This paper considers a hybrid relay network consisting of the source, the amplify-and-forward (AF) relay, the decode-and-forward (DF) relay, and the destination. We propose the optimal power allocation schemes between two different relays which maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from source. By solving the optimization problem to maximize the achievable rate for each relay network, the transmit power values in closed-form are derived. When the channel gains are the same, the optimal power allocation scheme for AF-DF relay network proves that a more power should be allocated at the first relay to maximize the achievable rate. In case of the DF-AF relay network, we derive the optimal power allocation scheme for the possible four cases. Under the same SNR condition at the first hop, we show that the achievable rate of AF-DF relay network is greater than that of DF-AF relay network when the channel gain between two relays is higher than that between the second relay and destination. Simulation results show that the proposed power allocation schemes provide a higher achievable rate than the equal power allocation schemes.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then the reconstructed SSTs from the RBFN method are compared with the results from the optimum interpolation (OI) method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and the average RMSE is 0.48°C for the RBFN method, which is quite smaller than the value of 0.69°C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

Suicidal behaviour remains difficult to predict and prevent, even for experienced mental health care professionals. The known distal risk factors for suicidal behaviour are not sufficiently specific to fully understand the complex dynamic processes that precede a suicide attempt. Real-time mobile monitoring data can be used to analyse proximal risk mechanisms within the suicidal process. At the same time smartphone-based safety planning and self-monitoring may enhance a patient’s self-management skills thereby increasing their capacity to respond to a suicidal crisis and to become more aware of crisis symptoms. The current paper describes the theoretical and conceptual rationale for the CASPAR study which applies an innovative approach to the study of suicidal processes. It uses basic science approaches to inform the implementation of an innovative suicide prevention intervention. We aim to develop and implement mobile safety plan in conjunction with real-time monitoring in order to both directly implement suicide prevention interventions and to study the ongoing dynamics of individual suicidal behaviour by applying network analysis.

Aim: This study aims to analyze the collaborative structure of Intestinal Behçet’s Disease (IBD) research by examining co-authorship networks in publications indexed in the Web of Science (WoS) Core Collection from 2000 to 2023. The goal is to identify key researchers, institutions, and collaboration patterns that have shaped the field over the past two decades. Method: This study utilized network analysis techniques to evaluate 484 publications related to IBD research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: From 2000 to 2009, the co-authorship network was highly fragmented with a low network density of 0.0114 and 84 distinct components, indicating limited collaboration. Key contributors during this period included Kim, Won Ho, and Cheon, Jae Hee (South Korea), who played central roles in connecting disparate research clusters. In the following decade (2010-2019), network density decreased slightly to 0.0086, and the number of components increased to 113, reflecting continued fragmentation despite growing research output. However, from 2020 to 2023, network density increased to 0.0171, and the number of components decreased to 73, indicating a trend towards greater integration and collaboration. Notable researchers in this period included Emmi, Giacomo (Italy), and Hatemi, Gulen (Turkey), who emerged as central figures, highlighting a shift in research leadership from Asia to Europe..Conclusion: The co-authorship network analysis of IBD research reveals an evolving collaborative landscape with increasing network density and decreasing fragmentation over time, particularly in the most recent period. From 2000 to 2019, Japanese and Korean researchers were prominent contributors, highlighting the significance of Asia in the global research arena. However, since 2020, researchers from Italy and Turkey have gained significant international prominence, suggesting a shift in the research focus from Asia to Europe. This transition may be attributed to changes in the incidence rates of IBD, where rapid increases were observed in Asian countries like Japan, Korea, and China until 2020, followed by stabilization or decline, while incidence rates have been increasing in European countries such as Italy and Turkey. These findings underscore the importance of expanding international collaborations to enhance the understanding of IBD. Fostering global partnerships could further advance the field, promoting more integrated and innovative research efforts, and addressing the shifting dynamics of IBD prevalence across regions.

This study presents the development of a Recurrent Neural Network (RNN) model for classifying smart home device data. Utilizing a dataset sourced from Kaggle, the study explored the processes of data gathering, loading, normalization, and model construction. The RNN, equipped with Long Short-Term Memory (LSTM), was trained and evaluated, demonstrating significant improvements in training and validation accuracy over 10 epochs, culminating in a test accuracy of 83.25%, and only 35.4% loss. The evaluation of the model on the test set provides a test accuracy result, accompanied by an in-depth analysis of ROC curves and Area Under the Curve (AUC) scores for multi-class classification, along with a confusion matrix. The AUC score of 0.9896 indicates outstanding performance in accurately classifying IoT device categories. These findings indicate that the RNN with LSTM exhibits superior learning efficiency and generalization capabilities, making it more suitable for IoT device classification tasks. This article emphasizes the concept of IoT and reviews recent studies on the application of deep learning models across various IoT domains, including smart homes, industrial systems, and healthcare. Future research could focus on improving real-time processing capabilities, and scalability, and incorporating diverse IoT data types to enhance and broaden the model's practical applications.

In this paper, we review and advance the application of deep learning algorithms for anomaly detection in Software Defined Networks (SDN). As SDN environments become more prevalent in modern networking infrastructures, their centralized control and dynamic nature make them susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized access. Traditional anomaly detection techniques often fall short in adapting to these evolving threats, necessitating more robust, adaptive solutions. This study evaluates three prominent deep learning architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders—for their effectiveness in detecting anomalous behavior in SDN environments. Through extensive experimentation, we compare these models in terms of accuracy, precision, recall, F1-score, and ROC-AUC, highlighting their strengths and limitations. Our results show that CNNs excel in detecting spatial anomalies, RNNs are wellsuited for temporal anomaly detection, and Autoencoders provide robust detection for previously unseen anomalies. Additionally, we examine the sensitivity of threshold settings in Autoencoders and assess the real-time feasibility of these models by measuring their inference times. The findings suggest that deep learning-based anomaly detection significantly enhances the security of SDNs, providing both accuracy and speed suitable for real-time applications. Finally, we propose future directions for optimizing deep learning models to handle large-scale, dynamic SDN deployments more effectively. This work contributes to the growing body of research focused on utilizing deep learning for improving the security and resilience of SDN-based infrastructures.

Opioid addiction disorder (OAD) affects millions of people worldwide. While it is known that OAD originates from many factors, including social and environmental factors, the role of genetic variants in developing the disease has also been reported. This study aims to investigate the genetic variants that may be associated with the risk of developing OAD upon exposure. Twenty-three subjects who had previously been given opioid-based painkillers to undergo small surgical treatment were recruited at Prisma Health Upstate clinic and elsewhere. Eleven of them were considered non-persistent opioid users (controls), and 12 of them were persistent opioid users (cases) after an initial surgery and at the time of sample collection. The subjects were asked to provide saliva samples, which were subjected to DNA sequencing at Clemson University Center for Human Genetics, and variant calling was performed. The GWAS for genes known to be associated with OAD resulted in 13 variants (intronic or SNV) with genome-wide significance (raw p-value < 0.01) and two missense variants, rs6265 (Val66Met in BNDF isoform a) and rs1799971 (Asn40Asp) in OPRM1 previously reported in the literature. Furthermore, extending the GWAS to find all the genomic variants and filtering the variants to include only variants found in cases (persistent opioid users) but not in controls (non-persistent opioid users) resulted in 11 new variants (p-value< 0.005). Considering that OAD is a complex disease and the effect may come from different variants in the same genes, we performed a co-occurrence analysis of variants on the genes and identified four genes, LRFN3, ZMIZ1, RYR3, and OR1L6 with three or more variants in the case subjects but not in control individuals.

