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Over the past years, remote sensing, radar, and imaging applications have all made use of ultra-wideband (UWB) technology. This study undertakes an extensive analysis of tree-shaped monopole antennas tailored for UWB systems. The intended antenna has an incomplete ground plane and a circular radiating patch. To increase bandwidth, two ears have been added to the circular structure. Possessing a dielectric constant of 4.3. The antenna substrate consists of FR-4 material with a dielectric constant of 4.3. To achieve optimal impedance matching for UWB systems, the antenna is fed via a coplanar waveguide (CPW). Design antenna is a simple structure, small size, easy design, and simple integration with the substrate with dimensions of 54 mm ×36 mm ×1.6 mm. All simulation results presented in this article were generated using computer simulation technology (CST) software. He monopole antenna exhibits an impressive impedance bandwidth of 9.6 GHz (146.68%), spanning from 1.99 GHz to 11.56 GHz. Furthermore, the simulated UWB circular monopole antenna exhibits omnidirectional radiation characteristics, boasting a peak gain of 8 dB, and a directivity of 8.2 dBi at the frequency of 5 GHz, and a remarkable radiation efficiency of 97%. With these attributes, the suggested monopole UWB antenna shows significant potential for ground penetrating radar (GPR) applications. [ABSTRACT FROM AUTHOR]

This research designs, analyzes, and studies a 2.45 GHz rectangular microstrip patch antenna (RMPA). The antenna design uses Rogers RT5880 (lossy) substrate material with 2.2 dielectric permittivity, 1.5 mm thickness, and 0.0009 loss tangent. Additionally, the antenna was designed and simulated using computer simulation technology (CST) studio 2019 software. Plot designs were again created using Origin Pro Software. The simulation results showed that the return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, bandwidth, efficiency, and surface current were -45.992 dB, 1.0101, 6.115 dBi, 6.534 dBi, 70.8 MHz, 93.59%, and 49.9 A/m, respectively. This paper aims to increase return loss to a typical VSWR value near 1. Besides boosting antenna gain, directivity, and efficiency, it can be used in future wireless applications, including mobile phones and wireless LANs. The proposed antenna design outperforms earlier experiments, demonstrating that the research has increased performance. [ABSTRACT FROM AUTHOR]

In this research, we explain comprehensive industrial and innovation results on using an artificial neural network (ANN) method to improve the performance of microstrip patch antennas for 5G, indoor-outdoor, and Ku band uses. To determine if an antenna is appropriate, this article discusses multiple methods, one of which is to do a simulation using validating software like high frequency structure simulator (HFSS) and Altair Feko. Based on the Rogers RT 5880 substrate, the antenna is constructed. There is a loss tangent of 0.0009 and its dimensions are 17.1053 mm in length and 16 mm in width. Its dielectric constant is 2.2. Despite its small size, it boasts an impressive maximum efficiency of almost 90% and a gain of approximately 8 dB. As an indicator of ANN model performance, we may look at the R-squared value (99%), the mean square error (MSE), which is approximately 0.0015, and the confidence interval (99%). The ANN models are the most accurate and have the lowest error rate when it comes to predicting efficiency and gain. The suggested antenna is a promising contender for the targeted Ku band, indoor/outdoor, and 5G uses, as verified by the clustering of computer simulation technology (CST), HFSS, and Altair Feko simulated results with the measured and predicted outcomes of ANN approach. [ABSTRACT FROM AUTHOR]

Hajj is an annual Islamic event attended by millions of pilgrims every year from around the globe. It is considered to be the biggest religious event that includes large human crowds in the world. Managing such crowds and detecting abnormal behaviors is one of the most significant challenges for the host country, particularly the crowds of pilgrims. Most of the current solutions can only handle small-scale crowd management issues, that involve simple and clear abnormal behaviors. Therefore, there is a need to have a human abnormal behavior detection approach that can deal with large-scale crowd situations. This study aims to propose a computer vision-based framework that automatically analyzes video sequences and detects human abnormal behaviors. The Convolutional LSTM Autoencoder is used for analyzing video scenes and extracting valuable spatial and temporal features. The proposed approach has achieved a good loss reduction of 0.176587 in detecting abnormal pilgrims' behavior. The results demonstrate a promising picture of the effectiveness of computer vision technologies to detect abnormal behavior in large-scale crowds. [ABSTRACT FROM AUTHOR]

