bluuebunny/crossref_metadata_2025 huggingface dataset:
Active Decision Support System for Observation Scheduling Based on Image Analysis at the BOROWIEC SLR Station
Authors: Suchodolski Tomasz
Published: October 2022

The dynamic exploration of the orbits from the LEO-to-GEO region, for the needs of telecommunication services, science, industry and defense, forces monitoring of the trajectory of such orbital objects for the safety of spacecraft traffic and, in the case of deorbitation, for the safety of ground infrastructure. First off all, the need for trajectory monitoring in order to avoid collisions can be distinguished, as well as the need to calibrate the satellite on-board devices. This is mainly carried out by radar measurements, by passive optical acquisition and active laser measurements. The number of orbital objects increases rapidly, and the number of tracking stations for the second is relatively small. This leads to a situation in which each tracking station must select which of the objects will be subject to the measurement task. In the case of the Satellite Laser Ranging (SLR) or passive optical set-up, the weather conditions are an important factor enabling the measurement of the orbital object trajectory. This paper presents an innovative observation scheduling support system based on the analysis of the images obtained from the Allsky camera. The information of the degree of cloud cover, the position of the Sun/Moon in connection with the graphical projections of the ephemeris trajectory of the orbital objects allows increasing the measurement efficiency. The presented solution is part of a larger number of improvements carried out by the author, which lead to the upgrade of SLR stations in terms of new technologies and safety of use.
An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators
Authors: Premkumar Ramesh , Mahdal Miroslav , Elangovan Muniyandy
Published: October 2022

Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image’s pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.
NiTi SMA Superelastic Micro Cables: Thermomechanical Behavior and Fatigue Life under Dynamic Loadings
Authors: Silva Paulo C. S. , Grassi Estephanie N. D. , Araújo Carlos J. , Delgado João M. P. Q. , Lima Antonio G. B.
Published: October 2022

Shape memory alloy (SMA) micro cables have a wide potential for attenuation of vibrations and structural health monitoring due to energy dissipation. This work evaluates the effect of SMA thermomechanical coupling during dynamic cycling and the fatigue life of NiTi SMA micro cables submitted to tensile loadings at frequencies from 0.25 Hz to 10 Hz. The thermomechanical coupling was characterized using a previously developed methodology that identifies the self-heating frequency. When dynamically loaded above this frequency, the micro cable response is dominated by the self-heating, stiffening significantly during cycling. Once above the self-heating frequency, structural and functional fatigues of the micro cable were evaluated as a function of the loading frequency for the failure of each individual wire. All tests were performed on a single wire with equal cross-section area for comparison purposes. We observed that the micro cable’s functional properties regarding energy dissipation capacity decreased throughout the cycles with increasing frequency. Due to the additional friction between the filaments of the micro cable, this dissipation capacity is superior to that of the single wire. Although its fatigue life is shorter, its delayed failure compared to a single wire makes it a more reliable sensor for structural health monitoring.
A Plasma Transmitting Source for Borehole Acoustic Reflection Imaging
Authors: Hao Xiaolong , Zhou Jing , Shang Haiyan , Xie Haiming , Wang Wei , Yang Cheng
Published: October 2022

The detection depth of current borehole acoustic reflection imaging is only tens of meters without high resolution. This considerably limits its wide application in the identification and fine description of unconventional reservoirs and in the optimization of drilling trajectories. Increasing the directional energy from the transmitter to a geological structure is an excellent way to solve this issue. In this study, a plasma source with a parabolic reflector was introduced during borehole acoustic reflection imaging. First, an experimental system was built for testing the plasma source. Next, the acoustic-electrical characteristics and directional radiation of the source were studied using experiments and a numerical simulation. Finally, the advantages, disadvantages, and feasibility of the plasma-transmitting source were analyzed; some suggestions for further work on the source and its logging application were proposed. The experimental and simulation results show that the use of a plasma source with a parabolic reflector can increase the detection depth of borehole acoustic reflection imaging to hundreds of meters with high resolution. This is crucial in imaging the geological structures near boreholes and enhancing oil–gas exploration and development.
A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges
Authors: Munir Tahir , Akbar Muhammad Soomair , Ahmed Sadia , Sarfraz Azza , Sarfraz Zouina , Sarfraz Muzna , Felix Miguel , Cherrez-Ojeda Ivan
Published: October 2022

