bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative effectiveness of manual intermittent cervical longitudinal mobilization versus pain modalities in cervical radiculopathy: A double blinded randomized controlled study
Authors: Deen Nazar , Akhtar Saeed , Qazi Fawad Hafeez , Uzair Rahila , Khan Mohammad , Tasneem Sharjeel
Published: December 2023

BACKGROUND: Cervical radiculopathy is a common condition encountered in clinical practice and is characterized by compression or irritation of the nerve roots in the cervical spine. OBJECTIVE: To compare the effectiveness of manual intermittent cervical longitudinal mobilization (mICLM) and therapeutic modalities in managing acute, sub-acute and chronic radiculopathy pain. METHODS: This was a multicenter, double-blinded, randomized controlled trial. Individuals with radiculopathies were randomized into interventional; (IG, n= 18) receiving mICLM and control group; (CG, n= 17), receiving Microwave Diathermy (MWD) and transcutaneous electrical nerve stimulation (TENS) at the cervico-dorsal area daily for two weeks. The neck pain disability index (NPDI), numeric pain rating scale (NPRS), and global rating of change score (GRoC) were used as outcome measures. Statistical analysis was conducted using SPSS version 23.0. RESULTS: Both mICLM and MWD with TENS were equally effective in reducing the pain and disability in either group. However, it was noted that mICLM had a better impact on acute, sub-acute and chronic cases of radiculopathy pain comparatively. Mean age was 42.3 (SD =± 10.9) years. 54.3% were between the age group of 40–60 years out of which 51.4% were male and 48.6% were female. 51.4% were in IG and 48.6% were in CG. The mean comparison of NPAD and NPRS within groups was significant with p< 0.01*. Post treatment mean comparison of NPAD outcomes in IG and CG remain significant with p= 0.004* and p< 0.01* respectively. The post NPAD and NPRS between IG and CG were found statistically insignificant with p= 0.75 and p= 0.57 respectively. CONCLUSION: mICLM and therapeutic modalities showed similar outcomes in managing radiculopathy pain. The study highlights the need for further research to understand the effectiveness of these interventions in larger patient populations.
Experience from a single-center study on multimodal medication therapy for patients with complex regional pain syndrome
Authors: Park Donghwi , Choi Jin-Woo , Chang Min Cheol
Published: December 2023

BACKGROUND: Despite the application of various therapeutic methods, pain caused by complex regional pain syndrome (CRPS) is not sufficiently managed and often progresses to a chronic stage. For the systematic and effective treatment of CRPS, we developed an algorithm for multimodal medication therapy based on the established pathophysiology of CRPS to control CRPS-related pain. OBJECTIVE: In this study, we present the outcomes of our novel algorithm for multimodal medication therapy for patients with CRPS, consisting of three major components: multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapy. METHODS: We retrospectively investigated patients with CRPS who received multimodal therapy. Pain severity scores were evaluated using a numerical rating scale at four time points (P1, pain at initial consultation; P2, pain after oral medication; P3, pain after ketamine treatment; and P4, pain after lidocaine treatment). The effect of the multimodal medication therapy algorithm on pain management was evaluated at each time point. RESULTS: In patients with CRPS, multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapies led to significantly improved pain control (p< 0.05). Additionally, the combination of these three therapies (through the multimodal medication therapy algorithm) resulted in significant pain relief in patients with CRPS (p< 0.05). CONCLUSIONS: Our multimodal medication therapy algorithm effectively controlled pain in patients with CRPS. However, further prospective studies with large sample sizes and randomized controlled trials are needed for more accurate generalization.
Intraoperative pain prediction of percutaneous kyphoplasty under local anesthesia by preoperative experimental pain assessment
Authors: Zhong Xiqiang , Shen Guangjie , Qian Yunfan , Li Yimin , Tang Chengxuan , Tang Xiaojun , He Shaoqi
Published: December 2023

