Recently, the number of vehicles on the road, especially in urban centers, has increased dramatically due to the increasing trend of individuals towards urbanization. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturer) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based DL techniques for automatic identification of Jordanian vehicles. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish the license plate detection, character recognition, and vehicle logo detection. While VGG16 (Visual Geometry Group) model was retrained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7,035 Jordanian vehicle images, the second dataset consist of 7,176 Jordanian license plates, and the third dataset consists of 8,271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection, respectively. While the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for the vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, mean average precision (mAP) measure was used to evaluate the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, F-measure were 98%, 98%, and 98%, respectively. The performance of vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

The rate of accidents in Morocco is experiencing a significant increase. Automatic license plate detection and recognition (ALPR) is an essential road safety technology. It facilitates applications such as traffic control, law enforcement, and toll collection by allowing for the automated recognition of vehicles on the road. In this study, we incorporated ShuffleNet V2 into the end-to-end YOLOV5 object detection system. The objective was to develop a model capable of identifying Moroccan license plates with an accuracy of 87%. The proposed model is intended to attain a high processing performance of 60 frames per second (FPS) while maintaining a low weight of 1.3 megabytes (MB) and a parameter count of 0.44 million floating point operations (MGFLOP). Our model maintains superior performance and is highly compatible with embedded systems compared to other models utilized in the same context.

This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.

A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

The concept of a Social License to Operate (SLO) has become increasingly important in the sustainability literature in recent years. Having its origins in the business discourse of the mining industry with respect to limiting opposition to mining projects, the notion of a social contract above and beyond legal requirements has since become applied across a number of different industries. Despite the concepts adoption confusion exists over the practices and outcomes of SLO, and particularly the nature of engagement. Given this situation it is surprising that not more attention to the role of marketing, and social marketing in particular, in operationalizing the concept. The paper discusses the potential of social marketing to contribute to SLO. Economic, political and social relations are complex in SLO and exchange is intricate in such relational environments. A community-based social marketing orientation is proposed as a means to improve exchange relations and enhance engagement. Seven models of SLO related social marketing models are discussed with community-based social marketing and adaptive co-marketing models being regarded as the most positive for the achievement of an SLO. Potential barriers to adoption of these approaches are noted.

Background: We prepared reference materials of fentanyl and amphetamines in hair, and established methods for the certification of reference materials of fentanyl and amphetamines in hair samples. Methods: Hair samples containing 15 drugs were prepared by immersion method (water-dimethylsulfoxide). On the basis of single-factor experiment, Box-Behnken design-response surface was used to optimize the pre-treatment conditions of samples. In MRM/SIM mode, 15 drugs were quantitatively analyzed by LC-MS/MS and GC-MS/MS methods. Results: The contents of 15 drugs in the mixed hair samples were: 1.050 ng mg-1~7.771 ng mg-1, RSDs<7%(n=11). The results showed that the two analytical methods developed were highly sensitive (LODs as low as 0.05 pg mg-1), reproducible (intra-day and inter-day precision RSDs were<10%) and accurate. F-test was performed on the prepared hair samples and the results showed that the homogeneity of the prepared hair samples was good. Conclusions: The established methods were used for the detection of fentanyl and amphetamine drugs in human hair, with high sensitivity, accuracy and specificity. It can be used as a certified method for the development of fentanyl and amphetamine matrix certified reference materials. The developed hair reference materials are expected to be applied in forensic drug abuse detection and laboratory quality control.

Background: Ankle fractures, comprising about 10% of musculoskeletal injuries, pose a significant healthcare burden with an increasing incidence, particularly among an aging population. This study focuses on isolated Danis-Weber type B fractures. Recent trends lean towards surgical intervention for anatomical restoration, quicker recovery, albeit with associated complications. This report aims to assess the safety and efficacy of NewClip narrow locking plate system with early weight bearing in treating isolated fibular fractures. Methods: The study includes patients with Danis-Weber type B fractures surgically treated with NewClip narrow locking plate system. Exclusions involved nondisplaced fractures, open fractures, or those requiring additional fixation.Results: Fifteen patients were enrolled in this study. Minor complications, including wound swelling, were observed in two cases. The Visual Analogue Scale (VAS) for pain exhibited a steady decline postoperatively, with almost complete resolution by the third month. At three months, the FAOS demonstrated excellent results, and final follow-up at twelve months revealed complete osseous healing without complications.Conclusions: The use of this type of plates in treating isolated fibular fractures with early weight bearing shows promising results in terms of fracture healing, pain resolution, and functional outcomes warranting further investigation with larger cohorts.

In this work, the analytical Lagrange-Kirchhoff model for the deformation for a taiko plate silicon is proposed. Under the conditions of validity of the linear theory of thin plates, the model is in agreement with the experimental data.

Aiming at the problems of high labor intensity and high cost of manual picking, high loss rate of mechanical picking seeds, low mechanization level and difficult to guarantee the quality of picking plates in the process of picking plates of edible sunflower. Based on the principle of manual plate taking, a plate taking control system for sunflower harvesting table was designed. Through the research and analysis of the principle of taking the plate of the edible sunflower harvesting table. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. To verify the working performance of the plate control system, with the absolute error, relative error and total loss rate of the harvest as objectives. The bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error is only 0.94 %, and the grain loss rate was not more than 2.26 %. It result showed that the system algorithm was reliable, the positioning accuracy was high, and the taking plate operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5 %. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32 %, and which indicated the control system meets the requirements of sunflower harvesting operation.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

In the contemporary accelerated pace of life, emphasizing the importance of sleep quality is essential for enhancing overall well-being and health. This study investigates the link between poor sleep quality and key factors affecting wellness, such as mental health and eating disorders. Through a cross-sectional analysis involving 407 participants, utilizing established measures including the Depression Anxiety Stress Scale (DASS-21), the Eating Disorder Examination Questionnaire Short (EDE-QS), and the single-item Sleep Quality Scale (SQS), data was collected and analyzed using SPSS v28 and R-Statistics. The findings reveal a significant correlation (p<0.05) between DASS-21, EDE-QS, and SQS, indicating that individuals experiencing poor sleep quality exhibit higher levels of depression, anxiety, and stress. Furthermore, multinomial logistic regression analysis highlights low sleep quality as a risk factor for both mental health (OR: 1.071, 95% CI: 1.042, 1.102, p<0.05, low vs. high sleep quality), and eating disorders (OR: 1.047, 95% CI: 1.004, 1.092, p<0.05, low vs. high sleep quality). Overall, these results underscore the critical role of sleep quality in mental health and suggest insomnia as a predictive factor for both poor mental well-being and disordered eating habits. Further research through randomized controlled trials is warranted to validate the findings of this cross-sectional study.

