Network Utilization Research Articles

Exploration of Facial Emotion Detection Systems Utilizing Convolutional Neural Networks: A Comprehensive Review

Facial emotion detection systems have witnessed significant advancements, particularly with the utilization of convolutional neural networks (CNNs). This paper provides a thorough survey of such systems, beginning with an introduction to artificial intelligence and the evolutionary trajectory of neural networks, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and CNNs. The paper elaborates on CNNs' architecture and functionality, elucidating key components such as convolutional layers, pooling layers, and fully connected layers, while also spotlighting prominent CNN architectures like AlexNet and ResNet. It delineates the broad scope and diverse applications of facial emotion detection systems across various domains, including marketing research, crowd testing, AI robots, banking, and entertainment. In the literature review section, recent research papers on CNN models for facial expression recognition are synthesized, highlighting variances in datasets, methodologies, and accuracy levels. The paper concludes that CNNs represent the current pinnacle of facial emotion classification techniques, surpassing previous methodologies such as eigenfaces. It underscores the efficacy of deep CNN architectures trained on extensive facial image datasets in proficiently identifying emotions from facial expressions. Moreover, the paper emphasizes the necessity for ongoing endeavors to enhance accuracy, particularly concerning complex emotions like disgust. In essence, CNNs exhibit substantial promise for the development of real-world facial emotion detection systems, heralding a new era of sophisticated emotion recognition technology.

Navigating the Dynamics of Squeeze Film Dampers: Unraveling Stiffness and Damping Using a Dual Lens of Reynolds Equation and Neural Network Models for Sensitivity Analysis and Predictive Insights

The squeeze film damper (SFD) is proven to be highly effective in mitigating rotor vibration as it traverses the critical speed, thus making it extensively utilized in the aeroengine domain. In this paper, we investigate the stiffness and damping of SFD using the Reynolds equation and neural network models. Our specific focus includes examining the structural and operating parameters of SFDs, such as clearance, feed pressure of oil, rotor whirl, and rotational speed. Firstly, the pressure distribution analytical model of the oil film inside the SFD based on the hydrodynamic lubrication theory is established, as described by the Reynolds equation. It obtained oil film forces, pressure, stiffness, and damping values under various sets of structural, lubrication, and operating parameters, including length, clearance, boundary pressure at both sides, rotational speed, and whirling motion, by applying difference computations to the Reynolds equation. Secondly, according to the significant analyses of the obtained oil film stiffness and damping, the following three parameters of the most significance are found: clearance, rotational speed, and rotor whirl. Furthermore, neural network models, including GA-BP and decision tree models, are established based on the obtained results of difference computation. The numerical simulation and calculation of these models are then applied to show their validity with all given parameters and the three significant parameters separately as two sets of model input. Regardless of either set of model inputs, these established neural network models are capable of predicting the nonlinear stiffness and damping of the oil film inside an SFD. These sensitive parameters merely require measurement, followed by the utilization of a neural network to predict stiffness and damping instead of the Reynolds equation. This process serves structural enhancement, facilitates parameter optimization in SFDs, and provides crucial support for refining the design parameters of SFDs.

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

BackgroundBreast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes.MethodsWe integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification.ResultsThe FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN.ConclusionOur study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

The Internet of Things (IoT) in World Practice: Review and Analysis

This article explores the contemporary realm of the Internet of Things (IoT) and its influence on society and the economy. The primary emphasis lies in examining the principal patterns within the realm of IoT, encompassing the escalation of connected device quantities and the utilisation of 5G networks. The article encompasses various facets, including an overview of the evolution and possibilities of IoT, encompassing the advancement of intelligent urban areas and the influence of 5G networks. The analysis of threats and vulnerabilities related to this technology focuses on detecting common flaws, including weak passwords, unsecured network services, and inadequate privacy measures.
 Furthermore, the essay presents concrete hazards in diverse sectors that employ IoT technology. Particular emphasis is given to potential cybersecurity vulnerabilities in healthcare, manufacturing, agriculture, retail, transport and logistics, energy, and smart cities. These businesses have distinct challenges and hazards due to incorporating IoT technologies.
 The authors also analyse the substantial financial commitment to IoT and its profound influence on the worldwide economy, including data on the expansion of the IoT market and investments in IoT technologies and smart cities. The findings emphasise that the Internet of Things (IoT) substantially influences society and the economy. However, it necessitates meticulous consideration of cybersecurity and privacy concerns. The authors underscore the significance of creating efficacious cyber security policies to safeguard against the possible hazards presented by IoT technology.
 
