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By comparing management in 1987 with today, this column examines the differences for library managers as technology including computers and the Internet has become an integral part of their lives. Technology has made completing many managerial functions easier and erased the barriers of time and space. Communication has become almost instantaneous. Opportunities for learning are now global. On the other hand, the time required to manage and effectively use technology has reduced time devoted to other tasks. The work-free weekend has disappeared; products must have a slick professional look; and form has sometimes become more important than content. Contemporary library managers must focus both for themselves and for their staff on ways to use technology effectively in meeting library goals. Returning to an idyllic fictionalized past is impossible, especially now that artificial intelligence will likely bring another cycle of radical changes. Library managers will need flexibility, experimentation, and harsh critical evaluation of results to achieve success. [ABSTRACT FROM AUTHOR]

This article proposes the use of the Karhunen Loève expansion in the design of computer experiments when the Gaussian Process (GP) model is used to model the output, Y(x). We start by finding the K-L expansion in the case of the univariate and multivariate processes. When the analytical solution does not exist, we apply several methods of approximation, such as Haar wavelet functions and Fourier expansions. Numerical examples are presented to illustrate the idea of the approximations. We use the approximated model to select the maximum entropy sampling design. [ABSTRACT FROM AUTHOR]

This research proposes a new image encryption method to improve the security of image encryption by integrating a multi-chaotic system and mixed pixel-bit level encryption. In the face of the growing use of communications and computer technology, as well as the need to protect sensitive information, this method utilizes chaotic algorithms to increase encryption security. Experimental results show increased security and resistance to various attacks, validating the method's effectiveness in protecting digital images. The measurement tools used include histograms, information entropy, adjacent pixel correlation coefficient, key sensitivity analysis, and robustness testing. In conclusion, this method offers an effective and robust approach to image encryption, providing an important contribution to the field of information security. [ABSTRACT FROM AUTHOR]

Fire is a serious threat to people's lives and property. Be aimed at the low detection accuracy of the current fire detection system, a fire detection system based on one-chip computer is proposed in this work. To achieve fire detection and warning, the system collects these signals such as smoke and temperature from the surrounding environment, and processes them with a one-chip computer. The simulation experiments of the system are conducted with Proteus, and the experimental results indicate that the system can detect accurately. [ABSTRACT FROM AUTHOR]

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines. [ABSTRACT FROM AUTHOR]

This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns. [ABSTRACT FROM AUTHOR]

South Africa lags significantly in mathematics achievement on international benchmarking tests, which has led to several interventions aimed at improving mathematics attainment in the country. Drawing on the theoretical work of Vygotsky, Leontiev and Engeström, this chapter reports on one such initiative that implemented computer technology into disadvantaged schools in the apple growing district of the Western Cape. Contrary to expectations, the object of the lesson became control over students' actions, rather than a mathematical object aimed at developing students' understanding of the subject. The teacher adopted what I call a defensive position in relation to the novel technology, tightening pace and sequencing in these lessons. I draw on Ratnam's work into 'excessive entitlement' to illustrate that this teacher's defensive posture regarding technology emanates from a need to exert complete power over the content taught in a lesson and leads her to reject the novel technology in favour of traditional methods. While interviews with the teacher in this study indicated that she felt she promoted student dialogue and more symmetrical power relations in her classes through group work, this is not seen in the data. This is explained in relation to teachers' excessive entitlement to 'owning' the knowledge in their classrooms through maintaining control over the rules of the system. I pull on Cultural-Historical Activity Theory (CHAT) to illuminate how the activity of teaching in a classroom affords and constrains what the teacher is able to achieve, often making them feel excessively entitled to push back reform. [ABSTRACT FROM AUTHOR]