The Internet of Things (IoT) is the network of physical objects embedded with sensors, software, electronics, and online connectivity systems. This study explores the role of IoT in clinical laboratory processes; this systematic review was conducted adhering to the PRISMA Statement 2020 guidelines. We included IoT models and applications across preanalytical, analytical, and postanalytical laboratory processes. PubMed, Cochrane Central, CINAHL Plus, Scopus, IEEE, and A.C.M. Digital library were searched between August 2015 to August 2022; the data were tabulated. Cohen’s coefficient of agreement was calculated to quantify inter-reviewer agreements; a total of 18 studies were included with Cohen’s coefficient computed to be 0.91. The included studies were divided into three classifications based on availability, including preanalytical, analytical, and postanalytical. The majority (77.8%) of the studies were real-tested. Communication-based approaches were the most common (83.3%), followed by application-based approaches (44.4%) and sensor-based approaches (33.3%) among the included studies. Open issues and challenges across the included studies included scalability, costs and energy consumption, interoperability, privacy and security, and performance issues. In this study, we identified, classified, and evaluated IoT applicability in clinical laboratory systems. This study presents pertinent findings for IoT development across clinical laboratory systems, for which it is essential that more rigorous and efficient testing and studies be conducted in the future.
Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications
Authors: Naqvi Rizwan Ali , Arsalan Muhammad , Qaiser Talha , Khan Tariq Mahmood , Razzak Imran
Published: October 2022

Sensor fusion is the process of merging data from many sources, such as radar, lidar and camera sensors, to provide less uncertain information compared to the information collected from single source [...]
Matched Filter Interpretation of CNN Classifiers with Application to HAR
Authors: Farag Mohammed M.
Published: October 2022

Time series classification is an active research topic due to its wide range of applications and the proliferation of sensory data. Convolutional neural networks (CNNs) are ubiquitous in modern machine learning (ML) models. In this work, we present a matched filter (MF) interpretation of CNN classifiers accompanied by an experimental proof of concept using a carefully developed synthetic dataset. We exploit this interpretation to develop an MF CNN model for time series classification comprising a stack of a Conv1D layer followed by a GlobalMaxPooling layer acting as a typical MF for automated feature extraction and a fully connected layer with softmax activation for computing class probabilities. The presented interpretation enables developing superlight highly accurate classifier models that meet the tight requirements of edge inference. Edge inference is emerging research that addresses the latency, availability, privacy, and connectivity concerns of the commonly deployed cloud inference. The MF-based CNN model has been applied to the sensor-based human activity recognition (HAR) problem due to its significant importance in a broad range of applications. The UCI-HAR, WISDM-AR, and MotionSense datasets are used for model training and testing. The proposed classifier is tested and benchmarked on an android smartphone with average accuracy and F1 scores of 98% and 97%, respectively, which outperforms state-of-the-art HAR methods in terms of classification accuracy and run-time performance. The proposed model size is less than 150 KB, and the average inference time is less than 1 ms. The presented interpretation helps develop a better understanding of CNN operation and decision mechanisms. The proposed model is distinguished from related work by jointly featuring interpretability, high accuracy, and low computational cost, enabling its ready deployment on a wide set of mobile devices for a broad range of applications.
Network Meta-Analysis of Trials Testing If Home Exercise Programs Informed by Wearables Measuring Activity Improve Peripheral Artery Disease Related Walking Impairment
Authors: Thanigaimani Shivshankar , Jin Harry , Silva Munasinghe Tharindu , Golledge Jonathan
Published: October 2022

