bluuebunny/crossref_metadata_2025 huggingface dataset:
go4cognition: Evaluation of a Newly Developed Multicomponent Intervention in Mild Cognitive Impairment
Authors: Lissek Vanessa J. , Orth Stefan , Suchan Boris
Published: April 2024

Background: Cognitive training and physical exercise show positive effects on cognitive decline in subjects with mild cognitive impairment (MCI). Multimodal interventions for MCI patients, combining physical and cognitive training in a social context seem to slow down cognitive decline. Objective: Based on a previous study, a new mobile gamification tool (go4cognition; https://www.ontaris.de/go4cognition) has been developed to train cognitive and physical functions simultaneously in a group setting. It involves tasks targeting various cognitive functions (short-term memory, working memory, executive functions). The computer-based setup allows for individual performance analysis. This study evaluated the effects of this tool. Methods: 30 participants with MCI, as defined by the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) cut-off-score, aged between 66 and 89 years, trained for one hour two days a week for twelve weeks. Additionally, standard neuropsychological assessment of memory and attention was carried out before and after the intervention. Results: The go4cognition device is highly effective in improving various cognitive functions. A significant improvement in the CERAD total score resulting in re-classification of 70% of former MCI patients into non-MCI patients was found. Additionally, an improvement of verbal fluency, verbal memory, spatial memory, and attention was observed. Furthermore, the CERAD total score was significantly correlated with performance in the go4cognition tool. Conclusions: The results of the intervention support the idea of the effectiveness of a combined cognitive and motor intervention by incorporating neuropsychological paradigms in a group setting and suggest a close relation between combined cognitive and physical exercise and cognitive performance.
Self-Reported Sleep Duration and Bedtime Are Associated with Dementia Risk in Community-Dwelling People Aged 40–74 Years: The Murakami Cohort Study
Authors: Bulycheva Irina , Watanabe Yumi , Kitamura Kaori , Kabasawa Keiko , Saito Toshiko , Takahashi Akemi , Kobayashi Ryosaku , Oshiki Rieko , Takachi Ribeka , Tsugane Shoichiro , Yamazaki Osamu , Watanabe Kei , Nakamura Kazutoshi
Published: April 2024

Background: Sleep is a potentially modifiable factor associated with dementia, including Alzheimer’s disease, but current evidence supporting this is insufficient. Objective: This study aimed to determine whether sleep duration and bedtime patterns are associated with the risk of dementia among middle-aged and older people. Methods: This cohort study had an eight-year follow-up period. Participants were 13,601 community-dwelling people aged 40–74 years living in Murakami (Niigata, Japan). Data were collected using a self-administered questionnaire. Predictors were self-reported sleep duration and bedtime, and the outcome was newly-diagnosed dementia determined using the long-term care insurance database. Covariates were demographic characteristics, body mass index, smoking, alcohol consumption, total physical activity, insomnia symptoms, disease history, and either bedtime or sleep duration. Cox proportional hazard models were used to calculate hazard ratios (HRs). Results: The mean age of participants at baseline was 59.2 years. Over a mean follow-up period of 8.0 years, 319 cases of dementia were observed. A long self-reported sleep duration relative to the reference sleep duration (7 hours) was associated with increased dementia risk, with the “8 hours” group (adjusted HR = 1.30, 95% CI:0.99–1.73) and “≥9 hours” group (adjusted HR = 1.46, 95% CI:1.00–2.15) having an increased risk (marginally significant) relative to the reference group. Early bedtime was associated with increased dementia risk (adjusted p for trend = 0.0010), with the “21 : 00 or earlier” group (adjusted HR = 1.61, 95% CI:1.14–2.28) having an increased risk relative to the reference (“23 : 00”). Conclusions: A long self-reported sleep duration and early bedtime are both associated with increased dementia risk in middle-aged and older people
Serum YKL-40 as a Predictive Biomarker of Cerebral Amyloid Angiopathy-Related Intracerebral Hemorrhage Recurrence
Authors: Xu Feifan , Xu Jiajie , Wang Qiong , Gao Feng , Fu Jiayu , Yan Tingmeng , Dong Qiang , Su Ya , Cheng Xin
Published: April 2024