Understanding user challenges with light dosimeters is crucial for designing more acceptable devices and advancing light exposure research. We systematically evaluated the usability and acceptability of the lido (light dosimeter) by 29 participants who wore the dosimeter near the corneal plane of the eye for 5 days. Common reasons for not wearing the dosimeter included exercise, recharging, wet environments, public settings, and discomfort. Despite these issues, participants adhered to using the dosimeter, with high compliance (89% of recording time). Our findings reveal a significant discrepancy between mean melanopic Equivalent Daylight Illuminance (300 lxmEDI) and median values (51 lxmEDI). This discrepancy indicates that, for most of the time, participants experienced significantly lower light levels. Specifically, participants were exposed to light levels above 250 lxmEDI for only 14% of their wearing time. This highlights the need for increased exposure to recommended light levels. In the evening, participants' light exposure was below the recommended 10 lxmEDI for 58% of the wear time, which is in line with the guidelines for reducing light exposure before sleep. This study highlights the critical need for strategies to enhance daily light exposure, aligning better with circadian health recommendations.

Wear-resistant steel/carbon steel composite plate not only has the double performance advantage of high strength and wear-resistance, but also can reduce energy consumption and production cost. Based on 50% reduction rate, the wear resistance of BTW1/Q345 composite was studied at different annealing temperatures, and the dry friction and wear tests of BTW1/Q345 composite at different annealing temperatures were carried out by using RETC MFT-5000. The macro-micro morphology and wear mechanism of wear marks at different annealing temperatures were analyzed by using white light interference three-dimensional surface profiler, Scanning electron microscope scanning electron microscope (SEM) and backscatter electron diffraction (EBSD) . The results show that with the increase of annealing temperature, the grain size near the scratch of BTW1 in the wear-resistant layer of the composite plate is refined, and the wear-resistant property is higher than that of the original sample.When the annealing temperature is 860℃,the wear resistance is the best.

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of Artificial Intelligence (AI), particularly Deep Learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Determining the general-name-derived semantic relationships between geographic names often requires consulting many geographic name documents for verification. In geographic name translation and geographic name knowledge graphs, this method cannot meet the needs of large-scale extraction of general-name-derived semantic relationships between geographic entities. To solve this problem, this paper proposes a general-name derivation relation recognition method based on Prompt learning and Semantic Spatiotemporal Distribution Attention Feature Fusion (SSD-AFF). This method adopts a pipe-based relationship recognition strategy. First, the Prompt learning method is adopted to form a general-name derivation pattern with the general-name-derived geographical name category and the original geographical name category as the prompt word, and then the derivation pattern is combined with the geographical name as the input of the model so that the recognition problem of the general-name derivation of the geographical name can be transformed into a classification problem using the bidirectional language model. Then, by analyzing the characteristics of the semantic association and geographical spatio-temporal distribution between the general-name-derived geographical name and the original geographical name, the semantic spatio-temporal distribution characteristics of the general-name-derived geographical name are constructed. Finally, the network model based on the semantic spatio-temporal distribution Attention Feature Fusion (SSD-AFF) is used to distinguish the general-name-derived semantic relationships. The experimental results show that the proposed method has good performance. It can effectively mine the general-name-derived semantic relations between the vector data of geographical entities and has important application value in geographical name translation and geographical name knowledge maps.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

The agricultural soil maps (ASMs) showing the agricultural land of Poland were prepared in 1:5000 scale in 1960s’ and 1970s’. These maps show land suitability group, soil type and soil texture (ST) to a depth of about 150cm. Nowadays, these maps are being digitalized and might be a basis for the preparation of modern soil maps at local, regional, national and international level. The agreement between the ST of topsoil derived from ASMs and recently evaluated for eleven fields located in three voivodeships (regions) of Poland was studied. The study of soil considered the examination of soil profiles or augerings and laboratory analysis of the ST. The agreement between soil status in the field and according to maps was field-specific. Complete agreement (purity) within the field was assessed for 5-79% of ST classes and for 23-100% of agronomic categories (ACs) – a groupings of similar ST classes. However, the averaged agreement, which treated adjacent ST classes as having partial agreement, varied from 37 to 88% for ST classes and from 61 to 100% for the ACs among studied fields. These results indicate on the variable quality of the information shown on the ASMs and on the necessity of improvement of these maps.

AI (Artificial Intelligence) chatbots has been widely applied. AI (Artificial Intelligence) chatbots has been widely applied. ChatGPT could enhance individual learning capabilities and clinical reasoning skills, facilitate students' understanding of complex concepts in healthcare education: (1) Background: There is currently less emphasis on its application in nursing education. The application of ChatGPT in nursing education needs to be verified; (2) Methods: A descriptive study is used to analyze the scores of ChatGPT on the registered nurse license exam(RNLE) and compare to the pass rate of candidates in Taiwan; (3) Results: The average score of four exams were around 51.6 to 63.75 by ChatGPT, and pass the RNLE in 2022 1st and 2023 2nd. However, ChatGPT may generate misleading or inaccurate explanations, or it could lead to hallucinations; (4) Conclusions: ChatGPT may have potentially to assist with nursing education. Although AI can simulate human behavior in context, it still cannot replace human creativity, critical thinking, and clinical reasoning.

Background: Although National Licensing Examinations (NLEs) may be a costly process, they can predict better performance for many following years. The current licensing requirements will not entail and new requirements will be requested. Therefore, this study suggests a framework for the development of the Medical Licensing Exam by exploring the opinions of Egyptian medical practitioners and educators regarding the exam format and criteria. Objective and methods: to describe the needed steps to develop the EMLE through a two-phase exploratory mixed-method study that had been conducted among the Egyptian medical sectors: medical practitioners and educators. Results: There were 50 clinical and academic educators shared in the online discussion about EMLE development. Then, a survey was carried out on 266 participants with various working places in which 198 of the participants were staff in the Higher Education Ministry and 68 physicians in the Ministry of Health. The input from both had contributed to the development of the following framework that is divided into two main sections; the Exam Logistics and the Exam Set up. The exam Logistics included the exam committee, prerequisites for the exam, the admission criteria and fees, and the validity of the license. While the Exam Set up included exam setting, structure, standard-setting, pass marks and reset policy. Conclusion: A Multidisciplinary team resources for the exam setting. Fairness and objectivity were highlighted through several factors; development of the exam blueprint, types of assessment methods, post-exam analysis, and standard-setting. Finally, the reset policy, fees, and validity of the license were recommended with a student-centered perspective that was suggested for the exam committee.

This paper presents a method for a decentralised peer-to-peer software license validation system using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for software developers to protect copyrighted works. Protecting software copyright has been an issue since the late 1970's and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. The method described creates an ecosystem in which the rights and privileges of participants are observed.

