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Because of its dimensional characteristics, two-dimensional (2D) materials exhibit many special properties. The key to researching their features is to prepare high-quality larger-area monolayer 2D materials. Metal-assisted mechanical exfoliation method offers the possibility. However, there is still an unclear mechanism. We take transition metal dichalcogenides (TMD) as examples to perform serials of first-principles calculations to study their interlayer interaction. The calculated results demonstrate that the main factors affecting the interlayer interaction of 2D TMD materials include their structural features and the elemental features. The smaller interlayer distance between the topmost two layers than that of bulk leads to the stronger interlayer bonding energy, which results in the difficulty to exfoliate the large-area monolayer 2D crystals successfully. Further, we selected Ti, Cr and Au as the assisted metal due to their enhanced density of states near the Fermi level to calculate the adhesion energies of TMD on their surfaces. We found that there exists strong adhesion energy between 2D crystal and metal, which stretches the spacing of the topmost two layers and leads to the electron density rearrangement. Therefore, metal assisted mechanical exfoliation facilitates the successful preparation of monolayer 2D crystal., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Pre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo-relevant documents, followed by re-ranking with a pre-trained model. However, the semantic information captured by pre-trained models from sentences or passages is usually only applied to document ranking, with limited use in query expansion. In fact, the semantic information within pseudo-relevant documents plays a critical role in selecting appropriate query expansion terms. Therefore, this paper proposes a novel approach that leverages pre-trained models to extract multi-dimensional semantic information from pseudo-relevant documents, offering more possibilities for query expansion. First, traditional sparse retrieval methods are used in the initial retrieval stage to ensure efficiency, and term-level weights are calculated based on statistical information. Then, the pre-trained model encodes both the query and the sentences and passages from the documents, extracting sentence-level and passage-level semantic similarities to the query. Finally, these semantic weights are combined with the term-level weights to generate an improved query for the second retrieval round. We conducted experiments on five TREC datasets and a medical dataset, showing improvements in official metrics such as MAP and P@10. The results demonstrate the effectiveness of utilizing multi-dimensional semantic information from pseudo-relevant documents to optimize query expansion. This study offers new insights into how the semantic information of pseudo-relevant documents can be effectively harnessed to enhance retrieval performance., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Passive temperature sensing systems based on the Internet of Things (IoT) present an efficient, reliable, and convenient solution for temperature monitoring with extensive application prospects and market value. This paper introduces a passive, battery-free, chipless, metasurface temperature sensing tag. The key insight is that the sensing tag uses vanadium dioxide ([Formula: see text]) to solve the problems of measuring distance, large size, and high cost related to active devices. The sensing tag fabricated with tungsten-doped [Formula: see text] powder demonstrated a significant variation in the reflection magnitude within the temperature range of 34-42 °C. It was achieved through coating, sintering, metasurface design, and ion beam etching. Experimental results showed that the square resistance of the prepared coating decreased from 1003 to 90 [Formula: see text] as the temperature increased from 34 to 42 °C. Additionally, the reflection magnitude of the tag significantly increased with the temperature decrease in the 3.5-5.27 GHz frequency band. These results indicate that the passive temperature sensing tags can achieve rapid and accurate temperature sensing within the 34-42 °C range., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection. The focus is on detecting voltage deviations caused by internal short circuits, external short circuits, and capacity degradation, which are primary indicators of battery faults. Firstly, data from real electric vehicles, operating under normal and faulty conditions, is collected over a period of 18 months. Secondly, the segmented regression method is utilized to segment the data based on the charging and discharging cycles and capture potential dependencies in battery behavior within each cycle. Thirdly, an optimized gated recurrent unit network is developed and integrated with the segmented regression to enable accurate cell voltage estimation. Lastly, an adaptive threshold algorithm is proposed to integrate driving behavior and environmental factors into a Gaussian process regression model. The integrated model dynamically estimates the normal fluctuation range of battery cell voltages for fault detection. The effectiveness of the proposed method is validated on a comprehensive dataset, achieving superior accuracy with values of 99.803% and 99.507% during the charging and discharging phases, respectively., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The brain's ability to prioritize behaviorally relevant sensory inputs (i.e., targets) while ignoring irrelevant distractors is crucial for efficient information processing. However, the role of emotional valence in modulating selective attention remains underexplored. This study examined how positive and negative emotions alter the spatial scope of visual selective attention using a modified Eriksen Flanker task. Participants viewed an emotional face cue (happy, angry, or neutral) randomly positioned on the screen and then identified the shape of a subsequent neutral target (bowtie or diamond) at the cued location. Adjacent stimuli either matched the target shape (congruent) or differed (incongruent). Results showed that happy faces increased susceptibility to distractors (i.e., a larger incongruency effect), suggesting a broadening of attentional scope, while angry faces reduced susceptibility (i.e., a smaller incongruency effect), indicating a narrowing of focus. Importantly, the magnitude of this emotion-driven attention modulation was negatively correlated with participants' self-reported levels of psychological distress. Participants with higher stress and depression exhibited weaker attention broadening in response to positive cues. Together, the findings provide behavioral evidence of how emotional valence influences attention scope, offering potential insights into the dynamic interplay between psychological distress, emotional processing, and attention modulation., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Emojis, utilizing visual means, mimic human facial expressions and postures to convey emotions and opinions. They are widely used in social media platforms such as Sina Weibo, and have become a crucial feature for sentiment analysis. However, existing approaches often treat emojis as special symbols or convert them into text labels, thereby neglecting the rich visual information of emojis. We propose a novel multimodal information integration model for emoji microblog sentiment analysis. To effectively leverage the emoji visual information, the model employs a text-emoji visual mutual attention mechanism. Experiments on a manually annotated microblog dataset show that compared to the baseline models without incorporating emoji visual information, the proposed model achieves improvements of 1.37% in macro F1 score and 2.30% in accuracy, respectively. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/Emojis/blob/main/weibo-emojis-annotation ., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The rapid expansion of varied network systems, including the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), has led to an increasing range of cyber threats. Ensuring robust protection against these threats necessitates the implementation of an effective Intrusion Detection System (IDS). For more than a decade, researchers have delved into supervised machine learning techniques to develop IDS to classify normal and attack traffic. However, building effective IDS models using supervised learning requires a substantial number of benign and attack samples. To collect a sufficient number of attack samples from real-life scenarios is not possible since cyber attacks occur occasionally. Further, IDS trained and tested on known datasets fails in detecting zero-day or unknown attacks due to the swift evolution of attack patterns. To address this challenge, we put forth two strategies for semi-supervised learning-based IDS where training samples of attacks are not required: (1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; (2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic. We have implemented both approaches and compared their performances using 10 recent benchmark IDS datasets. Our findings demonstrate that the OCC model based on the state-of-art anomaly detection technique called usfAD significantly outperforms conventional supervised classification and other OCC-based techniques when trained and tested considering real-life scenarios, particularly to detect previously unseen attacks., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers., Competing Interests: Competing interests: The authors declare no competing interests. Ethics statement: The studies involving human participants were reviewed and approved by School of Information Technology and Engineering, Guangzhou College of Commerce Ethics Committee (Approval Number: 2022.29384384). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations., (© 2024. The Author(s).)