 

A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images

Computed tomography (CT) is a rapid and precise medical imaging modality, but it poses the challenge of high radiation exposure to patients. To control this issue, stringent quality control (QC) evaluations are imperative for CT. One crucial aspect of CT QC involves the evaluation of phantom images, utilizing specifically designed phantoms for accuracy management and subsequent objective evaluation. However, CT QC has qualitative evaluation methods, particularly for evaluating spatial and contrast resolutions. To solve this problem, we propose a quality control method based on deep-learning object detection for quantitatively evaluating spatial and contrast resolutions, CT Attention You Only Look Once v8 (CTA-YOLOv8). First, we utilized the YOLOv8 network as the foundational model, optimizing it for enhanced accuracy. Second, we enhanced the network’s capabilities by integrating the Convolutional Block Attention Module (CBAM) and Swin Transformers, tailored for phantom image evaluations. The CBAM module was employed internally to pinpoint the optimal position for achieving peak performance in CT QC data. Similarly, we fine-tuned the code and patch size of the Swin Transformer module to align it with YOLOv8, culminating in the identification of the optimal configuration. Our proposed CTA-YOLOv8 network showed superior agreement with qualitative evaluation methods, achieving accuracies of 92.03% and 97.56% for spatial and contrast resolution evaluations, respectively. Thus, we suggest that our method offers nearly equivalent performance to qualitative methods. The utilization of the CTA-YOLOv8 network in evaluating CT phantom images holds potential for setting a new standard in quantitative assessment methodologies.

Real-Time Monitoring of Photovoltaic Systems and Control of Electricity Supply for Smart Micro Grid-PV using IoT

This paper aims to develop a photovoltaic (PV) performance monitoring system applied on a micro scale using the Internet of Things (IoT). Previous monitoring systems had limitations in platform flexibility, low-cost devices, hardware complexity, and stability of the data transfer process. For this reason, this research proposes an IoT architecture that uses Arduino devices, mini WIFI and an open-source platform, so that it can be easily developed further. This research also develops innovations in controlling the use of electrical energy sources from PV and utility networks. This monitoring system is applied to PV installations with a capacity of 1KW which is capable of monitoring electrical data in the form of current, voltage, power, energy and frequency obtained from PV panels, batteries, loads and electrical utilities. Monitoring data is displayed in a visual form that can be accessed via web-based and mobile applications. This research has developed an innovation in controlling the use of electrical energy sources from PV and Utility networks by using several sensors as parameters to determine the right time and condition in controlling the use of electrical resources.

Legal hypergraphs.

Complexity science provides a powerful framework for understanding physical, biological and social systems, and network analysis is one of its principal tools. Since many complex systems exhibit multilateral interactions that change over time, in recent years, network scientists have become increasingly interested in modelling and measuring dynamic networks featuring higher-order relations. At the same time, while network analysis has been more widely adopted to investigate the structure and evolution of law as a complex system, the utility of dynamic higher-order networks in the legal domain has remained largely unexplored. Setting out to change this, we introduce temporal hypergraphs as a powerful tool for studying legal network data. Temporal hypergraphs generalize static graphs by (i) allowing any number of nodes to participate in an edge and (ii) permitting nodes or edges to be added, modified or deleted. We describe models and methods to explore legal hypergraphs that evolve over time and elucidate their benefits through case studies on legal citation and collaboration networks that change over a period of more than 70 years. Our work demonstrates the potential of dynamic higher-order networks for studying complex legal systems, and it facilitates further advances in legal network analysis. This article is part of the theme issue 'A complexity science approach to law and governance'.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

Background and ObjectiveLeft ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). MethodsUtilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. ResultsFor LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). ConclusionsThis study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment

Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection.