Background: This study aimed to investigate whether home exercise programs informed by wearable activity monitors improved walking ability of patients with peripheral artery disease (PAD). Methods: A systematic literature search was performed to identify randomised controlled trials (RCT) testing home exercise that were or were not informed by wearable activity monitors. The primary outcome was the change in walking distance measured by a six-minute walking test or treadmill test over the course of the trial. Network meta-analysis (NMA) was performed using the gemtc R statistical package. The risk of bias was assessed using Cochrane tool for assessing risk of bias in RCTs (RoB 2.0). Results: A total of 14 RCTs involving 1544 participants were included. Nine trials used wearable activity monitors to inform the home exercise program tested, while five trials did not use wearable activity monitors to inform the home exercise program tested. Overall quality assessment showed 12 trials to be at low risk of bias and two trials at high risk of bias. Home exercise programs informed by wearable activity monitors significantly improved walking distance compared to non-exercise controls (Mean difference, MD: 32.8 m [95% credible interval, CrI: 6.1, 71.0]) but not compared to home exercise programs not informed by wearable activity monitors (MD: 4.7 m [95% CrI: −38.5, 55.4]). Conclusions: Home exercise informed by wearable activity monitors improve walking ability of patients with PAD. It is, however, unclear if activity monitoring informed exercise programs are more effective than exercise programs not using activity monitors.
A Step toward Next-Generation Advancements in the Internet of Things Technologies
Authors: Amin Farhan , Abbasi Rashid , Mateen Abdul , Ali Abid Muhammad , Khan Salabat
Published: October 2022

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.
IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning
Authors: Ahmad Ijaz , Shin Seokjoo
Published: October 2022

Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB–CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside–out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB–CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB–CPE to the privacy-preserving deep learning (PPDL) domain.
Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification
Authors: Alshammari Abdulaziz
Published: October 2022

Brain metastases (BMs) happen often in patients with metastatic cancer (MC), requiring initial and precise diagnosis of BMs, which remains important for medical care preparation and radiotherapy prognostication. Nevertheless, the susceptibility of automated BM (ABMS) diagnosis is unfairly great for minute BMs, and integrating into medical exercises to distinguish true metastases (MtS) from false positives remains difficult. For enhancing BM classification execution, MtS localization is performed through the NestNet framework. Subsequent to segmentation, classification is performed by employing the VGG16 convolution neural network. A novel loss function is computed by employing the weighted softmax function (WSF) for enhancing minute MtS diagnosis and for calibrating susceptibility and particularity. The aim of this study was to merge temporal prior data for ABMS detection. The proffered VGG16_CNN is capable of differentiating positive MtS among MtS candidates with high confidence, which typically needs distinct specialist analysis or additional investigation, remaining specifically apt for specialist reinforcement in actual medical practice. The proffered VGG16_CNN framework can be correlated with three advanced methodologies (moU-Net, DSNet, and U-Net) concerning diverse criteria. It was observed that the proffered VGG16_CNN attained 93.74% accuracy, 92% precision, 92.1% recall, and 67.08% F1-score.
No-Reference Quality Assessment of Stereoscopic Video Based on Temporal Adaptive Model for Improved Visual Communication
Authors: Gu Fenghao , Zhang Zhichao
Published: October 2022

An objective stereo video quality assessment (SVQA) strives to be consistent with human visual perception while ensuring a low time and labor cost of evaluation. The temporal–spatial characteristics of video make the data processing volume of quality evaluation surge, making an SVQA more challenging. Aiming at the effect of distortion on the stereoscopic temporal domain, a stereo video quality assessment method based on the temporal–spatial relation is proposed in this paper. Specifically, a temporal adaptive model (TAM) for a video is established to describe the space–time domain of the video from both local and global levels. This model can be easily embedded into any 2D CNN backbone network. Compared with the improved model based on 3D CNN, this model has obvious advantages in operating efficiency. Experimental results on NAMA3DS1-COSPAD1 database, WaterlooIVC 3D Video Phase I database, QI-SVQA database and SIAT depth quality database show that the model has excellent performance.
Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks
Authors: Benaddi Hafsa , Jouhari Mohammed , Ibrahimi Khalil , Ben Othman Jalel , Amhoud El Mehdi
Published: October 2022

Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion Detection System (IDS) is suggested as a robust mechanism to protect and monitor malicious activities in IIoT networks. In this work, we suggest a new mechanism to improve the efficiency and robustness of the IDS system using Distributional Reinforcement Learning (DRL) and the Generative Adversarial Network (GAN). We aim to develop realistic and equilibrated distribution for a given feature set using artificial data in order to overcome the issue of data imbalance. We show how the GAN can efficiently assist the distributional RL-based-IDS in enhancing the detection of minority attacks. To assess the taxonomy of our approach, we verified the effectiveness of our algorithm by using the Distributed Smart Space Orchestration System (DS2OS) dataset. The performance of the normal DRL and DRL-GAN models in binary and multiclass classifications was evaluated based on anomaly detection datasets. The proposed models outperformed the normal DRL in the standard metrics of accuracy, precision, recall, and F1 score. We demonstrated that the GAN introduced in the training process of DRL with the aim of improving the detection of a specific class of data achieves the best results.
Radiofrequency Energy Harvesting Systems for Internet of Things Applications: A Comprehensive Overview of Design Issues
Authors: Mouapi Alex
Published: October 2022

Radiofrequency energy harvesting (RF-EH) solutions have evolved significantly in recent years due to the ubiquity of electromagnetic waves in any environment. This review presents a comprehensive report on autonomous wireless sensor (WS) design considerations based on RF-EH. The obtainability of RF-EH-WS is driven by development efforts in the areas of RF-EH circuit design, known as rectifying antenna (Rectenna), the minimization of the energy budget of WS (MEB-WS), and finally, power management modules (PMM). The PMM aims to optimize the energy efficiency of the WS. In addition to these three factors, examining the RF power levels harvested related to the rectenna feeding technique (RFT) is essential. Since we did not find any review presenting a holistic view of these design considerations, we strived to provide a detailed picture of recent advances and new enhancements in this review. To address this issue, this review gives an overview of the seminal and contemporary studies in the RF-EH-WS field. The IoT issues are also discussed in terms of their basic requirement to support reduced size or miniaturized smart objects, which are common matters in current applications of WS nodes. Potential open issues that might be considered for future research are also discussed in this article. For a more detailed description of all presented concepts, many significant references are provided for the readers.
Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets
Authors: Ribeiro Miguel , Damas Bruno , Bernardino Alexandre
Published: October 2022

This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios.
Intrusion Detection Quantum Sensor Networks
Authors: Nagy Marius , Nagy Naya
Published: October 2022

This paper proposes a perimeter detection scheme based on the quantum physical properties of photons. Existing perimeter intrusion detection schemes, if using light, rely on the classical properties of light only. Our quantum sensor network uses the quantum property of spatial superposition of photons, meaning that a photon can simultaneously follow two different paths after going through a beam splitter. Using multiple Mach–Zehnder interferometers, an entire web of paths can be generated, such that one single photon occupies them all. If an intruder violates this web in some arbitrary point, the entire photon superposition is destroyed, the photon does not self-interfere any more and this event is detected by measurements. For one single photon, the intruder detection probability is limited theoretically but can be increased arbitrarily with the usage of a sequence of photons. We show both theoretical bounds as well as practical results of the proposed schemes. The practical results are obtained by simulation experiments on IBM Quantum platforms. The benefits of our quantum approach are: low power, invisibility to potential intruders, scalability and easy practical implementation.
RD-YOLO: An Effective and Efficient Object Detector for Roadside Perception System
Authors: Huang Lei , Huang Wenzhun
Published: October 2022