The paper presents a proposal for an in-house mathematical model of a liquid flat-plate solar collector. The proposed model is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all components of the collector. The formulated differential equations are solved iteratively using an implicit difference scheme. In order to obtain a stable numerical solution, it is necessary to use appropriate steps of the time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and the calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. Comparing, on an ongoing basis, the measured and the calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The model's simplicity and the low computational demands enable such comparisons in an online mode.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

Prompt removal of misfolded membrane proteins and misassembled membrane protein complexes is essential for membrane homeostasis. However, the elimination of these toxic proteins from the hydrophobic membrane environment has high energetic barriers. Transmembrane FtsH is the only known ATP-dependent protease responsible for this task, unlike other well-studied soluble ATP-dependent proteases. The mechanisms by which FtsH recognizes, unfolds, translocates, and proteolyzes its substrates remain unclear. Here, we report the crystal structures of the Thermotoga maritima FtsH periplasmic domain (PD) in an associative trimeric state at a 1.5-1.95 Å resolution. We also describe the pH-dependent oligomerization states of the isolated PD using dynamic light scattering. These observations help us understand how FtsH recognizes membrane-anchored misfolded proteins.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

It is commonly recognized that a ship’s ability to maintain stability is dependent upon the way its loads are distributed. This means making sure the center of gravity is sufficiently low and evenly distributed. Yet when a ship encounters turbulent water with extremely large wave amplitudes, load distribution is a critical factor in determining how stressed the ship becomes. In particular, this article examines several methods for increasing the ship's longitudinal strength to prevent ship structural damage during hogging and sagging. This research presents a number of proposed techniques to enhance the longitudinal strength of cargo ships in order to guarantee minimal stress and deformation during sagging and hogging. The first method is the stern-to-bow use of curved support structures to increase the longitudinal strength of a cargo ship. And another way to stiffen the lateral plate is the proposed use of different designs of the side plate, such as wavy curves, stars, etc. To further boost the strength, the lateral plate upper part is suggested to curve upward. A numerical simulation using ANSYS of the stiffened plate when subjected to uniform load distribution and fixed support on either end shows that there is a decrease in deformation of the plate when we use wavy curved plates compared with those of the other plate stiffening methods used, and furthermore, curving the plate on top increases its strength during sagging as well as hogging. Finally, the use of a curved support structure further enhanced the overall longitudinal strength of the vessel.

Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial-temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. We propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (chroma, spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the "Magnified 1/4 Size" configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors and complexity of character interactions.Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy.To address the dataset complexity and multi-targeting problems, feature extraction is optimized and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students raising their hands, reading, writing, playing cell phones, looking down, and lying on the table in high-definition images. And the accuracy and robustness of this network are verified.Compared with small target detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network has better detection performance and can efficiently deal with the behavior recognition of multiple students.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

This paper presents an approach to the Kirchhoff-Love plate theory (KLPT) using the finite difference method (FDM). The KLPT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally in the case of KLPT, the finite difference approximations are derived based on the fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection surface. The FOPH is made for the fourth and third derivative of the deflection surface while the SOPH is made for its second and first derivative; this leads to a 13-point stencil for the governing equation. In addition, the boundary conditions and not the governing equations are applied at the plate edges. In this paper, the FOPH was made for all of the derivatives of the deflection surface; this led to a 25-point stencil for the governing equation. Furthermore, additional nodes were introduced at plate edges and at positions of discontinuity (continuous supports/hinges, incorporated beams, stiffeners, brutal change of stiffness, etc.), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at the plate edges and to satisfy the boundary and continuity conditions. First-order analysis, second-order analysis, buckling analysis, and vibration analysis of plates were conducted with this model. Moreover, plates of varying thickness and plates with stiffeners were analyzed. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. In first-order, second-order, buckling, and vibration analyses of rectangular plates, the results obtained in this paper were in good agreement with those of well-established methods, and the accuracy was increased through a grid refinement.

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Background: The surgical approach to treating calcaneal fractures, specifically Sanders types II, III, and IV, is still a subject of controversy. The decision to opt for open reduction and internal fix-ation (ORIF) or minimally invasive osteosynthesis (MIOS) remains uncertain owing to varying complication rates. This study intends to examine each treatment’s pros and cons in relation to the incidence of complications. Methods: In this retrospective study, we examined 70 patients treated between January 2018 and September 2022: 50 received ORIF, and 20 underwent MIOS. We evaluated functional outcomes with the Maryland Foot Score and the Creighton-Nebraska Health Foundation Assessment Scale (CNHFAS). We also assessed the amount of anatomical restoration using Böhler’s and Gissane’s angles, determined by radiographic checks, and the frequency of postoperative complications. Results: A significant reduction in the risk of reinter-vention was observed in patients with high anesthetic risks, polytraumatized individuals, and those with comorbidities who underwent MIOS. The chi-square test analysis of postoperative complications highlighted no substantial differences between the ORIF and MIOS groups in rela-tion to tendinitis, chronic pain and swelling, algodystrophy, and surgical wound infections (p > 0.20 for all). Conclusion: ORIF is more suitable for patients who have high motor function re-quirements, a long-term activity schedule and can endure potential additional procedures. This preference is because ORIF provides better anatomical restoration and features a lower osteoar-thritis occurrence rate.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Modification of electrode surface with a selective layer gives the possibility to detect target ana-lytes of forensic interest. 3-(4-trifluoromethyl)-phenyl)-thiophene (ThPhCF3) was deposited on a graphite (G) electrode by electrochemical oxidation and characterized as a receptor for the recognition of synthetic stimulants (2-aminoindane, buphedrone, naphyrone). First, the structural characterization of the polymeric layer were obtained from Raman spectroscopy. Second, the electrochemical profiles of selected synthetic stimulants and their affinity to ThPhCF3/G-modified electrodes were studied using square wave voltammetry and electrochemical impedance spec-troscopy. The values of the adsorption constant show the importance of – PhCF3 groups in the interaction with synthetic stimulants, namely recognition by the formation of hydrogen bonds (Kads (buphedrone) < Kads (2-AI)) and interaction with aromatic H atoms (Kads (buphedrone) < Kads (naphyrone)). It was demonstrated that the polymeric layer derived from thiophene with – PhCF3 group is capable to increase the intensity of the electrochemical signal.

Heat exchangers play a vital role in numerous sectors like power generation and automotive manufacturing. Enhancing the energy efficiency of these exchangers stands as a significant hurdle. Utilizing nanofluids emerges as an optimal solution to augment heat transfer rates, leveraging their superior thermal conductivity while addressing economic constraints linked with traditional heat exchanger technologies. The motivation for this study is to consider the comprehensive synergistic features of MWCNTs, SiO₂, and hybrid MWCNTs–SiO₂ EG-based nanofluids in plate heat exchangers. This was achieved by simulating a plate heat exchanger using CFD (Fluent and Gambit softwares) considering the comparison of two SiO₂, MWCNTs, and their hybrid forms at different mass concentrations. The Reynolds-Averaged Navier-Stokes (RANS) method was employed to model turbulent flows, and the nanoparticles were compared in both single-phase and two-phase states, with their properties derived from experimental studies. Moreover, Nusselt number, convective heat transfer coefficient, and pressure drop variations were computed at different Reynolds numbers. The findings indicated an increase in the Nusselt number with the incorporation of the hybrid nanofluid, achieving a maximum enhancement of 11.19% in comparison to EG at a Reynolds number of 240 and a concentration of 0.86 wt.%. Noticeably, the pressure drop remained negligible at lower Reynolds numbers, exhibiting marginal variations up to 0.3% at higher Reynolds numbers. At a concentration of 0.86 wt.% and a Reynolds number of 480, the introduction of MWCNTs, hybrid, and SiO₂ nano-additives resulted in respective increases in the heat transfer coefficient by 21.02%, 10.76%, and 2.16% and corresponding alterations in pressure drop of 0.11%, 0.04%, and 0.02%.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological survey due to its low-cost, ease of use and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a new-developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views we can quickly detect the occurrence of collapse events, determine their location and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we have preliminary tested the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining collapse information proposed in this paper can extend the potentials of landslide monitoring system based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.