Background: Neuroinflammation is a major cause of secondary brain injury in intracerebral hemorrhage (ICH). To date, the prognostic value of YKL-40 (chitinase-3-like-1 protein), a biomarker of neuroinflammation, in cerebral amyloid angiopathy-related intracerebral hemorrhage (CAA-ICH) remains undiscovered. Objective: To evaluate the relationships between serum YKL-40 and CAA-ICH recurrence. Methods: Clinical and imaging information of 68 first-onset probable CAA-ICH cases and 95 controls were collected at baseline. Serum YKL-40 was measured by Luminex assay. Cox proportional hazards model was used to analyze the associations between YKL-40 level and CAA-ICH recurrence. Results: Serum YKL-40 level was significantly higher in CAA-ICH cases than healthy controls (median [interquartile range, IQR], 46.1 [19.8, 93.4] versus 24.4 [13.9, 59.0] ng/mL, p = 0.004). Higher level of YKL-40 predicted increased risk of CAA-ICH recurrence adjusted for age, ICH volume and enlarged perivascular space score (ePVS) (above versus below 115.5 ng/ml, adjusted hazard ratios 4.721, 95% confidence intervals 1.829–12.189, p = 0.001) within a median follow-up period of 2.4 years. Adding YKL-40 to a model of only MRI imaging markers including ICH volume and ePVS score improved the discriminatory power (concordance index from 0.707 to 0.772, p = 0.001) and the reclassification power (net reclassification improvement 28.4%; integrated discrimination index 11.0%). Conclusions: Serum YKL-40 level might be a candidate prognostic biomarker for CAA-ICH recurrence.
Sociodemographic and Clinical Characteristics of People Living with Dementia and Their Associations with Unmet Healthcare Needs: Insights from the Baseline Assessment of the InDePendent Study
Authors: Scharf Annelie , Kleinke Fabian , Michalowsky Bernhard , Rädke Anika , Pfitzner Stefanie , Mühlichen Franka , Buchholz Maresa , van den Berg Neeltje , Hoffmann Wolfgang
Published: April 2024

Background: The healthcare needs of People living with Dementia (PlwD) (such as Alzheimer’s disease) are often unmet. Information about the needs of community-dwelling PlwD and their association with sociodemographic and clinical characteristics is needed to fill the knowledge gap regarding factors influencing unmet needs among PlwD and to conduct a comprehensive needs assessment to develop tailored interventions. Objective: To describe sociodemographic and clinical characteristics of the InDePendent study population with particular reference to determinants of unmet needs. Methods: We analyzed baseline data of the multi-centre cluster-randomized controlled trial (InDePendent) using descriptive statistics to describe patients’ sociodemographic and clinical characteristics and Poisson regression models to predict unmet needs, separated by sex. Data were collected personally via face-to-face interviews. Results: Most of the n = 417 participating PlwD were mild to moderately cognitively impaired, were not depressed, had an average of 10.8 diagnoses, took 6.7 medications, and had, on average, 2.4 unmet needs (62% of PlwD had at least one unmet need) measured by the Camberwell Assessment of Need for the Elderly (CANE). Low social support, a high body-mass-index, a lower education, functional impairment, and worse health status were associated with more unmet needs, regardless of sex. In women, higher unmet needs were associated with more depressive symptoms, a poor financial situation, living alone and not being recently treated by a general practitioner. In males, unmet needs increased with the number of medications taken. Conclusions: PlwD had a broad array of unmet healthcare needs, indicating primary healthcare provision improvement potentials. The results underscore the significance of early assessment of patient’s clinical characteristics and unmet needs as a basis for individualized gender-sensible intervention strategies.∥ClinicalTrials.gov Identifier: NCT04741932, Registered on February 5, 2021
NB-02 Protects Neurons and Astrocytes from Oligomeric Amyloid-β-Mediated Damage
Authors: Miller Morgan R. , Lariviere Lavender , Pagnier Guillaume J. , Aygar Sema , Wieckiewicz Natalia , Maesako Masato , Bacskai Brian J. , Kastanenka Ksenia V.
Published: April 2024