In recent years, intelligent driving technology based on vehicle–road cooperation has gradually become a research hotspot in the field of intelligent transportation. There are many studies regarding vehicle perception, but fewer studies regarding roadside perception. As sensors are installed at different heights, the roadside object scale varies violently, which burdens the optimization of networks. Moreover, there is a large amount of overlapping and occlusion in complex road environments, which leads to a great challenge of object distinction. To solve the two problems raised above, we propose RD-YOLO. Based on YOLOv5s, we reconstructed the feature fusion layer to increase effective feature extraction and improve the detection capability of small targets. Then, we replaced the original pyramid network with a generalized feature pyramid network (GFPN) to improve the adaptability of the network to different scale features. We also integrated a coordinate attention (CA) mechanism to find attention regions in scenarios with dense objects. Finally, we replaced the original Loss with Focal-EIOU Loss to improve the speed of the bounding box regression and the positioning accuracy of the anchor box. Compared to the YOLOv5s, the RD-YOLO improves the mean average precision (mAP) by 5.5% on the Rope3D dataset and 2.9% on the UA-DETRAC dataset. Meanwhile, by modifying the feature fusion layer, the weight of RD-YOLO is decreased by 55.9% while the detection speed is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 71.9 frames/s (FPS) and achieves higher accuracy than the previous approaches with a similar FPS.
An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets
Authors: Khare Smith K. , Gaikwad Nikhil , Bokde Neeraj Dhanraj
Published: October 2022

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.
Domain Decomposition Spectral Method Applied to Modal Method: Direct and Inverse Spectral Transforms
Authors: Edee Kofi , Granet Gérard , Paladian Francoise , Bonnet Pierre , Al Achkar Ghida , Damaj Lana , Plumey Jean-Pierre , Larciprete Maria Cristina , Guizal Brahim
Published: October 2022

We introduce a Domain Decomposition Spectral Method (DDSM) as a solution for Maxwell’s equations in the frequency domain. It will be illustrated in the framework of the Aperiodic Fourier Modal Method (AFMM). This method may be applied to compute the electromagnetic field diffracted by a large-scale surface under any kind of incident excitation. In the proposed approach, a large-size surface is decomposed into square sub-cells, and a projector, linking the set of eigenvectors of the large-scale problem to those of the small-size sub-cells, is defined. This projector allows one to associate univocally the spectrum of any electromagnetic field of a problem stated on the large-size domain with its footprint on the small-scale problem eigenfunctions. This approach is suitable for parallel computing, since the spectrum of the electromagnetic field is computed on each sub-cell independently from the others. In order to demonstrate the method’s ability, to simulate both near and far fields of a full three-dimensional (3D) structure, we apply it to design large area diffractive metalenses with a conventional personal computer.
Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering
Authors: Fang Yuan , Wang Yibo , Liu Chuang , Cai Guowei
Published: October 2022

The ideal wind turbine power curve provided by the manufacturer cannot monitor the practical performance of wind turbines accurately in the engineering stage; in this paper, a modified approach of the wind turbine power curve is proposed based on improved Bins and K-means++ clustering. By analyzing the wind speed-power data collected by the supervisory control and data acquisition system (SCADA), the relationship between wind speed and output is compared and elaborated on. On the basis of data preprocessing, an improved Bins method for equal frequency division of data is proposed, and the results are clustered through K-means++. Then, the wind turbine power curve correction is realized by data weighting and regression analysis. Finally, an example is given to show that the power curve of the same type of wind turbines, which, installed in different locations, are discrepant and different from the MPC, and the wind turbine power curve obtained by using this method can reflect the output characteristics of the wind turbine operating more effectively in a complex environment.
A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning
Authors: Chen Junyan , Xiao Wei , Li Xinmei , Zheng Yang , Huang Xuefeng , Huang Danli , Wang Min
Published: October 2022

Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network’s spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.
Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge
Authors: D’Souza Ollencio , Mukhopadhyay Subhas Chandra , Sheng Michael
Published: October 2022

The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific “wake up” triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the “edge”, where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains.
The Watershed Communal Land Management and Livelihood of Rural Households in Kilte Awlaelo Woreda, Tigray Region, Ethiopia
Authors: Etsay Haftu , Oniki Shunji , Berhe Melaku , Negash Teklay
Published: October 2022