Recent advancements in brain stimulation and nanomedicine have ushered in a new era of therapeutic interventions for psychiatric and neurodegenerative disorders. This review explores the cutting-edge innovations in brain stimulation techniques, including their applications in alleviating symptoms of main neurodegenerative disorders and addiction. Emphasis is placed on the underlying main neurotransmitter modifications and their specific brain area location, particularly focusing on the dopaminergic system, which plays a critical role in these conditions. Furthermore, the review delves into the groundbreaking developments in nanomedicine, highlighting how nanotechnology can be utilized to target aberrant signaling in neurodegenerative diseases, with a specific focus on the dopaminergic system. The discussion extends to emerging technologies such as magnetoelectric nanoparticles (MENPs), which represent a novel intersection between nanoformulation and brain stimulation approaches. These innovative technologies offer promising avenues for enhancing the precision and effectiveness of treatments by enabling non-invasive, targeted delivery of therapeutic agents as well as on site, on-demand stimulation. By integrating insights from recent research and technological advances, this review aims to provide a comprehensive understanding of how brain stimulation and nanomedicine can be synergistically applied to address complex neuropsychiatric and neurodegenerative disorders, paving the way for future therapeutic strategies.

In the paper the experimental methodology for the single-crystal circular plate deformation measurement and subsequent procedure for mechanical properties quantitation are developed. The procedure is based on a new numerical-analytical solution of nonlinear boundary-value problem for finite deformations of a circular anisotropic plate. Using the developed method, a study of the deformation of single-crystal circular plates formed on the basis of a silicon-on-insulator structure was carried out. The values of residual stresses are determined and it is shown that the presence of these stresses increases the flexural rigidity of the plate in several times.

In general sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, an analysis and comparison is made between the results of Chladni plate experiments and Chladni plate simulations. We consider a finite range of frequencies and plate geometries and present a model that has been shown to successfully recreate the nodal line patterns to a first approximation. We discuss the benefits of such a model and the future work necessary to develop the model to its full predictive capacity.

The sleeping and grazing behaviors of horses play a crucial role in their health and welfare. In the horse industry, with the prevalent trend of individual single-stall farming in stable, accurately identifying various behaviors of horses within stables is imperative. This study has established a spatio-temporal dataset for horse postures and behaviors, proposed an algorithm utilizing a SE-SlowFast network with an enhanced loss function. This algorithm achieves automatic recognition of multiple postures and behaviors of horse simultaneously. The model developed in this study demonstrates high accuracy in identifying three postures within the stable: Standing, Crouching, and Lying, achieving respective accuracies of 92.73%, 91.87%, and 92.58%. Additionally, it accurately recognizes two behaviors: Sleeping and Grazing, achieving best accuracies of 93.56% and 98.77%. The overall best accuracy of the model is 93.9%. Experiments on video recognition confirm that the suggested model recognizes horse video data accurately. This study marks the first time computer vision technology has been used in the horse farming industry, achieving non-intrusive recognition of multiple horse postures and behaviors. This study provides valuable data support for livestock managers to evaluate the health conditions of horses, enhances the welfare of horses in stables, and contributes to the advancement of modern horse husbandry practices.

Identifying punch types as part of punch kinematic analysis is a key component for both coaches and players, providing critical insights into the variety and effectiveness of punches, which are essential for refining overall performance. Existing research indicates that punch type classification typically relies on data from either wearables or video data. Both approaches have their merits and demerits but require a significant amount of labeled data for modeling the classification algorithm. In this work, we propose a novel approach that significantly reduces the labeling effort by one-sixth by leveraging video data to enhance the training information of sensor data. This enriched training phase enables more accurate punch classification with reduced labeled data. Subsequently, the real-time punch identification and classification use only the wearable data. On a real dataset, with 15% of the labeling efforts, our approach achieved an accuracy rate of 91.41% for rear hand punch recognition and 91.91% for lead hand punch recognition, along with 92.33% and 94.56% for punch classification, respectively. Through the development of our Smart Boxer system, we aim to revolutionize punch analytics in boxing, providing comprehensive insights while enhancing the overall training and competitive experience for athletes and enriching fan engagement with the sport.

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues on steel pipes, the demand for glass fiber reinforced plastics (GFRP) pipes is increasing in the oil and gas gathering and transmission systems. However, the medium transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-、Ca2+、Mg2+、SO42-、CO32- and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped GFRP pipes with depths of 2mm and 5mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

Spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stress including herbicide, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for diagnosis of herbicide activity and mode of action. Crabgrass (Digitaria ciliaris) as a model weed was cultivated in multi-well plates and subsequently treated with 6 herbicides with different modes of action when they reached the 1-leaf stage, using only a quarter of the recommended dose. To detect plant response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and mode of action. Principal Component Analysis (PCA) using all spectral data successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments have shown that the multi-well plate assay combined with spectral image analysis can be successfully applied for herbicide bioassay. In addition, the use of spectral image sensors, especially in CF image, would facilitate HTS by enabling rapid observation of herbicide response, as early as 3 hours after herbicide treatment.

Rett syndrome (RTT) is an X-linked neurodevelopment disorder associated with the single monogenic mutation in methyl-CpG binding protein 2 (MeCP2) in up to 95% of cases. The growing number of genome-wide association studies and incomplete concordance for autoimmune diseases in monozygotic twins concur to support the involvement of environmental factors, like infectious agents or chemicals, in the breakdown of tolerance leading to autoimmunity. In fact, the coexistence of a perturbation of the immune system in RTT patients has been previously hypothesized. We herein explored the hypothesis that an autoimmune component derived from environmental bacterial infection of non typeable Haemophilus influenzae may coexist in RTT. At this purpose we screened sera from RTT syndrome patients, non-RTT pervasive developmental disorders patients and healthy controls with the hyperglucosylated adhesin protein HMW1ct(Glc) used as a precise antigen in ELISA in order to identify specific antibodies to N-glucosylation sites. Results showed that HMW1ct(Glc) is able to significantly discriminate antibodies among RTT sera and controls. Competitive ELISA confirmed the specific interaction between antibodies characteristic of RTT syndrome and the N-glucosylation motifs of the bacterial adhesin protein HMW1ct(Glc).