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with limited therapeutic strategies. NB-02 is a novel botanical drug that has shown promise as a protective and therapeutic treatment for AD in an APP/PS1 preclinical mouse model. In this paper, we investigate the underlying mechanisms by which NB-02 provides these therapeutic advantages using in vitro neuron-astrocyte co-cultures. Pretreatment with NB-02 prevented pathological calcium elevations in neurons and astrocytes after application of toxic soluble amyloid-β (Aβ) oligomers. NB-02 also prevented cell death associated with the addition of soluble Aβ oligomers suggesting NB-02 is effective at protecting both neurons and astrocytes from Aβ-mediated damage.
Biomarkers as Endpoints in Clinical Trials for Alzheimer’s Disease
Authors: Elghanam Yomna , Purja Sujata , Kim Eun Young
Published: April 2024

Background: Alzheimer’s disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. Objective: This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. Methods: In this retrospective study, trials were collected by searching clinicaltrials.gov using the term “Alzheimer”. Primary and secondary outcomes were analyzed separately for each phase. Results: Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-β (Aβ), blood Aβ and amyloid-PET; in phase 2, they were MRI, CSF Aβ, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aβ. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). Conclusions: The growing recognition of the importance of biomarkers in AD trial’ design and drug development is evident by the significant steady increase in biomarkers’ utilization in phases 2 and 3.
Evaluating the stealth of reinforcement learning-based cyber attacks against unknown scenarios using knowledge transfer techniques
Authors: Horta Neto Antonio Jose , dos Santos Anderson Fernandes Pereira , Goldschmidt Ronaldo Ribeiro
Published: April 2024

Organizations are vulnerable to cyber attacks as they rely on computer networks and the internet for communication and data storage. While Reinforcement Learning (RL) is a widely used strategy to simulate and learn from these attacks, RL-guided offensives against unknown scenarios often lead to early exposure due to low stealth resulting from mistakes during the training phase. To address this issue, this work evaluates if the use of Knowledge Transfer Techniques (KTT), such as Transfer Learning and Imitation Learning, reduces the probability of early exposure by smoothing mistakes during training. This study developed a laboratory platform and a method to compare RL-based cyber attacks using KTT for unknown scenarios. The experiments simulated 2 unknown scenarios using 4 traditional RL algorithms and 4 KTT. In the results, although some algorithms using KTT obtained superior results, they were not so significant for stealth during the initial epochs of training. Nevertheless, experiments also revealed that throughout the entire learning cycle, Trust Region Policy Optimization (TRPO) is a promising algorithm for conducting cyber offensives based on Reinforcement Learning.
Transfer learning based cross-process fault diagnosis of industrial robots
Authors: Wang Junchi , Xiao Hong , Jiang Wenchao , Li Ping , Li Zelin , Wang Tao
Published: April 2024

In the actual industrial application of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex. Utilizing the original fault diagnosis models in new working environments correspondingly leads to a decline in the performance and the generalization capability of the model. Moreover, the monitoring data collected in new working processes often has limited or no labels, making the diagnosis models trained with this data unable to identify faults accurately. In this paper, we propose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage knowledge from existing working processes for diagnosing faults in new working processes. DCFD uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to measure the difference between the current working processes and the previous working processes, enhancing the fault diagnosis capability of the robotic system in cross-process scenarios. DCFD achieves an average fault classification accuracy of 98% on 12 types of migration tasks, which demonstrates the effectiveness of DCFD on cross-process fault diagnosis classification tasks in real-time industrial application scenarios.
Can convolutional neural networks approximate the phylogenetic tree of human populations with face images?1
Authors: Aguilar-Canto Fernando , Luján-García Juan Eduardo , Espinosa-Juárez Alberto , Calvo Hiram
Published: April 2024