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Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood–brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast has several drawbacks, including nephrogenic systemic fibrosis, gadolinium deposition in the brain and bones, and allergic‐like reactions. As computer hardware and technology continues to evolve, machine learning has become a possible solution for eliminating or reducing the dose of gadolinium contrast. This review summarizes the clinical uses of gadolinium contrast, the risks of gadolinium contrast, and state‐of‐the‐art machine learning methods that have been applied to reduce or eliminate gadolinium contrast administration, as well as their current limitations, with a focus on neuroimaging applications. Evidence Level: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Warpage of the injection moulded parts is one of the most common defects after demoulding, which is intensively present in thin-walled moulded parts or parts with non-uniform wall thickness. The level of the warpage can be reduced by optimizing the mould tempering system in the phase of mould design, by optimizing the injection moulding parameters and recently by optimizing the shape of the mould cavity elements geometry – so-called inverse contouring. Computer simulation of injection moulding process can show the level of moulded part warpage after ejection from the mould, but it will not allow detailed measuring of critical moulded part measures deviations. Detailed analysis and prediction of the critical moulded part dimensions can be performed by aligning of the deformed moulded part design obtained with computer simulation, with moulded part reference CAD geometry. This paper shows the main steps and an example of the geometry aligning process for a specific thermoplastic part. [ABSTRACT FROM AUTHOR]

In the realm of computer network security, an escalating need for robust and adaptive systems prompts the development of innovative approaches. This paper introduces a novel framework, termed "ALPSO AutoLSTM-PSO Security Optimization Framework," designed for the optimization of computer network security systems. The framework synergistically integrates advanced techniques, including Autoencoder (Auto), Long Short-Term Memory (LSTM), and Particle Swarm Optimization (PSO). The Autoencoder, trained on normal network traffic data, serves as a feature learning mechanism, capturing essential representations. The LSTM, adept at modeling temporal dependencies, complements this by recognizing sequential patterns in network behavior. Furthermore, the PSO algorithm is employed to finely tune the parameters of both the Autoencoder and LSTM networks, enhancing their collective performance. The integrated model, forged through this holistic approach, forms the cornerstone of an improved neural network algorithm. To demonstrate the efficacy of the proposed ALPSO, comprehensive experiments are conducted using the NSL-KDD dataset. This dataset provides a realistic and diverse set of network traffic scenarios, enabling a thorough evaluation of the framework's capabilities. The algorithm, enriched by the dynamic fusion of Autoencoder and LSTM outputs, is adept at anomaly detection and security threat identification. This framework, coupled with efficient data searching techniques, enables real-time analysis of network traffic, thereby fortifying the security infrastructure. The ALPSO Framework represents a comprehensive solution that amalgamates state-of-the-art technologies to address the evolving challenges in computer network security. [ABSTRACT FROM AUTHOR]

AbstractWe consider an unknown multivariate function representing a system—such as a complex numerical simulator—taking both deterministic and uncertain inputs. Our objective is to estimate the set of deterministic inputs leading to outputs whose probability (with respect to the distribution of the uncertain inputs) of belonging to a given set is less than a given threshold. This problem, which we call Quantile Set Inversion (QSI), occurs for instance in the context of robust (reliability-based) optimization problems, when looking for the set of solutions that satisfy the constraints with sufficiently large probability. To solve the QSI problem we propose a Bayesian strategy, based on Gaussian process modeling and the Stepwise Uncertainty Reduction (SUR) principle, to sequentially choose the points at which the function should be evaluated to efficiently approximate the set of interest. We illustrate the performance and interest of the proposed SUR strategy through several numerical experiments. [ABSTRACT FROM AUTHOR]

