bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Carbon Nanocomposites Based on Sodium Alginate/Chitosan Reinforced with Graphene Oxide and Carbon Nanotubes
Authors: Khachatryan Gohar , Khachatryan Karen , Szczepankowska Joanna , Krzan Marcel , Krystyjan Magdalena
Published: February 2023

The aim of this study was to use a simple, low-cost and environmentally friendly synthesis method to design nanocomposites. For this purpose, carbon nanostructures were used to reinforce the chitosan/alginate bond in order to improve the mechanical, solubility, water absorption and barrier (protection against UV radiation) properties of the chitosan/alginate structure. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), ultraviolet and visible light absorption spectroscopy (UV-VIS) and color analysis were utilized, and the thickness and mechanical properties of the obtained films were determined. The tests that were carried out showed an equal distribution of nanostructures in the composite material and the absence of chemical interactions between nanoparticles and polymers. It was also proven that the enrichment of the polysaccharide composite with graphene oxide and carbon nanotubes positively affected its absorption, mechanical capabilities and color.
A Biodegradable, Bio-Based Polymer for the Production of Tools for Aquaculture: Processing, Properties and Biodegradation in Sea Water
Authors: Pavia F. Carfì , Brucato V. , Mistretta M. C. , Botta L. , La Mantia F. P.
Published: February 2023

Bio-based, biodegradable polymers can dramatically reduce the carbon dioxide released into the environment by substituting fossil-derived polymers in some applications. In this work, prototypes of trays for aquaculture applications were produced via injection molding by using a biodegradable polymer, Mater-Bi®. A characterization carried out via calorimetric, rheological and mechanical tests revealed that the polymer employed shows properties suitable for the production of tools to be used in aquaculture applications. Moreover, the samples were subjected to a biodegradation test in conditions that simulate the marine environment. The as-treated samples were characterized from gravimetrical, morphological and calorimetric point of views. The obtained data showed a relatively low biodegradation rate of the thick molded samples. This behavior is of crucial importance since it implies a long life in marine water for these manufacts before their disappearing.
Durability Performance Investigation for Engineering Fiber Cementitious Composites (ECC): Review
Authors: Zhang Ziyi , Ji Yongcheng , Ji Wenhao
Published: February 2023

Engineered Cementitious Composite (ECC) is currently receiving more and more attention due to its excellent tensile strain hardening and multiple cracking properties. However, due to the high material cost of polyvinyl alcohol (PVA) fiber and quartz sand, its widespread promotion and application in the market are limited. Therefore, scholars at home and abroad have conducted many active studies on improving ECC. This paper summarizes the development history and research status of ECC materials, summarizes the current domestic and foreign researchers’ improvement methods for ECC materials, and classifies the improvement methods into three categories: the type of fiber variation, the water-binder ratio variation and adding mineral admixtures, the influences of the above three factors on the mechanical properties and durability of ECC, such as compressive and flexural resistance, are described in detail, and the mechanism of action is explained. Furthermore, this paper introduces the most common uniaxial compression and uniaxial tension constitutive models of ECC. They are briefly classified and evaluated, hoping to help readers’ numerical simulation analysis. Finally, some suggestions for ECC research, such as the proportion of water binders and the application of composite fibers, require further research.
How Does Religious Belief Affect Happiness of Older Adults: The Participation Effect and Support Effect
Authors: Hou Bin , Wu Yuxin , Huang Yuqi
Published: February 2023

