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In this paper, we develop an approach for analyzing the increasingly important strand of research that deals with automated systems of governing. Such systems, which figure prominently in public policy and regulation, are designed to utilize the rapid advancement in computer technology, like artificial intelligence, with the purpose of governing something or someone. Drawing on a large sample of articles we present a comprehensive analysis of scholarly works where these systems are studied as political, rather than neutral, instruments of governing. We find that the current state of the art articulates the politics of automated systems of governing in three ways. Namely, as part of ontological, epistemological and ideological questions. We conclude that future research should investigate the complex forms of marketization nested in these systems, that it should move from theoretical examples to detailed empirical studies and that political science should get more involved with the issue. [ABSTRACT FROM AUTHOR]

Recent applications like mmWave technologies require antenna characteristics such as high gain and wide bandwidth for smooth operation with high speed. Hence, this paper presents a novel pi‐shaped patch antenna framed within a square structure for mmWave applications at 28 GHz. In the process of designing a four‐element linear‐planar antenna array (LPAA), a thin single antenna element is designed on a 0.275 mm Rogers 5880 substrate with a dielectric constant of 2.2. The proposed single‐element antenna provides a gain of 3 dBi at 28 GHz and a wide impedance bandwidth ranging from 22.482 GHz to 40.511 GHz. Then, this proposed structure is transformed into a four‐element LPAA with a compact dimension of 0.263 mm × 18.55 mm × 23.99 mm. To enhance and predict the impedance bandwidth of the proposed LPAA, mathematical modeling using response surface methodology and constrained numerical optimization is applied. In the optimization process of the antenna, independent factors such as substrate height (Hs) and interelement spacing (d) that influence the antenna's responses such as impedance bandwidth (BW) and operating frequency (Fr) are considered. These factors are varied repeatedly and simulated using computer simulation technology (CST) suite 2019 for preparing the dataset, which is used as an input for mathematical modeling. Then, response surface methodology (RSM) is employed to relate the responses with independent factors. Models are validated using analysis of variance (ANOVA). The optimum parameters determined by applying constrained numerical optimization are substrate height (Hs) and interelement spacing (d) of values 0.263 mm and 5.61 mm, respectively. The optimized LPAA provides a wide bandwidth of 11 GHz and a peak gain of 9.25 dBi. The antenna also gives radiation efficiency of ≥98.5% and VSWR of less than 2 in its operating frequency range. The results included here are simulated using the CST EM solver and validated using the Ansys High‐Frequency Simulation Software (HFSS). The proposed antenna provides high gain and wide bandwidth, which makes it a good candidate for 5G wireless communication applications. [ABSTRACT FROM AUTHOR]

Through the enrolment of big data, deep learning, sensor fusion, and computer vision technologies, Amazon Go and similar shops pursue the automated management of retail subjects, goods, and transactions. Tracing the logics of automated shop technology, the paper makes two contributions. First, it proposes a theory of “retail transparency” to attend to how automated shops reimagine space as a series of pockets of excess (actions that escape circuits of capitalist valuation) to be countered through acts of making‐transparent (datafication for integration into digital systems of control). Retail transparency is underpinned by interventions aimed at perceiving, incorporating, and productivising excess. Second, we argue that logics of deep learning raise important challenges to traditional conceptions of resistance in digital geographies, as these tend to rely on a celebration or cultivation of excess. Instead, we offer a speculative reflection outlining a politics of “circuit‐breaking” which refuses to engage algorithmic logics on their own terms. [ABSTRACT FROM AUTHOR]

The slowdown of Moore's Law necessitates an exploration of novel computing methodologies, new materials, and advantages in chip design. Thus, carbon-based materials have promise for more energy-efficient computing systems in the future. Moreover, sustainability emerges as a new concern for the semiconductor industry. The production and recycling processes associated with current chips present huge environmental challenges. Electronic waste is a major problem, and sustainable solutions in computing must be found. In this review, we examine an alternative chip design based on nanocellulose, which also features semiconductor properties and transistors. Our review highlights that nanocellulose (NC) is a versatile material and a high-potential composite, as it can be fabricated to gain suitable electronic and semiconducting properties. NC provides ideal support for ink-printed transistors and electronics, including green paper electronics. Here, we summarise various processing procedures for nanocellulose and describe the structure of exclusively nanocellulose-based transistors. Furthermore, we survey the recent scientific efforts in organic chip design and show how fully automated production of such a full NC chip could be achieved, including a Process Design Kit (PDK), expected variation models, and a standard cell library at the logic-gate level, where multiple transistors are connected to perform basic logic operations—for instance, the NOT-AND (NAND) gate. Taking all these attractive nanocellulose features into account, we envision how chips based on nanocellulose can be fabricated using Electronic Design Automation (EDA) tool chains. [ABSTRACT FROM AUTHOR]

Data transparency plays a critical role in understanding IoT privacy practices and making informed decisions. To gain a comprehensive understanding of transparency in the IoT environment, a systematic literature review of 58 academic articles is conducted to investigate the progress and status of existing data transparency studies from a design perspective. Data transparency was identified as a signifier to bridge the connection between user behavior and privacy risks. The level of transparency achieved was shaped by users' privacy perceptions, which in turn influenced their privacy behavior. GUI-based transparency design has been widely used in IoT, but it is not sufficient to provide users with accessible, understandable, and unified transparency information. A conceptual transparency design is proposed based on the extracted design opportunities and practices. This paper provides an important resource on transparency issues in the IoT environment, and will benefit the design and computer science communities. [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]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office 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.)