A dual-band broadband absorber based on frequency selective surface (FSS) is proposed. This double-layer absorber consists of two layers of FSS structure; each layer has a unique construction and plays its wave-absorbing function in different frequency bands. In the low-frequency band, layer I is a transmission channel, and layer II effectively absorbs electromagnetic waves. Furthermore, layer I exhibits significant absorption characteristics in the high-frequency band, whereas layer II provides effective reflection. The combination of the two layers results in a remarkably effective absorption. Its reflectivity below - 10 dB covers a bandwidth of 5.5-14.8 GHz and 22.9-33.1 GHz, and its fractional bandwidth is 128%. The structure prevents external electromagnetic interference from affecting the antenna's performance, ensuring communication quality in critical frequency bands while reducing unwanted absorption in unwanted frequency bands. It is worth mentioning that the structure still has good stability under 45° oblique incidence. The physical mechanism is analyzed in detail by using an equivalent circuit model (ECM). The measurement results are in good agreement with those of simulation and ECM., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Prototype-based methods in deep learning offer interpretable explanations for decisions by comparing inputs to typical representatives in the data. This study explores the adaptation of SESM, a self-attention-based prototype method successful in electrocardiogram (ECG) tasks, for electroencephalogram (EEG) signals. The architecture is evaluated on sleep stage classification, exploring its efficacy in predicting stages with single-channel EEG. The model achieves comparable test accuracy compared to EEGNet, a state-of-the-art black-box architecture for EEG classification. The generated prototypical components are exaimed qualitatively and using the area over the perterbation curve (AOPC) indicate some alignment with expected bio-markers for different sleep stages such as alpha spindles and slow waves in non-REM sleep, but the results are severely limited by the model's ability to only extract and present information in the time-domain. Ablation studies are used to explore the impact of kernel size, number of heads, and diversity threshold on model performance and explainability. This study represents the first application of a self-attention based prototype method to EEG data and provides a step forward in explainable AI for EEG data analysis., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token representation convergence. To address these issues, we introduce AIRHF-Net: an Adaptive Interaction Representation Hierarchical Fusion Network, named AIRHF-Net, designed to enhance pedestrian identity recognition in occluded scenarios. Our approach begins with the development of an Adaptive Local-Window Interaction Encoder (AL-WIE), which aims to overcome the inherent subjective limitations of traditional sparse attention mechanisms. This innovative encoder merges window attention, adaptive local attention, and interaction attention, facilitating automatic localization and focusing on visible pedestrian regions within images. It effectively extracts contextual information from window-level features while minimizing the impact of occlusion noise. Additionally, recognizing that ViTs may lose spatial information in deeper structural layers, we implement a Local Hierarchical Encoder (LHE). This component segments the input sequence in the spatial dimension, integrating features from various spatial positions to construct hierarchical local representations that substantially enhance feature discriminability. To further augment the quality and breadth of datasets, we adopt an Occlusion Data Augmentation Strategy (ODAS), which bolsters the model's capacity to extract critical information under occluded conditions. Extensive experiments demonstrate that our method achieves improved performance on the Occluded-DukeMTMC dataset, with a rank-1 accuracy of 69.6% and an mAP of 61.6%., (© 2024. The Author(s).)