While the degradation of natural resources has a substantial impact on the livelihood of farmers in rural areas, there is scant empirical evidence about livelihood status and benefits from communal resources, especially whether the benefits are equally distributed among local farmers. This study examines how the conservation of communal lands affects the food security status and the livelihood of the poor people in the Tigray region of Ethiopia. This paper employed both descriptive statistics and econometric analyses using the ordinary least square regression and quantile regression models. The food security status of rural households was found to be negatively associated with the direct use of natural resources generated on conserved communal lands. The study further affirms that households in the lower quantile harness more of the direct use of common property resources. However, households in the median and the upper quantiles tend to engage in the indirect use of resources generated on communal lands. These findings pose a critical policy implication regarding how to reconcile the trade-offs between the consequence of heavy dependence of the poor on the direct use of communal land-based resources and ensuring sustainable livelihood by allowing the poor to collect benefits from the conserved lands.
Internet, Green Innovation and Industrial Upgrading
Authors: Tong Lei , Rong Yafei
Published: October 2022

The internet and green innovation are important driving forces to promote industrial high-quality and sustainable development at present. Studying their independent and interactive effects on industrial upgrading is of great practical and theoretical significance. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper constructs a spatial error model based on four weight matrices to study independent and interactive effects of the internet and green innovation on industrial upgrading. The results show that the internet, green innovation, and industrial upgrading have significant spatial correlation, with all showing high–high and low–high agglomeration trends. Both the internet and green innovation contribute to industrial upgrading, and their interaction effect is more significant for enhancing industrial upgrading. The heterogeneity analysis finds that green innovation has a greater impact on industrial upgrading in eastern China, and the internet in the central and western regions has a greater impact on industrial upgrading. Few previous studies put the internet, green innovation, and industrial upgrading into the unified framework. This paper expands and enriches the research on the relationship among the three to analyze the independent and interactive effects of the internet and green innovation on industrial upgrading by spatial metrology. We also promote the construction, promotion, and application of the internet and optimization of the green innovation environment, taking the “Internet plus green innovation” strategy as the foothold and implementing differentiation and a dynamic strategy that provides a reference for how to realize the transformation and upgrading of China’s industrial structure through the internet and green-innovation strategy.
The Potential of Big Data Application in Mathematics Education in Malaysia
Authors: Jin Sim Jia , Abdullah Abdul Halim , Mokhtar Mahani , Abdul Kohar Umar Haiyat
Published: October 2022

The world is facing rapid changes after the emergence of innovative technologies. These changes aim to ensure that a country keeps track of current world developments, strengthens its economy, and reduces its dependence on imports. Hence, every country is now amid technological transformation in the industrial sector by replacing manpower with machines to increase production and efficiency, allowing for mass production. Technology advancements in control, information technology, and automation that are applied to business and industry production processes are referred to as ‘Industry 4.0’. The objective is to increase the autonomy, adaptability, and effectiveness of decision-making and production processes utilizing cyber-physical systems (CPS), Big Data (BD), artificial intelligence (AI), and the industrial Internet of Things (IoT). Specifically, this article first introduces Industry Revolution (IR) 4.0, followed by a delineation of the concept of BD. Correspondingly, we discuss BD in education and relate mathematics education with BD. The article concludes with the implications of BD for Malaysian teaching and learning practices.
The Effect of Digital Economy on Urban Green Transformation—An Empirical Study Based on the Yangtze River Delta City Cluster in China
Authors: Chang Xinfeng , Su Jian , Yang Zihe
Published: October 2022

Under the new development pattern, promoting urban green transformation efficiently addresses resource and environmental restrictions and is the foundation of high-quality development. However, the development of green transformation in Chinese cities faces constraints in three dimensions, production, life, and ecology, and it is not clear whether the digital economy, as a key driver of green development, can empower urban green transformation. Therefore, this paper measures the level of urban green transformation from the three dimensions of production, life, and ecological space and empirically tests the impact of the digital economy on urban green transformation from 2011 to 2020 by using panel data from 41 cities in the Yangtze River Delta urban agglomeration. The results indicate: (1) The development of the green transformation level in the Yangtze River Delta urban agglomeration demonstrates a constant upward trend, but the absolute difference fluctuates and declines; (2) the development of the digital economy has a positive impact on urban green transformation, as well as changes in urban production, living, and ecological space; (3) the digital economy helps the green transformation of cities by supporting the growth of an advanced and rationalized industrial structure. Nevertheless, the indirect influence of an advanced and rational industrial structure on urban green transformation is quite different.
Synthesis of a New Ag(I)-Azine Complex via Ag(I)-Mediated Hydrolysis of 2-(((1-(Pyridin-2-yl)ethylidene)hydrazineylidene) Methyl)phenol with AgClO4; X-ray Crystal Structure and Biological Studies
Authors: Altowyan Mezna Saleh , Soliman Saied M. , Al-Wahaib Dhuha , Barakat Assem , Ali Ali Eldissouky , Elbadawy Hemmat A.
Published: October 2022