The paper deals with the design issue of the computer simulation models of the traction inverter reflecting request on the rapid investigation of the efficiency performance under various operational conditions. According to this, a piece-wised linearized simulation tool PLECS is selected for model development, which considers the relevant loss behaviour of the main power circuit semiconductor components. The topologies, for which the simulation models are created, are topologies, one of which is commonly used across the automotive industry as the heart of the traction drive (VSI – Voltage Source Inverter), and its perspective alternative three-level traction inverter topology T-NPC – T-type Neutral Point Clamped. The simulation models created in the given simulation tool are thus subjected to the analysis of power parameters, which also include the calculated power losses of the entire system of semiconductor devices for both topologies of the traction inverter. Based on the obtained power parameters, the efficiency performance under various operational conditions of both topologies is evaluated. The presented methodology enables rapid investigation of operational performance enabling fast optimization methods. Any design of linearized simulation models in the PLECS simulation tool, whose core consists of semiconductor elements, can be analyzed by the proposed methodology. [ABSTRACT FROM AUTHOR]

Attentive User Interface (AUI), especially vision-based AUI systems, is a highly developed area of study. Cameras play a crucial role in modeling AUI inputs to monitor and understand user attention during interaction. This enables the optimization of interaction tasks between humans and machines. Typically, AUI systems are designed for desktop computers, employing various detection methods based on computer vision. This research introduces the exploration of AUI input through a low-cost Raspberry Pi 4 embedded system a smallsized system with limited voltage sources. This exploration serves as a reference for AUI design on embedded systems. The proposed AUI input design generating 8 AUI input categories. These include face detection, open or closed eye condition detection, and detection of forward, up, down, left, and right view directions. This study discusses three scenarios of AUI input modeling using various methods to identify the most effective design through Raspberry Pi 4. Based on experimental results and a series of tested tasks, Scenario 3, based on Mediapipe, yielded the best results with an average FPS value of 10 and 100% accuracy for facial landmark detection, detection of open eye conditions, detection of forward, up, down, right, and left gaze directions at various detection reading angles. [ABSTRACT FROM AUTHOR]

This document is a monthly report on project management in Spain, written by Alfonso Bucero. The report discusses the monthly meeting of the PMI Madrid Chapter, where Conflict Management through nonviolent communication (NVC) was the topic of discussion. The speaker, Alfonso Bucero, shared his experiences as a project manager and explained the benefits of the NVC model. The meeting was well-attended, with both in-person and virtual participants. The report also provides contact information for the PMI Madrid Spain Chapter for further details. [Extracted from the article]

Due to the complexity of search space and move evaluation, the game of Go has been a long-standing challenge for artificial intelligence (AI) to achieve a high level of proficiency. It was not until DeepMind proposed the deep neural network and tree search algorithm AlphaGo in 2014 that an efficient learning algorithm was developed, marking a significant milestone in AI technology. In light of the key technologies in AI Computer Go, this work examines move prediction across different Go rankings and sophisticatedly develops two deep learning models by combining and extending the feature extraction methods of AlphaGo. Specifically, effective modules for neural networks are proposed to guide learning through complicated Go situations based on the Inception module in GoogLeNet and the Convolutional Block Attention Module (CBAM). Subsequently, the two models are combined by ensemble learning to improve generalization, and these streamlined models significantly reduce the number of model parameters to the scale of one hundred thousand. Experimental results show that our models achieve prediction accuracies of 46.9% and 50.8% on two different Go datasets, outperforming conventional models by significant margins. This work not only advances AI development in the Go-game but also offers an innovative approach to related studies. [ABSTRACT FROM AUTHOR]

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications. [ABSTRACT FROM AUTHOR]

The current article discusses Sierpinski networks, which are fractal networks with certain applications in computer science, physics, and chemistry. These networks are typically used in complicated frameworks, fractals, and recursive assemblages. The results derived in this study are in mathematical and graphical format for particular classes of these networks of two distinct sorts with two invariants, K-Banhatti Sombor (KBSO) and Dharwad, along with their reduced forms. These results can facilitate the formation, scalability, and introduction of novel interconnection network topologies, chemical compounds, and VLSI processor circuits. The mathematical expressions employed in this research offer modeling insights and design guidelines to computer engineers. The derived simulation results demonstrate the optimal ranges for a certain network. The optimization assistant tool deployed in this work provides a single maximized value representing the maximum optimized network. These ranges can be put into service to dynamically establish a network according to the requirements of this paper. [ABSTRACT FROM AUTHOR]