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers., (© 2024. The Author(s).)

Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98., (© 2024. The Author(s).)

With the advancement of 3D scanning technologies and deep learning theories, point cloud-based deep learning networks have gained considerable attention in the fields of 3D vision and computer graphics. Leveraging the rich geometric information present in 3D point clouds, these networks facilitate more accurate feature learning tasks. However, existing networks often suffer from generalization defects caused by variations in pose and inconsistent representations of training data. In this paper, we propose a novel data augmentation framework to overcome these limitations. Our approach utilizes principal component analysis (PCA) to generate four aligned copies of a point cloud. These copies are then input into a multi-channel structure, which is compatible with popular backbones of point cloud-based deep networks. Finally, the outputs of the multi-channel structure are merged to generate rotation-invariant feature learning results. Experimental evaluations demonstrate the efficacy of our framework, showcasing significant improvements in various existing point cloud-based deep learning methods. Notably, our method exhibits enhanced robustness in classification tasks, particularly when dealing with point clouds containing random pose variations and non-uniform densities. Project link: https://github.com/LAB123-tech/PCAlign ., (© 2024. The Author(s).)

Quantum secret sharing is a basic quantum cryptographic primitive, which has a lot of applications in information security and privacy preservation. An efficient multiparty quantum secret sharing protocol (Kuo et al. in EPJ Quantum Technol 10(1):29, 2023) based on a novel structure and single qubits was reported recently. In this paper, we give a cryptanalysis of this protocol and show that it cannot satisfy the security requirement for secret sharing because an unauthorized set of agents can gain access to some information on the dealer's secret by a special collusion attack. Furthermore, we put forward a way to deal with the security problem., (© 2024. The Author(s).)

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering., (© 2024. The Author(s).)

Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool., (© 2024. The Author(s).)

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders., (© 2024. The Author(s).)

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines., (© 2024. The Author(s).)