Inferring phylogenetic trees in human populations is a challenging task that has traditionally relied on genetic, linguistic, and geographic data. In this study, we explore the application of Deep Learning and facial embeddings for phylogenetic tree inference based solely on facial features. We use pre-trained ConvNets as image encoders to extract facial embeddings and apply hierarchical clustering algorithms to construct phylogenetic trees. Our methodology differs from previous approaches in that it does not rely on preconstructed phylogenetic trees, allowing for an independent assessment of the potential of facial embeddings to capture relationships between populations. We have evaluated our method with a dataset of 30 ethnic classes, obtained by web scraping and manual curation. Our results indicate that facial embeddings can capture phenotypic similarities between closely related populations; however, problems arise in cases of convergent evolution, leading to misclassifications of certain ethnic groups. We compare the performance of different models and algorithms, finding that using the model with ResNet50 backbone and the face recognition module yields the best overall results. Our results show the limitations of using only facial features to accurately infer a phylogenetic tree and highlight the need to integrate additional sources of information to improve the robustness of population classification.
Breast density classification in mammograms using VGG convolutional networks
Authors: Cruz Elsy , Santos Lourdes , Calvo Hiram , Anzueto-Rios Álvaro , Villuendas-Rey Yenny
Published: April 2024

In recent years, multiple studies have highlighted the growing correlation between breast density and the risk of developing breast cancer. In this research, the performance of two convolutional neural network architectures, VGG16 and VGG19, was evaluated for breast density classification across three distinct scenarios aimed to compare the masking effect on the models performance. These scenarios encompass both binary classification (fatty and dense) and multi-class classification based on the BI-RADS categorization, utilizing a subset of the ABC-Digital Mammography Dataset. In the first experiment, focusing on cases with no masses, VGG16 achieved an accuracy of 93.33% and 90.00% for two and four-class classification. The second experiment, which involved cases with benign masses, yielded a remarkable accuracy of 95.83% and 93.33% with VGG16, respectively. In the third and last experiment, an accuracy of 88.00% was obtained using VGG16 for the two-class classification, while VGG19 delivered an accuracy of 93.33% for the four-class classification. These findings underscore the potential of deep learning models in enhancing breast density classification, with implications for breast cancer risk assessment and early detection.
BiLSTM-CNN with fixed weight approach for tracking speech articulatory features
Authors: Pillai Leena G. , Muhammad Noorul Mubarak D. , Sherly Elizabeth
Published: April 2024

Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications.
Single image dehazing using attention layers in convolutional neural networks
Authors: Rao Vishisht Srihari , Vinay P. , Uma D.
Published: April 2024

A hazy image is characterized by atmospheric conditions that reduce the image’s clarity and contrast, thereby making it less visible. This degradation in image quality can hinder the performance of advanced computer vision tasks such as object detection and identifying open spaces which need to perform with high accuracy in important real world applications such as security surveillance and autonomous driving. In the recent past, the use of deep learning in image processing tasks have shown a remarkable improvement in performance, in particular, Convolutional Neural Networks (CNNs) perform superior to any other type of neural network in image related tasks. In this paper, we propose the addition of Channel Attention and Pixel Attention layers to four state-of-the-art CNNs, namely, GMAN, U-Net, 123-CEDH and DMPHN, used for the task of image dehazing. We show that the addition of these layers yields a non-trivial improvement on the quality of the dehazed images which we show qualitatively with examples and quantitatively by obtaining PSNR and SSIM scores of 28.63 and 0.959 respectively. Through the experiments, we show that the addition of the mentioned attention layers to the GMAN architecture yields the best results.
Hybrid Machine Learning Approach for Early Diagnosis of Polycystic Ovary Syndrome with Stable Features
Authors: Reka S , Karthik Sainadh Reddy Dwarampudi , Dhiraj Inti , Suriya Praba T
Published: April 2024