A new Ag(I)-azine complex of the formula [Ag(La)]2(ClO4)2.1/2(Lb) was synthesized and characterized using elemental analysis, FTIR, XPS and single-crystal X-ray diffraction. The [Ag(La)]2(ClO4)2.1/2(Lb) complex was obtained via Ag(I)-mediated hydrolysis of 2-(((1-(pyridin-2-yl)ethylidene)hydrazineylidene)methyl)phenol (L) with AgClO4 leading to the formation of the azines (1E,2E)-1,2-bis(1-(pyridin-2-yl)ethylidene)hydrazine (La) and 2,2’-((1E,1’E)-hydrazine-1,2-diylidene bis(methanylylidene)) diphenol (Lb). The former underwent complexation with AgClO4, while the latter was co-crystallized with the resulting Ag(I) complex, leading to the complex formula [Ag(La)]2(ClO4)2.1/2(Lb). In this complex, the azine La is acting as a bridged bidentate chelate connecting the two Ag(I) sites, leading to a tetra-coordinated Ag(I) with a distorted square planar coordination environment. Hirshfeld analysis indicated the importance of the H…H (38.4%), O…H (17.9%), H…C (13.2%), C…C (9.0%) and N…H (8.9%) interactions in the molecular packing. The antimicrobial, antioxidant and cytotoxic activities of the [Ag(La)]2(ClO4)2.1/2(Lb) complex were examined and compared with those of [Ag(La)]2(ClO4)2. The former was found to have lower bioactivity than the latter, which shed light on the lowering of biological actions as a consequence of the incorporation of the azine Lb within the Ag(I) complex. In other words, lowering the %Ag decreases the biological actions. The [Ag(La)]2(ClO4)2.1/2(Lb) complex (ΜIC = 39 μg/mL) has higher activity against the fungus A. fumigatus than the control Ketoconazole (ΜIC =156 μg/mL). This complex also has good cytotoxic activity against colon carcinoma and weak antioxidant activity.
Trajectories of CD4+/CD8+ T-Cells Ratio 96 Weeks after Switching to Dolutegravir-Based Two-Drug Regimens: Results from a Multicenter Prospective Cohort Study
Authors: Taramasso Lucia , Falletta Antonio , Ricci Elena , Orofino Giancarlo , Squillace Nicola , Menzaghi Barbara , De Socio Giuseppe , Molteni Chiara , Pellicanò Giovanni , Gulminetti Roberto , Madeddu Giordano , Sarchi Eleonora , Vichi Francesca , Celesia Benedetto , Bonfanti Paolo , Di Biagio Antonio
Published: October 2022