In this paper, we review metaheuristic optimization techniques for machine learning with Graph Neural Networks (GNNs), focusing on two major categories: single-solution-based and population-based metaheuristics. We provide an in-depth analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), highlighting their effectiveness in optimizing GNN hyperparameters. By exploring the strengths and weaknesses of these methods, we aim to provide insights into their application and performance in enhancing the efficiency and accuracy of GNN models. We will also review the similarities of supervised learning and metaheuristic solutions.

With the urgent need of precise positioning faced by internet of things (IoT) applications, the universality and cost of indoor positioning devices become key factors. Since the 5G network has been widely deployed, new opportunity is brought by tightly fusing the traditional low-cost sensors, i.e., the magnetometer. In this study, using 5G channel state information (CSI) and geomagnetic data, a multi-input convolutional neural network (CNN) localization system was proposed. First, to generate a data tensor that is easy for CNN processing, the raw data was reconstructed individually. Then, to comprehensively incorporate the features of 5G CSI and geomagnetic strength data, the ReLU function was chosen as the activation function of the convolutional layer. After that, a multi-input CNN was trained using the incorporated geomagnetic strength and CSI amplitude in the off-line side, and the trained CNN was recorded as a location fingerprint, which can be used for the user position prediction. Finally, in the online side and using a multi-input CNN, the 2-D coordinates are estimated and tested indoors in a typical conference room scenario. The results showed that longer sampling time of fingerprint data result in better uniqueness of the reference point, while the data collection time of locating points does not need to be long. Taking the positioning efficiency and accuracy into consideration, a sampling time of 3s at the reference point and 0.2s at the locating point are recommended. The positioning accuracy using the proposed method was 1.41 m, with an improvement of 22.9% compared with the 5G positioning, and an improvement of 18.0% compared with the 5G and geomagnetic fusing positioning using single CNN.

There are considerable benefits in applying Artificial Intelligence to automation in modern industries including Oil and Gas Industries. Oil and Gas Industries have many complex systems in their production and flow line. The failure of gas equipment will have serious consequences including financial and environmental issues. Condition monitoring of oil and gas equipment provides the condition of components, machines, systems, equipment, data hardware, and even software. The predictive maintenance and industry 4.0 applications have more advantages in petrochemical industries to make a safer economic environment and this paper addresses the Artificial Intelligence (AI) application of condition monitoring for Oil and Gas Industries. To understand the effectiveness of AI in condition monitoring for oil and gas industries, a case study related to the condition monitoring of drilling machine is conducted and applied Artificial Neural Network (ANN) algorithm to analyze and predict the potential failures. The novelty of this work is the proposal of an approach for tool wear monitoring in drilling using acoustic emission sensors for feature extraction and considering wavelet packet decomposition for further analysis. The extracted features from WPD are given as input for ANN to identify the healthiness of the drill bit and machine. This work aims to find the effectiveness of AI-based condition monitoring in enhancing effectiveness of monitoring and the safety of equipment in Oil and Gas Industries.

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is crucial to understand people's emotions and the life incidents they experience both before and after a disaster strikes. In this case study, we mined the emotions and life incidents expressed on Twitter during the 2017 Hurricane Harvey. Between August 20 and August 30, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. Following emotion prediction, we applied various natural language processing techniques to analyze the content of the tweets, aiming to uncover the 'topics' discussed alongside specific 'emotions.' To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. This step ensured that related tweets were accurately grouped together based on their content and context. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster. This advanced approach allowed us to identify and label life incidents automatically, providing a nuanced understanding of the key issues faced by the community during the hurricane. The results revealed significant emotional shifts as the hurricane reached its most devastating phase. These shifts coincided with pressing concerns and demands from the population, such as evacuation plans, animal safety, safety updates, public policy, help requests, flood impacts, and the spread of fake news. By combining GNN and LLM for automatic event name prediction, our study not only mapped out the emotions but also accurately categorized the specific life incidents that were most relevant during the disaster, offering critical insights for disaster preparedness and response strategies.

In this study a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, design of optimal neural-network based controller is done automatically. Several common reference types are studied including step, square, sine, sawtooth and trapezoid functions. Expected reference-disturbance pairs are used to train the system for finding optimal neural-network controller parameters. A separate test set is used to test the system for unexpected reference-disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. Real DC motor’s parameters are estimated using particle swarm optimization (PSO) algorithm. Initially, a proportional-integral (PI) controller is designed using PSO algorithm for finding simple controller’s parameters optimally and automatically. Starting with neural-network equivalent of optimal PI controller, optimal neural-network controller is designed using PSO algorithm for training again. Simulations are conducted with estimated parameters for diverse set of training and test patterns. Results are compared with optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained suggesting further research in the proposed direction.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

Machine-learning methods used for classification are challenged by the class imbalance problem, where a certain class is underrepresented. Over/under-sampling the majority/minority class observations or model selection for ensemble methods alone might not be effective if the class imbalance ratio is very high. To address this concern, a novel method is presented for generating synthetic data for minority class observations in conjunction with an optimal tree ensemble classifier (OTEC). This novel synthetic method first generates minority class instances to balance the training data. Then it applies OTEC, where models are selected based on their performance using out-of-bag observations and training subsamples. A total of 20 benchmark problems on binary classification with moderate to extreme class imbalance are used to assess the efficacy of the proposed method against other well-known methods, including optimal tree ensemble, smote random forest, under-sampling random forest, oversampling random forest, k-nearest-neighbor, support vector machine, tree, and artificial neural network, using classification error rate, sensitivity, specificity, precision, recall and F1-score as performance metrics to assess the efficacy of the proposed method. The analyses presented in this study revealed that the proposed method based on data balancing and model selection yielded better results than other methods.

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMS) perform real-time monitoring functions, but their performances must be carefully assessed. In this work the damage detection performances of a strain-based SHMS, were evaluated on a composite helicopter rotor blade root. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through Finite Element analyses performed on the component. The effects of the loads variability and of damages were decoupled by including a load recognition step in the algorithm. Load recognition was accomplished in different ways: version#1 adopted an Artificial Neural Network (ANN) trained on blades in pristine condition; version#2 was trained also on damaged blades; version#3 adopted a calibration matrix method. Anomaly detection, damage assessment, and localization were performed by using an ANN. Results showed a higher load identification and anomaly detection accuracy for version#1 and version#2 compared to version#3. Gaussian noise reduced anomaly detection and damage assessment performances, and reduced damage localization accuracy for bigger damages. The damage detection performance were obtained for a fibre optic based-SHMS, including the trade-off between probability of detection and false alarm rate for different damage sizes.

This study examines the causal relationships between interest rates in the Korean financial market using a vector error correction model (VECM) and a Bayesian network. The Bayesian network, a novel approach in this context, identifies two distinct transmission channels: one led by call rates (reflecting monetary policy) and another by 10-year Treasury bond yields (reflecting fiscal policy). While the call rate channel aligns with traditional views, affecting bank lending rates, corporate bond spreads, and commercial paper rates, the 10-year Treasury yield channel highlights a separate fiscal policy transmission mechanism, influencing commercial paper rates indirectly through 30-year Treasury yields and directly impacting merchant bank lending rates. This finding suggests that fiscal intervention could potentially interfere with monetary policy, emphasizing the need for coordinated macroeconomic policy measures. The study also emphasizes the role of 10-year Treasury bonds as a benchmark in the Korean bond market, reflecting medium- to long-term economic outlooks and serving as the underlying asset for various derivatives.