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the periglacial environment. Deep learning algorithms and red-green-blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from QiLian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (Mean Intersection over Union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer's accuracy of 93.68% and a user's accuracy of 94.18%. Furthermore, the model was employed to two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

Vibrations of plate structures placed in a supersonic flow is considered. The undisturbed fluid flow is parallel to the plate. Two specific problems are treated: in the first one the plate is in the form of an infinite strip and the flow is in the direction of its finite length. Rigid walls extend from the sides of the plate indefinitely. In the second problem, the plate is a finite rectangle and the flow is parallel to one of its sides. The first problem is a limiting case of the second problem. The flow is modeled by piston theory which assumes that the fluid pressure on the plate is proportional to local slope. This approximation is widely used at high speeds, and reduces the interaction between the fluid flow and the vibrations of the plate to an additional term in the vibration equation. The resulting problem can be solved by assumed mode methods. In this study, the solution is also carried out by using the collocation method. The main result is the flutter velocity of the free fluid flow under which the plate vibrations become unstable. Finally, simple expressions are proposed between the various non-dimensional parameters that allows quick estimation of flutter velocity.

The paper describes the Field Boundary Element Method applied to the fracture analysis of a 2D rectangular plate made of Functionally Graded Material to calculate Mode I Stress Intensity Factor. The object of the Field Boundary Element Method is the transversely isotropic plane plate. Its material presents an exponential variation of the elasticity tensor depending on a scalar function of position, i.e., the elastic tensor results from multiplying a scalar function by a constant taken as a reference. Several examples using a parametric representation of the structural response show the suitability of the method that constitutes a sight of Stress Intensity Factor evaluation of Functionally Graded Materials plane plates even in the case of more complex geometries.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

The Ionian Sea is a crucial intersection for various water masses in the Mediterranean. Its hydrography and dynamics play a significant role in the seawater budgets and biogeochemistry of the neighboring sub-basins. Multiple theories have been formulated to gain a better understanding of the Ionian dynamics. These theories primarily attribute the variability of the near-surface Ionian circulation to internal processes. Here, we utilize horizontal currents and temperature-salinity profiles from the Copernicus reanalysis to examine the contribution of baroclinic modes to the variability of the basin horizontal circulation. Our findings demonstrate that, although the basin vertical structure is characterized by three layers, the primary patterns of the Ionian circulation can be attributed to the first baroclinic mode. This mode, along with the barotropic mode, accounts for over 85% of the overall variability in the Ionian circulation, suggesting that only one of the three interfaces separating the different water masses in the basin is dynamically active. We estimate the depth of this interface to be about 490 m. Additionally, our analysis shows that more than 90% of the kinetic energy over the water column is localized above this interface, indicating that the deep layer of the Ionian is dynamically nearly inert.

Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Fibreglass composite plates are used for applications ranging from aerospace and automotive industries to construction industry. Vibroacoustic properties of the fibreglass plate has been investigated for different boundary conditions. The theory of the plate vibration and sound radiation has been used to determine the deflection of the composite plate at different locations on its surface. 3-Dimentional (3D) deformations and their corresponding contour velocities of plate have been computed for a range of frequencies. It has been shown that clamping the plate at four edges delayed first resonance of the plate by 15 Hz and second resonance of the plate by 30 Hz. Vibroacoustic indicators and the radiation impedance matrix of the plate have also been calculated. The results of this study show that fibreglass plate could be used in building industry to dampen the vibration of machine and structural vibration of buildings, and to sandwich building structures to form panels in order to attenuate airborne sound and to lower noise transmission of structural borne sound

The motion of a circular elastic plate floating on the surface is investigated in the time–domain. The solution is found from the single frequency solutions and the method to solve for the circular plate is given using the eigenfunction matching method. Simple plane incident waves with a Gaussian profile in wavenumber space are considered and a more complex focused wave group is considered. Results are given for a range of plate and incident wave parameters are investigated. Code is provided to show how to simulate the complex motion.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, Named Entity Recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the Multiple Fine-Grained Feature Fusion (MFGFF) module with a BiLSTM neural network and employs an Efficient Global Pointer (EGP) for predicts the entity location encoding. In MFGFF module we designed three encoders, character encoder, word encoder and sentence encoder, this design effectively captures fine-grained features and improves the recognition accuracy of nested entities. Experimental results show that the model performs robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of knowledge graph and intelligent question-answering system for chicken diseases but also provides a viable solution for extracting disease information that can be applied to other livestock species.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

A large part of our understanding of the biological substrates of sex-recognition and mate-recognition is derived by studying animal models. In performing those tasks, rodents rely mostly of pheromones and other olfactory cues, whereas humans rely mostly on visual cues. That may hinder the translation of rodents’ biology to humans’ biology, especially at the neural-networks level, where those cues traverse different networks in humans and rodents brains. That may be called the “pheromonal-visual gap”. A theoretical model presented here addresses those issues. The model merges observations from humans and model-animals, as reported in specific scientific reports, and general biological principles that are accepted by the scientific community. The model suggests that the voices of men and women are the innate cues based on which humans learn to use visual cues in sex-recognition and mate-recognition. Children learn the two tasks in associative learning mechanisms, by being immersed in their community, and observing adult role-models in innocuous, non-sexual scenarios. The model proposes that the human medial-geniculate-nucleus (MGN) is the analog of the rodents’ accessory-olfactory-bulb (AOB) and the main-olfactory-bulb (MOB), and that the human MASH pathway (MGN, amygdala, bnST, hypothalamus) is the analog of the rodents’ VNOP (Vomeronasal-organ-pathway). Considering the differences in the pathways should facilitate the translation from rodents’ brain nuclei and tracks to humans’. Also, the model hypothesizes that innate direct and indirect connections between auditory centers, e.g., MGN, and sex-control centers, e.g., hypothalamus, vary across three groups of children, and those variations determine the individual’s mate-recognition that emerges at puberty.