[Background and Objective] In order to overcome the difficulty about laying electric wires in the field and realize unmanned filed irrigation, improving the intellectualization and precision of irrigation in rice planting area in Anhui Province and exploring the feasibility of intelligent control of irrigation sluice by using new generation electronic information technology would be carried out in the research. [Method] An intelligent irrigation control system based on AT89S52 single chip computer was designed in this paper, which included integrated lifting sluice, irrigation hardware subsystem and irrigation software subsystem. The gate of the sluice was adopted integrated design, which was integrated central processing unit module, sensor sensing module and solar power supply module based on AT89S52 MCU. Keil C developed the control software for the terminal of the gate, and the upper computer control software of the system was developed by C++. [Result] The application test showed that the automatic control response time of the motor was less than 1 s, the gate of the sluice opening control error was less than 1.5 mm, the water level control error was less than 8 mm, the maximum packet loss rate of data transmission was 0.11%, the calculation error of free discharge flow was less than 1%, and that of submerged outflow flow was less than 1.2%. [Conclusion] This system run safely and stably, and could meet the requirement of intelligent and precise water use in the complex environment of field irrigation of rice planting area. [ABSTRACT FROM AUTHOR]

This paper presents the design, simulation, and test measurements of a microstrip bandstop filter operating at 1.5 GHz, incorporating six split ring resonator (SRR) unit cells. The substrate employed is an FR-4 with a thickness of 1.6 mm and tangent losses of 0.025. In the initial phase, the design is conceptualized, simulated using computer simulation technology (CST) studio and advanced design system (ADS) Agilent simulators, and validated through test measurements. Building upon this foundation, the filter is transformed into a reconfigurable variant by integrating four SMV2019 varactor diodes. These varactors are modeled to ensure the reconfigurability of the bandwidth. The integration of varactors introduces dynamic tuning capabilities to the considered bandstop filter. [ABSTRACT FROM AUTHOR]

The present work aims to develop an object tracking controller for the Stewart platform using a computer vision-assisted machine learning-based approach. This research is divided into two modules. The first module focuses on the design of a motion controller for the Physik Instrumente (PI)-based Stewart platform. In contrast, the second module deals with the development of a machine-learning-based spatial object tracking algorithm by collecting information from the Zed 2 stereo vision system. Presently, simple feed-forward neural networks (NN) are used to predict the orientation of the top table of the platform. While training, the x, y, and z coordinates of the three-dimensional (3D) object, extracted from images, are used as the input to the NN. In contrast, the orientation information of the platform (that is, rotation about the x, y, and z-axes) is considered as the output from the network. The orientation information obtained from the network is fed to the inverse kinematics-based motion controller (module 1) to move the platform while tracking the object. After training, the optimised NN is used to track the continuously moving 3D object. The experimental results show that the developed NN-based controller has successfully tracked the moving spatial object with reasonably good accuracy. [ABSTRACT FROM AUTHOR]

Brain tumor is a severe condition that occurs due to the expansion of unnatural brain cells. Because tumors are rare and can take many different forms, it is challenging to estimate the endurance rate of a tumor affected patient. Examining the images obtained from Magnetic Resonance Imaging (MRI) is the fundamental method in locating the tumor affected part in the brain and detecting it with those MRI images is a labor-intensive and difficult process that may yield inaccurate findings. Implementing computer-aided methods is extremely important to overcome these limitations. With the support of the advancement in computer technologies like Artificial Intelligence (AI) and Deep learning (DL), we made use of one of the finest model in deep convolutional neural network (CNN), AlexNet to identify the tumor from MRI images. We incorporated an Enhanced AlexNet (EAN) in line to the proposed layers to categorize the images effectively. Needed data augmentation methods are used to progress the accuracy of our EAN model. From the investigation our EAN model performed well than the other traditional models with respect to accuracy, F1 score, recall and precision with minimum error rate. Our model has managed to produce accuracy rate of 99.32% in terms of classifying the brain tumor from the MRI Images. [ABSTRACT FROM AUTHOR]

A fundamental characteristic of computer networks is their topological structure. The question of the description of the structural characteristics of computer networks represents a problem that is not completely solved. Search methods for structures of computer networks, for which the values of the selected parameters of their operation quality are extreme, have not been completely developed. The construction of computer networks with optimum indices of their operation quality reduces to the solution of discrete optimization problems over graphs. A k-geodetic graph is one in which each pair of nonadjacent vertices has at most k paths of minimum length between them. A k-geodetic graph is geodetic when k = 1, bigeodetic when k = 2, trigeodetic when k = 3, and so on. This paper describes in detail the advantages of the practical use of k-geodetic graphs in the topological design of computer networks as an alternative for the solution of the fundamental problems mentioned above which, we believe, are still open. Also, the topological analysis and synthesis of some classes of these networks have been performed. [ABSTRACT FROM AUTHOR]

The Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings. [ABSTRACT FROM AUTHOR]

In the digital age, information has become a highly valuable asset, attracting not only individuals and companies but also criminals seeking to exploit it for illicit purposes. Cybercrime, defined as any criminal activity involving the use of computer technology and communication networks, has experienced exponential growth in recent years. This article explores the protection of personal data in the context of cybercrime, focusing on Ecuador's legal framework established by the 2008 Constitution and the 2021 Organic Law on Personal Data Protection. The article provides a comprehensive analysis of various forms of cybercrime, such as unauthorized access, computer fraud, and the distribution of illegal content, examining their impact on data privacy and security. It also addresses the challenges faced by authorities in enforcing laws due to encryption and data anonymization, as well as the use of blockchain and cryptocurrencies in illicit activities. The increasing incidence of cybercrime in Ecuador, exemplified by the Ola Bini case, is discussed alongside the role of artificial intelligence and Big Data in privacy concerns. The article concludes by highlighting necessary cybersecurity strategies in the financial sector to protect sensitive data and ensure economic stability. [ABSTRACT FROM AUTHOR]

Ancient Chinese murals have a splendid history in ancient times, experiencing thousands of years and countless dynasties. There have been artistic classics such as Dunhuang murals that are still applauded, and the murals of Goguryeo in Ji'an are also impressive. Through systematically combing and analyzing the lotus pattern in the Goguryeo murals in Ji'an, this paper reveals the artistic expression, style changes, and cultural factors behind the lotus pattern in different historical periods. It is found that the lotus pattern in the Goguryeo period not only showed an evolution trend from simplicity to complexity and from realism to abstraction in form but also reflected the profound transformation from nature worship to Buddhist belief to royal power symbolism in meaning. However, due to the age of the murals and the interference of natural climate and artificial damage, most of the murals have been damaged, falling off, cracks, and other pathological problems, which need to be protected urgently. Therefore, the combination of mural restoration and computer technology, the use of digital technology, vision and pattern recognition technology, the restoration of mural images for automated pattern recognition and analysis of mural paintings to assist in virtual restoration, not only for artificial restoration to provide an important and practical significance of the value of the reference, but also after the restoration of the mural paintings for the digitization of longterm preservation. [ABSTRACT FROM AUTHOR]

With the rapid development of big data computing and computer technology, consumers are more and more inclined to choose online shopping, especially in today's increasingly fierce competition among airlines, Internet Word of Mouth (IWOM) also reputation becomes more important. IWOM has communication characteristics such as fast spreading speed, wide influence, and strong persistence, which has a great impact on consumers' choice decisions. Therefore, an in-depth understanding of IWOM has certain guiding significance for big data analysis of service quality and customer perception of the influencing factors. Based on the theory of perceived value and purchase intention, this paper constructs a SOR model using computer technology from the dual perspectives of consumer perceived value and airline environment. The obtained data were analyzed using Smart PLS3.0 software, and the conclusions drawn were: word-of-mouth information has a positive impact on perceived value, and perceived value indirectly affects customers' willingness to buy, and perceived value has different mediating effects. [ABSTRACT FROM AUTHOR]

Among the first order tasks for all the political and administrative authorities of our country is to guarantee an adequate amount of production and an appropriate system of commercialization of agricultural products. It is objective of the present work to show the results obtained in the frame of a project for the development of a computerized system for the management and distribution of the agricultural productions in the municipalities. The methodology elaborated for the study included the collection of the guidelines and policies regarding this topic, as well as the analysis of the organizational structures of the entities involved. It included the analysis of the process of development of the net of entities for the commercialization of agricultural products in the revolutionary period and the types of entities that offer of these products to the population. The work concludes with the design of a computer system for its permanent control at municipal level, based on tools of the System of Geographical Information and the collection of information in the involved entities. [ABSTRACT FROM AUTHOR]