While there is a growing body of research that explores the integration of music and coding in learning environments, much of this work has either emphasised the technical aspects of computer language design or music as a motivational context within which to learn computer science concepts. In this paper, we report on a study in which five undergraduate students with experience in both music and coding completed two creative musical tasks: one using conventional instruments and tools and one using Python code in an online music + coding environment. Inspired by the work of Christopher Small (1998. Musicking: The Meanings of Performing and Listening. University Press of New England), we describe music + coding as a set of interlocking processes which we call computational musicking and explore how practices from both domains are reimagined in this new hybrid context. We introduce semiotic theories of translation and transcription to make sense of the computational musicking process and describe strategies that participants devised in their creative process. [ABSTRACT FROM AUTHOR]

AbstractOne of the most common strategies to achieve optimum designs for physical and computer high-dimensional experiments with no model pre-specification and limited budget is to uniformly spread the design points over the experimental domain, which are called uniform designs. A uniform design provides good design space coverage (space-filling), resulting in more accurate estimates using fewer points. Algorithmic search is commonly used for finding such designs, but this approach becomes ineffective for large problems. Though the construction of uniform designs theoretically is challenging, intriguing and encouraging findings have recently been accomplished for highly specialized cases. This paper gives a new theoretical construction method for high-dimensional experiments with a mixture of four-level and sixteen-level factors via an adjusted version of the multiple quadrupling algorithm (Elsawah, Communication in Mathematics and Statistics 10:623-652, 2022). The new technique (called AMQA) is simple to use since it only needs small suitable four-level designs, which may be easily generated theoretically or by algorithmic search. The quality of the small designs ensures the quality of the large designs. The effectiveness of the AMQA is investigated both theoretically and computationally. The main findings demonstrate that, compared with other widely used techniques, the AMQA provides more accurate results for high-dimensional screening using a variety of models, including polynomial, spline, and kriging models, as well as variable selection techniques, including AIC, BIC, RIC, SCAD, and LASSO. To further improve performance, a hybrid algorithm that combines the AMQA with the iterative threshold accepting algorithm is proposed. [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]

Embedded computer systems refer to specialized computer systems that integrate operating systems and functional software into computer hardware systems, are application centric, based on computer technology, and have tailorable software and hardware. They have strict requirements for real-time performance, reliability, cost, volume, and power consumption. The performance and security evaluation of embedded systems is an important aspect of the development, development, and integration process of embedded systems, which can provide various quantitative analysis basis for the development and technical transformation of embedded products. The performance and security evaluation of embedded systems is a very complex issue, and evaluators may have different understandings of the problem due to their different starting points and observation perspectives. The embedded computers' performance evaluation is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted geometric Bonferroni mean (GWGBM) operator is built for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number generalized power geometric BM (SVNNGPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNGPGBM operator and power geometric (PG) operator. Finally, an example about embedded computers' performance evaluation and some comparative analysis were given to demonstrate the SVNNGPGBM method. [ABSTRACT FROM AUTHOR]

The improvement in the reliability and lifespan of insulated gate bipolar transistor (IGBT) devices and the enhancement of system efficiency under high-frequency pulse conditions are receiving increasingly wide attention, and all of these are closely related to the evaluation of the power loss of the IGBT. The traditional method for assessing the power loss of the IGBT is through curve fitting of the dual-pulse experimental results, without delving into the conduction process of the IGBT and without considering the IGBT model itself and the parasitic parameters present in the test circuit. In response to the condition of a pulse duration of 100 ns for IGBT single devices, a universal test circuit has been designed to measure the collector-emitter voltage waveform and the collector current waveform. A method based on Computer Simulation Technology for field-circuit co-simulation to evaluate the power loss of the IGBT has been proposed, which takes into account the parasitic parameters in the actual circuit. In this method, the 3D model of the IGBT device, as well as the circuit model of the test circuit, is established. The power loss curve of the IGBT was obtained from the evaluation model, and the results agree well with the power loss curve obtained from experiments. This method provides guidance for seeking lower power loss in order to improve the reliability and lifespan of the IGBT, as well as enhance system efficiency. It also provides theoretical guidance for further research on the thermal damage mechanism of the IGBT. [ABSTRACT FROM AUTHOR]

To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop. [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]

Copyright of Studii de Ştiintă şi Cultură is the property of Studii de Stiinta si Cultura 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 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]

