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This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations. [ABSTRACT FROM AUTHOR]

The authors discuss a problem of ensuring the optimal temperature conditions of the technological process for obtaining transparent conductive coatings using spray pyrolysis. To solve this problem, a system of automatic control of the substrate temperature is proposed, which, unlike previously used "manual" temperature setting methods, ensures the required accuracy of maintaining the substrate temperature along with minimal temperature gradient across the substrate surface. This allows achieving ideal temperature conditions for the evaporation of aerosol droplets during the pyrolysis process. Functionally, the developed automatic control system consists of an automatic temperature controller, a power regulator, an infrared heater with halogen lamps, and a temperature sensor. A variational problem of minimizing energy consumption when turning on the infrared heater has been solved. To eliminate surge currents, it is proposed to optimize the heater activation conditions. The proposed automatic control system has been synthesized and analyzed, including the calculation of its time and frequency characteristics. Acceptable response speed, zero static control error, zero overshoot, and significant phase and amplitude stability margins have been confirmed. The application of such automatic temperature control system contributes to the creation of optimal evaporation conditions for aerosol droplets, as well as stabilizes the substrate temperature and temperature gradient, thereby ensuring the stability of the characteristics of the resulting films. The studied transparent conductive coatings are used in adjustable transparency glasses and as layers in solar cells. [ABSTRACT FROM AUTHOR]

This article highlights the importance of Air Traffic Control in ensuring the security and efficiency of international aviation operations. Traditional RADAR systems face limitations in power consumption, coverage, and accuracy, leading to the development of innovative solutions like the Automatic Dependent Surveillance-Broadcast (ADS-B) system. However, the open nature of ADS-B's unencrypted signals poses security challenges, allowing attackers to intercept and manipulate data. To address this, the paper emphasizes the need for symmetric authentication, which uses shared secret keys through schemes like Message Authentication Codes and block cipher encryption. The choice between these methods depends on factors such as security, performance, and usability. Symmetric authentication is a practical solution, requiring robust shared keys and careful encryption algorithm selection. The research underscores the need for secure ADS-B systems and the trade-offs required in designing authentication schemes. [ABSTRACT FROM AUTHOR]

The fast automatic differentiation technique and its advantages as applied to the numerical solution of optimal control problems are described. Results obtained by applying this technique to the optimal control of thermal processes with phase transitions are presented. [ABSTRACT FROM AUTHOR]

Automatic train operation (ATO) is a critical component of automatic train control (ATC) systems. The ATO automatically adjusts the speed of trains, ensuring the safety of trains and increasing the passenger capacity of urban railway networks. The traditional ATO models employ a linear approximation approach to fit the nonlinear control model of the train, such as proportional–integral–derivative (PID) or model predictive control (MPC). However, due to the complexity of actual train operations, these methods fail to accurately fit train models. Thus, they are unable to shorten the operational intervals of trains and meet the demand for improving system efficiency of ATC. In recent years, machine learning-based methods have been employed for deriving more accurate train control models. However, these methods require trains to offload massive amounts of data to central servers for centralized training, which is challenging in CBTC systems due to limited wireless bandwidth. In this paper, we propose using federated learning (FL)-based edge computing to train the ATO model without transmitting a large amount of data. The onboard computing devices are used to perform local federated learning when the train is idle. Additionally, we present a Tab-Transformer-based machine learning model for ATO policy to improve the prediction accuracy of federally trained models. Our extensive simulation results demonstrate that the proposed federated learning scheme improves the efficiency of model training, and the Tab-Transformer-based ATO model achieves better driving performance. [ABSTRACT FROM AUTHOR]

The article provides a detailed analysis of causes leading to defects formed on the working surfaces of conical discs of variable automatic transmissions. The defect morphology was analyzed at the macro- and microlevels. Technologies for preventing defects are proposed. In particular, they include new type partially regular microreliefs applied to the working surface of conical discs. [ABSTRACT FROM AUTHOR]

Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs' internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios. To address this challenge, we propose an ultra-lightweight neural network (ULNN). This network incorporates a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion technique. Additionally, we introduce data augmentation (DA) based on signal phase offsets during the model training process, aimed at improving the model's generalization ability and preventing overfitting. Through experimental validation on the public dataset RML2016.10 A, the ULNN we proposed achieves an average precision of 62.83% with only 8815 parameters and reaches a peak classification accuracy of 92.11% at SNR = 10 dB. The experimental results show that ULNN can achieve high recognition accuracy while keeping the model lightweight, and is suitable for UAV platform with limited resources. [ABSTRACT FROM AUTHOR]

Extractive document summary is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm to select difference sentences in semantic space and we improve the base BERT to score sentences. We evaluate our model and perform significance testing using ROUGE on the CNN/DailyMail datasets compare with six baselines, which include two traditional methods and four state-of-art deep learning model. The results validate the effectiveness of our approach, which leverages K-means algorithm to produce more accurate and less repeat sentences in semantic summaries. [ABSTRACT FROM AUTHOR]