The aim of the present study was to evaluate CD4/CD8 dynamics in patients on dolutegravir (DTG)-based two-drug regimens (2DRs) and compare them with DTG-containing triple-drug regimens (3DRs). A prospective observational study was performed in the context of the SCOLTA cohort. Experienced PWH with HIV-RNA < 50 copies/mL were included if they were on the DTG-2DR, the DTG + tenofovir/emtricitabine (TDF/FTC) regimen, the DTG + tenofovir alafenamide (TAF)/FTC regimen, or the DTG + abacavir/lamivudine (ABC/3TC) regimen; they were followed-up for at least one year. A total of 533 PWH were enrolled, 120 in the DTG + 3TC group, 38 in the DTG + protease inhibitors (PI) group, 67 in the DTG + rilpivirine (RPV) group, 49 in the DTG + TDF/FTC group, 27 in the DTG + TAF/FTC group, and 232 in the DTG + ABC/3TC group. After one year, the CD4/CD8 ratio significantly increased in the PWH treated with DTG + 3TC (+0.08 ± 0.26), DTG + TDF/FTC (+0.1 ± 0.19), and DTG + ABC/3TC (+0.08 ± 0.25). At two years, the CD4/CD8 increase was confirmed for PWH on DTG + TDF/FTC (+0.16 ± 0.28) and DTG + ABC/3TC (+0.1 ± 0.3). In the SCOLTA cohort, PWH on 2DRs experienced a CD4/CD8 increase only in the DTG + 3TC group. Controlled studies with longer follow-up will clarify the long-term immunological and clinical impacts of DTG-2DR.
HIV, HTLV, and Hepatitis B and C Infection in Blood Donors in Bahia, Brazil from 2008 to 2017
Authors: Luz Estela , Marques Marinho , Netto Eduardo Martins , Campos Luisa Meireles , Amaral Sávio , Santana Iraildes , Marques Eduardo Luz , Brites Carlos
Published: October 2022

Although blood transfusion is an important therapeutic resource, transfusion-transmitted infections (TTIs) are still a cause for concern. Measures to mitigate this risk involve improvement of donor screening criteria and improvements in laboratory tests, especially the use of nucleic acid test (NAT). In this retrospective study we evaluated HIV, HTLV, HCV and HBV infection rates in blood donors of the Hematology and Hemotherapy Foundation of Bahia (Hemoba), Brazil, through serological and NAT results and the characteristics of donors. From February/2008 to December/2017, 777,446 blood donations were made. Most donors were male, aged 25–44 years, black and mixed race, and single or divorced. The density-type incidence (DTI; per 100,000) for each virus was 91.1 for HBV; 66.5 for HCV; 54.3 for HIV; and 33.9 for HTLV, with a decreasing trend observed over the period studied, except in the last biennium. NAT detected only 1 donor in immunological window for HIV (0.46/100,000 donations) and 3 donors in immunological window for HBV (1.8/100,000 donations). Serological positivity for all viruses studied was higher in the metropolitan region of Salvador, the state capital. Conclusion: DTI rates show a decreasing trend over the years studied, with a predominance of HBV infection. NAT allowed the detection of donors in immunological window periods, having an important role in improving transfusion safety.
Investigation Of Separability Of DMC/Methanol Azeotropic Mixtures By Chitosan Hybrid Membrane
Authors: YENİHAN YÜZER Nazlı , HASIRCI Güler , HİLMİOĞLU Nilüfer
Published: October 2022

Dimethyl carbonate (DMC) is a green solvent and an environmentally friendly fuel additive. It is produced by carbonylation of methanol The production cost of DMC is very high by conventional processes because DMC and methanol form azeotrope and cannot be separated by distillation. Its separation can be performed by pervaporation (PV) economically. Pervaporation (PV) is also an environmentally friendly membrane process in which liquid-liquid mixtures are separated under vacuum with high selectivity. Pervaporation selectivity is dependent on sorption selectivity and diffusion selectivity. Membrane used in the pervaporation process is a semi-permeable barrier and provides selective separation and transport. In PV development of an appropriate membrane material is important for obtaining pure substances. Hydrophilic membrane can separate methanol which is a polar component. Chitosan is a harmless, biodegradable, natural polymer and, despite having hydrophilic properties, cannot withstand aqueous conditions. Therefore, it should be modified by crosslinking. Tetraethoxysilan (TEOS) and tungstophosphoric acid (TPA) can be used as crosslinking agents and additive blending components in membrane production for increasing chemical and mechanical stability. In this study, sorption tests were carried out to determine the usability of chitosan-tetraethoxysilan (TEOS) and chitosan-Tungstophosphoric acid (TPA) hybrid membranes in separation of DMC-Methanol mixture with PV.