Polycystic Ovary Syndrome (PCOS) is a hormonal condition that typically affects female during the time of their reproduction. It is identified by the disruptions in hormonal balance, particularly an increase in levels of androgen (male hormone) in the female body. PCOS can lead to various symptoms and health complications including irregular menstrual cycles, ovarian cysts, fertility issues, insulin resistance, weight gain, acne, and excess hair growth. The real-world PCOS detection is a challenging task whilst PCOS specific cause is unknown and its symptoms are unclear. Thus, accurate and timely diagnosis of PCOS is crucial for effective management and prevention of long-term complications. In such cases, Machine learning based PCOS prediction model support diagnostic process, address potential errors and time constraints. Machine learning algorithms can analyze large set of patient data, including medical history, hormonal profiles, and imaging results, to assist in the diagnosis of PCOS. In particular, the performance of data analysis chore and prediction model is improved by ensemble feature selection strategies. These methods concentrate on selecting a subset of pertinent features from a broader range of features. The unstable nature of the outcome of feature selection algorithm is a frequent issue in practical applications, when it is applied multiple times on similar dataset or with slight modifications in the data. Thus, evaluating the robustness of feature selection algorithm is most important. To address these issues and quantify the robustness, this study uses Jenson-Shannon divergence, an information theoretic approach with ensemble feature selection method to handle the various findings, such as complete ranking, half ranking and top-k lists (without ranking). Furthermore, this article proposes a hybrid machine learning classifier with SMOTE – SVM for the prompt detection of PCOS and the performance of the model is compared with a number of other individual classifiers including KNN (K-Nearest Neighbour), Support Vector Machine (SVM), AdaBoost, LR –Logistic Regression, NB –Nave Bayes, RF –Random Forest, Decision Tree. The proposed SWISS-AdaBoost classifier surpassed other models with 97.81% of accuracy and AUC of 99.08%.
Mitigating filter bubbles: Diverse and explainable recommender systems
Authors: Tahir Kidwai Umar , Akhtar Nadeem , Nadeem Mohammad , Alroobaea Roobaea Salim
Published: April 2024

In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play.
GeoCovaxTweets: A global analysis of COVID-19 vaccines and vaccination discourse on social media
Authors: Singh Pardeep , Lamsal Rabindra , Singh Monika , Shishodia Bhawna , Sitaula Chiranjibi , Chand Satish
Published: April 2024

Social media platforms play a crucial role in providing valuable information during crises, such as pandemics. The COVID-19 pandemic has created a global public health crisis, and vaccines are the key preventive measure for achieving herd immunity. However, some individuals use social media to oppose vaccines, undermining government efforts to eliminate the virus. This study introduces the “GeoCovaxTweets” dataset, consisting of 1.8 million geotagged tweets related to COVID-19 vaccines from January 2020 to November 2022, originating from 233 countries and territories. Each tweet includes state and country information, enabling researchers to analyze global spatial and temporal patterns. An extensive set of analyses are performed on the dataset to identify prominent topic clusters and explore public opinions across different vaccines and vaccination contexts. The study outlines the dataset curation methodology and provides instructions for local reproduction. We anticipate that the dataset will be valuable for crisis computing researchers, facilitating the exploration of Twitter conversations surrounding COVID-19 vaccines and vaccination, including trends, opinion shifts, misinformation, and anti-vaccination campaigns.
Joint computational and optimized resource allocation in edge computing networks
Authors: Bukya Hanumanthu , Bhukya Raghuram , Harshavardhan A.
Published: April 2024

Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined.
RETRACTED: Application of an adaptive genetic algorithm and machine learning in an English text analysis teaching system
Authors: Lu Yang , Liu Fengjun , Cao Bin
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Improved deep learning intrusion detection model based on GAN data enhancement
Authors: Shuangyuan Li , Qichang Li , Mengfan Li , Yanchang Lv
Published: April 2024

With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security.
Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model
Authors: Immanuel Rajeswari Rajesh , Sangeetha S.K.B.
Published: April 2024

Human emotions are the mind’s responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application.
Measurement of body size parameters and body weight prediction in beef cattle based on image analysis
Authors: Zheng Z. , Gao J.B. , Weng Z.
Published: April 2024

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing.
Outlier-resistant non-fragile observer-based fuzzy boundary control for interconnected nonlinear PDE systems
Authors: Zheng Danjing , Song Xiaona , Song Shuai , Peng Zenglong
Published: April 2024