Fast and accurate assessment of slope topography and geological information and understanding of slope deformation evolution are of great significance in emergency investigation and monitoring of landslides. In this study, using the geometric information contained in terrestrial laser scanning (TLS) point clouds, an automatic discontinuity identification algorithm and a quantitative overall displacement evaluation method were developed. The automatic discontinuity identification algorithm is based on a k-means fuzzy cluster, allowing fast visual identification of any discontinuity from the acquired point cloud data to obtain accurate geometric information including occurrence, spacing, and continuity. It can provide adequate assessment of potentially unstable blocks in landslides during emergency disposal. The quantitative overall displacement evaluation method characterizes the overall displacement trend of local areas based on statistics from the M3C2 algorithm results. It can be used to quantify the deformation/displacement of relatively independent blocks (or local areas of slopes) and provides technical support for multi-temporal monitoring of potentially unstable blocks during emergency landslide disposal. These methods were applied to the Baige landslide during emergency disposal in 2019. Based on a comprehensive geological survey and monitoring results, the overall remnant slope was in a relatively stable state after the Baige landslide. However, the back edge of the slope was somewhat broken, and there were two large blocks with a relatively high risk of instability. The results show that these methods can provide effective technical support for emergency investigation, monitoring, and risk assessment of large-scale landslides. [ABSTRACT FROM AUTHOR]

Eye movements offer valuable insights for clinical interventions, diagnostics, and understanding visual perception. The process usually involves recording a participant's eye movements and analyzing them in terms of various gaze events. Manual identification of these events is extremely time-consuming. Although the field has seen the development of automatic event detection and classification methods, these methods have primarily focused on distinguishing events when participants remain stationary. With increasing interest in studying gaze behavior in freely moving participants, such as during daily activities like walking, new methods are required to automatically classify events in data collected under unrestricted conditions. Existing methods often rely on additional information from depth cameras or inertial measurement units (IMUs), which are not typically integrated into mobile eye trackers. To address this challenge, we present a framework for classifying gaze events based solely on eye-movement signals and scene video footage. Our approach, the Automatic Classification of gaze Events in Dynamic and Natural Viewing (ACE-DNV), analyzes eye movements in terms of velocity and direction and leverages visual odometry to capture head and body motion. Additionally, ACE-DNV assesses changes in image content surrounding the point of gaze. We evaluate the performance of ACE-DNV using a publicly available dataset and showcased its ability to discriminate between gaze fixation, gaze pursuit, gaze following, and gaze shifting (saccade) events. ACE-DNV exhibited comparable performance to previous methods, while eliminating the necessity for additional devices such as IMUs and depth cameras. In summary, ACE-DNV simplifies the automatic classification of gaze events in natural and dynamic environments. The source code is accessible at https://github.com/arnejad/ACE-DNV. [ABSTRACT FROM AUTHOR]

The experimental investigations performed in this work and practice have shown that the main defects significantly reducing the service life of enamel-coated pipes are the roughness of the inner surface of a pipe and the presence of small bubbles and layers of macro- and microcracks. The problem of the occurrence of defects in the enamel coating that destroy the integrity of the enamel is considered in this article. The causes of these defects are associated with an incorrect choice of the technological regime when applying an enamel coating to the inner surface of the pipes. The choice of the technological regime on application of a coating depends on the geometric dimensions and technological parameters of the pipes. It is known that imprecise implementation of the technology for applying enamel coatings to the inner surface of pipes effects the formation of microcracks. Taking this into account, the main objectives of this work are to automatically adjust the parameters of the technological regime of the device and to adjust the control system so as to prevent the formation of microcracks during coating application and to improve the automatic regulation of the process. [ABSTRACT FROM AUTHOR]

Computer-assisted diagnosis (CAD) plays a key role in cancer diagnosis or screening. Whereas, current CAD performs poorly on whole slide image (WSI) analysis, and thus fails to generalize well. This research aims to develop an automatic classification system to distinguish between different types of carcinomas. Obtaining rich deep features in multi-class classification while achieving high accuracy is still a challenging problem. The detection and classification of cancerous cells in WSI are quite challenging due to the misclassification of normal lumps and cancerous cells. This is due to cluttering, occlusion, and irregular cell distribution. Researchers in the past mostly obtained the hand-crafted features while neglecting the above-mentioned challenges which led to a reduction of the classification accuracy. To mitigate this problem we proposed an efficient dual attention-based network (CytoNet). The proposed network is composed of two main modules (i) Efficient-Net and (ii) Dual Attention Module (DAM). Efficient-Net is capable of obtaining higher accuracy and enhancing efficiency as compared to existing Convolutional Neural Networks (CNNs). It is also useful to obtain the most generic features as it has been trained on ImageNet. Whereas DAM is very robust in obtaining attention and targeted features while negating the background. In this way, the combination of an efficient and attention module is useful to obtain the robust, and intrinsic features to obtain comparable performance. Further, we evaluated the proposed network on two well-known datasets (i) Our generated thyroid dataset (ii) Mendeley Cervical dataset (Hussain in Data Brief, 2019) with enhanced performance compared to their counterparts. CytoNet demonstrated a 99% accuracy rate on the thyroid dataset in comparison to its counterpart. The precision, recall, and F1-score values achieved on the Mendeley Cervical dataset are 0.992, 0.985, and 0.977, respectively. The code implementation is available on GitHub. https://github.com/naveedilyas/CytoNet-An-Efficient-Dual-Attention-based-Automatic-Prediction-of-Cancer-Sub-types-in-Cytol. [ABSTRACT FROM AUTHOR]

Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset. [ABSTRACT FROM AUTHOR]

Thanks to technical progress and the availability of virtual data, sex estimation methods as part of a biological profile are undergoing an inevitable evolution. Further reductions in subjectivity, but potentially also in measurement errors, can be brought by approaches that automate the extraction of variables. Such automatization also significantly accelerates and facilitates the specialist's work. The aim of this study is (1) to apply a previously proposed algorithm (Kuchař et al. 2021) to automatically extract 10 variables used for the DSP2 sex estimation method, and (2) to test the robustness of the new automatic approach in a current heterogeneous population. For the first aim, we used a sample of 240 3D scans of pelvic bones from the same individuals, which were measured manually for the DSP database. For the second aim a sample of 108 pelvic bones from the New Mexico Decedent Image Database was used. The results showed high agreement between automatic and manual measurements with rTEM below 5% for all dimensions except two. The accuracy of final sex estimates based on all 10 variables was excellent (error rate 0.3%). However, we observed a higher number of undetermined individuals in the Portuguese sample (25% of males) and the New Mexican sample (36.5% of females). In conclusion, the procedure for automatic dimension extraction was successfully applied both to a different type of data and to a heterogeneous population. [ABSTRACT FROM AUTHOR]

The development of automatic video understanding technology is highly needed due to the rise of mass video data, like surveillance videos, personal video data. Several methods have been presented previously for automatic video captioning. But, the existing methods have some problems, like more time consume during processing a huge number of frames, and also it contains over fitting problem. This is a difficult task to automate the process of video caption. So, it affects final result (Caption) accuracy. To overcome these issues, Automatic Video Captioning using Tree Hierarchical Deep Convolutional Neural Network and attention segmental recurrent neural network-bi-directional Long Short-Term Memory (ASRNN-bi-directional LSTM) is proposed in this paper. The captioning part contains two phases: Feature Encoder and Decoder. In feature encoder phase, the tree hierarchical Deep Convolutional Neural Network (Tree CNN) encodes the vector representation of video and extract three kinds of features. In decoder phase, the attention segmental recurrent neural network (ASRNN) decode vector into textual description. ASRNN-base methods struck with long-term dependency issue. To deal this issue, focuses on all generated words from the bi-directional LSTM and caption generator for extracting global context information presented by concealed state of caption generator is local and unfinished. Hence, Golden Eagle Optimization is exploited to enhance ASRNN weight parameters. The proposed method is executed in Python. The proposed technique achieves 34.89%, 29.06% and 20.78% higher accuracy, 23.65%, 22.10% and 29.68% lesser Mean Squared Error compared to the existing methods. [ABSTRACT FROM AUTHOR]

In addition to higher-order executive functions, underlying sensory processing ability is also thought to play an important role in Attention-Deficit/Hyperactivity Disorder (AD/HD). An event-related potential feature, the mismatch negativity, reflects the ability of automatic sensory change processing and may be correlated with AD/HD symptoms and executive functions. This study aims to investigate the characteristics of visual mismatch negativity (vMMN) in adults with AD/HD. Twenty eight adults with AD/HD and 31 healthy controls were included in this study. These two groups were matched in age, IQ and sex. In addition, both groups completed psychiatric evaluations, a visual ERP task used to elicit vMMN, and psychological measures about AD/HD symptoms and day-to-day executive functions. Compared to trols, the late vMMN (230–330 ms) was significantly reduced in the AD/HD group. Correlation analyses showed that late vMMN was correlated with executive functions but not AD/HD symptoms. However, further mediation analyses showed that different executive functions had mediated the relationships between late vMMN and AD/HD symptoms. Our findings indicate that the late vMMN, reflecting automatic sensory change processing ability, was impaired in adults with AD/HD. This impairment could have negative impact on AD/HD symptoms via affecting day-to-day executive functions. [ABSTRACT FROM AUTHOR]

Purpose: Research and clinical attention in psychology has focused heavily on negative automatic thoughts and their role in symptoms of psychopathology and maladaptive behavior; however, the role of thoughts that appear to be overly positive in content has received much less attention. Recent work in the cognitive-behavioral treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) has identified overly positive thoughts that may be associated with avoidance and functional impairment. Method: We defined, described, and measured Avoidant Automatic Thoughts (AAT) in the daily lives of 101 undergraduate students using ecological momentary assessment and tested hypotheses about the association of these thoughts with ADHD symptoms and in-the-moment avoidance and negative emotion. Data were collected at baseline and up to three times per day for six days and analyzed using multilevel modeling. Results: We found that AAT were frequent daily occurrences for the undergraduates in our sample and that recent presence of AAT was associated with greater task avoidance and inattentive symptoms at the momentary level. AAT were not, however, associated with momentary negative emotion. Participants' general level of ADHD symptoms predicted greater momentary AAT, task avoidance, negative emotion and negative thoughts and less positive emotion. Conclusions: This study introduces AAT as a construct with potential research and clinical utility for understanding, predicting, and intervening in problematic avoidance behaviors that reduce people's quality of life and prevent them from reaching their meaningful goals. [ABSTRACT FROM AUTHOR]