Multiple sclerosis (MS) is a common autoimmune neurological disease affecting patients' motor, sensory, and visual performance. Stem Cell Transplantation (SCT) is a medical intervention where a patient is infused with healthy stem cells with the purpose of resetting their immune system. SCT shows remyelinating and immunomodulatory functions in MS patients, representing a potential therapeutic option. We conducted this systematic review and meta-analysis that included randomized control trials (RCTs) of SCT in MS patients to investigate its clinical efficacy and safety, excluding observational and non-English studies. After systematically searching PubMed, Web of Science, Scopus, and Cochrane Library until January 7, 2024, nine RCTs, including 422 patients, were eligible. We assessed the risk of bias (ROB) in these RCTs using Cochrane ROB Tool 1. Data were synthesized using Review Manager version 5.4 and OpenMeta Analyst software. We also conducted subgroup and sensitivity analyses. SCT significantly improved patients expanded disability status scale after 2 months (N = 39, MD =  - 0.57, 95% CI [- 1.08, - 0.06], p = 0.03). SCT also reduced brain lesion volume (N = 136, MD = - 7.05, 95% CI [- 10.69, - 3.4], p = 0.0002). The effect on EDSS at 6 and 12 months, timed 25-foot walk (T25-FW), and brain lesions number was nonsignificant. Significant adverse events (AEs) included local reactions at MSCs infusion site (N = 25, RR = 2.55, 95% CI [1.08, 6.03], p = 0.034) and hematological disorders in patients received immunosuppression and autologous hematopoietic SCT (AHSCT) (N = 16, RR = 2.33, 95% CI [1.23, 4.39], p = 0.009). SCT can improve the disability of MS patients and reduce their brain lesion volume. The transplantation was generally safe and tolerated, with no mortality or significant serious AEs, except for infusion site reactions after mesenchymal SCT and hematological AEs after AHSCT. However, generalizing our results is limited by the sparse number of RCTs conducted on AHSCT. Our protocol was registered on PROSPERO with a registration number: CRD42022324141., (© 2024. The Author(s).)

In this paper, a double-layer patterned graphene-based frequency-selective absorber (DGFSA) is proposed as a means of reducing an antenna's radar cross-section (RCS) while simultaneously increasing its gain. The antenna consists of a patch antenna with Multi-Graphene Frequency Selective Absorber (MGFSA) mounted on top. The DGFSA consists of double-layer patterned graphene and a band-pass frequency selective surface (FSS). Two patterned graphene lossy layers with different square resistances are used, which broaden the electromagnetic (EM) wave absorption bandwidth of the DGFSA, thus greatly reducing the out-band monostatic RCSs of the patch antenna. Meanwhile, due to the quasi-Fabry-Perot (F-P) effect, the gain of the proposed antenna is enhanced by 2.4 dB. Additionally, the low-RCS antenna reduces the monostatic RCS from 1.32 to 17 GHz under y-polarization and from 1.4 to 16.8 GHz under x-polarization, respectively. Furthermore, a decrease in the bistatic RCS is accomplished. Results from simulations and measurements match up nicely, which means the antenna we proposed has a good application on the stealth platform., (© 2024. The Author(s).)

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge-Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space., (© 2024. The Author(s).)

Bit is the most basic unit of a digital image in the spatial domain, and bit-level encryption is regarded as an important technical means for digital image privacy protection. To address the vulnerability of image privacy protection to cryptographic attacks, in this paper, a bit-level image privacy protection scheme using Zigzag and chain-diffusion is proposed. The scheme uses a combination of Zigzag interleaving scrambling with chaotic sequences and chain-diffusion method images are encrypted at each bit level, while using non-sequential encryption to achieve efficient and secure encryption. To balance security and efficiency, the encryption strategy for each bit layer is weighted. The chaos-based sequences used for encryption depend on the previous hash value, thus the effect of chain-diffusion is achieved. To further enhance the encryption effect, a non-sequential encryption technique by non-linearly rearranging the bit cipher image is employed, so that the attacker cannot crack the protection scheme by analyzing the encrypted image. The ciphertext image hidden by discrete wavelet transform (DWT) also provides efficient encryption, higher level of security and robustness to attacks. This technology provides indistinguishable secret data embedding, making it difficult for attackers to detect or extract hidden information. Experimental results show that this scheme can effectively protect the confidentiality of the image and can resist various common cryptographic attacks. The scheme proposed in this paper is a preferred digital image privacy protection technology, so it has broad application prospects in image secure transmission occasions., (© 2024. The Author(s).)

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era., (© 2023. The Author(s).)

Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R 2 ), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions., (© 2023. The Author(s).)

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible., (© 2023. The Author(s).)