The term "computer-mediated communication" (CMC) describes the transmission of messages and information using computer technology. With the extensive use of email, instant messaging, social media, and other digital platforms in Nepal in recent years, this style of communication has grown in popularity. The article examines how computer-mediated communication (CMC) and artificial intelligence (AI) have grown in significance in Nepal's undergrad English education. This article allows students to collaborate on projects and tasks, which improves their learning experience, by examining the potential of AI and CMC. The facts of Nepal's education have been examined using the four different basic literature reviews. Despite the need for further literature on artificial intelligence and computer-mediated communication in Nepal's English classes for undergrads between 2018 and 2023, the studies reviewed shed light on the possibilities of technology and AI in language acquisition. Undergraduate class observation, self-administered text analysis, social information processing theory and experimental research, which involves adjusting variables to see how they affect learning results, were used to gather data. The study's findings also demonstrate how using CMC platforms encourages students to practice their digital literacy. They gain knowledge on how to move through online spaces, efficiently communicate with a variety of digital tools, and assess the reliability of information sources. Students can communicate with their teachers outside of the classroom via video conferencing software and online discussion boards. This may result in more in-depth discussions and debates as well as greater student engagement. [ABSTRACT FROM AUTHOR]

We live in an age of artificial intelligence (AI). Open AI, an American artificial intelligence organization, claims that generative chatbots are here to stay. Although computer technology remains unevenly distributed, the presence of AI continues to be felt in many areas of daily life. Further, AI development happens in few countries whose cultural practices and beliefs tend to be embedded in AI output and functionality. Western and Global North perspectives tend to dominate conversations about AI. How might AI technology function equitably in an ethno‐racially and linguistically diverse world? This paper explores intersections of technology, coloniality, and bias to contextualize the user‐experience for individuals in groups characterized as high, medium, and low‐resourced languages. For whom does AI function in our globalizing world? Engaging with this question through a languacultural (Agar, 1994) lens, I employ a language and culture perspective to guide inquiry into AI’s output and impact, based on a children’s storybook. Specifically, I deploy Kenyan, Indonesian, and Taiwanese languacultural perspectives of three participants in a culturally cognizant reading (Washington, 2023) of a product of chatbot‐human composing, ‘Alice and Sparkle’, a children’s book illustrated by MidJourney and co‐authored by ChatGPT and Ammaar Reshi. Overall, through scrutiny of encoded cultural tropes and values inscribed within text and images, this paper proposes considerations for literacy pedagogy and research that stem from a pursuit of inclusive AI. [ABSTRACT FROM AUTHOR]

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system's historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model's adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks. [ABSTRACT FROM AUTHOR]

The rapid advancement of deep learning and computer vision technologies has given rise to a concerning class of deceptive media, commonly known as deepfakes. This paper addresses emerging trends in deepfakes, including the creation of hyper-realistic facial manipulations, the incorporation of synthesized human voices, and the addition of fabricated subtitles to video content. To effectively combat these multifaceted deepfake threats, we introduce an ensemble-based deepfake detection framework called the "D-Fence" layer. The D-Fence layer consists of two uni-modal classifiers designed to identify tampered facial and vocal elements, as well as two cross-modal classifiers for interactions between Video-Audio and Audio-Text domains to detect deepfakes across multiple modalities. To evaluate the effectiveness of our framework, we introduce two novel adversarial attacks: the "Bogus-in-the-middle" attack, which strategically inserts counterfeit video frames within authentic sequences, and the "Downsampling attack", designed to create deceptive audio. A comparative study of the D-Fence layer against various state-of-the-art multi-modal deepfake detection systems is conducted, demonstrating that our ensemble architecture outperforms existing classifiers. Under diverse adversarial conditions, our D-Fence layer achieves an impressive detection accuracy of 92%, showcasing its ability to detect deepfakes efficiently and reliably. [ABSTRACT FROM AUTHOR]

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system. [ABSTRACT FROM AUTHOR]