Vehicle exhaust emissions are a significant factor causing urban environmental pollution in China. Anomaly detection in vehicle exhaust remote sensing data presents challenges due to unreasonable interference data that are difficult to manually identify and eliminate. This study addresses the need for advanced methods to accurately and efficiently identify these anomalies. We propose an enhanced convolutional neural network (CNN) Inception algorithm for recognizing vehicle exhaust data, which converts multidimensional exhaust parameters into images for feature extraction and recognition. Utilizing measured data from Xiqing Saida Road in Tianjin, Meishan area in Sichuan, and Gucheng County in Hebei Hengshui, we established a comprehensive dataset focusing on nitrogen oxides, hydrocarbons, and carbon monoxide concentrations. Three CNN algorithms (AlexNet, VGG16, and Inception) were selected for their proven effectiveness in image recognition tasks and their capability to handle complex data patterns. These models were evaluated for their performance in vehicle exhaust recognition. Experimental results demonstrate that all three algorithms can effectively identify abnormal vehicle exhaust data, with the improved Inception CNN achieving the highest accuracy of 98.7%.The findings underscore the superior performance of the Inception network, establishing it as a robust tool for filtering unreasonable data from remote sensing datasets and efficiently recognizing vehicles with substandard exhaust emissions. This research provides a critical foundation for assessing vehicle compliance and contributes significantly to urban air quality monitoring and pollution control initiatives.Highlights: This paper propose an enhanced convolutional neural network (CNN) Inception algorithm for recognizing vehicle exhaust data. This algorithm has strong effectiveness and the ability to handle complex data patterns in image recognition tasks. This algorithm has been successfully applied to recognition of real vehicle exhaust remote sensing data and has obtained high recognition accuracy. [ABSTRACT FROM AUTHOR]

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization algorithm called hyper-heuristic fitness-distance balance success-history-based adaptive differential evolution (HH-FDB-SHADE). The hyper-heuristic algorithms have two main structures: a hyper-selection framework and a low-level heuristic (LLH) pool. In the proposed algorithm, the FDB method is preferred as a high-level selection framework to evaluate the LLH pool algorithms. In addition, a total of 10 different strategies is derived from five mutation operators and two crossover methods for using them as the LLH pool. Balancing the exploration and exploitation capability of FDB is the main reason for being the selection framework of the proposed algorithm. The success of the HH-FDB-SHADE algorithm was tested on CEC-17 and CEC-20 benchmark test suits for different dimensional search spaces, and the obtained solutions from the HH-FDB-SHADE were compared to 10 different LLH pool algorithms. In addition, the HH-FDB-SHADE algorithm has been applied to optimize the control parameters of PID, PIDF, FOPID, and PIDD2 in the optimal automatic voltage regulator (AVR) design problem to reveal the improved algorithm's performance more clearly and prove its success in solving engineering problems. The results obtained from the AVR system are compared with five other effective meta-heuristic search algorithms such as the fitness-distance balance Lévy Flight distribution, differential evolution, Harris–Hawks optimization, Barnacles mating optimizer, and Moth–Flame optimization algorithms in the literature. The results of the statistical analyses indicate that HH-FDB-SHADE is the best-ranked algorithm for solving CEC-17 and CEC-20 benchmark problems and gives better results compared to the LLH pool algorithms. Besides, the proposed algorithm is more effective and robust than five other meta-heuristic algorithms in solving optimal AVR design problems. [ABSTRACT FROM AUTHOR]

Clinical lesions progress continuously but previous grading strategies are not fine-grained enough to model the continuously changing features of lesions. For lack of temporal sequential medical data to provide lesion progression information, we propose to use the severity ranking of disease lesions as spatial ranking label to represent temporal progression. Absolute ranking labels and relative ranking labels are calculated from severity ranking of datasets. A two-branch framework with spatial-temporal feature encoder is designed which using ranking labels to exploit the ranking relation between query and reference images. Furthermore, ranking loss is designed to enforce that sample features should be distributed in the feature space based on ranking scores. Our model achieves five-grade accuracy of 0.9204 on myopic maculopathy dataset. Compared with discrete grading, great improvement for automatic diagnosis is achieved. Experiments on B-mode fatty liver ultrasound dataset and glaucoma dataset also show generality of our algorithm. [ABSTRACT FROM AUTHOR]