BACKGROUND: Percutaneous kyphoplasty (PKP) is the preferred treatment for osteoporotic vertebral compression fractures (OVCF) Currently, the preoperative anesthesia methods for PKP are mainly local anesthesia and tracheal intubation general anesthesia. OBJECTIVE: To assess whether patient sensitivity to pain measured preoperatively could predict the patients’ pain response during PKP treatment under local anesthesia, to facilitate the development of an optimal preoperative anesthesia plan for patients. METHODS: Fifty-five female patients diagnosed with osteoporotic single vertebral fracture who were treated with PKP under local anesthesia were selected. The patients’ pain sensitivities, including pain threshold and pain tolerance threshold, were evaluated with a pain test device on the day before the operation in the ward. Heart rate (HR), mean arterial pressure (MAP), and blood oxygen saturation (SpO2) were recorded before anesthesia, post-anesthesia, after needle puncture, and after balloon dilatation. At the same time, blood was drawn at the above time points to determine the level of norepinephrine (NA) as an indicator of intraoperative pain stress response. The numerical rating scale (NRS) during surgery was recorded at the end of the surgery. RESULTS: The preoperative pain tolerance threshold of 55 surgical patients was correlated with the intraoperative NRS score (r=-0.768, P< 0.001), as well as with the preoperative and intraoperative changes in HR (r=-0.791, P< 0.001), MAP (r=-0.819, P< 0.001), and NA (r=-0.553, P< 0.001). Thus, the lower the preoperative pain tolerance threshold, the more severe the patient’s response to pain during PKP treatment under local anesthesia, and the greater the hemodynamic changes. Consequently, the intraoperative experience becomes worse. However, there was no correlation between preoperative pain threshold and NRS scores (r=-0.069, P= 0.616) nor between the preoperative and intraoperative changes in HR (r= 0.103, P= 0.453), MAP (r= 0.086, P= 0.535), and NA (r=-0.058, P= 0.674). CONCLUSION: The results indicated that preoperative pain assessment could predict the level of pain response in OVCF patients during PKP surgery under local anesthesia.
The distribution of pressure forces of the foot on the ground during gait in patients with hip osteoarthritis
Authors: Pawłowska Katarzyna Maria , Pawłowski Jakub , Grochulska Agnieszka
Published: December 2023

BACKGROUND: Hip osteoarthritis is a major civilizational challenge of the ageing population, usually due to the reduced function of gait. In the light of this fact, gait analysis has become an important issue for the doctor, the physiotherapist and the patient, as it serves as a useful tool in diagnosis, recovery, and rehabilitation. Pedobarography is one of the most modern gait analysis methods. OBJECTIVE: The aim of this study is to assess the distribution of pressure forces of the foot on the ground during gait in patients with hip osteoarthritis. METHODS: Dynamic foot tests were performed by means of a two-meter RSscan® International platform, equipped with 16.384 sensors with the scanning frequency level exceeding 500 Hz. Patients were divided into two groups: the research group of N= 60 individuals with hip osteoarthritis, aged 52–84; and the control group of N= 32 individuals without hip osteoarthritis, aged 50–74. Data distribution analysis was performed with the Shapiro-Wilk test, followed by a non-parametric Mann-Whitney U test. RESULTS: The differences between the mean results of maximal peak values of pressure and force in such areas as the big toe, metatarsal I, III, IV, V, lateral and medial heel are significantly lower in the research group than in the control group. Only in the area of metatarsal II, the mean value of pressure is higher in the research group than in the control group. For all metatarsals, the midfoot and medial heel mean results of the contact area are significantly higher in the research group than in the control group. The differences between mean load rate were also tested. The test revealed statistical significance of metatarsal II and III, and the medial heel while metatarsal II displayed a higher mean value in the research group than in the control group. CONCLUSION: The distribution of the pressure forces of the foot can indicate imbalances which cause degenerative changes. Therefore, early detection of changes can help in prevention or delay of hip osteoarthritis in conjunction with proper therapy.
Effectiveness of cryo plus ultrasound therapy versus diathermy in combination with high-intensity laser therapy for pain relief in footballers with muscle injuries: A prospective study
Authors: Scaturro Dalila , de Sire Alessandro , Vitagliani Fabio , Lo Nardo Davide , Tomasello Sofia , Ammendolia Antonio , Letizia Mauro Giulia
Published: December 2023