Evaluating the satisfaction and utility of social networks in medical practice and continuing medical education

BackgroundDigital health has surged during the Covid health crisis, and the use of social media, already prevalent in medicine, has significantly increased. There are Social Networks groups dedicated to physicians with an educational purpose. These groups also facilitate peer discussions on medical questions and the sharing of training materials.ObjectivesThe aim of our study was to assess the value of these new tools and their contribution to medical education.MethodsAn anonymous questionnaire was conducted among members of a Social Networks community group for physicians. The survey received responses from 1451 participants.ResultsThe majority of participants believed they had enriched their medical knowledge and accessed documents they would not have accessed without the group. Subgroup analysis showed that the contribution of this tool is more pronounced for general practitioners and doctors practicing in limited healthcare access.ConclusionIt is essential to develop digital tools that enhance physician training, and social networks represent a valuable educational tool.

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

Generalisation of Feed-Forward Neural Networks and Recurrent Neural Networks

This paper presents an in-depth analysis of Feed-Forward Neural Networks (FNNs) and Recurrent Neural Networks (RNNs), two powerful models in the field of artificial intelligence. Understanding these models and their applications is crucial for harnessing their potential. The study addresses the need to comprehend the unique characteristics and architectures of FNNs and RNNs. These models excel at processing sequential and temporal data, making them indispensable in tasks. Furthermore, the paper emphasises the importance of variables in FNNs and proposes a novel method to rank the importance of independent variables in predicting the output variable. By understanding the relationship between inputs and outputs, valuable insights can be gained into the underlying patterns and mechanisms driving the system being modelled. Additionally, the research explores the impact of initial weights on model performance. Contrary to conventional beliefs, the study provides evidence that neural networks with random weights can achieve competitive performance, particularly in situations with limited training datasets. This finding challenges the traditional notion that careful initialization is necessary for neural networks to perform well. In summary, this paper provides a comprehensive analysis of FNNs and RNNs while highlighting the importance of understanding the relationship between variables and the impact of initial weights on model performance. By shedding light on these crucial aspects, this research contributes to the advancement and effective utilisation of neural networks, paving the way for improved predictions and insights in various domains.

Opportunity Assessment of Virtual Power Plant Implementation for Sustainable Renewable Energy Development in Indonesia Power System Network

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.

Assessing the economic impact of mobile telecommunications in Egypt: a structural model approach

PurposeThe purpose of this study is to use the structural model to determine the influence of mobile telecommunication on Egypt’s economic growth from 2000 to 2009. By focusing on mobile unique subscribers and mobile broadband-capable device penetration as indicators of telecommunications adoption, the authors seek to understand their overarching effects on the nation’s economic landscape.Design/methodology/approachThe paper uses quarterly time-series data set over the period 2000–2019 and uses a structural econometric model based on an aggregate production function, a demand function, a supply function and an infrastructure function to detect causality and examine long-run relationships between variables.FindingsThe findings of the structural model reveal that both mobile unique subscribers and mobile broadband-capable device penetration significantly contributed to Egypt’s gross domestic product (GDP) growth from 2000 to 2019. Specifically, a 1% increase in mobile unique subscriber penetration and mobile broadband-capable device adoption is estimated to result in an average annual contribution to GDP growth of 0.172% and 0.016%, respectively.Research limitations/implicationsThe scarcity of panel data is the main research limitation for comparative study with other Middle East and North African Region (MENA) countries. Research extensions would include testing the significance of complementarities such as improving governance measures and building human capacity for both households and firms, which are necessary to boost the impact of telecommunication on economic growth in the MENA region.Practical implicationsBased on these findings, the study puts forth policy recommendations aimed at maximizing investment in network utilization, including mobile and internet services, as well as fixed broadband subscriptions. It highlights the crucial role of these investments in promoting social and economic development, not only in Egypt but also across the MENA region as a whole.Social implicationsThe findings of this research emphasize the importance of strategic investments in network utilization, encompassing mobile, internet services and fixed broadband subscriptions. Such investments are pivotal for fostering social and financial inclusion. The study underscores the potential of these investments to drive social and economic progress, not just within Egypt but throughout the entire MENA region.Originality/valueOverall, existing literature generally supports the notion that the telecommunications sector has a positive economic impact. However, there is a gap in the literature when it comes to understanding the specific effects of the Egyptian telecommunications sector on the country’s economy, particularly in relation to the Egypt Vision 2030. The study aims to fill this gap by focusing specifically on Egypt and providing additional insights into the direct and indirect effects of the Egyptian telecommunications sector on the economy. By conducting a thorough analysis of the sector’s role, the authors aim to contribute to the existing literature by providing context-specific findings and recommendations.