We propose an automatic layout method for indoor scenes that effectively satisfies specific constraints. Our approach involves enhancing the existing scene representation method to accommodate complex constraints, including the precise placement of doors, windows, and user-specified furniture. To achieve this, we construct a conditional vector that encapsulates the necessary constraints. Moreover, our automatically constrained layout approach is implemented by training a conditional variational autoencoder model. Given the constraints and randomly sampled vectors, the decoder module can generate diversified reasonable indoor layout results. Evaluations show that our model outperforms the existing methods. Furthermore, our model exhibits a lower parameter count and faster execution speed compared with the existing approaches. [ABSTRACT FROM AUTHOR]

Mosaic ceramics are not limited to use solely as building materials, they also possess artistic value. Artists can create images by arranging and combining mosaic ceramics, resulting in a perfect fusion of large-scale public art for external walls and ceramic materials. However, the current approach for artists to create mosaic ceramic exterior wall art images involves manual laying and assembling of individual mosaic ceramics. This manual process suffers from issues such as low efficiency, eye fatigue, selection errors, and the risk of high-altitude operations. These challenges significantly impact the quality and efficiency of creating mosaic ceramic exterior wall art image images. To address these problems, this paper proposes an automatic mosaic ceramic art image stitching method based on subpixel edge fitting positioning and collaborative operation of multiple robotic arms. Additionally, a new U + I type conveying method is designed for efficient and space-saving transportation of mosaic ceramics. Experimental results demonstrate a high success rate of recognition, absorption, and placement of multi-color mosaic ceramics using this method reaching 95.45%, with a positioning error within 0.5 mm. The method can also adapt to varying levels of light intensity or noise interference. This approach effectively enhances the quality and efficiency of creating mosaic ceramic exterior wall art images and promotes the development of mosaic ceramic exterior wall public art creation. [ABSTRACT FROM AUTHOR]

The efficacy of automatic voltage regulator (AVR) systems is contingent on crucial parameters like voltage regulation, response time, stability, and efficiency. Integration of controllers with AVR systems facilitates centralized monitoring and regulation, enhancing voltage output efficiency. This study employs a modified sinh cosh optimizer (m-SCHO) and a modified time-domain metrics-based objective function to fine-tune a fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVR system control. The m-SCHO is strengthened with an adaptive local search mechanism and an experience-based perturbed learning strategy and enhances solution diversity and navigational efficacy, leading to improved optimization quality. This investigation illustrates the superior performance of the m-SCHO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, surpassing other techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with various tuning algorithms. Results indicate that the proposed m-SCHO-based FOPIDD2 controller achieves superior performance metrics, showcasing its capability. The study extends its scope by considering nineteen different controllers reported in the literature for a comprehensive comparison which also exhibits the best stability, further affirming its effectiveness. [ABSTRACT FROM AUTHOR]

Analytical differentiation for a smooth and accurate sensitivity field is typically used for efficient structural and multidisciplinary optimization. However, it can be challenging for multiphysics and non-linear problems. An alternative approach is automatic differentiation (AD). For large problems with many design variables AD can be computationally expensive and memory demanding and thus its use is still limited. To address some of these challenges, we propose to exploit the sparsity of the level set topology optimization (LSTO) in combination with a hybrid mode when using AD. The modularized LSTO used here enables the use of AD in combination with the classical level set method. In our method, we utilize the operator overloading (OO) approach, and we start our development by comparing different OO libraries. Next, a sparse AD implementation is proposed to take advantage of the sparsity of the level set method, in which sensitivities are only required within a narrow band close to the level set boundary. The obtained results indicate that this sparsity can improve the efficiency of the implementation. However, the reduction in memory requirements is not as significant. To improve the memory consumption as well, we introduce a hybrid mode, where instead of computing the total sensitivity directly with OO, the expression is first derived analytically and then OO is used to obtain the partial derivatives. Our studies show that combining the hybrid mode with the sparsity of the LSTO results in improvements in efficiency, with almost an order of magnitude less computational time for the biggest mesh size studied. Finally, the combination of the forward and reverse modes depending on the partial derivatives at hand is exploited to further improve memory requirements and computational cost. [ABSTRACT FROM AUTHOR]

Ancient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model. [ABSTRACT FROM AUTHOR]

Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO ('you only look once') and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings. [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in drought monitoring, flood forecasting, and water resource management. Data assimilation methods can integrate the strengths of land surface models (LSM) and remote sensing data to generate high-precision and spatio-temporally continuous soil moisture products. However, one of the challenges of the land data assimilation system (LDAS) is how to accurately estimate model and observation errors. To address this, we had previously proposed a dual-cycle assimilation algorithm that can simultaneously estimate the model and observation errors, LSM parameters, and observation operator parameters. However, this algorithm requires a large ensemble size to guarantee stable parameter estimates, resulting in low efficiency and limiting its large-scale applications. To address this limitation, the authors employed the following approaches: (1) using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM; and (2) replacing the ensemble Kalman filter framework with the extended Kalman filter (EKF) framework to improve the efficiency of parameter optimization for the dual-cycle algorithm. The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h. To evaluate the dual-cycle LDAS at the regional-scale, it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau, and soil moisture estimates were validated using in situ observations from four different climatic areas. The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture, land surface temperature, and evapotranspiration than did the open loop with default parameters. Furthermore, the spatial distribution of soil parameters (sand content, clay content, and porosity) obtained from the LDAS was more reasonable than those of default values. The EKF-based dual-cycle algorithm developed in this study is expected to improve the assimilation skills of land surface, ecological, and hydrological studies. [ABSTRACT FROM AUTHOR]