This study focuses on the increased risk of high heat release and asphyxiation (toxic gas poisoning) in the event of a fire of polyurethane (PU) and MDF-based building materials, which are commonly used in building fires. Among them, polyurethane (PU) building materials are essential for buildings except in Europe and some other countries due to their excellent thermal insulation performance, but problems of short-term heat release and spread of toxic gases in the event of a fire continue to occur. To overcome these problems, researchers are actively working on introducing various flame retardants into building materials. Therefore, in this study, we produced a laboratory-sized (500mm*500mm) plate flame retardant board that can be utilized as a building material with a lower heat release rate and a lower toxicity index by mixing expanded graphite and ceramic binder as flame retardants in a main material based on the cellulose of waste paper by replacing the existing building materials with a hot-press method. According to the ISO-5660-1 test on the heat release rate of the plate flame retardant board, the THR value was 2.9 (MJ/m2) for 10 minutes, showing an effect of reducing the THR value by 36.3 (MJ/m2) compared to the THR value of 39.2 (MJ/m2) of the specimen made using only paper. In addition, the toxicity index of the flame retardant board was checked through the NES 713 test. As a result, the test specimen showed a toxicity index of 0.7, which is 2.4 lower than the toxicity index of 3.1 of MDF, which is utilized as a conventional building material. Based on the results of this study, the cellulose fire retardant board showed the effect of reducing the heat release rate and toxicity index of building materials in a building fire, which reduces the risk of rapid heat spread and smoke toxicity. This has the potential to improve the evacuation time (A-SET) of evacuees in fires. It is also important to show that recycling waste paper and utilizing it as the main material for building materials can be an alternative in terms of sustainable development.

Monitoring is an important component of pest management, to prevent or mitigate outbreaks of native pests, and to check for quarantine organisms. Surveys often rely on trapping, especially when the target species respond to semiochemicals. Many traps are available for this purpose, but they are bulky in most cases, which raises transportation and deployment issues, and they are expensive, which limits the size and accuracy of any network. To overpass these difficulties, entomologists have used recycled material, such as modified plastic bottles, producing cheap and reliable traps but at the cost of recurrent handywork, not necessarily possible for all end-users (e.g., for national plant protection organizations). These bottle-traps have allowed very large surveys which would have been impossible with standard commercial traps, and we illustrate this approach with a few examples. Here we present, under a Creative Commons BY-SA License, the blueprint of a fan-trap, a foldable model, laser-cut from a sheet of polypropylene, that can rapidly be produced in large numbers, and could be transported and deployed in the field with very little efforts. Our first field comparisons show that fan-traps are as efficient as bottle-traps, and we describe two cases where they are being used for monitoring.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

The article evaluates the use of machine learning methods to predict valve plate failures in hydraulic pumps. To conduct this assessment, a laboratory setup was developed that allowed the creation of a training dataset containing both the normal operating state of the pump and the operating state with three different valve plate damages. Five different predictive models were evaluated, including three composite models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Additionally, the attribute importance analysis revealed that leak flow, pressure output together with output flow, are the most important parameters for accurate prediction.

Multi-gap Resistive Plate Chamber has been attracting increasing attention and has been studied in recent decades because of its good efficiency, high time resolution and relatively low cost. It has become a new standard technology for the Time of Flight system in particle and high energy physics experiments. In this paper, we introduce the operation principle and structure of the MRPC detector and review the applications on the Time of Flight system in several famous experiments, such as STAR, CBM and SoLID. The performances including time resolution and particle identification are discussed in detail. Some recent advances and perspectives with regard to the future development of the next generation MRPC are also outlined.

In this paper, the elemental construct of an I-shaped fin cooling a heat generating volume is studied through the entropy production perspective. Using the constructal design method framework with entropy production minimization as an objective leads to different optimal shape ratio, different from the ones obtained using the excess of temperature. Moreover, the optimal shape ratio evolution is not necessary the same for local entropy production minimization (excess of entropy production) and global entropy production (entropy produced in the whole area). Finally, an analysis on a possible dimensionless number is introduced for the study of constructal plates.

One of the most important pieces of equipment used in labs in culturing populations of fruit flies (Drosophila sp.) is that of the “CO₂ gas plate”, which is used to anesthesize individuals during “fly-pushing”. This piece of equipment consists of a box with a porous top into which carbon-dioxide is pumped. Flies placed on its surface are left immobilized, permitting the sorting, categorizing and/or counting of flies during population culturing and experimental assays. Unfortunately, commercially available gas plates are typically expensive. Here, we describe a new design for a gas plate that can be easily produced using a 3D printer and a laser cutter, which we are making freely available to the fly community.

The plate fin heat exchanger is the compact heat exchanger applied in many industries because of its high thermal performance. To enhance the heat transfer of plate fin heat exchanger in further, three new kinds of wavy plate fins, namely perforated wavy fin, staggered wavy fin and discontinuous wavy fin are proposed and investigated by CFD simulations. The effects of key design parameters, including that of waviness aspect ratios, perforation diameters, stagger ratios and breaking distance are investigated, respectively, with the Reynolds number changes from 500 to 4500. It is found that due to the swirl flow and efficient mixing of fluid, the perforation, serration and breaking techniques are beneficial for the enhancement of heat transfer compared to the traditional wavy fin. At the same time, serration is beneficial to reduce the friction factor, and the breaking technique can reduce heat transfer area as well as enhance heat transfer performance. Through the performance evaluation criteria, the staggered wavy fin has an advantage over the small waviness aspect ratio compared to the perforated wavy fin. The maximum performance evaluation criteria (PEC), as high as 1.24, can be obtained for the perforated wavy fin at the largest waviness aspect ratio.

Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.

To date, the development of unmanned technologies using autonomous underwater vehicles (AUV) has become an urgent demand for solving the problem of inspection of industrial sub-sea structures. A key issue here is precise localization of AUVs relative to underwater objects. However, the well-known challenges of recognizing and coordinating underwater objects (impossibility to use GPS, undercurrents, etc.) do not allow high-precision navigation if based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative is a technology of processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to a technology of acoustic measurement processing. In this study, in the context of the problem of subsea production system inspection, we propose a method to recognize underwater objects and provide coordinate reference of AUV to them, based on stereo-image processing. Its distinctive features are the use of a non-standard technique for generating a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object.

In this study, the efficiencies of flat-plate and U-tube solar collectors were investigated experimentally when an Al₂O₃ nanofluid was used as a working fluid and compared to those of solar collectors using water. The energy saving and CO₂ and SO₂ generated were calculated and compared to those of solar collectors using water. In addition, based on the experimental results, an economic analysis of the use of solar collectors in various countries was performed. As the concentration of the Al₂O₃ nanofluid increased, the performance of the solar collector improved. The highest efficiency for the solar collectors was shown at the concentration of 1.0 vol% with the nanoparticle size of 20 nm. The maximum efficiencies of the flat-plate and U-tube solar collectors using 1.0 vol%-Al₂O₃ nanofluid with 20-nm nanoparticles was 74.9% and 72.4%, respectively, when the heat loss parameter was zero. The efficiencies of the flat-plate and U-tube solar collectors using Al₂O₃ nanofluid were 14.8% and 10.7 higher, respectively, than those using water. When 50 EA flat-plate solar collectors were operated for one year using Al₂O₃ nanofluid, the coal use, generated CO₂, and generated SO₂ were 189.99 kg, 556.69 kg, and 2.03 kg less than those of solar collectors using water, respectively. In addition, the largest electricity cost reduction was in Germany.

The article proposes to use a radio holography for the detection and identification of unmanned aerial vehicles (UAVs).