Religion can influence the realization of happiness in older adults. However, the relationship between religious belief and happiness of older adults and its mechanisms are not very clear. Using 5177 individuals aged ≥ 60 years in China for empirical analysis, the results show that there is a significant positive relationship between religious belief and older adults’ happiness. The frequency of religious participation has a significantly moderating effect on the relationship between institutional religious affiliation and happiness; participation has an effect on happiness for those involved in institutional religion, but not an affect for those with diffused beliefs, which reflects the participation effect. Social support plays a mediating role between religious belief and older adults’ happiness. People with religious beliefs are more likely to form social relationships and gain social support from religious members and groups to improve their happiness, which reflects the support effect. In addition, the influence of religious belief on happiness with low socioeconomic status is more significant, and the influence in rural areas is also more significant, showing the heterogeneity of individual socioeconomic status and registered residence. The freedom of older adult-religious believers to participate in the legal activities of religious organizations should be fully guaranteed. The religious faith of older adults in the process of active aging should be given attention.
Data Preparation Impact on Semantic Segmentation of 3D Mobile LiDAR Point Clouds Using Deep Neural Networks
Authors: Mahmoudi Kouhi Reza , Daniel Sylvie , Giguère Philippe
Published: February 2023

Currently, 3D point clouds are being used widely due to their reliability in presenting 3D objects and accurately localizing them. However, raw point clouds are unstructured and do not contain semantic information about the objects. Recently, dedicated deep neural networks have been proposed for the semantic segmentation of 3D point clouds. The focus has been put on the architecture of the network, while the performance of some networks, such as Kernel Point Convolution (KPConv), shows that the way data are presented at the input of the network is also important. Few prior works have studied the impact of using data preparation on the performance of deep neural networks. Therefore, our goal was to address this issue. We propose two novel data preparation methods that are compatible with typical density variations in outdoor 3D LiDAR point clouds. We also investigated two already existing data preparation methods to show their impact on deep neural networks. We compared the four methods with a baseline method based on point cloud partitioning in PointNet++. We experimented with two deep neural networks: PointNet++ and KPConv. The results showed that using any of the proposed data preparation methods improved the performance of both networks by a tangible margin compared to the baseline. The two proposed novel data preparation methods achieved the best results among the investigated methods for both networks. We noticed that, for datasets containing many classes with widely varying sizes, the KNN-based data preparation offered superior performance compared to the Fixed Radius (FR) method. Moreover, this research allowed identifying guidelines to select meaningful downsampling and partitioning of large-scale outdoor 3D LiDAR point clouds at the input of deep neural networks.
A Study on the Retrieval of Ozone Profiles Using FY-3D/HIRAS Infrared Hyperspectral Data
Authors: Xie Mengzhen , Gu Mingjian , Hu Yong , Huang Pengyu , Zhang Chunming , Yang Tianhang , Yang Chunlei
Published: February 2023

Atmospheric ozone is a pollutant gas that has an important influence on the process of atmospheric radiation transmission and climate change. The Fengyun-3D (FY-3D) satellite Hyperspectral Infrared Atmospheric Sounder (HIRAS) has better spectral performance than other remote sensing payloads. Its observation radiation data contains abundant atmospheric vertical information, which can be used for ozone retrieval, but there are no ozone profile business products being generated at present. Therefore, for the mainland of Hong Kong, based on HIRAS infrared hyperspectral observation data, we used the traditional one-dimensional variational (1D-VAR) physical retrieval algorithm, combined with the radiative transfer model for TOVS (RTTOV), and selected the spectrum channel according to the optimal sensitive profile algorithm. The artificial neural network (ANN) algorithm was used to optimize the prior profiles, and the atmospheric ozone profile retrieval system was established. Finally, a set of ozone profile retrieval schemes suitable for FY-3D/HIRAS were summarized. We used ERA5 reanalysis data and World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data to determine true values. The retrieval results were compared with Global Forecast System (GFS) forecast data, Ozone Mapping and Profile Suite (OMPS) ozone products, and Atmospheric Infrared Sounder (AIRS) ozone products. The results show that our ozone profile retrieval scheme makes up for the shortcomings of the conventional physical methods in some atmospheric pressure levels. The overall root-mean-square error (RMSE) of the ozone from the ground to the top of the stratosphere is within 30% on average, which was better than that for the GFS forecast data; the retrieval accuracy RMSE (%) was less than 20% in the pressure layer with the highest ozone concentration (15–25 hPa), which is better than that of OMPS ozone products and AIRS ozone products. The retrieval results prove that FY3D/HIRAS observation data allow ozone profile retrieval. This paper provides a reference for generating independent HIRAS ozone profile product data sets in business, and provides support for the subsequent application of Fengyun-3 series meteorological satellites in atmospheric parameter remote sensing.
Design and Manufacture of an Optimised Side-Shifted PPM EMAT Array for Use in Mobile Robotic Localisation
Authors: McMillan Ross , Hampson Rory , Tabatabaeipour Morteza , Jackson William , Zhang Dayi , Tzaferis Konstantinos , Dobie Gordon
Published: February 2023