As one of the most important parts in the field of architectural environment research, outdoor scene environment design is closely related to people's daily life. Deeply exploring it can further improve the quality of living environment and the overall efficiency of buildings. Driven by this positive impact, this article conducted a series of research and analysis on outdoor scene environment design simulation through a multi-channel optical imaging classification automation model. This article first analyzes the principle of multi-channel optical imaging, using the classification process of imaging as the foundation. Then, it delves into the automation of optical imaging classification and systematically analyzes its working principle. Finally, a classic feature based dimensionality reduction algorithm, Principal Component Analysis (PCA), was proposed to extract potential information with the same structure from the dataset. This article not only analyzes image conversion in outdoor scene environments, but also conducts a comprehensive study of two environmental simulation processes and tests their core functions. The results of research and testing have found that multi-channel optical imaging automatic classification methods can effectively assist in the environmental design of outdoor scenes, providing a new reference path for the sustainable development of this field. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. Methods: A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland–Altman plot were used to evaluate the performance of the cascade HRNet model. Results: The PCK of the cascaded HRNet model was 97.9–100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989–0.999, R = 0.991–0.999, MAE = 0.63–1.65, MSE = 0.61–4.06, RMSE = 0.78–2.01). Conclusion: The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application. [ABSTRACT FROM AUTHOR]

Tashlhiyt is a low-resource language with respect to acoustic databases, language corpora, and speech technology tools, such as Automatic Speech Recognition (ASR) systems. This study investigates whether a method of cross-language re-use of ASR is viable for Tashlhiyt from an existing commercially-available system built for Arabic. The source and target language in this case have similar phonological inventories, but Tashlhiyt permits typologically rare phonological patterns, including vowelless words, while Arabic does not. We find systematic disparities in ASR transfer performance (measured as word error rate (WER) and Levenshtein distance) for Tashlhiyt across word forms and speaking style variation. Overall, performance was worse for casual speaking modes across the board. In clear speech, performance was lower for vowelless than for voweled words. These results highlight systematic speaking mode- and phonotactic-disparities in cross-language ASR transfer. They also indicate that linguistically-informed approaches to ASR re-use can provide more effective ways to adapt existing speech technology tools for low resource languages, especially when they contain typologically rare structures. The study also speaks to issues of linguistic disparities in ASR and speech technology more broadly. It can also contribute to understanding the extent to which machines are similar to, or different from, humans in mapping the acoustic signal to discrete linguistic representations. [ABSTRACT FROM AUTHOR]

Accurate and automated diagnosis of focal liver lesions is critical for effective radiological practice and patient treatment planning. This study presents a deep learning model specifically developed for classifying focal liver lesions across eight different MRI sequences, categorizing them into seven distinct classes. The model includes a feature extraction module that derives multi-level representations of the lesions, a feature fusion attention module to integrate contextual information from the various sequences, and an attention-guided data augmentation module to enrich the training dataset. The proposed model achieved a patient-wise classification accuracy of 0.9302 and a lesion-wise accuracy of 0.8592, along with an F1-score of 0.8395, a recall of 0.8296, and a precision of 0.8551. These findings demonstrate the effectiveness of combining multi-sequence MRI with advanced deep learning methodologies, providing a robust tool to support radiologists in accurately classifying liver lesions in clinical settings., Competing Interests: Declarations Ethics Approval This study has been granted ethical approval by the Institutional Review Board (IRB) of The Affiliated LiHuiLi Hospital of Ningbo University, Ningbo, Zhejiang, China, under the approval number KY2022SL446-01. The study was conducted in strict compliance with the ethical standards outlined in the Declaration of Helsinki. Given the non-invasive approach employed by the research and the anonymization process applied to the data, the IRB has provided an exemption from the requirement for informed consent. Competing Interests The authors declare no competing interests., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The diagnosis of Autism Spectrum Disorder (ASD) is mainly based on some diagnostic scales and evaluations by professional doctors, which may have limitations such as subjectivity, time, and cost. This research introduces a novel assessment and auto-identification approach for autistic children based on the appearance of children, which is a relatively objective, fast, and cost-effective approach. Initially, a custom social interaction scenario was developed, followed by a facial data set (ACFD) that contained 187 children, including 92 ASD and 95 children typically developing (TD). Using computer vision techniques, some appearance features of children including facial appearing time, eye concentration analysis, response time to name calls, and emotional expression ability were extracted. Subsequently, these features were combined and machine learning methods were used for the classification of children. Notably, the Bayes classifier achieved a remarkable accuracy of 94.1%. The experimental results show that the extracted visual appearance features can reflect the typical symptoms of children, and the automatic recognition method can provide an auxiliary diagnosis or data support for doctors. [ABSTRACT FROM AUTHOR]

Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction of architectural heritage. This study segments façade elements of architectural heritage semantically using YOLOv9. A parameterized expression for the semantic façade model is designed. In addition, the façade layer graph (FLG) and element layer graph (ELG) algorithms are developed based on topological, geometric, and structural constraints to automatically reconstruct the semantic façade model for architectural heritages. The results showed that the average precision (AP) and mean intersection over union (MIoU) achieved using YOLOv9 + FLG-ELG are 86.91% and 85.63%, respectively, on the dataset concerning façade elements of architectural heritages. The AP values obtained from the proposed method are 98.5% on the ECP2011 dataset and 95.3% on the Graz2012 dataset. The YOLOv9 + FLG-ELG method automatically reconstructs regular, irregular, and complex façade layouts with high accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we propose TAWE (title assignment with word embedding), a new method to automatically assign titles to topics inferred from sets of documents. This method combines the results obtained from the topic modeling performed with, e.g., latent Dirichlet allocation (LDA) or other suitable methods and the word embedding representation of words in a vector space. This representation preserves the meaning of the words while allowing to find the most suitable word that represents the topic. The procedure is twofold: first, a cleaned text is used to build the LDA model to infer a desirable number of latent topics; second, a reasonable number of words and their weights are extracted from each topic and represented in n-dimensional space using word embedding. Based on the selected weighted words, a centroid is computed, and the closest word is chosen as the title of the topic. To test the method, we used a collection of tweets about climate change downloaded from some of the main newspapers accounts on Twitter. Results showed that TAWE is a suitable method for automatically assigning a topic title. [ABSTRACT FROM AUTHOR]

Developers often encounter challenges with their introductory programming tasks as part of the development process. Unfortunately, rectifying these mistakes manually can be time-consuming and demanding. Automated program repair (APR) techniques offer a potential solution by synthesizing fixes for such errors. Previous research has investigated the utilization of both symbolic and neural techniques within the APR domain. However, these approaches typically demand significant engineering efforts or extensive datasets and training. In this paper, we explore the potential of using a large language model trained on code, specifically, we assess ChatGPT's capability to detect and repair bugs in simple Python programs. The experimental evaluation encompasses two benchmarks: QuixBugs and Textbook. Each benchmark consists of simple Python functions that implement well-known algorithms and each function contains a single bug. To gauge repair performance in various settings, several benchmark variations were introduced including addition of plain English documentation and code obfuscation. Based on thorough experiments, we found that ChatGPT was able to correctly detect and fix about 50% of the methods, when code is documented. Repair performance drops to 25% when code is obfuscated, and 15% when documentation is removed and code is obfuscated. Furthermore, when compared to existing APR systems, ChatGPT considerably outperformed them. [ABSTRACT FROM AUTHOR]

In this work, two area systems of renewable energy sources (RES), such as tidal, wind and solar, are interconnected with the deep residual network (DRN) controller in a microgrid. The DRN controller is used to control load frequency, which is optimally designed by green anaconda optimization (GAO) algorithm. The GAO algorithm is used to optimally select the DRN layers that improve the control of a two-area interconnected power system. GAO algorithm is utilized for the parameter section of a DRN controller in the interconnected power system with two areas, which provides optimal solutions based on the natural behaviour of green anacondas. The reason for choosing the GAO method is that it has high capability in the exploration and exploitation phase, thus creating effective tuning of controllers. The DRN controller is chosen because it has fewer additional parameters and increases the accuracy level, thus enhancing control of the two areas. The proposed model is implemented in MATLAB tool, and the results are compared with the existing methods of proportional-integral derivative-whale optimization algorithm (PID-WOA), PID-Harris hawks optimization (PID-HHO) and two degrees of freedom (2DOF)-PID (2DOF-PID). The comparison results indicate that the proposed GAO-based DRN controller achieves 98% efficiency and 95% frequency deviation, thus providing high-performance over counterpart methods. [ABSTRACT FROM AUTHOR]

Observing actions evokes an automatic imitative response that activates mechanisms required to execute these actions. Automatic imitation is measured using the Stimulus Response Compatibility (SRC) task, which presents participants with compatible and incompatible prompt-distractor pairs. Automatic imitation, or the compatibility effect, is the difference in response times (RTs) between incompatible and compatible trials. Past results suggest that an action's animacy affects automatic imitation: human-produced actions evoke larger effects than computer-generated actions. However, it appears that animacy effects occur mostly when non-human stimuli are less complex or less clear. Theoretical accounts make conflicting predictions regarding both stimulus manipulations. We conducted two SRC experiments that presented participants with an animacy manipulation (human and computer-generated stimuli, Experiment 1) and a clarity manipulation (stimuli with varying visual clarity using Gaussian blurring, Experiments 1 and 2) to tease apart effect of these manipulations. Participants in Experiment 1 responded slower for incompatible than for compatible trials, showing a compatibility effect. Experiment 1 found a null effect of animacy, but stimuli with lower visual clarity evoked smaller compatibility effects. Experiment 2 modulated clarity in five steps and reports decreasing compatibility effects for stimuli with lower clarity. Clarity, but not animacy, therefore affected automatic imitation, and theoretical implications and future directions are considered. [ABSTRACT FROM AUTHOR]