The Internet of Things (IoT) involves the gathering of all those devices that connect to the Internet with the purpose of collecting and sharing data. The application of IoT in the different sectors, including health, industry has also picked up the threads to augment over the past few years. The IoT and, by integrity, the IIoT, are found to be highly susceptible to different types of threats and attacks owing to the networks nature that in turn leads to even poor outcomes (i.e., increasing error rate). Hence, it is critical to design attack detection systems that can provide the security of IIoT networks. To overcome this research work of IIoT attack detection in large amount of evolutions is failed to determine the certain attacks resulting in a minimum detection performance, reinforcement learning-based attack detection method called sliding principal component and dynamic reward reinforcement learning (SPC-DRRL) for detecting various IIoT network attacks is introduced. In the first stage of this research methodology, preprocessing of raw TON_IoT dataset is performed by employing min-max normalization scaling function to obtain normalized values with same scale. Next, with the processed sample data as output, to extract data from multi-sources (i.e., different service profiles from the dataset), a robust log likelihood sliding principal component-based feature extraction algorithm is applied with an arbitrary size sliding window to extract computationally-efficient features. Finally, dynamic reward reinforcement learning-based IIoT attack detection model is presented to control the error rate involved in the design. Here, with the design of dynamic reward function and introducing incident repository that not only generates the reward function in an arbitrary fashion but also stores the action results in the incident repository for the next training, therefore reducing the attack detection error rate. Moreover, an IIoT attack detection system based on SPC-DRRL is constructed. Finally, we verify the algorithm on the ToN_IoT dataset of University of New South Wales Australia. The experimental results show that the IIoT attack detection time and overhead along with the error rate are reduced considerably with higher accuracy than that of traditional reinforcement learning methods., (© 2023. The Author(s).)

Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations' variations, supported by genotypes' principal component analysis. Results show that the Egyptian's reference metabolic genes were clustered together with the Europeans', Ad-Mixed Americans', and South-Asians'. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations., (© 2023. The Author(s).)

Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation., (© 2023. The Author(s).)

Alzheimer's, Parkinson's, and Huntington's are the most common neurodegenerative diseases that are incurable and affect the elderly population. Discovery of effective treatments for these diseases is often difficult, expensive, and serendipitous. Previous comparative studies on different model organisms have revealed that most animals share similar cellular and molecular characteristics. The meta-SNP tool includes four different integrated tools (SIFT, PANTHER, SNAP, and PhD-SNP) was used to identify non synonymous single nucleotide polymorphism (nsSNPs). Prediction of nsSNPs was conducted on three representative proteins for Alzheimer's, Parkinson's, and Huntington's diseases; APPl in Drosophila melanogaster, LRRK1 in Aedes aegypti, and VCPl in Tribolium castaneum. With the possibility of using insect models to investigate neurodegenerative diseases. We conclude from the protein comparative analysis between different insect models and nsSNP analyses that D. melanogaster is the best model for Alzheimer's representing five nsSNPs of the 21 suggested mutations in the APPl protein. Aedes aegypti is the best model for Parkinson's representing three nsSNPs in the LRRK1 protein. Tribolium castaneum is the best model for Huntington's disease representing 13 SNPs of 37 suggested mutations in the VCPl protein. This study aimed to improve human neural health by identifying the best insect to model Alzheimer's, Parkinson's, and Huntington's., (© 2023. The Author(s).)

Diabetes retinopathy (DR) is one of the leading causes of blindness globally. Early detection of this condition is essential for preventing patients' loss of eyesight caused by diabetes mellitus being untreated for an extended period. This paper proposes the design of an augmented bioinspired multidomain feature extraction and selection model for diabetic retinopathy severity estimation using an ensemble learning process. The proposed approach initiates by identifying DR severity levels from retinal images that segment the optical disc, macula, blood vessels, exudates, and hemorrhages using an adaptive thresholding process. Once the images are segmented, multidomain features are extracted from the retinal images, including frequency, entropy, cosine, gabor, and wavelet components. These data were fed into a novel Modified Moth Flame Optimization-based feature selection method that assisted in optimal feature selection. Finally, an ensemble model using various ML (machine learning) algorithms, which included Naive Bayes, K-Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forests, and Logistic Regression were used to identify the various severity complications of DR. The experiments on different openly accessible data sources have shown that the proposed method outperformed conventional methods and achieved an Accuracy of 96.5% in identifying DR severity levels., (© 2023. The Author(s).)