Guided wave Electro Magnetic Acoustic Transducers (EMATs) offer an elegant method for structural inspection and localisation relative to geometric features, such as welds. This paper presents a Lorentz force EMAT construction framework, where a numerical model has been developed for optimising Printed Circuit Board (PCB) coil parameters as well as a methodology for optimising magnet array parameters to a user’s needs. This framework was validated experimentally to show its effectiveness through comparison to an industry built EMAT. The framework was then used to design and manufacture a Side-Shifted Unidirectional Periodic Permanent Magnet (PPM) EMAT for use on a mobile robotic system, which uses guided waves for ranging to build internal maps of a given subject, identifying welded sections, defects and other structural elements. The unidirectional transducer setup was shown to operate in simulation and was then manufactured to compare to the bidirectional transmitter and two-receiver configurations on a localisation system. The unidirectional setup was shown to have clear benefits over the bidirectional setup for mapping an unknown environment using guided waves as there were no dead spots of mapping where signal direction could not be interpreted. Additionally, overall package size was significantly reduced, which in turn allows more measurements to be taken within confined spaces and increases robotic crawler mobility.
DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification
Authors: Kim Eunbeen , Moon Jaeuk , Shim Jonghwa , Hwang Eenjun
Published: February 2023

Animal sound classification (ASC) refers to the automatic identification of animal categories by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based models have shown good performance in ASC when training data is sufficient, but suffer from severe performance degradation if not. Recently, generative adversarial networks (GANs) have shown the potential to solve this problem by generating virtual data. However, in a multi-class environment, existing GAN-based methods need to construct separate generative models for each class. Additionally, they only consider the waveform or spectrogram of sound, resulting in poor quality of the generated sound. To overcome these shortcomings, we propose a two-step sound augmentation scheme using a class-conditional GAN. First, common features are learned from all classes of animal sounds, and multiple classes of animal sounds are generated based on the features that consider both waveforms and spectrograms using class-conditional GAN. Second, we select data from the generated data based on the confidence of the pretrained ASC model to improve classification performance. Through experiments, we show that the proposed method improves the accuracy of the basic ASC model by up to 18.3%, which corresponds to a performance improvement of 13.4% compared to the second-best augmentation method.
A Study of an Online Tracking System for Spark Images of Abrasive Belt-Polishing Workpieces
Authors: Huang Jian , Zhang Guangpeng
Published: February 2023

During the manual grinding of blades, the workers can estimate the material removal rate based on their experiences from observing the characteristics of the grinding sparks, leading to low grinding accuracy and low efficiency and affecting the processing quality of the blades. As an alternative to the recognition of spark images by the human eye, we used the deep learning algorithm YOLO5 to perform target detection on spark images and obtain spark image regions. First the spark images generated during one turbine blade-grinding process were collected, and some of the images were selected as training samples, with the remaining images used as test samples, which were labelled with LabelImg. Afterwards, the selected images were trained with YOLO5 to obtain an optimisation model. In the end, the trained optimisation model was used to predict the images of the test set. The proposed method was able to detect spark image regions quickly and accurately, with an average accuracy of 0.995. YOLO4 was also used to train and predict spark images, and the two methods were compared. Our findings show that YOLO5 is faster and more accurate than the YOLO4 target detection algorithm and can replace manual observation, laying a specific foundation for the automatic segmentation of spark images and the study of the relationship between the material removal rate and spark images at a later stage, which has some practical value.
Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm
Authors: Mukundan Arvind , Tsao Yu-Ming , Cheng Wen-Min , Lin Fen-Chi , Wang Hsiang-Chen
Published: February 2023