Taking the motivation of the knowledge aggregation of age-friendly design resources as the starting point, this paper compares the development history of age-friendly design for interactive interfaces, understands the research status, hotspots, and development trend of age-friendly design for interactive interfaces from 2003 to 2022 worldwide, and summarizes and analyses them. This paper explores five aspects of aging-friendly design in the field of interactive interface design extracted from the Web of Science core collection database, including keyword co-occurrence analysis of global research literature, country/region information analysis, fundamental research areas, major research institutions, and future research directions. It explores the design methods, design trends, and design laws of aging-friendly design for interactive interfaces from a multidisciplinary perspective. The results show that: (a) Smart Home, Ambient Assisted Living (AAL), User Experience, Internet of Things (IoT), etc., are the hot topics at the forefront of interface aging design. (b) USA is the world's leading research site for interface aging design and works closely with China, where the population aging is more serious. (c) The main research areas of interface aging are computer science and engineering ethics. (d) Udice French Research Universities, the primary publishing institution, is worth attention. (e) This review proposes three research hotspots as important development directions for interactive interface aging design, including "smart home product interaction interface aging design will become a hot topic,""fusion of multi-source data assisted interface aging design," and "interdisciplinary interface aging design." [ABSTRACT FROM AUTHOR]

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people's daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique. [ABSTRACT FROM AUTHOR]

The study of computer-related careers is a perfect opportunity for young people due to the high impact that technology has today since there is an increase in job opportunities for qualified personnel in this area. In addition to having a high offer of jobs, most of these are the best paid today. The most encouraging part of preparing in this area is that in the immediate future, it is envisioned to continue with this demand and have a career that will not be absorbed by automation and the development of artificial intelligence. On the other hand, the means of communication where young people can find out about the academic offer are isolated, or they must have a clear idea of a university, or it is generally achieved by recommendation of another professor or by having seen a lecture by a particular professor. Notice that students do not have a general vision of the academic offer. This article presents an initiative to concentrate information on careers related to Computing in Mexico and presents statistics on the data collected. This study aims to present an academic offer to young people in careers related to computing. The academic degrees are bachelor's/engineering, master's, and doctoral. The data presented corresponds to 33 bachelor's/engineering, 30 master's, and 21 doctoral programs. The careers related to computing and information technology most offered in Mexico in 2022 were: computer systems engineering and computer engineering. The information presented here is available at: http://computo.fismat.umich.mx/smcc/mapaComputacion/. [ABSTRACT FROM AUTHOR]

With the development of computer technology, image stylization has become one of the hottest technologies in image processing. To optimize the effect of artistic image style conversion, a method of artistic image style conversion optimized by attention mechanism is proposed. The CycleGAN network model is introduced, and then the generator is optimized by the attention mechanism. Finally, the application effect of the improved model is tested and analyzed. The results show that the improved model tends to be stable after 40 iterations, the loss value remains at 0.3, and the PSNR value can reach up to 15. From the perspective of the generated image effect, the model has a better visual effect than the CycleGAN model. In the subjective evaluation, 63 people expressed satisfaction with the converted artistic image. As a result, the cyclic generative adversarial network model optimized by the attention mechanism improves the clarity of the generated image, enhances the effect of blurring the target boundary contour, retains the detailed information of the image, optimizes the image stylization effect, and improves the image quality of the method and application value of the processing field. [ABSTRACT FROM AUTHOR]

Schizophrenia is a neuropsychiatric disorder affecting approximately 1 in 300 people worldwide. It is characterized by a range of symptoms, including positive symptoms (delusions, hallucinations, and formal thought disorganization), negative symptoms (anhedonia, alogia, avolition, asociality, and blunted affect), and cognitive impairments (impaired memory, attention, executive function, and processing speed). Current treatments, such as psychopharmacology and psychotherapy, often do not fully address these symptoms, leading to impaired everyday functionality. In recent years, there has been a growing interest in neuromodulation due to computer and engineering science making extraordinary computational advances. Those put together have reinitiated the spark in the field of neurofeedback (NF) as a means for self-regulation and neuromodulation with the potential to alleviate the daily burden of schizophrenia. We review, in a systematic way, the primary reports of electroencephalogram (EEG)-based NF as a therapeutical tool for schizophrenia. The main body of research consists mostly of case studies and case reports. The results of a few randomized controlled studies, combined with case studies/series, underscore the potential use of NF as an add-on treatment option for improving the lives of suffering individuals, being sustained by the changes in brain function and symptomatology improvement. We aim to provide important evidence of neuromodulation using NF in patients with schizophrenia, summarizing the effects and conclusions found in several clinical trials. [ABSTRACT FROM AUTHOR]