Deep brain stimulation (DBS) in thalamic reticular nucleus (TRN) neuron provides a novel treatment for drug-resistant epilepsy via the induced electrical field (EFs). However, the mechanisms underlying EF effects remain unclear. This paper investigated how EFs regulate low-threshold dendritic Ca 2+ (dCa) response and thus contribute to the input-output relationship of TRN cell. Our results showed that EFs modulate firing modes differently in a neuronal state-dependent manner. At the depolarized state, EFs only regulate the spike timing of a somatic stimulus-evoked single action potential (AP) with less contribution in the regulation of dCa response but could induce the transition between a dendritic stimulus-evoked single AP and a tonic burst of APs via the moderate regulation of dCa response. At the hyperpolarized state, EFs have significant effects on the dCa response, which modulate the large dCa response-dependent burst discharge and even cause a transition from this type of burst discharge to a single AP with less dCa response. Moreover, EF effects on stimulation threshold of somatic spiking prominently depend on EF-regulated dCa responses and the onset time differences between the stimulus and EF give rise to the distinct effect in the EF regulation of dCa responses. Finally, the larger neuronal axial resistance tends to result in the dendritic stimulus-evoked dCa response independent of somatic state. Interestingly, in this case, the EF application could reproduce the similar somatic state-dependent dCa response to dendritic stimulus which occurs in the case of lower axial resistance. These results suggest that the influence of EF on neuronal activities depends on neuronal intrinsic properties, which provides insight into understanding how DBS in TRN neuron modulates epilepsy from the point of view of biophysics., (© 2023. Springer Nature Limited.)

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings., (© 2023. Springer Nature Limited.)

Thua Nao is a Thai traditional fermented soybean food and low-cost protein supplement. This study aimed to evaluate the bacterial community in Thua Nao from northern Thailand and assess potentially active short-chain fatty acids (SCFAs)-related bacteria. Sixty-five Thua Nao consisting of 30 wet and 35 dried samples were collected from six provinces: Chiang Rai, Chiang Mai, Mae Hong Son, Lampang, Lamphun, and Phayao. Bacterial diversity was significantly higher in the wet samples than in the dried samples. The dominant phyla were Firmicutes (92.7%), Proteobacteria (6.7%), Actinobacteriota (0.42%), and Bacteroidota (0.26%). The genus Bacillus (67%) was the most represented in all samples. Lactobacillus, Enterococcus, and Globicatella were enriched in the wet samples. Assessment of the SCFA-microbiota relationships revealed that high butyrate and propionate concentrations were associated with an increased Clostridiales abundance, and high acetate concentrations were associated with an increased Weissella abundance. Wet products contained more SCFAs, including acetate (P = 2.8e-08), propionate (P = 0.0044), butyrate (P = 0.0021), and isovalerate (P = 0.017), than the dried products. These results provide insight into SCFA-microbiota associations in Thua Nao, which may enable the development of starter cultures for SCFA-enriched Thua Nao production., (© 2023. The Author(s).)

Antibiotic resistance is one of the biggest threats to global health resulting in an increasing number of people suffering from severe illnesses or dying due to infections that were once easily curable with antibiotics. Pseudomonas aeruginosa is a major pathogen that has rapidly developed antibiotic resistance and WHO has categorised this pathogen under the critical list. DNA aptamers can act as a potential candidate for novel antimicrobial agents. In this study, we demonstrated that an existing aptamer is able to affect the growth of P. aeruginosa. A computational screen for aptamers that could bind to a well-conserved and essential outer membrane protein, BamA in Gram-negative bacteria was conducted. Molecular docking of about 100 functional DNA aptamers with BamA protein was performed via both local and global docking approaches. Additionally, genetic algorithm analysis was carried out to rank the aptamers based on their binding affinity. The top hits of aptamers with good binding to BamA protein were synthesised to investigate their in vitro antibacterial activity. Among all aptamers, Apt31, which is known to bind to an antitumor, Daunomycin, exhibited the highest HADDOCK score and resulted in a significant (p < 0.05) reduction in P. aeruginosa growth. Apt31 also induced membrane disruption that resulted in DNA leakage. Hence, computational screening may result in the identification of aptamers that bind to the desired active site with high affinity., (© 2023. The Author(s).)

The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models., (© 2023. The Author(s).)

To determine the path of disease in different types of networks, a new method based on compressive sensing is proposed for identifying the disease propagation paths in two-layer networks. If a limited amount of data from network nodes is collected, according to the principle of compressive sensing, it is feasible to accurately identify the path of disease propagation in a multilayer network. Experimental results show that the method can be applied to various networks, such as scale-free networks, small-world networks, and random networks. The impact of network density on identification accuracy is explored. The method could be used to aid in the prevention of disease spread., (© 2023. The Author(s).)

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi's fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders., (© 2023. The Author(s).)

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare  applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network., (© 2023. The Author(s).)

Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1-6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies., (© 2023. The Author(s).)

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it's important to assess software maintainability. Many software maintainability prediction models' compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms., (© 2022. The Author(s).)

Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model-convolutional neural network (CNN), however, cannot be used with such kind of tabular clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors-apart from the time series data-cannot be used with CNN, but with other classification models such as Recurrent Neural Networks for tabular data or Random Forest. We have proposed three novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image tabular data to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset., (© 2022. The Author(s).)

Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners., (© 2022. The Author(s).)

Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs' CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99., (© 2022. The Author(s).)

Sleep research often relies on large retrospective clinical datasets. However, as the data is usually stored in proprietary formats specific for each sleep software, the raw data cannot be easily accessed and analyzed with external tools. While the raw data can usually be exported to more common data formats, this is often a cumbersome and labor-intensive task as it is not required for clinical purposes. Additionally, the recordings often include sensitive patient information which must be removed before the data can be shared or analyzed externally. This anonymization can be difficult to perform manually without the correct tools or knowledge of the file types and how they internally store the data. The STAR sleep recording export software provides a simple tool that can be used to perform the sleep study exports automatically. This allows the user to easily export a batch of sleep studies with minimal effort. In addition, the software can also be used to automatically anonymize the exported sleep recordings allowing researchers to save time and personnel resources as these do not need to be allocated for exporting and anonymizing sleep studies. The software supports Noxturnal, RemLogic, Profusion PSG and Sleepware G3 and it is free and openly available for anyone to download and use., (© 2022. The Author(s).)

Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls (n = 14,927) from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables (loneliness/social isolation and depression) and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35-53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls., (© 2022. The Author(s).)

Our sense of time is fallible, often resulting in the sensation of time flying by quickly or dragging slowly. It has been suggested that changes in sympathetic (SNS) and parasympathetic nervous system (PNS) activity may influence the perceived passage of time, however this proposition has never been tested during real-world temporal experience. The current study directly tested the relationship between the passage of time and SNS-PNS activity in the real-world. Sixty-seven participants completed a normal day's activities whilst wearing sensors to capture electrocardiography (ECG), electrodermal activity (EDA) and movement. They also provided hourly rating of the subjective speed at which time was passing. Results revealed that greater SNS activity (e.g., increased heart rate, frequency of phasic skin conductance response) was associated with time passing more quickly. PNS activity was not related to time experience. Whilst the findings support previous suggestions that changes in physiological arousal are associated with distortions to the passage of time, the effects are small and other factors are likely to contribute to real-world temporal experience., (© 2022. The Author(s).)

In this work, we demonstrate an effective anion capturing in an aqueous medium using a highly porous carbon paper decorated with ZnO nanorods. A sol-gel technique was first employed to form a thin and compact seed layer of ZnO nanoparticles on the dense network of carbon fibers in the carbon paper. Subsequently, ZnO nanorods were successfully grown on the pre-seeded carbon papers using inexpensive chemical bath deposition. The prepared porous electrodes were electrochemically investigated for improved charge storage and stability under long-term operational conditions. The results show effective capacitive deionization with a maximum areal capacitance of 2 mF/cm 2 , an energy consumption of 50 kJ per mole of chlorine ions, and an excellent long-term stability of the fabricated C-ZnO electrodes. The experimental results are supported by COMSOL simulations. Besides the demonstrated capacitive desalination application, our results can directly be used to realize suitable electrodes for energy storage in supercapacitors., (© 2022. The Author(s).)

An improved interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering the risk preference of decision-makers is proposed to solve the multi-attribute group decision-making problem with interval-valued intuitionistic fuzzy numbers and the condition that the attribute weight information is completely unknown. Firstly, the decision-maker weight of each attribute is determined by combining similarity and proximity. In order to consider the influence of the decision-maker's risk preference on the decision result and avoid the asymptotic behavior of interval-valued intuitionistic fuzzy matrix, the risk aversion coefficient of the decision-maker is introduced and combined with the determined decision-maker's weight aggregation to form a group decision matrix. Then, the information of group decision matrix is mined, and the interval-valued intuitionistic fuzzy entropy is used to determine the attribute weight and relative weight. Based on the interval-valued intuitionistic fuzzy distance measure formula and the TODIM method, the overall superiority of each scheme relative to other schemes is obtained by calculating the superiority between schemes, and the optimal scheme is determined by comparing and sequencing. Finally, the rationality and effectiveness of the proposed method are verified by an example of mechanical assembly supplier selection decision., (© 2022. The Author(s).)