In the present era, there is no longer a necessity to rely on outdated and laborious methods for attendance management in the field of education. Managing attendance for large groups of students in a classroom can be a challenging and time-consuming task when it comes to recording data. Fortunately, modern technologies offer us the opportunity to streamline attendance management and make it more efficient. One effective approach is Real-Time Face Recognition, which simplifies the process of tracking attendance for numerous students daily. We employ the Haar cascade classifier to identify positive and negative facial attributes, along with the LBPH algorithm for accurate face recognition. These techniques are implemented using Python programming language and the OpenCV library while incorporating a user-friendly Tkinter GUI (Graphical User Interface) for seamless usability. By embracing these advancements, we can revolutionize how attendance is managed in educational environments.

We investigate the performance of a novel flat photovoltaic-thermal (PV-T) module under high-vacuum through a 1D numerical model based on steady-state energy balance, with the aims of optimizing the simultaneous production of thermal and electrical energy. In the proposed design, the photovoltaic (PV) cell is positioned directly above the selective solar absorber (SSA), in a multilayer or fully integrated PV-SSA structure, which allows full exploitation of spectral solar radiation. In fact, in this configuration the losses related to non-absorption of low-energy photons and thermalization, typical of a classical single-junction PV cell, are reduced. The present study is conducted as the emittance and energy bandgap of the PV layer varied, thus admitting a wide variety of materials into the analysis. The dependence of the temperature coefficient, β(%/K), on the energy bandgap of the PV cell is also included. In the last part of the work, we discuss the performance of the proposed evacuated PV-T equipped with a SSA layer and thin film solar cells, namely those made of CdTe, CdS and GaAs. Overall, the paper highlights the great advantage of using high vacuum insulation, which suppresses conductive losses, and the versatility of the proposed system, which could be adapted to the user's needs simply by choosing the appropriate material for the photovoltaic layer.

Electrospinning has gained a lot of attention in recent years due to its ability to easily produce high-quality polymeric nanofibers. However, electrospinning suffers from limited production capacity and a method to readily scale up this process is needed. One obvious approach includes the use of multiple electrospinning needles operating in parallel. Nonetheless, such an implementation has remained elusive, partly due to the uneven electric field distribution resulting from the Coulombic repulsion between the charged jets and needles. In this work, the uniformization of the electric field was performed for a linear array of twenty electrospinning needles using lateral charged plates as auxiliary electrodes. The effect of the auxiliary electrodes was characterized by investigating the semi-vertical angle of the spun jets, as well as the deposition area and diameter of the fibers. Finite element simulation was also used to analyze the impact of the auxiliary electrodes on the electric field intensity below each needle. Implementing parallel lateral plates as auxiliary electrodes was shown to help achieve uniformization of the electric field, the semi-vertical angle of the spun jet, and the deposition area of the fibers for the multi-needle electrospinning process. The high-quality morphology of the polymer nanofibers obtained by this improved process was confirmed by scanning electron microscopy (SEM). These findings help resolve one of the primary challenges that have plagued the large-scale industrial adoption of this exciting polymer processing technique.

This study investigated the mechanical properties and fatigue behavior of FH36 steel plate rolled in different directions. By adopting a sequential rolling process in the rolling direction (RD) and transverse direction (TD), the differences in properties between RD and TD were eliminated, resulting in nearly identical tensile properties. The steel plate exhibited a yield strength of approximately 420 MPa, a tensile strength of approximately 506 MPa, and an elongation of approximately 25%. Fatigue tests conducted at room temperature and -60℃ showed a fatigue limit of the maximum stress of 488 MPa at room temperature and 500 MPa at -60℃, indicating improved low-temperature fatigue performance. The fracture surfaces displayed distinct characteristics, such as oblique shearing fractures for high-cycle fatigue and cup-cone shape fractures for low-cycle fatigue. The fatigue source zone were both on the surface of the specimens. As the maximum stress increased, the area of the crack propagation zone for high-cycle fatigue decreased, while the area of the final fracture zone, the number of dimples and the proportion of the LAGBs increased.

The analysis concerned a thin-walled plate with a circular central hole made of CFRP (Carbon Fiber Reinforced Polymer) material. The test object was subjected to a tensile test to investigate the cracking mechanism of the composite structure. The tests were performed using two independent test methods experimentally and numerically. In the experiment, the force was recorded as a function of elongation of the tensile plate and the elongation of the plate was investigated using an Aramis non-contact optical measurement system. As part of the numerical analysis, an FEM model adequate to the experimental conditions was performed in the Abaqus system. The fracture process was modeled using the extended finite element method XFEM. This approach enabled the structure of the composite to be investigated over the full range of tensile loading. The behaviour of the circular hole plate before the damage symptoms was investigated, the damage initiation force was determined, then the initial cracking and delamination of the layers were analysed, as well as the crack propagation process leading to the complete failure of the composite structure (cracking of all laminate layers).

The preparation of gold nanoparticles (AuNPs) involves a variety of chemical and physical methods. These methods use toxic and environmentally harmful chemicals. Consequently, the synthesis of AuNPs using green chemistry has been under investigation to develop eco-friendly nanoparticles. One method to achieve this is the use of plant-derived phytochemicals capable of reducing gold ions to produce AuNPs. The aim of this study was to implement a facile microtitre-plate method to screen a large number of aqueous plant extracts to determine the optimum concentration (OC) to bio-synthesize the AuNPs. Several AuNPs of different sizes and shapes were successfully synthesized and characterized from seventeen South African plants. The characterization was done using Ultra Violet-Visible Spectroscopy, Dynamic Light Scattering, High Resolution Transmission Electron Microscopy and Energy-Dispersive X-ray Spectroscopy. We also studied the effects of temperature on the synthesis of the nanoparticles and measured its effect on the particle size of the synthesized AuNPs and the data showed that changes in temperatures affect the size and dispersity of the generated AuNPs. Further, some of the synthesized AuNPs were stable upon incubation with different biological solutions in vitro.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

The ability to express and recognize emotion via facial expressions is well known to change with age. The present study investigated the differences in the facial recognition and facial expression of the elderly (n = 57) and the young (n = 115) and measure how each group uses different facial muscles for each emotion with Facial Action Coding System (FACS). In facial recognition task, the elderly did not recognize facial expressions better than young people and reported stronger feelings of fear and sad from photographs. In making facial expression task, the elderly rated all their facial expressions as stronger than the younger, but in fact, they expressed strong expressions in fear and anger. Furthermore, the elderly used more muscles in the lower face when making facial expressions than younger people. These results help to understand better how the facial recognition and expression of the elderly change, and show that the elderly do not effectively execute the top-down processing concerning facial expression.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, mass spectrometry analyses must therefore interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct MHC I presentation but poor substrates for cross-presentation. This is an important caveat because cancer cells are poor antigen-presenting cells and the immune system therefore depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We therefore postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs represent an attractive strategy for cancer treatment.

