demonstrates that the WT code efficiently and reliably predicts the optical response of thin films containing alternating lossless-lossless materials. The optical responses are obtained for different numbers of thin film layers and at various values of external transverse potential. The results show that the impact of the thickness of layers is greater than that of applying transverse voltage. In other words, the effect of the propagation matrices is stronger than the interface matrices. The variance in resistivity of adjacent layers partially enhances the influence of the interface matrices when applying a transverse voltage, leading to more significant effects on the curves of reflections and transmissions. The significance of the obtained results lies in their potential applications in optoelectronic devices and Nano photonics. Understanding and controlling the optical properties of thin films under external electrical fields can enhance the performance of devices such as optical filters, waveguides, and photodetectors. Moreover, the findings contribute to the fundamental understanding of light-matter interactions in layered structures and offer insights into the design and optimization of future nanostructured systems (Malek in Opt Quantum Electron 55:196, 2023; Al-Dossari in Opt Quantum Electron 55:484, 2023; Aly in Ismail Polym Bull, 2023; Aly in Sci Rep 13:9422, 2023). This research elucidates the optical behaviour of multilayer thin films under external transverse voltage and establishes a foundation for further investigations in the field (Shabat in Phys Scr 67:147, 2003; Taya in Optik Stuttg 122:666, 2011). The outcomes contribute to both applied and fundamental aspects of thin film optics and pave the way for future advancements in developing innovative optoelectronic devices and Nano photonic systems. [ABSTRACT FROM AUTHOR]

The magnetic characters of VPd2Br4 spinel were calculated using different spin orientations, and electronic properties were investigated using different potential approaches. The ferromagnetic (FM) phase was the most stable magnetic state according to magnetic orientation calculations. The lattice parameter of VPd2Br4 spinel in the FM phase was obtained as 11.17 Å. Elastic analyses showed that the FM phase was mechanically stable. Cauchy's pressure (C12–C44), B/G ratio, and Poisson's ratio (ν) values were obtained as 22.819 GPa, 5.89, and 0.419 at 0 GPa pressure, respectively. Therefore, VPd2Br4 spinel was ductile at 0 GPa pressure. As a result of elastic calculations, Debye temperature was obtained as 136 K. The total magnetic moment value of VPd2Br4 spinel in the FM phase was obtained as 2.00 µB/f.u. The V-atom with a magnetic moment value of 1.868 µB was the most partial contribution to the total magnetic moment. According to electronic properties, minority electron states of VPd2Br4 spinel had semiconductor character, while majority electron states showed metallic character. The band gaps in the minority electron states were calculated as 1.358 eV, 1.705 eV, 2.011 eV, and 2.253 eV according to the GGA, GGA + 1 eV, GGA + 2 eV, and GGA + 3 eV approaches, respectively. These band gaps in the minority electron states and the metallic characters in the majority electron states indicated that VPd2Br4 spinel had 100% spin polarization. Thus, using different approaches, VPd2Br4 spinel was obtained as a true half-metallic material in the FM phase. [ABSTRACT FROM AUTHOR]

To make the interpretation of the complex xCsBr.(100 − x)(CaO:SiO2) glasses easier to study, the structure of glasses in a binary composition CaO–SiO2 is being initially investigated. The changes in the crystallization behavior of glasses and the local environment surrounding silicon atoms could be easily followed using X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) spectroscopy. The XRD pattern of the binary 50CaO–50SiO2 glass showed that its structure was amorphous. But when (CaO:SiO2) is replaced with CsBr, some sharp diffraction lines appear in system of the ternary xCsBr.(100 − x)(CaO:SiO2) glass composition. A Polycrystalline Cs2Ca(SiO3)2 structure is the primary phase in CsBr rich glasses. The results based on transmission electron microscopy (TEM-EDP) and X-ray diffraction pattern (XRD) are in excellent agreement, indicating that crystalline-clustered species develop in glasses enriched with CsBr. Both the NMR and FTIR spectra are clearly defined, and they contain different features that distinguish between different silicate structural subunits. One and two bridging oxygen atoms (BO) can be found in the main SiO4 structural units. Such units become less shielded due to increasing of nonbridging oxygen atoms (NBO) in the silicate network by increasing CsBr at the expense of both SiO2 and CaO. There is a good correlation between the data obtained from FTIR and NMR spectroscopy. Both techniques could differentiate between BO and NBO involved in the silicate structural units. [ABSTRACT FROM AUTHOR]