In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required.
On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification
Authors: Pokorny Tomas , Vrba Jan , Fiser Ondrej , Vrba David , Drizdal Tomas , Novak Marek , Tosi Luca , Polo Alessandro , Salucci Marco
Published: February 2023

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.
Discharge Monitoring in Open-Channels: An Operational Rating Curve Management Tool
Authors: Paoletti Michele , Pellegrini Marco , Belli Alberto , Pierleoni Paola , Sini Francesca , Pezzotta Nicola , Palma Lorenzo
Published: February 2023

An aspect correlated with climate change is certainly represented by the alternation of severe floods and relevant drought periods. Moreover, there is evidence that changes in climate and land cover are inducing changes in stream channel cross-sections, altering local channel capacity. A direct consequence of a significant change in the local channel capacity is that the relationship between the amount of water flowing at a given point in a river or stream (usually at gauging stations) and the corresponding stage in that section, known as a stage–discharge relationship or rating curve, is changed. The key messages deriving from the present work are: (a) the more frequent and extreme the floods become, the more rapid the changes in the stream channel cross-section become, (b) from an operational point of view, the collection and processing of field measurements of the stage and corresponding discharge at a given section in order to quickly and frequently update the rating curve becomes a priority. It is, therefore, necessary to define a control system for acquiring hydrological data capable of keeping river levels and discharges under control to support flood early warnings and water management. The proposed stage–discharge management system is used by the Civil Protection Service of the Marche Region (east-central Italy) for the monitoring of river runoff in the regional watersheds. The Civil Protection Service staff performs stage–discharge field measurements using water level sensors and recorders (e.g., staff gauges, submersible pressure transducers, ultrasound and radar sensors) and a current meter, acoustic doppler velocimeter, acoustic doppler current profilers, portable mobile radar profiler and salt dilution method equipment, respectively. Power functions are fitted to the stage–discharge field data. Furthermore, extrapolation is performed to cover the full range of flow measurements; in general, extrapolation is not an easy task because of sharp changes in the stream cross-section geometry for very high or very low stages. In the present work, we also focused attention on the application problems that occur in practice and the need for frequent updating.
A Mapless Local Path Planning Approach Using Deep Reinforcement Learning Framework
Authors: Yin Yan , Chen Zhiyu , Liu Gang , Guo Jianwei
Published: February 2023

The key module for autonomous mobile robots is path planning and obstacle avoidance. Global path planning based on known maps has been effectively achieved. Local path planning in unknown dynamic environments is still very challenging due to the lack of detailed environmental information and unpredictability. This paper proposes an end-to-end local path planner n-step dueling double DQN with reward-based ϵ-greedy (RND3QN) based on a deep reinforcement learning framework, which acquires environmental data from LiDAR as input and uses a neural network to fit Q-values to output the corresponding discrete actions. The bias is reduced using n-step bootstrapping based on deep Q-network (DQN). The ϵ-greedy exploration-exploitation strategy is improved with the reward value as a measure of exploration, and an auxiliary reward function is introduced to increase the reward distribution of the sparse reward environment. Simulation experiments are conducted on the gazebo to test the algorithm’s effectiveness. The experimental data demonstrate that the average total reward value of RND3QN is higher than that of algorithms such as dueling double DQN (D3QN), and the success rates are increased by 174%, 65%, and 61% over D3QN on three stages, respectively. We experimented on the turtlebot3 waffle pi robot, and the strategies learned from the simulation can be effectively transferred to the real robot.
Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals
Authors: Aminosharieh Najafi Taraneh , Affanni Antonio , Rinaldo Roberto , Zontone Pamela
Published: February 2023