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

Background: Olecranon fractures are common injuries of the upper limb in adults. Simple dis-placed trasverse fractures are generally surgically treated with tension-band wiring (TBW) or plate fixation (PF). The purpose of this retrospective study is to compare the clinical-functional outcome, complications and reoperation rates between TBW and PF for Mayo IIA fractures. Methods: 72 patients treated with PF or TBW at our institution, completed our survey and clinical evaluation and their demographic and clinical data were recorded and analysed. The clini-cal-functional outcomes were evaluated assessing ROMs and three validated scoring systems: the Disabilities of the Arm, Shoulder, and Hand (DASH), the Mayo Elbow Performance Score (MEPS) and the Patient American Shoulder and Elbow Surgeons Standardized Elbow Assess-ment score (pASES-e). Results: 38 patients (53%) underwent TBW and 34 (47%) PF. The mean DASH, MEPS and pASES-e scores were respectively 14.5±17.2, 80.5±14.7 and 83.6±12.4 in the TBW group and 21±21.7, 75.6±15.3 and 75.1±19.2 in the PF group (p=0.16, p=0.17 and p=0.03). The mean duration of surgery and hospitalisation period were longer in the PF group (p=0.002, p=0.37) whereas the complication and reoperation rates were higher after TBW (p=0.15, p=0.24). Conclusion: According to the literature, both TBW and PF resulted comparable valid surgical op-tions for the treatment of simple isolated displaced olecranon fractures, revealing that they both provide good/excellent outcomes with no significant differences.

Carbon fiber reinforced polymers (CFRP) are extensively employed in the aerospace industry due to their excellent properties. Delamination damage occurring at critical locations in CFRP can seriously reduce the safety of in-service components. The detection and localization of delamination damage using Lamb waves hold significant potential for widespread application in non-destructive testing. However, the choice of damage localization algorithm may produce different delamination damage localization results. This research presented an IRAPID (improved reconstruction algorithm for probabilistic inspection of defects) method derived from the ORAPID (original reconstruction algorithm for probabilistic inspection of defects) method, aiming to improve the accuracy and reliability of delamination damage localization. Three CFRP bending plates, including a healthy bending plate and two bending plates with delamination damage sizes of Φ20 mm and Φ40 mm, were prepared in the experiment, respectively. The detection experiment of the CFRP bending plate using lead zirconate titanate (PZT) as a transducer to excite and receive Lamb waves was conducted, and the influence of excitation signal frequency on the performance of the proposed method was discussed. Under the condition of excitation signal frequency of 220 ~ 320 kHz and step size of 10 kHz, the accuracy of the delamination damage localization method proposed in this paper was compared with that of the existing methods. The experimental results indicate that the IRAPID algorithm exhibits good stability in the localization of delamination damage across the frequency variations range considered. The localization error of the IRAPID algorithm for delamination damage is significantly lower than that of the DaS (delay-and-sum) algorithm and the ORAPID algorithm. As the size of the delamination damage increases, so does the localization error. The accuracy of delamination damage localization is lower in the X-axis direction than in the Y-axis direction. By averaging the localization results across various frequencies, we can mitigate the potential localization errors associated with single-frequency detection to a certain extent. For the localization of delamination damage, Lamb waves at multiple frequencies can be employed for detection, and the detection results at each frequency are averaged to enhance the reliability of localization.

High Vacuum Flat Plate Collectors (HVFPCs) are the only type of Flat Plate Thermal Collectors capable of producing thermal energy for middle-temperature applications (until 180 °C). As the trend of research plans to develop new Selective Solar Absorbers to extend the range of HVFPC application until 250 °C, it is necessary to correctly evaluate the collector efficiency up to such temperature to predict the energy production accurately. We propose an efficiency model for these collectors based on the selective absorber optical properties. The proposed efficiency model explicitly includes the radiative heat exchange with the ambient, which is the main source of thermal losses for evacuated collectors at high temperatures. It also decouples the radiative losses that depend on the optical properties of the absorber adopted from the other thermal losses due to HVFPC architecture. The model has been validated by applying it to MT-Power HVFPC manufactured by TVP-Solar, and it has been used to predict the efficiency and energy production of HVFPC equipped with new, optimized selective solar absorbers developed in recent years.

Given the existing movable rotating plate type grading device for potatoes with a high safety factor and heavy mass, the body frame of the grading device and movable rotating plate were optimized and a lightweight design was carried out. Based on ANSYS Workbench 2022, the static analysis of the body frame before and after improvement was compared, and it was verified that the strength and deformation of the optimized body frame met the design requirements, and the weight of the optimized body frame was reduced by 32.3%. Based on the direct optimization module of ANSYS Workbench 2022, the plate length, width, and height were used as design variables for the lightweight design of the movable rotating plate. The MOGA algorithm was used to generate sixty design solutions, and the optimal combination of size parameters for the plate was 610 mm, 95mm, and1.2 mm. Compared with the whole grading device before optimization, the total mass was reduced by 22.86% after optimization. The research of this article provides a reference for the structural design and optimization analysis of potato grading devices.

The aim of this study was to determine the correlation between the external friction angle of cereal kernels and the roughness of a steel friction plate. The experiment was performed on the kernels of five principal cereals: wheat, rye, barley, oats and triticale. Flat seed units composed of three spaced kernels joined by adhesive tape were analyzed in each experimental variant. The external friction angle of flat seed units was determined on 9 steel friction plates with different roughness. Measurements were performed in 3 replications with a photosensor device which registered the external friction angle of cereal kernels. On friction plates with surface roughness Ra=0.36 to Ra=6.72, the average values of the angle of external friction ranged from 17.56° in rye kernels to 34.01° in oat kernels. The greatest similarities in the angle of external friction were observed between wheat and triticale kernels, whereas the greatest differences were noted between barley and oat kernels and between barley and triticale kernels. Friction plates made of ST3S steel should be characterized by the lowest surface roughness to minimize energy consumption during grain processing. The optimal surface roughness of steel friction plates was determined at Ra=0.9.

The aim of this study was to determine the correlation between the external friction angle of cereal kernels and the roughness of a steel friction plate. The experiment was performed on the kernels of five principal cereals: wheat, rye, barley, oats and triticale. Flat seed units composed of three spaced kernels joined by adhesive tape were analyzed in each experimental variant. The external friction angle of flat seed units was determined on 9 steel friction plates with different roughness. Measurements were performed in 3 replications with a photosensor device which registered the external friction angle of cereal kernels. On friction plates with surface roughness Ra=0.36 to Ra=6.72, the average values of the angle of external friction ranged from 17.56° in rye kernels to 34.01° in oat kernels. The greatest similarities in the angle of external friction were observed between wheat and triticale kernels, whereas the greatest differences were noted between barley and oat kernels and between barley and triticale kernels. Friction plates made of ST3S steel should be characterized by the lowest surface roughness to minimize energy consumption during grain processing. The optimal surface roughness of steel friction plates was determined at Ra=0.9.