Background: Accurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images. Methods: A total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients. Results: On the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications. Conclusion: Our algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference. [ABSTRACT FROM AUTHOR]

This study presents laboratory measurements of P and S wave velocities of two carbonate rocks (porous limestone and yellow cemented limestone). The experimental results were validated and compared with the numerical simulation outputs using the 3D Fast Lagrangian Analysis of Continua software (FLAC3D). The main aim of this study is to evaluate the effect of frequency and mode of emission on ultrasonic pulse velocity (UPV) by applying an automatic method for the determination of P and S wave velocities. Based on the results, automatic detection of UPV can provide reliable outputs. The difference between numerical simulation results and laboratory measurement in terms of P and S wave velocities was, on average, around 7%, suggesting the applicability of the automatic detection method. Our study implies less noise in the perfect shear (PS) mode than in the single zone (SZ) emission mode. In summary, higher frequencies and the PS mode of emission are recommended.Highlights: The applicability of the numerical method in ultrasonic wave propagation was proven. Automatic detection of UPV needs to be adjusted based on the type of transducer. Increasing transducer frequency causes a more straightforward form of wave emission with a lower amount of noise. The perfect shear (PS) emission mode yielded more precise results than the single zone (SZ) emission mode and decreased the extra noise propagation. [ABSTRACT FROM AUTHOR]

Accurate and timely diagnosis of COVID-like virus is of paramount importance for lifesaving. In this work, deep learning techniques are applied to lung CT image segmentation for accurate disease diagnosis. We discuss the limitations of current diagnostic methods, such as RT-PCR, and highlights the advantages of deep learning, including its ability to automatically learn features and handle complex lesion morphology and texture. We, therefore, propose a novel deep learning framework, GAHFNet, specifically designed for automatic segmentation of COVID-19 lung CT images. The proposed method addresses the challenges in lung CT image segmentation, such as the complex image structure and difficulties of distinguishing COVID-19 pneumonia lesions from other pathologies. We provide the detailed description of the proposed GAHFNet. Finally, comprehensive experiments are carried out to evaluate the performance of GAHFNet, and the proposed method outperforms other traditional and the state-of-the-art methods in various evaluation metrics, demonstrating the effectiveness and the efficiency of the proposed method in this task. GAHFNet is able to facilitate the application of artificial intelligence in COVID-19 diagnosis and achieve accurate automatic segmentation of infected areas in COVID-19 lung CT images. [ABSTRACT FROM AUTHOR]

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70–180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future. [ABSTRACT FROM AUTHOR]

The improvement of temperature measurement accuracy using air monitoring systems at various sites, including hazardous industries, is considered. Specifics of such systems include a significant distance between the measuring device and the temperature sensors, such as resistance thermometers, which are widely used in the environmental parameter collection and control systems. It is shown that the main source of temperature measurement errors when connecting sensors remotely is the resistance of the connecting lines, determined by their length. The main methods for reducing measurement errors caused by the effect of resistance of the sensor connection line on the temperature measurement results are analyzed. The advantages of two-wire circuits in comparison with three- and four-wire circuits are formulated. A two-wire temperature measurement method with an integrating converter, implemented based on a device with a built-in microcontroller, is described. By using this method, it becomes possible to reduce measurement time, implement jamming and quantization noise avoidance, and consequently, reduce the temperature measurement error compared to similar two-wire measurement methods. An alternative two-wire temperature measurement method with automatic adjustment of the measurement algorithm parameters is proposed, aimed at increasing measurement accuracy by mitigating the effect of the time constant. The optimal time for the first integration step was found. Experimental studies and evaluation calculations were conducted to confirm the effectiveness of the proposed solution. When the temperature sensor resistance varies from 1 to 4 kOhm, the coefficient of variation constitutes ± 0.06%, while the variation interval of the relative resistance measurement error with automatic adjustment within the specified range has decreased by more than four times. The experimental results suggest the possibility of applying the proposed method in control and regulation systems with remote temperature sensor connections. [ABSTRACT FROM AUTHOR]

Loop-efficient automatic parallelization has become increasingly relevant due to the growing number of cores in current processors and the programming effort needed to parallelize codes in these systems efficiently. However, automatic tools fail to extract all the available parallelism in irregular loops with indirections, race conditions or potential data dependency violations, among many other possible causes. One of the successful ways to automatically parallelize these loops is the use of speculative parallelization techniques. This paper presents a new model and the corresponding C++ library that supports the speculative automatic parallelization of loops in shared memory systems, seeking competitive performance and scalability while keeping user effort to a minimum. The primary speculative strategy consists of redundantly executing chunks of loop iterations in a duplicate fashion. Namely, each chunk is executed speculatively in parallel to obtain results as soon as possible and sequentially in a different thread to validate the speculative results. The implementation uses C++11 threads and it makes intensive use of templates and advanced multithreading techniques. An evaluation based on various benchmarks confirms that our proposal provides a competitive level of performance and scalability. [ABSTRACT FROM AUTHOR]