Deep Neural Network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO: Energy-efficient Computation Harnessing Onilne Arithmetic - A MSDF-based accelerator for DNN inference, designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/ most-significant digit first (MSDF) arithmetic which performs computations in a digit-serial manner. The MSDF digital serial computation of online arithmetic enables overlapped computation of successive operations leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction of energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. Experimental results demonstrate promising power and throughput improvements compared to contemporary methods. In particular, the proposed design achieved an average improvement in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42 were observed when comparing the proposed design to conventional bit-serial designs for VGG-16, ResNet-18, and ResNet-50 models respectively.

Yeast two-hybrid approaches, which are based on fusion proteins that must co-localise to the nucleus to reconstitute the transcriptional activity of GAL4 have greatly contributed to understand the nitrogen interaction network of cyanobacteria, whose main hubs are the trimeric PII and the monomeric PipX regulators. The bacterial two-hybrid system, based on the reconstitution in the E. coli cytoplasm of the adenylate cyclase of Bordetella pertussis, should provide a relatively faster and presumably more physiological assay for cyanobacterial proteins than the yeast system. Here we use the bacterial two-hybrid system to gain additional insights into the cyanobacterial PipX interaction network while simultaneously assessing the advantages and limitations of the two most popular two-hybrid systems. Comprehensive mutational analysis of PipX and bacterial two-hybrid assays were performed to compare outcomes between yeast and bacterial systems. We detected interactions previously recorded in the yeast two-hybrid system as negative as well as a “false positive”, the self-interaction of PipX, which is rather an indirect interaction dependent on PII homologs from the E. coli host, a result confirmed by Western analysis with relevant PipX variants. This is, to our knowledge, the first report of the molecular basis of a false positive in the bacterial two-hybrid system.

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, and Regression Tree—are employed and compared for performance. Evaluation metrics such as Mean Absolute Deviation, Root Mean Square Error, Coefficient of Correlation, and Mean Absolute Percentage Error are used. After preprocessing 1030 samples, the dataset is split into 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are po-tentially applicable in artificial and spiking neural networks, memory arrays, and many other de-vices and circuits for artificial intelligence. In this paper, a complete electronic realization of ana-log circuit model of modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an im-portant step towards the forthcoming design of complex memristor-based neural networks for ar-tificial intelligence, for implementation in very high-density integrated circuits and chips.

In this paper, we propose a method for assessing the effectiveness of entropy features using several chaotic mappings. We anlyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) on four discrete mappings: the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn is shown to have improved global efficiency (GEFMCC) in the classification task compared to NNetEn. At the same time, there are local areas of the time series dynamics in which the classification efficiency NNetEn is higher than FuzzyEn. The results of using horizontal visibility graphs (HVG) instead of the raw time series are also shown. GEFMCC decreases after HVG time series transformation, but there are local areas of the time series dynamics in which the classification efficiency increases after including the HVG. The scientific community can use the results to explore the efficiency of entropy-based classification of time series.

This work studies the discontinuities features of sedimentary flysch materials in a 100 km2 area belonging to the Basque Arc. Such materials are common in this Spanish Alpine region located in the north of the Iberian Peninsula. A total of 33 outcrops are investigated by an intensive geotechnical investigation including geomechanical stations, boreholes and mechanical laboratory tests. Two flysch units are characterized: the Upper Aguinaga Formation or siliciclastic flysch, and the Lower Itziar Formation or calcareous flysch. Differences between both flysch formation are found. Joints in the siliciclastic flysch formation present an undulated roughness, with a spacing narrower and a persistence lower than in the calcareous flysch formation, which exhibits higher friction angles, although roughness is essentially planar. In addition, the potential of using Artificial Intelligence (AI) techniques, particularly Artificial Neural Networks and Support Vector Machine, to estimate the Geological Strength Index (GSI) from the Rock Quality Design (RQD) and some discontinuities features (spacing, persistence, aperture and roughness) is investigated. AI techniques are found to be satisfactory, being the Support Vector Machine with a linear kernel the technique which achieves the best performance.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.

The models of partial least squares (PLS) regression and artificial neural network (ANN) for evaluation of texture properties of cooked germinated brown rice (GBR) using the Fourier transform near infrared (NIR) spectra of uncooked whole grain combined with data separation methods, spectral pretreatment methods were investigated in this study. The ANN was outperformed in evaluation of hardness by back extrusion test of cooked GBR using the smoothing combined with standard normal variate pretreated NIR spectra in the range of 12,500-4,000 cm−1 of 188 whole grain samples where the calibration sample set was separated from prediction set by Kennard-Stone method where the best ANN model for hardness from hidden layers of 25 and 8 iteration time provided R2, r2, RMSEC, RMSEP, Bias and RPD of 0.9987, 0.9447, 0.1021 N, 0.7699 N, 0.0216 N and 4.3 respectively. The PLS regression of 64 sample KDML GBR group and 64 sample various variety GBR group, provided models for the hardness of the former and the toughness of the latter which developed by using 7506−5446.3, 4605.4−4242.9 cm−1 which included the amylose vibration band of 6834 cm−1 while toughness model was from 9403.8-6094.3 cm−1 where included 6834 and 8316 cm−1 vibration bands of amylose which influenced the texture of cooked rice. The PLS regression models for hardness and toughness were with the r2 of 0.85 and 0.82, and the RPD of 2.9 and 2.4, respectively. The ANN model for hardness of cooked GBR should be implemented to the practical use in GBR production factories for the quality assurance and further updating using more samples and several brands to obtain the robust models.

India ranks among the top ten nations in the world for grape production. Fungal pathogens inflict damage to crop plants in turn making cultivators bear huge economical losses. With an output of 1.21 million tons (about 2% of 57.40 million tons produced globally). 1.2% of the nation’s total fruit cropland is covered by grapes. But due to fungal diseases the effect of the yield produced ranges from 5-80% depending on the severity of diseases which will affect the yield of grape vineyard. In precision agriculture, new sensing technologies and artificial intelligence could be used to automatically identify grapevine and disease pest symptoms. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Timely effective and precise evaluation of grape diseases is admitted as a critical step in the field management. In this paper, we are explaining about different optical sensing methods applied for RGB, Multispectral and Thermal cameras. Section-wise we will be describing environmental set up for image-aquation, data-preprocessing, different modelling methods, evaluation matrix, result, and reviewer’s comment.

Underwater wireless sensor networks (UWSNs) represent a specialized category of WSNs with versatile applications including acoustic monitoring, oil and gas exploration, and military surveillance. UWSNs face formidable challenges such as limited energy resources, extended propagation delays, and harsh conditions. Existing clustering and multi-hop routing protocols often unevenly distribute nodes geographically, causing network fragmentation and disproportionately draining the battery life of nodes near the sink due to higher data transmission demands. In this paper, we introduce an Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP), inspired by the collective behavior of fish swarms. EAFSCP is a decentralized clustering algorithm designed for acoustic monitoring in UWSNs. Its decentralized nature makes it particularly well-suited for large-scale UWSNs, where centralized algorithms may not be feasible. Through comprehensive comparisons with existing cluster-based routing protocols, our findings indicate that EAFSCP consistently outperforms them across multiple key performance metrics, including network lifetime, energy consumption, packet delivery ratio, packet loss rate, and throughput. According to the results, EAFSCP represents an effective clustering algorithm that enhances network performance, prolongs network lifespan by reducing energy consumption, promotes scalability, and provides valuable guidance for emergency response efforts.

Underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, deploying the UASN is a challenging problem due to the harsh environment and high deployment cost. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of routing void, packet delivery delay, and energy utilization. In this paper, an adaptive support-vector-machine-based routing (ASVMR) protocol is proposed for the UASN to prolong the network lifetime and reduce the end-to-end packet delivery delay. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real-time by considering four types of node state information. Moreover, the ASVMR protocol establishes a "routing vector" spanning from the current node to the sink node, and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well, in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments.

Cancer is a condition in which the body's cells proliferate unchecked. Skin cancer is one of the deadliest diseases that impacts the skin on many levels. There are several different types of the disease, including melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevus. Due to its increased prevalence, skin cancer, in particular melanoma, is be-coming a serious health problem. Early identification of skin lesions is crucial for successful treatment. Due to their resemblance, many skin lesions are misclassified, which is a severe issue. Researchers seek computer-aided diagnostic tools for early malignant tumor detection. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, to get the desired outcomes, the current YOLOv5 model is changed by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The pro-posed approach improves melanoma detection with a real-time speed of 0.4 ms non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to cur-rent melanoma detection approaches, the provided approach is more efficient using deep fea-tures.

As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation to overcome treatment inefficacy in cancer. Given the high attrition rate of de novo drug discovery, drug screening, and drug repurposing have offered time- and cost-effective alternative strategies for the identification of potentially effective therapeutics. In contrast to large-scale drug screens, computational approaches for drug repurposing leverage the increasing amounts of biomedical data to predict candidate therapeutic agents prior to testing in biological models. Current studies in drug repurposing for cancer therapy prediction have increasingly focused on the prediction of combination therapies, as combination therapies have numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity from lowered drug doses, and a reduced risk of resistance due to multiple non-overlapping mechanisms of action. This review provides a summary of several classes of computational methods used for drug combination therapy prediction in cancer research, including networks, regression-based machine learning, classifier machine learning models, and deep learning approaches, with the goal of presenting current progress in the field, particularly to non-computational cancer biologists. We conclude by discussing the need for further advancements in technologies that incorporate disease mechanisms, drug characteristics, multi-omics data, and clinical considerations to generate effective patient-specific drug combinations, as holistic data integration will inevitably result in optimal targeted therapeutics for cancer.

In recent times, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks which include Denial-of-Service (DoS), Distributed-Denial-of-Service (DDoS), Man-in-the-Middle (MitM) attacks, and this may have been caused majorly by the integration of open network to Operation Technology (OT) as a result of low-cost network expansion. The connection of the OT to the internet for firmware updates, third-party support, or vendor interventions, has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations and proposes the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network so as to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in the simulation of process control. The outcome of the experiments shows the reliability and functionality of the different machine-learning algorithms in detecting these anomalies with significant precise attack detections identified using a coarse tree algorithm.

An approach to the implementation of neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable especially for onboard communication systems in unmanned aerial vehicles (UAV) because of suitability to hardware implementation. In this study we evaluate the possibility to build such a system in hardware implementation at FPGA. The proposed integrated neural network approach for real-time cryptographic data protection was based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern hardware components with programmable structure. The development and implementation of the on-board neural network system for cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; hardware and software modification and making them suitable for data encryption and decryption. The tabular-algorithmic method for calculation of the scalar product has been improved that makes it possible to perform fast calculation of the scalar product of fixed-point and floating-point input data by bringing them to the largest common order of weights and forming tables of macro-partial products. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

The ubiquitous connectivity for the space-air-ground integrated network (SAGIN) of the beyond fifth generation of communication and sixth generation of communication (B5G/6G) is envisaged to meet the needs for the demanded quality of service (QoS), green communication, and "dual carbon" target. However, the offloading and computation of massive latency-sensitive tasks dramatically increases the energy consumption of the network. Furthermore, the traditional power supply technology of the network base stations (BSs) enhances the carbon emission. To address these issues, we first propose a SAGIN architecture with energy harvesting devices, where the BS is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power the unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighbouring BSs is designed to fully utilize RE for reduce carbon emission. Secondly, on the basis of task offloading decision, UAV trajectory, and RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emission.

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms, and our results demonstrate that the neural network-based classifier yields an accuracy as high as 92% for the RSNA dataset which is a new dataset that provides two views and additional features such as age. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. The two popular methods of producing a deepfake are based on either convolutional neural networks (CNN), or autoencoders. Deepfakes created using CNN comparatively show higher qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increase usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with accuracies around 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival and other prognostic endpoints in multiple cancer types, including Lower-Grade Gliomas (LGG), Breast Invasive Carcinoma (BRCA) and many others. We also showed significant association of MNAI with genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Futhermore, we found that patients with high MNAI benefit more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated on LGG and BRCA that the multimodal integration of MNAI and the AI-empowerd Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet which uses deep neural networks for detecting flowers from multiview image sequences for high throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Aiming at the problem of low charging efficiency caused by the scattered sensor nodes in traditional wireless rechargeable sensor networks (WRSNs), a UAV-assisted WRSN Online Charging Strategy Based on Dynamic Queue and Improved K-means (UOCS) is proposed. The scheme assumes that the energy consumption of nodes is unpredictable, and only generates charging requests when the energy is lower than a threshold, and performs on-demand responses to nodes that issue charging requests. The scheme combines the characteristics of one-to-one charging of UAVs, the selection and allocation timing of waiting queues and the number of UAVs, and the improved K-means partitioning based on space-time coordination(SPKM), which simplifies the problem of coordinated charging of multiple UAVs and maximizes energy. Using the efficiency and charging success rate, the optimal charging trajectory can be found under the constraint that the node will not starve to death due to power shortage. Finally, a simulation comparison experiment is carried out with the existing UAV charging scheduling strategy. UOCS achieves the optimal node survival rate with low algorithm complexity.

Rationale: The 14-3-3 protein family is known to interact with many proteins in non-cardiac cell types to regulate multiple signaling pathways, particularly those relating to energy and protein homeostasis; and the 14-3-3 network is a therapeutic target of critical metabolic and proteostatic signaling in cancer and neurological diseases. Although the heart is critically sensitive to nutrient and energy alterations, and multiple signaling pathways coordinate to maintain the cardiac cell homeostasis, neither the structure of cardiac 14-3-3 protein interactome, nor potential functional roles of 14-3-3 protein-protein interactions (PPIs) in heart has been explored. Objective: To establish the comprehensive landscape and characterize the functional role of cardiac 14-3-3 PPIs. Methods and Results: We evaluated both RNA expression and protein abundance of 14-3-3 isoforms in mouse heart, followed by co-immunoprecipitation of 14-3-3 proteins and mass spectrometry in left ventricle. We identified 52 proteins comprising the cardiac 14-3-3 interactome. Multiple bioinformatic analyses indicated that more than half of the proteins bound to 14-3-3 are related to mitochondria; and the deduced functions of the mitochondrial 14-3-3 network are to regulate cardiac ATP production via interactions with mitochondrial inner membrane proteins, especially those in mitochondrial complex I. Binding to ribosomal proteins, 14-3-3 proteins likely coordinate protein synthesis and protein quality control. Localizations of 14-3-3 proteins to mitochondria and ribosome were validated via immunofluorescence assays. The deduced function of cardiac 14-3-3 PPIs is to regulate cardiac metabolic homeostasis and proteostasis. Conclusions: Thus, the cardiac 14-3-3 interactome may be a potential therapeutic target in cardiovascular metabolic and proteostatic disease states, as it already is in cancer therapy.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p &lt; 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

In a changing climate, extreme weather events such as heat waves will be more frequent and could affect grain weight and the quality of crops such as wheat, one of the most significant crops in terms of global food security. In this work, we characterized the response of Triticum turgidum spp. durum wheat to a short-term heat-stress (HS) treatment at transcriptomic and physiological levels during early grain filling in glasshouse experiments. We found a significant reduction in grain weight and size from HS treatment. Grain quality was also affected, showing a decrease in starch content in addition to increments in grain protein levels. Moreover, an RNA-seq analysis of durum wheat grains allowed us to identify 1590 differentially expressed genes related to photosynthesis, response to heat, and carbohydrate metabolic process. A gene regulatory network analysis of HS-responsive genes uncovered novel transcription factors (TFs) controlling the expression of genes involved in abiotic stress response and grain quality, such as a member of the DOF family predicted to regulate glycogen and starch biosynthetic processes in response to HS in grains. In summary, our results provide new insights into the extensive transcriptome reprogramming that occurs during short-term HS in durum wheat grains.

Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. Satellite communication services became recently attractive to mega-companies that foresee an excellent opportunity to connect disconnected remote regions, serve emerging machine-to-machine communication, Internet-of-things connectivity, and more. Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. In addition, when signals approach the ground station, it has to overcome buildings blocking the direct access to the ground station. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes LTSM, an artificial recurrent neural network technology that provides a time-dependent prediction of the expected attenuation level due to rain and fog and the signal strength that remained after crossing physical obstacles surrounding the ground station. The satellite transmitter is calibrated accordingly. The satellite outgoing signal strength is based on the predicted signal strength to ensure it will remain strong enough for the ground station to process it. The instant calibration eliminates the excess use of energy resulting in energy savings.

Green innovation has become an important combination of high-quality economic growth and sustainable development of ecological environment. In this paper, the super-efficiency network SBM model is used to measure the two-stage green innovation efficiency of industrial science and technology R&amp;D and achievement transformation in 30 provinces and cities from 2009 to 2019, and exploratory Data Analysis (ESDA) and spatial econometric model are used to investigate the spatial-temporal evolution characteristics and influencing factors of green innovation efficiency. The results show that: firstly, the overall efficiency of industrial green innovation is low, and the efficiency of scientific research and development and achievement transformation has experienced three stages of "upward-declining-revitalized period". The low efficiency of achievement transformation is an important factor hiding the improvement of the efficiency of industrial green innovation. Secondly, The industrial green innovation efficiency gradually increases from northwest to southeast, forming a centralized "line" and "block" distribution. The high efficiency area is still concentrated in the eastern coastal region, and the balanced development trend is obvious in the central and western regions. Finally, openness has a positive impact on the two-stage green innovation efficiency; Industrial structure and government investment in science and technology have a positive impact on the efficiency of science and technology research and development, but have no significant effect on the efficiency of achievement transformation. Enterprise size has a positive effect on achievement transformation efficiency, but has no significant effect on R&amp;D efficiency. Environmental regulation has a positive impact on R&amp;D efficiency and a negative impact on achievement transformation efficiency.

Epithelial-mesenchymal transition (EMT) networks are essential in acquiring the drug resistance and cancer malignant features in cancer stem cells (CSCs). In this regard, gene expression profiles in diffuse- and intestinal-type gastric cancer (GC) have been analyzed to reveal the network pathways in EMT and CSCs, since the diffuse-type GC has much more mesenchymal features than intestinal-type GC that has the intestinal features. The study results revealed that the activation state of several canonical pathways related to cell cycle regulation was altered. The canonical pathway on Cell cycle: G1/S checkpoint regulation was activated in diffuse-type GC, and canonical pathways on Cell cycle control of chromosomal replication and Cyclins and cell cycle regulation were activated in intestinal-type GC. Canonical pathway related to Role of BRCA1 in DNA damage response was activated in intestinal-type GC, where BRCA1, which is related to G1/S phase transition was up-regulated in intestinal-type GC. Several microRNAs (miRNAs), including mir-10, mir-17, mir-19, mir-194, mir-224, mir-25, mir-34, mir-451, and mir-605, were identified to have direct relationships of RNA-RNA interaction in Cell cycle: G1/S checkpoint regulation pathway. Additionally, cell cycle regulation may be altered in EMT conditions. The alterations in activation states of the pathways related to cell cycle regulation in diffuse- and intestinal-type GC would indicate the significance of cell cycle regulation in EMT.

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). Robustness and security of the IoVT systems are essential to a sustainable urban environment. While blockchain technology has shown great potentials to enable trust-free and decentralized security mechanisms, directly embedding crypto-currency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. Leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. Decentralized database (DDB) is adopted to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks.

A more sustainable society and economy also implies a more sustainable behavior in the consumption of financial products. A possible change in focus can come from the demand side, so that more sustainable consumption of financial products have to go hand to hand with financial literacy. However, financial literacy, potential favoring of this sustainable behavior, is far from reaching an international consensus about its definition, object and scope. Two objectives are analyzed; the different interpretations of financial literacy in the academic literature, as well as its evolution and how, in what context and with what other concepts the term is used in social networks. Scientometric techniques and content analysis have been used to carry out a systematic review of literature and also NLP to analyze the comments on Social Networks. Critical moments are identified in the definition of financial literacy. Also ten sentiments are analyzed in social networks in wich positivity, trust, and anticipation predominate. Greater attention to this issue is necessary both from the private initiative and from public policies, so that financial literacy is an effective tool for a more sustainable behavior by consumers. Finally, a new definition is proposed based on our findings.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threats (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD data set. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and their timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain-Computer Interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes Steady-State Visually Evoked Potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital EEG responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40 and band power, throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20-40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age related cognitive decline.

Reemergence of deep Neural Networks (CNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is possible because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be a lot more challenging and for various reasons using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper,we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data is easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other learning competing approaches.

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Parmigiano Reggiano cheese is one of the most appreciated and consumed food worldwide, especially in Italy, for its high content of nutrients and for its taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed in order to investigate the potentiality of this tool to distinguish rind percentage in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples in terms of percentage, seasoning and rind working process were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them with sensors responses. Data analysis consisted of two stages: multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results are promising in terms of correct classification of the samples. The classification rate is higher for ANNs than PLS-DA, reaching also 100% values.

Recently, cloud technology has become popular for smart societies. The Cloud technology has made dynamical network changes by enabling the construction of a logical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake VNFs and the encryption of communication between entities. In this paper, we proposed an LW_PKI mechanism that detects a fake VNF and guarantees data security through mutual authentication between VNFs. To evaluate the LW_PKI, we built a MANO environment to test the performance of authentication and key generation for data security. In addition, we applied the artificial intelligence algorithm to detect abnormal behavior by using real attack data in the MANO environment. The LW_PKI guaranteed the reliability of a smart service by enhancing the security of the cloud environment.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