In the field of biology, the 'biomarkers' of the different animal species are of great relevance, as well as the relationships between them and the quality of the habitat. A study was proposed, analysing the antlers of the Iberian deer, to measure the bilateral asymmetry of its branches. The antlers are characterised in a 3D CAD model of their axial structure obtained via photogrammetric restitution from two photographs. This method quantifies the global and partial asymmetries of the antler's structural features. The research evaluated 48 deer antlers from hunting days and taxidermy workshops. To quantify asymmetries, the measurements required are the same as those considered to assess the hunting quality of antlers. Bilateral deviations are estimated for each structural trait and the whole antler. Preliminary results showed a convergence between the values of global asymmetry and hunting quality. The methodology proved to be fast, inexpensive, and easy to implement, which facilitates its use in future asymmetry studies of other biological elements of animal or plant origin. It is only necessary that the starting data, which is the bilateral axial structure of the analysed element, is characterised in a 3D model. [ABSTRACT FROM AUTHOR]

The world is becoming increasingly dependant in computing intensive applications. The appearance of new paradigms, such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) are creating a demand for high-performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. These devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all these applications, there is a recent tendency to offload these computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address these problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of these devices in the Cloud. [ABSTRACT FROM AUTHOR]

In the laboratory analysis of geological data, a number of problems arise due to the insufficient accuracy of the results. For example, different laboratories may provide different end results for the same samples, which creates a problem. This can lead to unreliable results, which can ultimately reduce the quality of the assessment. Machine learning allows to speed up the processing of radar data, as well as avoid the above-mentioned «misunderstandings». The problem of conducting scientific research at specialized landfills for a comprehensive assessment of the possibilities of using computer technology in the interpretation of georadar profiles is urgent. This makes it possible to objectively evaluate the result of automatic interpretation of georadar data. The several machine-learning algorithms described in the article are designing to improve the analysis and interpretation of data by incorporating various methods for optimizing georadar data processing processes. These methods include regression, classification and clustering. By incorporating these methods of optimizing the processing of georadar data into several machine-learning algorithms, the software can provide a comprehensive analysis and interpretation of the data obtained. This allows for a better understanding of the relationships, patterns and trends in the data, which ultimately leads to more informed decision-making and improved understanding. To improve the understanding of the results, the following quantitative indicators were obtained: correlation coefficient - 0.7072, determination coefficient - 0.5001, all these indicators correspond to these models. The deviation from the regression line is on average 22.37 units. Based on the classification results, the soil was determined to be wet. Errors in the sets do not exceed 1 %. [ABSTRACT FROM AUTHOR]

Major depression is a severe psychological disorder typically diagnosed using scale tests and through the subjective assessment of medical professionals. Along with the continuous development of machine learning techniques, computer technology has been increasingly employed to identify depression in recent years. Traditional methods of automatic depression recognition rely on using the patient's physiological data, such as facial expressions, voice, electroencephalography (EEG), and magnetic resonance imaging (MRI) as input. However, the acquisition cost of these data is relatively high, making it unsuitable for large-scale depression screening. Thus, we explore the possibility of utilizing a house-tree-person (HTP) drawing to automatically detect major depression without requiring the patient's physiological data. The dataset we used for this study consisted of 309 drawings depicting individuals at risk of major depression and 290 drawings depicting individuals without depression risk. We classified the eight features extracted from HTP sketches using four machine-learning models and used multiple cross-validations to calculate recognition rates. The best classification accuracy rate among these models reached 97.2%. Additionally, we conducted ablation experiments to analyze the association between features and information on depression pathology. The results of Wilcoxon rank-sum tests showed that seven of the eight features significantly differed between the major depression group and the regular group. We demonstrated significant differences in HTP drawings between patients with severe depression and everyday individuals, and using HTP sketches to identify depression automatically is feasible, providing a new approach for automatic identification and large-scale screening of depression. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, intelligent cockpit has increasingly become a trend in on-board related equipment. The intelligent cockpit system can not only provide auxiliary support for users in the driving environment, but also greatly improve driving efficiency. With the advancement of technology and the improvement of people's material lives, people's demand for driving environment safety and intelligence is gradually increasing. In order to effectively enhance the interactivity of the intelligent cockpit and ensure the safety of users' driving, this article effectively analyzed the performance and functional requirements of the intelligent cockpit, and combined speech recognition algorithms to construct an artificial intelligence (AI) speech interaction system. On this basis, this paper provides an in-depth study of intelligent cockpit design. To verify the effectiveness of the intelligent cockpit application, it was tested from four aspects: system operation accuracy, command response time, system security, and user experience. The test findings showed that at the level of system command response time, the response time of driving assistance commands, entertainment media commands, and traffic information commands was basically concentrated below 300 milliseconds. From the specific test results, it can be seen that the intelligent cockpit based on the artificial intelligence voice interaction system can quickly respond to users' different command needs through precise speech recognition. [ABSTRACT FROM AUTHOR]