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects’ Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.
Modified Two-Point Correction Method for Wide-Spectrum LWIR Detection System
Authors: Zhang Di , Sun He , Wang Dejiang , Liu Jinghong , Chen Cheng
Published: February 2023

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed-pattern noise (FPN) and seriously affects the image quality of long-wave infrared (LWIR) detection systems. The two-point correction (TPC) method is commonly used to reduce image FPN in engineering. However, when a wide-spectrum LWIR detection system calibrated with a black body is used to detect weak and small targets in the sky, FPN still appears in the image, affecting its uniformity. The effects of atmospheric transmittance characteristics of long-range paths on the non-uniformity of wide-spectrum long-wave infrared systems have not been studied. This paper proposes a modified TPC model based on spectral subdivision that introduces atmospheric transmittance. Additionally, the effects of atmospheric transmittance characteristics on the long-wave infrared non-uniform correction coefficient are analyzed. The experimental results for a black body scene and sky scene using a weak and small target detection system with a long-wave Sofradir FPA demonstrate that the wide-spectrum LWIR detection system fully considers atmospheric transmittance when performing calibration based on the TPC method, which can reduce the non-uniformity of the image.
Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results
Authors: Plucińska Renata , Jędrzejewski Konrad , Malinowska Urszula , Rogala Jacek
Published: February 2023

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification. We analyzed the original data from 29 participants with 20 distinct recorded sessions each, as well as 23 additional impostors with only one session each. We applied raw coefficients of power spectral density estimate, and the coefficients of power spectral density estimate converted to the decibel scale, as the input to a shallow neural network. Our study showed that the variance introduced by multiple recording sessions affects sensitivity. We also showed that increasing the number of sessions above eight did not improve the results under our conditions. For 15 training sessions, the achieved accuracy was 96.7 ± 4.2%, and for eight training sessions and 12 test sessions, it was 94.9 ± 4.6%. For 15 training sessions, the rate of successful impostor attacks over all attack attempts was 3.1 ± 2.2%, but this number was not significantly different from using six recording sessions for training. Our findings indicate the need to include data from multiple recording sessions in EEG-based recognition for training, and that increasing the number of test sessions did not significantly affect the obtained results. Although the presented results are for the resting-state, they may serve as a baseline for other paradigms.
Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control
Authors: Siribunyaphat Nannaphat , Punsawad Yunyong
Published: February 2023

Brain–computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.
Resolution Enhanced Array ECT Probe for Small Defects Inspection
Authors: Long Cai , Zhang Na , Tao Xinchen , Tao Yu , Ye Chaofeng
Published: February 2023

It is a continual and challenging problem to detect small defects in metallic structures for array eddy current testing (ECT) probes, which require the probe to have ultra-high resolution and sensitivity. However, the spatial resolution of an ECT array probe is limited by the size of the induction coils. Even if it is possible to increase the spatial resolution by using smaller coils, the sensitivity of the sensor also decreases. To obtain finer spatial resolution without sacrificing sensitivity, this paper proposes a resolution enhanced ECT array probe with four rows of coils attached to a flexible printed circuit board (FPCB). The distance between each two adjacent coils in a row is 2 mm and the position of each row is offset by 0.5 mm along the horizontal direction related to its prior row. The outputs of the four rows are aligned and interpolated in a line, and in this way the image resolution of the probe is increased to 0.5 mm. The probe is configured to operate with the differential setting, namely two differential coils operate simultaneously at each time. The currents in the two coils can be controlled to have the same flowing direction or opposite flowing direction, resulting in different distributions of the induced eddy current and two sets of output images. A patch-image model and an image fusion method based on discrete wavelet transforms are employed to suppress the noise and highlight the defects’ indications. Experimental results show that small defects with dimensions as small as length × width × depth = 1 mm × 0.1 mm × 0.3 mm on a 304 stainless-steel sample can be detected from the fused image, demonstrating that the probe has super sensitivity for small defects inspection.
Prediction Models of Shielding Effectiveness of Carbon Fibre Reinforced Cement-Based Composites against Electromagnetic Interference
Authors: Narayanan Shilpa , Zhang Yifan , Aslani Farhad
Published: February 2023