Aim: This study aims to analyze the evolution of co-author networks in Single-Balloon Enteroscope (SBE) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The objective is to provide a structural overview of researcher collaborations in this specialized field, highlighting key contributors and the impact of international cooperation. Method: I conducted a co-author network analysis on 149 SBE-related articles indexed in the WoS Core Collection between 2000 and 2023. Using Python (Version 3.10.5) within the PyCharm development environment (Software Version 2022.1.3), I assessed macro-level indicators including network density, clustering coefficient, number of components, and average distance. Micro-level indicators such as degree centrality, closeness centrality, and betweenness centrality were also evaluated to understand the roles of individual researchers in the network.Result: The analysis revealed that the co-author networks in SBE research have evolved over the studied period, with varying degrees of connectivity and collaboration. From 2000 to 2009, the network showed moderate clustering but high fragmentation, with a few key researchers like Nista, Enrico C., and Spada, Cristiano playing central roles. In the 2010-2019 period, network density decreased, indicating expansion in the author pool but less frequent co-authorships. However, the clustering coefficient remained high, suggesting localized collaboration. Central figures such as Itoi, Takao emerged as influential in bridging network gaps. In the most recent period (2020-2023), network density moderately increased, reflecting improved collaboration. Key contributors included Tanisaka, Yuki, and Ryozawa, Shomei, who enhanced connectivity within the network. Despite these advancements, the network remained fragmented with isolated components throughout all periods. Conclusion: The co-author networks in SBE research have shown an overall increase in collaboration over the past two decades, with key contributors playing pivotal roles in connecting disparate groups. However, the persistent fragmentation suggests a need for more integrated efforts to enhance international collaboration. Understanding these network dynamics can inform future strategies for fostering global cooperation and advancing SBE research.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Course recommendation is an important service of Intelligent tutoring systems (ITS), the rapid development of large-scale online courses has generated a substantial amount of learning process data for online learning platforms, providing valuable support for understanding learners' interests and preferences. However, despite made considerable achievements, there are still unresolved challenges:(1) Current research neglects the sequential relationship between the course learners learn. (2) It does not take advantage of the differences in learners' interest in different courses during the knowledge dissemination process. To overcome these challenges, we propose an innovative solution called PGDB (Preference-aware graph diffusion network and BiLSTM) for course recommendation. Specifically, we analyze learners' preferences for courses and relational paths using a relation-aware multi-head attention network, learn the semantic diversity of different contexts, effectively distinguish the difference of learners' interest in different courses in the process of knowledge transmission. In addition, the temporal preference modeling module employs a bi-directional long short-term memory network to mine learners' interest evolution patterns, generating learner-dependent representations, efficient use of the sequential relationship between the courses taken by learners. Experimental results on real datasets demonstrate that the proposed model greatly exceeds the performance of the most recent baseline models, thereby validating the model's effectiveness.

Drilling and blasting is the conventional method used for rock fragmentation in open pit mining. Blast-induced damage can reduce the level of stability of benches and pit slopes. To develop an optimal blast design, an adequate knowledge of the rock properties and in-situ fractures is needed. Fractures are generally the paths of least resistance for explosive energy and can affect the intensity of blast-induced damage. Discrete Fracture Networks (DFN) are 3D representations of joint systems used for estimating the distribution of in-situ fractures in a rock mass. The combined finite/discrete element method (FDEM) can be used to simulate the complex rock breakage process during a blast. The objective of this paper is to develop a methodology for assessing the influence of in-situ joints on the post-blast fracturing and the associated wall damage in 2D bench blasts scenarios. First, a simple one-blasthole scenario is analyzed with the FDEM software Irazu 2D and calibrated based on a laboratory-scale blasting experiment available from previous literature. Secondly, more complex scenarios consisting of one-blasthole models at the bench-scale were simulated. A bench blast without DFN (base case) and one with DFN, were numerically simulated. The model with DFN demonstrated that the growth path and intensity of blast-induced fractures were governed by pre-existing fractures which led to smaller wall damage area. The damage intensity for the base case scenario is about 42% higher than for the blast model with DFN included, which highlights the significance of in-situ fractures in the resulting blast damage intensity. The methodology for developing the DFN-included blasting simulation provides a more realistic modeling process for blast-induced wall damage assessment. This results in a better characterization of the blast damage zone and can lead to improved slope stability analyses.

Valles Marineris (VM), the most prominent example of a Martian Valley Network, has been extensively studied for over 50 years, yet no detailed examination of the principal azimuths of the system exists. To address this, two methods are presented to precisely calculate the principal azimuths, a Bezier Spline analysis, and a GIS technique. The medial axis of the main canyon of VM was determined analytically from cubic polynomial splines fitted to 93 coordinate points along both north and south edges of the canyon. These splines were optimized, and medial axis points were calculated through numerical techniques that ensured orthogonality between the tangents of each spline and their connecting normal lines. 1,000 medial axis points were extracted, and various regression models constructed, including fitting to sinusoidal and cubic polynomial curves, achieving accuracies with R² values of 0.98 and 0.99, respectively. Principal azimuths were obtained using the sinusoidal equation with the slope of the tangent at any point x simply determined by the derivative of the curve’s equation. This analytic approach was cross-validated by a GIS method (using QGIS software), where a vector medial axis was obtained which produced principal azimuths that agreed with values from the analytic study with a correlation coefficient of 1.00, and a p value of 6.43e-65. The findings demonstrate that an azimuthal framework can be rigorously constructed as a potential standard reference in VM geoscience, replacing less precise and ambiguous compass bearings with the accurate azimuths necessary for high-resolution spatial analysis for future investigations.

You Only Look One (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, choosing selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (e.g., COCO, PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple intersection-over-union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. Thus, YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.

Metabolomics studies require a deep understanding of enzyme dynamics and their responses to environmental disturbances within a complex metabolic network. This paper presents a novel approach to modeling enzyme activity as a dynamic topological manifold, where each enzyme is represented as a node, and their interactions are described by differential equations. These equations account for both compensatory interactions and external perturbations, simulating how enzymes dynamically stabilize their activities in response to varying conditions. We explore the application of this model to visualize enzyme interactions, using graph theory to represent the network structure and color gradients to illustrate interaction intensities. Multiple disturbances are introduced to analyze the network's resilience and adaptability over time. The results provide insights into the compensatory mechanisms within enzymatic networks, offering a comprehensive visualization through both static and dynamic representations. Our approach allows for a better understanding of how enzymatic systems buffer against mutations and environmental stressors, contributing to the broader field of systems biology and metabolomics.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

In aerospace, robotics, network engineering and mechanical engineering, there exist complex and urgent challenges that can be abstracted into mathematical optimization problems. Therefore, exploring solutions to these optimization problems is a crucial task. Metaheuristic algorithms have been applied across various fields.The Snake Optimization Algorithm (SO) is an innovative metaheuristic method recognized for its efficient solving capabilities.However, this algorithm encounters challenges such as diminished search efficiency in the later stages and a propensity to become ensnared in local optima. To mitigate these issues, this paper introduces an enhanced Snake Optimization Algorithm (ISO). ISO integrates the RIME Algorithm(RIME)and introduces two effective enhancement strategies to improve its capability to evade local optima.To assess the effectiveness of ISO,the population distribution state was first measured using three algorithms, including Star Discrepancy, and the exploration and exploitation performance was analyzed using the CEC2017 test functions. Subsequently, utilized 23 benchmark functions for testing.The evaluation outcomes indicate that ISO exhibits outstanding performance in terms of convergence velocity and robustness.Additionally, it was applied to engineering fields including UAV path planning,Robot path planning, Wireless sensor network node deployment and Pressure vessel design.In comparison to the SO algorithm, the ISO algorithm demonstrates superior stability, converges earlier, and improves its ability to solve for optimal .These findings highlight the significant potential of the ISO algorithm in diverse test functions and a wide range of interdisciplinary problems.