Prediction of Cumulative Biomethane Yield Using Artificial Neural Network - Case Study of an Industrial Biogas Plant

There are many facets to the applications of Artificial Intelligence (AI) in the energy sector however, this research focuses on the utilization of Artificial Neural Networks (ANN) as parts of AI technique to simulate and model the operating performance of an industrial biogas plant data set. In this study, eight (8) model network architectures were developed using the ANN tool of MATLAB 2016a version and it was found that the best result was obtained based on the model performance evaluation metrics used such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Determination Coefficient (R2) was as a result of the combination of two activation functions namely: tansig and logsig. The model, that produced the best result was a result of the architecture that contains 2 hidden neurons and the training algorithm of Scaled Conjugate Gradient (SCG). It was also observed that the ANN-predicted network diagram is better than the observed.

Secrecy and Throughput Performance of Cooperative Cognitive Decode-and-Forward Relaying Vehicular Networks with Direct Links and Poisson Distributed Eavesdroppers

Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the secrecy and throughput performance of an underlay cooperative cognitive vehicular network, where a pair of secondary vehicles communicate through a direct link and the assistance of a decode-and-forward (DF) secondary relay in the presence of Poisson-distributed colluding eavesdroppers and under an interference constraint set by the primary receiver. Considering mixed Rayleigh and double-Rayleigh fading channels, we design a realistic relaying transmission scheme and derive the closed-form expressions of secrecy and throughput performance, such as the secrecy outage probability (SOP), the connection outage probability (COP), the secrecy and connection outage probability (SCOP), and the overall secrecy throughput, for traditional and proposed schemes, respectively. An asymptotic analysis is further presented in the high signal-to-noise ratio (SNR) regime. Numerical results illustrate the impacts of network parameters on secrecy and throughput and reveal that the advantages of the proposed scheme are closely related to the channel gain of the relay link compared to the direct link.

Carbon Dioxide (CO<sub>2</sub>) Capture and Utilization Targeting

The global increase in CO2 emissions is attributable to this study. Carbon capture and storage (CCS) is a potential method for reducing CO2 emissions. However, reducing CO2 emissions by storing it in a geological reservoir without using it may have limitations over time. Using a CO2 integration-based strategy, this study presents an algebraic targeting method for determining the optimal utilisation network. Along with CCS development, the concept of CO2 capture and utilisation via CO2 integration is presented. The qualified CO2 captured from CO2 emissions sources is injected into a CO2 pipeline or header in order to meet the CO2 utilisation needs of a variety of industries. Prior to injecting the remaining CO2 into a geological reservoir for storage, the CO2 sources and needs are matched. The CO2 headers can meet the CO2 requirements of industries located along the headers that use CO2 as a feedstock or raw material. The estimated integration of CO2 capture and CO2 utilisation will minimise the amount of CO2 sent to storage and increase the geological reservoir's carbon storage life span. The CO2 capture and usage targeting tool that was made has led to about 220.5 t/h of integrated CO2 source and demand, with 47% less CO2 that needs to be sent to storage.

A multi-branch convolutional neural network for snoring detection based on audio

Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prominent characteristic of this disorder. Therefore, the exploration of a concise and effective method for detecting snoring has consistently been a crucial aspect of sleep medicine. As the easily accessible data, the identification of snoring through sound analysis offers a more convenient and straightforward method. The objective of this study was to develop a convolutional neural network (CNN) for classifying snoring and non-snoring events based on audio. This study utilized Mel-frequency cepstral coefficients (MFCCs) as a method for extracting features during the preprocessing of raw data. In order to extract multi-scale features from the frequency domain of sound sources, this study proposes the utilization of a multi-branch convolutional neural network (MBCNN) for the purpose of classification. The network utilized asymmetric convolutional kernels to acquire additional information, while the adoption of one-hot encoding labels aimed to mitigate the impact of labels. The experiment tested the network’s performance by utilizing a publicly available dataset consisting of 1,000 sound samples. The test results indicate that the MBCNN achieved a snoring detection accuracy of 99.5%. The integration of multi-scale features and the implementation of MBCNN, based on audio data, have demonstrated a substantial improvement in the performance of snoring classification.