Considering the disadvantages of traditional railway operation and maintenance manual inspection, including low efficiency, high cost, and detection blind spots, to solve the problems faced by drones in data acquisition, data processing and application, this paper innovatively proposed a route planning algorithm with a safe buffer zone. The algorithm adopted a partition operation strategy, and used two methods of orthography and side-view shooting to effectively improve the operation efficiency and the security of data acquisition. In addition, based on the data of UAV inspection aerial photography project, this paper formed a typical disease sample library in railway operation and maintenance environment through relevant processing, and used YOLOv7 target detection algorithm to realize the rapid automatic detection of three typical diseases of color steel tile, dustproof net and plastic film. Combined with human-computer interaction, the intelligent extraction and rapid filing of external environmental hazards were realized, and a set of intelligent monitoring technology scheme of railway operation and maintenance environment based on UAV platform was formed. The results show that, the effective combination of UAV technology, target detection technology and computer technology can realize rapid automatic hidden danger identification, which can improve the efficiency of railway operation and maintenance inspection by 150%, and provide important technical support for railway safety management. [ABSTRACT FROM AUTHOR]

Copyright of Al-Adab / Al-ādāb is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

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]

As technology advances, the field of electrical and computer engineering continuously demands the introduction of innovative new tools and methodologies to facilitate the effective learning and comprehension of fundamental concepts. This research addresses an identified gap in technology-augmented education capabilities and researches the integration of virtual reality (VR) technology with real-time electronic circuit simulation to enable and enhance the visualization of non-observable concepts such as voltage distribution and current flow within these circuits. In this paper, we describe the development of our immersive educational platform, which makes understanding these abstract concepts intuitive and engaging. This research also involves the design and development of a VR-based circuit simulation environment. By leveraging VR's immersive capabilities, our system enables users to physically interact with electronic components, observe the flow of electrical signals, and manipulate circuit parameters in real-time. Through this immersive experience, learners can gain a deeper understanding of fundamental electronic principles, transcending the limitations of traditional two-dimensional diagrams and equations. Furthermore, this research focuses on the implementation of advanced and novel visualization techniques within the VR environment for non-observable electrical and electromagnetic properties, providing users with a clearer and more intuitive understanding of electrical circuit concepts. Examples include color-coded pathways for current flow and dynamic voltage gradient visualization. Additionally, real-time data representation and graphical overlays are researched and integrated to offer users insights into the dynamic behavior of circuits, allowing for better analysis and troubleshooting. [ABSTRACT FROM AUTHOR]

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]

Building facade completion is an important part of digitizing the structures of buildings using computer technology. Due to the intricate textures and structures in building facade images, existing image-completion algorithms cannot accurately restore the rich texture and detailed information. In response, this paper proposes a novel network to simultaneously recover the texture and semantic structural features of building facades. By incorporating dynamic convolutions into each layer of the feature encoder, the shallow layers of the completion network can create a global receptive field, thus enhancing the model's feature-extraction capability. Additionally, a spatial attention branch is integrated into the dynamic convolution module to boost the correlation between the completion area and its surrounding edge area, resulting in improved edge clarity and accuracy of the completed facade image. Experimental results on multiple public image datasets demonstrate that the proposed model in this paper achieves state-of-the-art results when applied to real-world datasets. [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]

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB's optimization algorithms with COMSOL's meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

IoT applications revolutionize industries by enhancing operations, enabling data-driven decisions, and fostering innovation. This study explores the growing potential of IoT-based facial recognition for mobile devices, a technology rapidly advancing within the interconnected IoT landscape. The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV's Haar Cascade, Dlib's CNN Face Detector, and Mediapipe's Face. The resulting system demonstrates high accuracy in real-time and operates efficiently on low-powered devices like the Raspberry Pi 400. The evaluation involves the use of the multilayer perceptron (MLP) and support vector machine (SVM) classifiers. The system primarily functions as a closed set identification system within a computer engineering department at the College of Engineering, Mustansiriyah University, Iraq, allowing access exclusively to department staff for the department rapporteur room. The proposed system undergoes successful testing, achieving a maximum accuracy rate of 99.976%. [ABSTRACT FROM AUTHOR]

Exploiting simulation modeling of graphene synaptic field‐effect transistors is extremely important for helping researchers to construct carbon‐based neuromorphic computing systems. Here, lateral‐ionic‐gated graphene synaptic FETs with different gate lengths are fabricated, and they are modeled by using basic physic models combined with the ions migration‐diffusion model and graphene material model. The feasibility and accuracy of the proposed modeling are validated by showing an excellent agreement between simulations and experimental results. The slicing technique of the modeling is proposed to analyze the influence of ionic concentration and diffusion coefficient on the ions movement to reveal their working mechanism. The effect of key parameters about gate length, ionic concentration, and diffusion coefficient on synaptic behavior such as short‐term plasticity, and long‐term plasticity is simulated and discussed. In addition, three kinds of spike‐timing‐dependent plasticity are obtained by the device modeling. This research opens up promising avenues for the development of artificial synapse modeling and paths to new opportunities for the construction of carbon‐based neuromorphic networks. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering 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.)

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]

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector. [ABSTRACT FROM AUTHOR]

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work. [ABSTRACT FROM AUTHOR]