This research paper deals with a directional high gain PCB 1 × 8 antenna array for 2.4 ISM band utilizations. To achieve this antenna array, a well-matched equal-split 9 dB-power splitter is designed and integrated with the suggested antenna array. It exhibits a wide operating range of 506 MHz (2.022–2.528 GHz) and splits the feed power to 8 equal-in-phase output quantities. Then eight identical patch elements with good reflection coefficient, high gain and excellent radiation efficiency are connected at the eight-output ports of the 1 × 8-power divider in order to obtain an array antenna consisting of eight radiating elements. A quarter-wave impedance adapter is utilized to obtain a perfect matching of impedance between patches and the power divider. The suggested directional antenna resonates at 2.4 GHz with good impedance matching via offering reflection coefficient S 1 1 = − 2 4. 3 4 dB and voltage standing wave ratio (VSWR) of 1.16. Moreover, it offers good radiation traits like the enhanced gain of 14.76 dB, an excellent radiating efficiency equals about 98.86% and a directional radiation pattern. Initially, the proposed planar directional antenna array has been designed and simulated utilizing high-frequency structure simulator (HFSS) EM simulation tool, the results are validated with another simulation tool via computer simulation technology (CST) software. This printed antenna array is a potential candidate to operate at around 2.4 GHz for RFID reader utilizations due to its outstanding impedance and radiation characteristics. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

This article presents a novel method for constructing computer experiment designs based on the theory of area-interaction point processes. This method is essential for capturing the interactions between different elements within a modeled system, offering a more flexible and adaptable approach compared with traditional mathematical modeling. Unlike conventional rough models that rely on simplified equations, our method employs the Markov Chain Monte Carlo (MCMC) method and the Metropolis–Hastings algorithm combined with Voronoi tessellations. It uses a new dynamic called homogeneous birth and death dynamics of a set of points to generate the designs. This approach does not require the development of specific mathematical models for each system under study, making it universally applicable while achieving comparable results. Furthermore, we provide an in-depth analysis of the convergence properties of the Markov Chain to ensure the reliability of the generated designs. An expanded literature review situates our work within the context of existing research, highlighting its unique contributions and advancements. A comparison between our approach and other existing computer experiment designs has been performed. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and network communication technology, embedded micro control system has been widely used. It is crucial in the fields of large-scale chemical equipment, mechanized production base, transportation and future robots. Combined with the current Internet of Things technology, the equipment needs to be kept on the network during operation, real-time data comparison, data feedback, data reception and other network operations to provide strong help for human society. Focusing on the network communication level, due to the improvement of hardware and software technology, the basic link work has become the foundation part of realizing network communication, and the real problem is often the information transmission. For example, for wireless channel estimation and optimization based on embedded system, it is necessary to eliminate the interference of irresistible factors as far as possible, overview the defects in algorithm level and logic analysis level since the development of network communication technology, discuss the impact of network communication module of embedded monitoring system in practical application as far as possible, and make the error infinite to zero under the premise of unchanged hardware conditions. Based on the above conditions, the lightweight channel estimation method and the optimization under the condition of sparse matrix are proposed. This paper studies the network communication of embedded devices, especially the channel link in network communication, and focuses on the channel estimation algorithm. This paper proposes the advantages of LS and MMSE algorithms. According to the experimental results, although in terms of speed indicators, it still maintains better than the traditional algorithm in terms of average throughput, transmission delay, response time, transmission rate and so on in a static environment. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

Designs for computer experiments in quantitative factors use columns with many levels. Filling the experimental space is their most important property, and there are many criteria that assess aspects of space‐filling. Recently, Tian and Xu proposed a stratification pattern for assessing the stratification‐related space‐filling properties of designs for quantitative experimental variables whose number of levels is a power of a – usually small – integer. Such designs have been named GSOAs, in generalization of the earlier proposal of strong – or stratum – orthogonal arrays (SOAs). Latin hypercube designs (LHDs) with a suitable number of levels are special cases of GSOAs. Tian and Xu proposed to use the stratification pattern as a means to ranking (G)SOAs. They reported a small simulation study in which arrays that fared well in that ranking performed well in predicting an unknown function. Shi and Xu refined the criterion and also demonstrated success of a design that fares well on their refined criterion. This paper explains the ideas behind the stratification pattern and the related ranking criteria. A practical example and several toy examples aid the illustration. The stratification pattern can be calculated using the R package SOAs, which does not only provide the pattern itself but also provides more detail in a dimension by weight table, in the spirit of the refinement by Shi and Xu. [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]

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]

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]

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]

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]

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]

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]

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]

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]