The effect of Nb for Ti substitution on the electrical, modulus, dielectric and conductivity behaviors of a ceramic perovskite, barium lanthanum titanate (BLTi1-xNb4x/5), Ba0.97La0.02Ti1-xNb4x/5O3, for x = 0.07 and 0.10, was investigated at a 0 V d.c. bias potential. The flux reaction method was used to create our solid solutions. With a rising niobium amount, electrical conductivity rose. Jonscher's universal power law was used to fit the experimental results. The fact that the exponent n is marginally greater than 1 suggests that the hopping takes place between neighboring sites. As frequency rose, the dielectric constant dropped. Maxwell–Wagner (M-W) interface polarization is responsible for this behavior. Complex impedance spectroscopy was used to inspect the electrical characteristics in the frequency band (100–106 Hz), at a 0 V d.c. bias potential. With increasing Nb concentration, the real and imaginary components of impedance rose. Each sample was represented by a single impedance semicircle on the complex impedance plot that was visible over both high and low frequencies. The measurement of the complex electrical modulus spectrum revealed the impact of grain boundaries and grains on electrical properties. A mismatch between the two maxima of the impedance and electrical modulus normalization curves was observed, which points to the possibility of short-range motion of charge carriers. [ABSTRACT FROM AUTHOR]

A novel technique based on cavity perturbation for the characterization of dielectric properties of medium and low loss homogeneous cylindrically shaped materials using a single iris coupled rectangular resonator in Ka and millimeter-wave band frequencies is presented in this paper. The relative permittivity and the dielectric loss have been calculated using the conventional perturbation method. Simultaneously, simulation measurements are also done for unperturbed and loaded cavity resonator. Simulated results demonstrate the degradation in accuracy of the derived relative dielectric constant with increasing sample radius. Hence, a first-order correction factor has been added with conventional expression of dielectric permittivity proposed in cavity perturbation method to stabilize the value of dielectric permittivity at higher sample radius. This proposed method eliminates any cavity perturbation, originating from the variations in sample size. The dielectric samples are tested at Ka-band frequency of 36.624 GHz and also at mm-wave band having frequency of 52.263 GHz. [ABSTRACT FROM AUTHOR]

Recent developments in surface wave methods, which enable to produce fine subsurface shear-wave velocity models by processing array data of near-field seismic surface waves, are mainly concerned with the innovation in their constituent approaches. In this paper, we present a scheme to achieve a high horizontal resolution in Rayleigh wave dispersion-based estimations of 2D-subsurface ground section. It is to apply to all steps of the method some newly updated approaches: the method based on sparse signal representation and reconstruction for generation of high-resolution phase velocity dispersion image and mode separation, the cross-correlation method combined with phase-shift scanning for extraction of dispersion curves from consecutive pair of traces, and the thin-layer stiffness matrix method and trust-region-reflective algorithm for inversion procedure. Effectiveness of the attempt was shown by the synthetic analysis for models with dipping interface of 20° and single step-shaped interface, and by a real-field data result. [ABSTRACT FROM AUTHOR]

This article delves into the Ramsauer-Townsend effect regarding the realm of quaternionic relativistic quantum mechanics. The investigation centers on studying the Dirac equation within this framework. The article presents a thorough examination of various facets of the subject matter, including a comprehensive analysis of quaternionic potential wells, wave functions, probability currents, reflection coefficients, and transmission coefficients. [ABSTRACT FROM AUTHOR]

In this paper, we study the approximate analytical solutions for bound states of the l-wave Klein-Gordon equation with a position-dependent mass subjected to a hyperbolic cotangent vector potential by using the concept of the supersymmetric quantum mechanics approach. Within the framework of the proper approximation of the centrifugal term, we obtain the bound state energy eigenvalues and the corresponding normalized wavefunctions written down in terms of the Jacobi polynomials. Furthermore, it is found that the solutions in the case of constant mass for nonzero l-values are identical to the ones obtained in the literature. Among these cases, Hulthén potential, Coulomb potential, and nonrelativistic limit are discussed [ABSTRACT FROM AUTHOR]

A new extended bubble dynamical equation has been recently proposed by the authors Jin-Ze Liu, Wen-Shan Duan et al. Based on this equation, the dependence of the mass loss of a solid boundary material on the bubble parameters, such as the initial bubble radius, the distance from the center of the bubble to a solid boundary, and the intensity of external disturbances, is given in this paper. Our results can explain why there is mass loss when there are bubbles close to a solid boundary. Based on our results, it is also possible to estimate the mass loss of a solid boundary material due to bubble collapse. [ABSTRACT FROM AUTHOR]

Fission fragments mass distribution and fission cross section for proton-induced fission of some isotopes of Thorium and Uranium at low energies are simulated. A phenomenological method is employed to estimate the fragments mass distribution of 230 , 232 , 234 Th and 233 , 235 , 238 U isotopes. The Prokofiev semi-empirical method was also employed in order to obtain the fission cross sections of proton-induced fission of 232 Th and 233 , 234 , 235 , 236 , 238 U isotopes. The adjustable parameters of these phenomenological approaches are obtained using available experimental data in a given energy by the least squares method. Consequently, the calculated fragments mass distribution and fission cross sections are compared with the available experimental data. The obtained results reveal that the fragments mass distribution and the proton-induced fission cross sections are in a good agreement with the available experimental data. [ABSTRACT FROM AUTHOR]