A significant challenge encountered in mmWave and Sub-terahertz systems used in 5G and 1 the upcoming 6G networks is the rapid fluctuation in signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments in device orientation or the presence of blockers capable of causing substantial fluctuations in link quality along a designated path. This issue poses a major obstacle because numerous applications with low-latency requirements necessitate precise forecasting of network quality from many directions and cells. The method that is proposed in this research demonstrates an avant-garde approach for assessing the quality of multi-directional connections in mmWave systems by utilizing the Liquid Time Constant network (LTC) instead of the conventionally used Long Short-Term Memory (LSTM) technique. The method’s validity was tested through an optimistic simulation involving the monitoring of multi-cell connections at 28 GHz in a scenario where humans and various obstructions were moving arbitrarily. The results with LTC are significantly better than those obtained by conventional approaches such as LSTM. The latter resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB, while the former, 0.25 dB, demonstrating a 13-fold improvement. For better interpretability and to illustrate the complexity of prediction, an approximate mathematical expression is also fitted to the simulated signal data using Symbolic Regression.

An innovative concrete that incorporates the advantages of self-compacting concrete with geopolymer is called self-compacting geopolymer concrete. The objective of this study was the Manufacturing of M30-grade self-compacting geopolymer concrete by adding industrial materials like fly ash (FA), ultrafine ground granulated blast furnace slag (UFGGBS), and silica fume (SF) as minerals. The disposal of these industrial wastes can be substantially overcome, as they lead to land pollution. The fixed powder content was 500 kg/m³. A mixture of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH) created an alkaline solution that activated the cementitious binders. In this study, slump flow, L-box, V Funnel, T50 V Funnel, and J ring tests were carried out to determine the fresh qualities of the concrete. These tests were conducted under the regulations for self-compacting concrete provided by EFNARC (2005). To improve the flexural strength of the SCGC, 1.5 % glass fibre was added. This study illustrates how an artificial neural network (ANN) model can determine the concrete mix proportion. The characteristics of SCGC, both fresh and hardened properties test were investigated for molarities of 8, 10, and 12. The microstructural outcomes were determined using Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and X-ray diffraction (XRD).

Cancer is one of the most dreaded illnesses of the 20th century, cancer is continuing to spread and is becoming more common in the 21 st century.As of right now, breast cancer seems to be the most common cancer worldwide and the leading cause of cancer-related fatalities.In present study we used different network pharmacology tools for identification of target that involved in breast cancer that network pharmacology tool used target identification and validation process in drug discovery.In present study we used UniprotKB as text mining tool for identification of breast cancer gene in that we got the 2,658 gene in that we take the 165 in study the we done the GO and KEGG enrichment analysis in that we take pathways related to the breast cancer further we used STRING for determination protein-protein interaction in that we got the interaction between NDUFA1 with NDUFA3 and EREG,MYO1B with ACTN4 and XRCC3 with XRCC2.The interaction network was drawn and the network was analyzed by Cytoscape.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

This study describes the implementation of IoT sensors in drinking water systems and fog collector systems in low-income communities in Ecuador. The influence and handling of these sensors are analyzed, as well as the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices, especially in mountain areas, is highlighted. In addition, it is mentioned that the cost-benefit of using IoT sensors in fog catchers and drinking water networks depends on several factors, such as the scale of the project, specific objectives, and available resources. Finally, it is highlighted that the use of IoT sensors in smart construction and water collection systems has proven to be beneficial in preventing effects on the operation.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p&lt;0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

The automated transcription of handwritten characters into a legible output is a multi-faceted process with diverse applications. In this paper, a novel approach to optical character recognition (OCR) for handwritten digits is proposed that, in certain components, exceeds current architectures in terms of accuracy, effectiveness, adjustability, temporal efficiency, and/or computational simplicity. This model succeeds in the adoption and enhancement of deprecated or obsolete algorithms across eight steps of image pre-processing—normalization, grayscaling, thresholding/binarization, noise removal, skew-correction, skeletonization/thinning, line separation, and character segmentation. The aforementioned model is evaluated with the use of a Convolutional Neural Network (CNN) leveraging the Balanced eMNIST dataset for training and testing. By suggesting contour-based feature extraction methods as alternatives to pixel-by-pixel iteration, the proposed paradigm demonstrates its capacity to serve as a suitable alternative to current, commonly used algorithms and computational techniques for textual image classification.

This study examined the effect of the media usage environment, particularly TV and SNS devices, on advertisement acceptance, lifestyle, and psychological satisfaction. Hypothesis pathways and comparative structural mediating effects were analyzed. Media and Consumer Research public data were used for empirical analysis. A total of 6,640 people who participated in sports activities were included in the study. The results demonstrated that TV and SNS device usage environments had similar effects on advertisement acceptance. While TV did not influence the pursuit of personal life, SNS device use environments affected the pursuit of personal life. SNS device use environments had a greater effect on individual psychological satisfaction than TV. Adoption had a mediating effect on the influence of media usage environments on the pursuit of personal life. The pursuit of a personal life had a significant mediating effect on the influence of media use environments on psychological stability. This study revealed no differences in the effects of new and traditional media on advertisement acceptance, while new media had a greater influence on lifestyle and psychological satisfaction.

5G low-earth orbit satellite communications offer enhanced wireless coverage beyond the reach of 5G terrestrial networks, with important implications, particularly for automated and connected vehicles. Such wireless automotive mass-market applications demand well-designed compact user equipment antenna terminals offering non-terrestrial jointly with terrestrial communications. The antenna should be low-profile, conformal, and meet specific parameter values for gain and operational frequency bandwidth, tailored to the intended applications, in line with the aesthetic design requirements of passenger cars. This work presents an original concept for a dual-band nested circularly polarized automotive user terminal that operates at the S-band frequencies around 3.5 GHz and Ka-band frequencies around 28 GHz, namely within the 5G new-radio bands n78 and n257, respectively. The proposed terminal is designed to be integrated into the plastic components of a passenger vehicle. The arrays consist of 2×2 aperture-coupled corner-truncated microstrip slot patch antenna elements for the n78 band and of 4×4 single-layer edge-truncated microstrip circular slot patch antenna elements for the n257 band. The embedded arrays offer, across the two bands, respectively 9.9 and 13.7 dBi measured realized gain and 3-dB axial ratio bandwidths of 100 and 1500 MHz for the n78 and n257 bands along the broadside direction. Detailed link budget calculations anticipate uplink data rates of 21 and 6 Mbit/s, respectively, deeming it suitable for various automotive mobility and internet-of-things applications.

Cyanobacteria, microorganisms performing oxygenic photosynthesis, must adapt their metabolic processes to environmental challenges such as day and night changes. PipX, a unique regulatory protein from cyanobacteria provides a mechanistic link between signalling protein PII, a widely conserved (in bacteria and plants) transducer of carbon/nitrogen/energy richness, and the transcriptional regulator NtcA, which controls a large regulon involved in nitrogen assimilation. PipX is also involved in translational regulation by interaction with the ribosome-assembly GTPase EngA. However, increases of the PipX/PII ratio are toxic, presumably by abnormally increased binding of PipX to other partner(s). Here we present mutational and structural analyses of reported PipX-PII and PipX-NtcA complexes, leading to identification of single amino acid changes that decrease or abolish PipX toxicity. Four out of 11 mutations decreasing toxicity did not decrease PipX levels, suggesting that the targeted residues (F12, D23, L36, R54) provide toxicity determinants. In addition, one of those four mutations (D23A) argued against the over-activation of NtcA as the cause of PipX toxicity. Since most mutations at residues contacting PII decreased PipX levels, PipX stability would depend on its ability to bind to PII, a conclusion supported by the light-induced decrease in PipX levels observed in Synechococcus elongatus PCC7942.