This paper investigates an observer-based boundary controller design for interconnected nonlinear partial differential equation (PDE) systems. First, the Takagi–Sugeno (T–S) fuzzy model is adopted to accurately describe the target systems. Then, boundary measurements are employed to reduce the number of sensors. Next, considering the phenomenon of abnormal interference that may lead to measurement outliers and observer parameters’ uncertainties, an outlier-resistant non-fragile observer expressed by a saturation function is designed to guarantee the desired control objectives. Moreover, the boundary control approach is employed to trade-off the cost of system design and system performance. Furthermore, utilizing the membership function-dependent Lyapunov functions and free-weight matrixes, sufficient conditions ensuring the closed-loop systems’ exponential stability are obtained while decreasing the conservativeness of the system stability analysis. Finally, the proposed method’s feasibility and effectiveness are validated by an example.
Multi-model of ammonia nitrogen in aquaculture water based on EM algorithm1
Authors: Wang Wei , Xu Dehao , Lv Jing , Rong Jian , He Donggang , Li Shuangshuang
Published: April 2024

The factors of water quality in the intensive marine stichopus japonicus aquaculture process are changing with seasons, so water temperature, salinity, pH value and nitrite were selected as auxiliary variables to measure the concentration of ammonia nitrogen. FCM (Fuzzy C-means) algorithm was adopted to classify them. Based on the EM (Expectation Maximization) algorithm, fuzzy sub-models of ammonia nitrogen concentration were constructed around each operating point, and finally the fuzzy sub-models were combined according to the posterior distribution of the characteristics of the sampling data. Based on the data collected at Xinyulong Marine Biological Seed Technology Co., Ltd, in Dalian China, the ammonia nitrogen concentration prediction model was tested and verified.
Dual rectangular fuzzy complex matrix equations: extended solution, algebraic solution, solution and their calculating1
Authors: Gong Zengtai , Zhang Yuanyuan
Published: April 2024

In this paper, we focus on generalized fuzzy complex numbers and propose a straightforward matrix method to solve the dual rectangular fuzzy complex matrix equations C · Z ˜ + L ˜ = R · Z ˜ + W ˜ , in which C and R are crisp complex matrices and Z ˜ , L ˜ and M ˜ are fuzzy complex number matrices. The existing methods for solving fuzzy complex matrix equations involve separately calculating the extended solution and the corresponding parameters of the real and imaginary parts, whereby we obtain the algebraic solution of the equations. By means of the interval arithmetic and embedding approach, the n × n dual rectangular fuzzy complex linear systems could be converted into 2n × 2n fuzzy linear systems, which are also equivalent to the 4n × 4n real linear systems. By directly solving the 4n × 4n real linear systems, the algebraic solutions can be obtained. The general dual rectangular fuzzy complex matrix equations and dual rectangular fuzzy complex linear systems are investigated by the generalized inverses of matrices. Finally, some examples are given to illustrate the effectiveness of method.
Increased knee joint circumference after total knee arthroplasty, should we care?
Authors: Mazy David , Manche Eric , Geulette Bernard , Beckers Gautier
Published: April 2024

BACKGROUND: Knee swelling is common after total knee arthroplasty (TKA). However, there is limited literature on its clinical implications. OBJECTIVE: The study aimed to determine whether a correlation exists between increased postoperative knee circumference, pain, functional score and range of motion. METHOD: Between November 2019 and June 2020, 114 patients were retrospectively enrolled in this study. Primary endpoints included the correlation between knee circumference change (KCC, and 1) postoperative pain 2) Oxford Knee Score, and 3) postoperative range of motion. These data were collected on day 2 and at 6 months postoperatively. RESULTS: The mean knee circumference (KC) increase on postoperative day 2 was 9.27%. No statistically significant correlation was found between KCC and pain (p = 0.853), KCC and patient-controlled analgesia (PCA) consumption (p = 0.265), KCC and knee flexion (p = 0.729) and extension (p = 0.157). At 6 months postoperatively, no statistically significant correlation was found between KCC and pain (p = 0.909), and Oxford Knee score (p = 0.137). However a statistically significant correlation was observed between increased KC of more than 10% and extension deficit at 6 month postoperatively (p = 0.012), as well as between KCC on day 2- and at 6 months postoperatively. CONCLUSION: After TKA the average increase in KC is around 10%. Increased postoperative knee effusion is neither an indicator for increased pain nor for unfavorable functional scores. At 6 months postoperatively, a correlation exists between increased KC and extension deficit for knees with more than 10% KCC. This study addresses common patient inquiries to physiotherapists regarding knee swelling following TKA.
Detection method for unrecognized spatial disorientation based on optical flow stimuli
Authors: Hao Chenru , Su Rui , Dong Chunnan , Zhang Jingjing , Chi Ziqiang , Meng Fanzhen , Zhao Ruibin , Wu Yanru , Wang Linlin , Li Pengfei , Chen Chengwei , Lian Qingjie , Cheng Li
Published: April 2024