Smart Home Technologies (SHTs) have recently become popular for a variety of purposes, including healthcare, entertainment, and convenience, among others. While SHT manufacturers promise to provide a range of services relating to home security, health and wellness, automated domestic tasks, entertainment, and beyond, user perceptions vary widely in terms of benefits and drawbacks. Moreover, surveillance studies researchers have warned against normalizing technologies that may exacerbate uneven power dynamics between users and household members, marketing companies, insurance brokers, law enforcement, and others. Through an analysis of the interdisciplinary literature stemming from computer science and engineering, gerontology, the social sciences, and related fields, we explore the extent to which these potential risks and related concerns are reflected upon by smart home users. This scoping review aims to explore SHT user perceptions of privacy attitudes, the purposes of smart home surveillance, risks and benefits, and impacts on home safety. Through our review of sixty-eight relevant studies, we found that many smart home users reported satisfaction over perceived benefits such as an increased sense of safety and home security. Many others displayed limited understandings of data collection practices or expressed privacy concerns. Nonetheless, SHT usage prevailed among these users. Others report a perceived trade-off between privacy and other factors, such as convenience, and some may have resorted to privacy cynicism, a coping mechanism for dealing with ubiquitous surveillance. In order to better understand SHT adoption trends despite concerns, exploring the conflict between user perceptions of privacy, understanding of SHT data collection purposes, risks and benefits, and home safety, is essential. [ABSTRACT FROM AUTHOR]

Purpose. Study of hydraulic-mechanical transmissions in the process of braking a mining diesel locomotive and determining their efficiency. Methodology. The paper uses the technique of matrix analysis of transmissions by Prof. V.B. Samorodov, based on the division of the kinematic scheme into structural elements. The method was chosen as the basic method due to its universality and the possibility of implementation with the help of computer technology. The developed mathematical models of the vehicle braking process take into account the possibility of a smooth change (decrease and increase) of the transmission ratio and the torque applied to the executive bodies. Findings. The advantages and disadvantages of the use of hydromechanical step-less transmissions in the braking modes of the diesel locomotive are determined. It was mathematically proven that such transmissions are efficient, it is limited by methods of implementing braking. The results of the modelling allow to justify the braking control strategies of the locomotive equipped with a hydromechanical transmission. Originality. The technique of matrix mathematical modelling of transmissions that work as part of mining and transport machines has got further development. The developed mathematical model of the braking process of a mine diesel locomotive with HMT allowed to study the change in the kinematic and power parameters of the transmission in different operating conditions of diesel locomotives – movement on traction and transport ranges, on descent and ascent, at different initial braking speeds for all possible ways of implementing the process braking. The obtained results made it possible to substantiate the braking control strategies depending on the initial speed and traction power of the mining diesel locomotive. Practical value. The obtained results testify to the operational efficiency of the considered transmissions due to correctly selected strategies of braking control, which exclude the possibility of the occurrence of emergency modes of operation, failure, and reduction of the service life of the transmission elements. The practical value of the paper is confirmed by the social effect due to the improvement of labour safety in locomotive transport. [ABSTRACT FROM AUTHOR]

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA. [ABSTRACT FROM AUTHOR]

Maximin distance designs and orthogonal designs are two attractive classes of space-filling designs for computer experiments, but their theoretical constructions are challenging, especially the construction of optimal designs in terms of both the maximin distance and orthogonality criteria. This paper presents a systematic method for constructing orthogonal maximin distance designs with flexible numbers of runs and factors. The method is carried out by rotating the subarrays of a saturated two-level regular design in Yates order or its circular shifting version. The principal objective is to construct high-level designs from two-level designs, and the method is effective because the performance of high-level designs is determined by that of two-level designs under both the maximin distance and orthogonality criteria. The proposed method is also generalized by rotating the subarrays of a saturated two-level nonregular design such that the resulting designs have flexible run sizes. Comparison results reveal that the resulting orthogonal designs are well worthy of recommendation under the maximin distance criterion. An illustrative example is provided to show that the proposed designs have a good two-dimensional stratification property. An application is given to present the effectiveness of the proposed designs in building statistical surrogate models. [ABSTRACT FROM AUTHOR]