In this study, we report the first outbreak of highly pathogenic avian influenza (HPAI) A H5N8, clade 2.3.4.4b in Kosovo on May 19, 2021. The outbreak consisted of three temporal phases: May–June 2021, September–November 2021, and January–May 2022. In total, 32 backyard and 10 commercial holdings tested positive for the virus, affecting 179,198 poultry. Interestingly, the third and last phase of the outbreak coincided with the massive H5N1 clade 2.3.4.4b epidemic in Europe. Phylogenetic analyses of 28 viral strains from Kosovo revealed that they were closely related to the H5N8 clade 2.3.4.4.b viruses that have been circulating in Albania, Bulgaria, Croatia, Hungary, and Russia in early 2021. Whole genome sequencing of the 25 and partial sequencing of 3 H5N8 viruses from Kosovo showed high nucleotide identity, forming a distinctive cluster and suggesting a single introduction. The results of the network analysis were in accordance with the three epidemic waves and suggested that the viral diffusion could have been caused by secondary spreads among farms and/or different introductions of the same virus from wild birds. The persistent circulation of the same virus over a one-year period highlights the potential risk of the virus becoming endemic, especially in settings with non-adequate biosecurity.

International trade has been one of the most significant economic activities among countries, and its contributions towards a country’s economic growth and sustainable development have been acknowledged. In recent times, international trade and global trade networks have received preeminent attention due to the sharp growth in trade volumes and its contributions to poverty reduction and general welfare. Despite the growth in global trade volumes, there are worrying concerns about the unprecedented changes in international trade patterns and growing imbalances in trade in recent times. Whereas China’s trade volumes have more than tripled over the last decades and have maintained the largest trade surplus spot ahead of Germany, other major trade surplus countries such as Japan, the USA, etc. have become trade deficit countries with their trade volumes increasing at a much slower pace. In view of the growing imbalances in global trade volumes, we use an augmented gravity model of trade that incorporates social network measurement, the real effective exchange rate which is a measurement of international competitiveness, as well as the Linder theory of trade, to investigate the factors that determine the export performance of countries. Using data from 51 countries for 41 years, our results indicate that the GDP of both the home country and the partner country affects the home country’s exports positively. Similarly, the real effective exchange rate REER, trade openness OPEN and dummy variables BORDER, ENGLISH, and EU have produced positive and statistically significant coefficient estimates, and these are in line with our theoretical expectations.

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. The epileptic seizure prediction models face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model that enhances the capability by utilizing the significant amount of seizure patterns from globally distributed patients with data privacy. The determination of the preictal state is influenced by global and local model-assisted decision-making by modeling the two-level edge layer. Integrating the Spiking Encoder (SE) with Graph Convolutional Neural Network (Spiking-GCNN) works as the local model trained using the bi-timescale approach. Each local model utilizes the aggregated seizure knowledge from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. The proposed seizure prediction is evaluated using benchmark datasets by comparing them with the existing works to demonstrate the potential results.

In this article, a method for the moisture mapping of the soil surface of agrophytocenosis was proposed using neural network based on synchronized radar and multispectral optoelectronic data of Sentinel-1,2. To verify the developed method, data from two experimental plots were used. These plots were located two on irrigated soybean crops. The first of them was located on the right bank (1st plot) and the second one on the left bank (2nd plot) of the down part of Volga River. Two experimental soil moisture geo-datasets were done by measurements and geo-referencing points using gravimetric method (1st plot) and with proximal sensing method (2nd plot) using Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the index (calculated on the basis of data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). Such way calculated index reveals showed a significantly greater dependence on soil moisture than on vegetation height that was been used in previous studies. The retrieved values of soil moisture were compared with the soil moisture measured in-situ. The proposed method with a determination coefficient of 0.44-0.65 and a standard deviation of 2.4%-4.2% for the 1st plot as well as with and of the same metrics for the 2nd allows predicting the soil moisture of both a test plots covered by soybean plants, relative to soil moisture measured in-situ. The conducted research created the scientific basis for a new technology for remote sensing the moisture content of the soil surface of agrophytocenosis as an element of the precision farming system and agroecology.

Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Thus, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis identified two potent drugs (Cialis and Finasteride) that can efficiently inhibit AKT1 and MDM2 protein signatures respectively, and with better therapeutic properties. We proposed that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

Healthcare workers (HCW) have been the professional category most exposed to SARS-CoV-2. The pandemic’s impact on HCW was analyzed in terms of COVID-19-related temporary disability (TD) between February 15 2020 and May 1 2021. TDs in HCW for COVID-19 infection or quarantine were described. TD quarantine/infection ratios and TDs per 100,000 affiliated HCW were compared with the cumulative incidence (CI) of COVID-19 cases notified to the National Network of Epidemiological Surveillance. TDs rates by economic activity and occupation were computed. A total of 429,127 TDs were recorded, 36,6% for infection. Three-quarters (76%) were women. The median TD quarantine/infection ratio was 2.5 (Interquartile range [IQR] 1.5-3.9). TDs rates in HCW were always above the CI except for the last two months of the fourth wave. Hospital activities accounted for 84% of TDs and showed the highest TD rate for infection (8,279/100,000). The highest TDs rates were registered among Nursing assistants, Nursing professionals and Physicians: 7,426, 6,925 and 5,508/100,000, respectively. The results demonstrate the high impact of COVID-19 on HCW in Spain and it’s inequalities. They also confirm that TDs represent a complementary source of information for epidemiological and public health surveillance and could provide an early warning of new emerging infections.

Liquid crystal elastomers (LCEs) are shape morphing materials whose large and reversible shape transformation results from the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape changing behaviors under the stimuli are largely directed by LC orientations, and therefore various strategies have been developed to spatially modulate the LC alignments. But most of the methods suffer from complex fabrication technologies or intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some types of LCEs, such as polysiloxane side-chain LCEs or thiol-acrylate main-chain LCEs, etc, have been achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2D and 3D shape changing performances achieved by mechanically programming the polydomain LCE between the two crosslinking steps. The resulting LCEs exhibited reversible thermal induced shape transformation between the initial and programmed shapes due to the two-way memory between the first network structure and the second network structure. Our work would be potential for expanding the applications of LCE materials in actuators, soft robotics and smart structures where arbitrary and easily programmed shape morphings are needed.

Many problems in the fields of finance and actuarial science can be transformed into the problem of solving backward stochastic differential equations (BSDE) and partial differential equations (PDE) with jumps, which are often difficult to solve in high-dimensional cases. To solve this problem, this paper applies the deep learning algorithm to solve a class of high-dimensional nonlinear partial differential equations with jump terms and their corresponding backward stochastic differential equations (BSDE) with jump terms. Using the nonlinear Feynman-Kac formula, the problem of solving this kind of PDE is transformed into the problem of solving the corresponding backward stochastic differential equations with jump terms, and the numerical solution problem is turned into a stochastic control problem. At the same time, the gradient and jump process of the unknown solution are separately regarded as the strategy function and they are approximated respectively by using two multilayer neural networks as function approximators. Thus, deep learning-based method is used to overcome the “curse of dimensionality” caused by high-dimensional PDE with jump, and the numerical solution is obtained. In addition, this paper proposes a new optimization algorithm based on the existing neural network random optimization algorithm, and compares the results with the traditional optimization algorithm, and achieves good results. Finally, the proposed method is applied to three practical high-dimensional problems: Hamilton-Jacobi-Bellman equation, bond pricing under the jump Vasicek model and high-dimensional option pricing model with default risk. The proposed numerical method has obtained satisfactory accuracy and efficiency. The method has important application value and practical significance in investment decision-making, option pricing, insurance and other fields.