BACKGROUND: Flight accidents caused by spatial disorientation (SD) greatly affect flight safety. OBJECTIVE: Few studies have been devoted to the evaluation of SD. METHODS: 10 pilots and 10 non-pilots were recruited for the experimental induction of SD. Videos for giving optical flow stimuli were played at two different flow speeds to induce SD. Subjective judgment and center of foot pressure (CoP) data were collected from the tests. The data were combined to determine the occurrence of SD and analyze the SD types. RESULTS: The number of self-reported SD events was slightly smaller in the pilots than in the non-pilots. The average upper bound of the confidence interval for the standard deviation of CoP was 0.32 ± 0.09 cm and 0.38 ± 0.12 cm in the pilots and non-pilots, respectively. This indicator was significantly lower in the pilots than in the non-pilots (P= 0.03). The success rate of the experimental induction of unrecognized SD was 26.7% and 45.0% in the pilots and non-pilots, respectively. CONCLUSION: The method offered a new to analyze unrecognized SD. We could determine the occurrence unrecognized SD. This is an essential means of reducing flight accidents caused by unrecognized SD.
Pedogenic Characterization of Alluvium-Derived Soils in the Arid Region of Rajasthan, India
Authors: Joshi Dinesh Chandra
Published: April 2024

South eastern part of the arid region of Rajasthan (India) is an alluvial plain developed from the sediments deposited by the river Luni and its tributaries, arising from the Aravali hill range. Soils of the region are medium to fine textured. Mean annual rainfall ranges between 350-400 mm, the soil temperature regime is hyperthermic, and the moisture regime is aridic, which restricts pedogenic processes. The objective of the study is to characterize the uniformity of the parent material and illuviation process. Extensive field work for the study of morphological characteristics of soil profiles revealed two major types of soils, viz., medium texture non-calcareous and fine texture calcareous. Sand fractionation and related parameters were worked out to ascertain the uniformity of the parent material. In medium texture non-calcareous profiles, medium sand (1-2 Φ) and fine sand (2-3 Φ) are dominant fractions, while in fine texture calcareous alluvium, the major particle sizes are fine sand (Φ 2-3), very fine sand (3-4 Φ), and coarse silt (Φ 4-5). The sorting coefficient in medium texture (1.49 to 1.61) and fine textured (1.41 to 1.81) soils indicated the well-sorted nature of the sediments (values less than 2.5). In both medium and fine texture soils, the depth-wise uniform distribution of all size sand fractions and the sorting coefficient indicated the uniformity of the parent material. Parameters such as clay/sand ratio, HA-carbon/FA-carbon ratio, CEC, and CaCO3, considered together, indicated that in medium texture non-calcareous soils, the clay and humus fractions have moved up to 80 cm depth, whereas in fine texture calcareous soils, the clay, humus fractions, and CaCO3 have moved up to 60 cm depth. These observations suggest that both medium and fine texture soils have undergone the illuviation process, but the process is more pronounced in fine texture calcareous soils.
Lymphoma: Potential Viral Antagonism between HTLV-1 and JCV Associated with Increased Survival Time
Authors: Tomasović Ante
Published: April 2024