With the rapid development of communication technology as well as a rapid rise in the usage of electronic devices, a growth of concerns over unintentional electromagnetic interference emitted by these devices has been witnessed. Pioneer researchers have deeply studied the relationship between the shielding effectiveness and a few mixed design parameters for cementitious composites incoporating carbon fibres by conducting physical experiments. This paper, therefore, aims to develop and propose a series of prediction models for the shielding effectiveness of cementitious composites involving carbon fibres using frequency and mixed design parameters, such as the water-to-cement ratio, fibre content, sand-to-cement ratio and aspect ratio of the fibres. A multi-variable non-linear regression model and a backpropagation neural network (BPNN) model were developed to meet the different accuracy requirements as well as the complexity requirements. The results showed that the regression model reached an R2 of 0.88 with a root mean squared error (RMSE) of 2.3 dB for the testing set while the BPNN model had an R2 of 0.96 with an RMSE of 2.64 dB. Both models exhibited a sufficient prediction accuracy, and the results also supported that both the regression and the BPNN model are reasonable for such estimation.
Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification
Authors: Elshewey Ahmed M. , Shams Mahmoud Y. , El-Rashidy Nora , Elhady Abdelghafar M. , Shohieb Samaa M. , Tarek Zahraa
Published: February 2023

Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.
Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
Authors: Morshed Khaled M. , Karmokar Debabrata K. , Esselle Karu P.
Published: February 2023

Two novel antennas are presented for mobile devices to enable them to access both licensed shared access (LSA) bands (1452–1492 and 2300–2400 MHz) and all the long-term evolution (LTE) mid (1427–2690 MHz) and high (3400–3800 MHz) bands, together with the GSM1800, GSM1900, UMTS, and 3.3 GHz WiMAX bands. These antennas do not require any passive or active lumped elements for input impedance matching. One of them is a dual-band antenna and the other is a wideband antenna. Both antennas have high efficiency in all the LSA bands, as well as the mid- and high-LTE bands, and nearly omnidirectional radiation patterns in the mid band. In the high band, the radiation patterns of the wideband antenna are less directional than those of the dual-band antenna. The wideband antenna was fabricated and tested and the measurements demonstrated that it had good wideband performance in a wide frequency range from 1.37 to 4 GHz, covering all the above-mentioned bands.
DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks
Authors: Zhang Rui , Zhang Jing , Wang Qiqi , Zhang Hehe
Published: February 2023

In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively.
Lossless Reconstruction of Convolutional Neural Network for Channel-Based Network Pruning
Authors: Lee Donghyeon , Lee Eunho , Hwang Youngbae
Published: February 2023

Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider reconstruction after pruning in order to apply the network to actual devices. This study proposes a reconstruction process for channel-based network pruning. For lossless reconstruction, we focus on three components of the network: the residual block, skip connection, and convolution layer. Union operation and index alignment are applied to the residual block and skip connection, respectively. Furthermore, we reconstruct a compressed convolution layer by considering batch normalization. We apply our method to existing channel-based pruning methods for downstream tasks such as image classification, object detection, and semantic segmentation. Experimental results show that compressing a large model has a 1.93% higher accuracy in image classification, 2.2 higher mean Intersection over Union (mIoU) in semantic segmentation, and 0.054 higher mean Average Precision (mAP) in object detection than well-designed small models. Moreover, we demonstrate that our method can reduce the actual latency by 8.15× and 5.29× on Raspberry Pi and Jetson Nano, respectively.
Long-Term Paracetamol Treatment Impairs Cognitive Function and Brain-Derived Neurotrophic Factor in Adult Rat Brain
Authors: Lalert Laddawan , Tantarungsee Nutnicha , Chotipinit Tipthanan , Ji-au Wilawan , Srikiatkhachorn Anan , Maneesri-le Grand Supang
Published: February 2023