In order to improve the comprehensive ability evaluation effect of students majoring in preschool education, this paper combines computer technology to evaluate the comprehensive ability of students majoring in preschool education. Through linearization, this paper deduces the state space model of data load of students' comprehensive ability and the transfer function model of student's comprehensive ability data load. Moreover, this paper establishes an analytical expression between the model coefficients of the transfer function of student's comprehensive ability data load and the transfer parameters of student's comprehensive ability data load. In addition, this paper analyzes and selects the identification method of the parameters of students' comprehensive ability data load model. Since the parameters of the student comprehensive ability data load model have physical meanings, the parameter identification of the student comprehensive ability data load model is a gray box identification, and constraints are required when identifying the parameters of the student comprehensive ability data load model. The experimental study has verified that the comprehensive ability evaluation model of students majoring in preschool education based on computer technology can play an important role in the comprehensive ability evaluation of students majoring in preschool education. This article adopts the comprehensive load model in the Comprehensive Stability Calculation Program (PSASP) to eliminate the shortcomings of factor analysis in traditional teaching comprehensive evaluation, and has a certain reference value for the promotion of intelligent teaching in the future. [ABSTRACT FROM AUTHOR]

Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model's detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection. [ABSTRACT FROM AUTHOR]

Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam's imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system's reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data's precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations. [ABSTRACT FROM AUTHOR]

IEC 61131-3 is an international standard for developing standardized software for automation and control systems. Machine vision systems are a prominent technology in the field of computer vision and are widely used in various industries, such as manufacturing, robotics, healthcare, and automotive, and are often combined with AI technologies. In industrial automation systems, software developed for defect detection or product classification typically involves separate systems for automation and machine vision programs, leading to increased system complexity and unnecessary resource wastage. To address these limitations, this study proposes an IEC 61131-3-based integrated development environment for programmable machine vision. We selected 11 APIs commonly used in machine vision systems, evaluated their functions in an IEC 61131-3 compliant development environment, and measured the performance of representative machine vision applications. This approach demonstrates the feasibility of developing PLC and machine vision programs within a single-controller system. We investigated the impact of controller performance on function execution. [ABSTRACT FROM AUTHOR]

Copyright of Revista Educación en Ingeniería is the property of Asociacion Colombiana de Facultades de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditionally, experts considered several blockades, including sea and land, with the development of aircraft transportation - air, and with the advent of space technology and special satellites - space. Also, information blockades have been introduced with the rapid development of computer technology and communication tools. Each type of blockade had its historical characteristics; they differed in costs and, of course, efficiency. In the second half and at the end of the 20th century, scientists operated the term cyber blockade and restrictions in cyberspace. The paper sequentially considers physical environments (domains): air, land, sea, outer space, information space, and cyberspace. Multiple definitions of cyberspace have emerged as this concept evolves and becomes more nuanced in response to rapid developments in the technical, technological, economic, and social dimensions of modern life. Due to their potential, cyber-attacks and cyber warfare have garnered significant attention considering the ongoing development of society and business. Cyber-attacks are carried out regularly - against government agencies, military and civilian departments, and private businesses. The growing dependence of countries on cyberspace can play a negative role in the process of confrontation. The enemy can attack those areas where cyberspace is a decisive element. The authors discussed the questions of cyber-blockade efficiency. [ABSTRACT FROM AUTHOR]

The aim of this paper is to give a solution to three conjectures from Euclidean geometry concerning the location of the Mittenpunkt. The first two are solved without dependence on computer technology and with only a moderate amount of calculations. They were initially tackled by heavy calculations using computer algebra systems. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Educational Psychological Studies (EPS) is the property of Refaad for Studies, Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cihan University-Erbil Scientific Journal is the property of Cihan University-Erbil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides a review of cyber risk research accomplished in different disciplines, with a primary goal to aid researchers in the field of insurance and actuarial science in identifying potential research gaps as well as leveraging useful models and techniques that have been considered in the literature. We highlight the recent advancements in cyber risk prediction, modeling, management, and insurance achieved in different domains including computer engineering, actuarial science, and business studies. The surveyed works are classified according to their respective modeling approaches, allowing readers to more easily compare the technical aspects of the surveyed works and spot out research gaps based on the research tools of their liking. We conclude this paper with a summary of possible research directions that are identified from the review. [ABSTRACT FROM AUTHOR]