This paper points to a potential antagonism between Human T-lymphotropic virus 1 (HTLV-1) and John Cunningham virus (JCV). Similarities between HTLV1 protease and JCV capsid protein, when compared in silico, offer a possibility of an interaction between the two viral proteins which could influence lymphoma survival rates and the risk of developing this disease.
Angle Trisection with Growth Rate of The Golden Ratio
Authors: Mousavi Seyed Kazem , Mansouri Hossein , Mousavi Seyed Mehdi
Published: April 2024

In this paper, we explore the triangulation of angles using the golden ratio and geometric compass principles. By dividing angles into three equal parts based on the golden ratio, we demonstrate a method for solving this geometric problem. Furthermore, we extend our exploration into the third dimension, where a geometric compass can effectively divide a two-dimensional angle into three equal parts. Additionally, our investigation delves into the realm of six-dimensional space-time, where we observe the simultaneous movement of two arms of the compasses based on the golden ratio to achieve the division of every angle into three equal parts. We emphasize the significance of the growth rate associated with the golden ratio in resolving complex mathematical and physical problems. This study provides valuable insights into the application of geometric principles and the golden ratio in solving challenging problems within mathematics and physics.
Ansiedad social en la adolescencia: un estudio transnacional entre Francia y España
Authors: Delgado Domenech Beatriz Beatriz , Torrecillas Martinez Manuel , Martínez Monteagudo María Carmen , Navarro Soria Ignasi
Published: April 2024

La adolescencia es una etapa que supone nuevas demandas de interacción social que llevan a algunos jóvenes a experimentar ansiedad social y limitar su desarrollo. Este estudio transnacional surge como necesidad de entender si existen diferencias culturales en la prevalencia de la ansiedad social en los adolescentes. El estudio tiene como objetivo analizar las manifestaciones de la ansiedad social en adolescentes franceses y españoles teniendo en cuenta el sexo. Para ello, se administró la Escala de Ansiedad Social para Adolescentes (SAS-A) a 2.533 estudiantes de Educación Secundaria españoles y franceses de 11 a 18 años (Medad = 14,36, DT = 1,65 España; Medad = 14,45, DT = 1,88 Francia). Se encontró una prevalencia de ansiedad social del 25,5 % en los adolescentes franceses y del 29,7% en los españoles. Los adolescentes franceses manifestaban más miedo a la evaluación negativa, mientras que los españoles evitaban más situaciones sociales, si bien las diferencias variaban en función del sexo. Así, las chicas españolas, en comparación con las chicas francesas, mostraban menos miedo a la evaluación negativa y más evitación social y angustia en general, y los chicos españoles, respecto a los chicos franceses, presentaban más malestar y evitación ante situaciones sociales. Los resultados resaltan la importancia de abordar la ansiedad social en los adolescentes, teniendo en cuenta los aspectos diferenciales por cultura y sexo, para diseñar intervenciones eficaces que desarrollen las habilidades sociales y promuevan el bienestar emocional.
PrescripTech : Enhancing Healthcare Access through Generic Prescription Conversion
Authors: Jadhav Shubham , Shetty Rakesh , Gujare Manoj , Prof. D. D. Sarpate
Published: April 2024

PrescripTech introduces a pioneering approach to healthcare access and affordability by converting brand-name conventions into original general druthers. Using sophisticated algorithms and integration with electronic health records (EHR), PrescripTech seamlessly analyzes patient data to recommend cost-effective drug options. This innovative platform addresses the pressing issue of drug affordability by promoting the use of generics, which offer similar efficacy at significantly lower costs. By easing cost-conscious prescribing practices, PrescripTech not only reduces fiscal burdens on cases but also contributes to the sustainability of healthcare systems worldwide. Also, PrescripTech has the implicit to profoundly impact healthcare access, particularly for underserved populations. By lowering drug costs, it ensures that essential treatments are accessible to individualities anyhow of their socioeconomic status. This democratization of healthcare aligns with broader pretensions of health equity and social justice, fostering positive issues for communities encyclopaedically. As a lamp of healthcare invention, PrescripTech exemplifies the transformative power of technology in optimizing healthcare delivery and advancing the principles of affordability and availability.