Paracetamol (acetaminophen, APAP) is known as a safe pain reliever; however, its negative effects on the central nervous system have gradually been reported. We examined alterations in learning and memory, and brain-derived neurotrophic factor (BDNF) expression in the frontal cortex and hippocampus at different durations of APAP treatment in rats. Novel object recognition (NOR) and Morris water maze (MWM) paradigms were used to assess learning and memory in rats fed with 200 mg/kg APAP at single-dose, 15-day or 30-day treatments. BDNF expression was evaluated through immunohistochemistry and Western blotting. The single-dose APAP treatment did not alter the NOR performance. However, deficits in the NOR and MWM capacities were detected in the rats with longer durations of APAP treatment. An analysis of BDNF expression revealed no significant change in BDNF expression in the single-dose APAP treatment, while rats given APAP for extended periods as treatment showed a significant decrement in this protein in the frontal cortex and hippocampus. Short-term APAP treatment has no effect on learning and memory, or BDNF expression; however, long-term APAP exposure causes cognitive impairment. The diminishment of the BDNF level in the frontal cortex and hippocampus due to the long period of treatment with APAP may at least in part be involved in altered learning and memory in rats.
Mechanisms for Choosing PV Locations That Allow for the Most Sustainable Usage of Solar Energy
Authors: Mian Syed Hammad , Moiduddin Khaja , Alkhalefah Hisham , Abidi Mustufa Haider , Ahmed Faraz , Hashmi Faraz Hussain
Published: February 2023

The electrical power need in the Kingdom of Saudi Arabia (KSA) has been escalating at a rapid rate of about 7.5% annually. It has the third highest usage rate in the world as stated by World Energy Council statistics. The rising energy demand has a significant impact on the country’s economy since oil is considered to be its mainstay. Additionally, conventional energy production using fossil fuels is a leading contributor to ecological degradation and adversely influences human health. As a result, Saudi Arabia has taken significant steps to shift from its current status of total reliance on oil to new frontiers of exploration of other kinds of renewable energies. Photovoltaic (PV) solar energy is the most preferred renewable energy to be harnessed in Saudi Arabia. In accordance with Vision 2030, the KSA intends to generate at least 9.5 GW of electricity from green sources, a significant portion of which will come from solar PV power. Since the site peculiarities have a huge influence on the project’s technical and economic dimensions, the scaled-up deployment of solar projects calls for a judicious selection of PV sites. Undoubtedly, performing a thorough solar site survey is the foremost step to establishing a financially viable and successful solar project. Multiple criterion decision-making (MCDM) strategies can be very helpful in making judgments, given that a number of criteria might influence PV site selection. The objective of this research is to provide valuable information on various MCDM approaches that can be utilized to select optimal locations for PV solar plants. A number of variables, including topography, air temperature, dust storms, solar radiation, etc., are considered in this analysis. This study has combined various MCDM techniques in order for the strengths of each method to outweigh the weaknesses of the others. It has been deduced from this analysis that the most crucial factors in choosing PV sites are solar radiation and sunshine hours. It has also been concluded that of the surveyed cities, Tabuk is the optimum location for the construction of a solar power plant due to its high GHI value of 5992 W/m2/day and abundant sunshine hours of 12.16 h/day. Additionally, the FAHP-VIKOR method is noted as being the most rigorous, whereas Entropy-GRA is the simplest method.
Approaching Urban Food Waste in Low- and Middle-Income Countries: A Framework and Evidence from Case Studies in Kibera (Nairobi) and Dhaka
Authors: Pedrotti Michele , Fattibene Daniele , Antonelli Marta , Castelein Bob
Published: February 2023

Increased urbanization rates pose crucial challenges in terms of food systems’ sustainability, including urban food waste (FW). The global narrative around FW has focused mainly on Western countries, but recent evidence shows that FW is also a major issue in the developing world. The objective of this article is to define the characteristics and drivers of urban FW in low- and middle-income countries (LMIC). We firstly present a tailor-made three-step approach to identify urban FW hotspots in LMIC, understand the main drivers and design and implement prevention and reduction interventions considering LMIC food system characteristics. We then draw on results from four different urban FW case studies based in Nairobi (Kenya) and Dhaka (Bangladesh) and discuss their characteristics in light of the proposed approach. The case of Nairobi focuses on quantifying and understanding possible drivers of household FW in Kibera and characterizing FW disposal through a household survey (N = 774). The other three studies examine FW at retail, food service and institutional levels for onions, mangoes and beef in Dhaka. The results confirm that FW happens at the urban supply chain level, particularly among mobile vendors but also among households living below the poverty line. The article thus urges LMIC municipalities to consider urban FW strategies as a key action to tackle food security, environmental issues and FW management challenges.
Implementing a Pediatric Residency Program in Central Asia in Compliance with ACGME-International Standards: First Experience from Kazakhstan
Authors: Poddighe Dimitri , Pignatelli Massimo
Published: February 2023

The Graduate Medical Education (GME) became an essential part of the physician’s professional curriculum all over the world, no matter the specific model developed across different countries. The “Residency” represents the main GME model in North America, where it is regulated and organized according to the standards of the Accreditation Council for Graduate Medical Education (ACGME). This model has been exported outside North America through the ACGME International (ACGME-I) program. This article reports the first experience of implementing an ACGME-oriented Pediatric Residency Program in Central Asia at the Nazarbayev University School of Medicine in the Republic of Kazakhstan. The vision, general project, main organizational aspects, and challenges of this Pediatric Residency Program are described and discussed in this article.
Enhancement of Municipal Solid Waste Management in Hong Kong through Innovative Solutions: A Review
Authors: Li Chi Ho , Lee Tsz Ting , Lau Stephen Siu Yu
Published: February 2023

With the world population having reached 8 billion people, waste generation is expected to continue its growth in the coming years. Solid waste management (SWM) is an essential part of sustainable development that aims to minimize adverse environmental impacts. Although waste problems have been monitored for decades, some regions still struggle to achieve their sustainability goals in this area. In particular, Hong Kong is still affected by overloaded landfills, with an average daily disposal of 14,739 tonnes. The major waste category is municipal solid waste (MSW), which originates from households and commercial activities. This paper provides an overview of the current SWM strategies and the advancement of technologies adopted in the SWM industry. Supported by a comprehensive literature review, this paper identifies the challenges of SWM in Hong Kong and suggests possible solutions. The findings lead to the direction of future research, which should include innovative solutions for improving recycling behaviors. Adopting technologies such as virtual reality on education platforms and developing mobile applications with a theme of SWM could promote a stronger awareness of waste reduction and recycling among the public.
Tourism Development to Enhance Resident Well-Being: A Strong Sustainability Perspective
Authors: Dwyer Larry
Published: February 2023

Tourism research must recognise recent advances in sustainability theory if it is to progress conceptually and in the policy domain. By applying the method of critical review, this paper demonstrates the relevance of the capitals approach to sustainable tourism development, with human well-being identified as the ultimate objective of the process. Distinguishing between weak and strong sustainability, a policy framework is developed to merge the capitals approach with well-being outcomes to determine the direct and indirect benefits of tourism developments to stakeholders and destination residents. Several challenges must be addressed if sustainability principles and practices are to be embedded in tourism policymaking.