BACKGROUND: Muscle injuries are common traumatic events in the clinical practice of the rehabilitation field. There is still a gap in the scientific literature on the effectiveness of physical agent modalities in the management of muscle injuries in athletes. OBJECTIVE: The aim of this study was to assess the effectiveness of cryo plus ultrasound therapy com-pared to diathermy in combination with high-intensity laser therapy (HILT) for pain relief in professional footballers with muscle injuries. METHODS: A case-control study was conducted on 31 professional footballers with a muscle injury of the lower limbs. Of these, 17 patients, assigned to a Group A (AG), were treated with HILT and cryoultrasound therapy; the remaining 14 patients, assigned to a Group B (BG), underwent HILT and diathermy. We assessed the extent of the pain, the size of the muscle injury, frequency of recurrence and number of days to recovery, at the time of recruitment, at the end of the rehabilitation and 3 months after the injury. RESULTS: Group A athletes had a greater benefit on pain (4.65 ± 0.61 vs 3.24 ± 0.63; p< 0.05) and muscle injury recurrence. The return to play in the athletes of group A took place 4.73 days earlier. CONCLUSION: HILT and cryo plus ultrasound therapy, in combination with therapeutic exercise, rep-resent a valid strategy in the treatment of muscle injuries in professional footballers.
Novel odor sensing intelligence and surveillance capabilities in controller-responder robots
Authors: Gandhi Serena , Abraham Ajith
Published: December 2023

The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.
Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder
Authors: Alonso Serafín , Morán Antonio , Pérez Daniel , Prada Miguel A. , Fuertes Juan J. , Domínguez Manuel
Published: December 2023

Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.
Highly compressed image representation for classification and content retrieval
Authors: Łażewski Stanisław , Cyganek Bogusław
Published: December 2023

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.
TSAGNN: Temporal link predict method based on two stream adaptive graph neural network
Authors: Zhu Yuhang , Guo Jing , Li Haitao , Liu Shuxin , Li Yingle
Published: December 2023

Temporal link prediction based on graph neural networks has become a hot spot in the field of complex networks. To solve the problems of the existing temporal link prediction methods based on graph neural networks do not consider the future time-domain features and spatial-domain features are limited used, this paper proposes a novel temporal link prediction method based on two streams adaptive graph neural networks. Firstly, the network topology features are extracted from the micro, meso, and middle perspectives. Combined with the adaptive mechanism of convolution and self-attention, the preprocessing of the feature extraction is more effective; Secondly, an extended bi-directional long short-term memory network is proposed, which uses graph convolution to process topological features, and recursively learns the state vectors of the target snapshot by using the future time-domain information and the past historical information; Thirdly, the location coding is replaced by the time-coding for the transformer mechanism, so that past information and future information can be learned from each other, and the time-domain information of the network can be further mined; Finally, a novel two-stream network framework is proposed, which combines the processing results of point features and edge features. The experimental results on 9 data sets show that the proposed method has a better prediction effect and better robustness than the classical graph neural network methods.
Diagnostic Model for Alzheimer’s Disease Based on PANoptosis-Related Genes
Authors: Zhang Erdong , Dai Fengqiu
Published: December 2023

Background: The pathophysiology of Alzheimer’s disease (AD) involves the interplay of three different processes: pyroptosis, apoptosis, and necroptosis. Objective: To explore role of PANoptosis, a novel pro-inflammatory programmed cell death pathway, in AD patients. Methods: We performed a consensus clustering analysis to identify distinct transcriptional profiles in the samples using the R package “ConsensusClusterPlus”. The PANoptosis key genes were obtained by crossing the WGCNA brown module and differentially expressed PANoptosis genes. We accomplished regression analyses using the LASSO-Cox method, combined with pathological status and gene expression data. At the same time, we also constructed PANscore system. The expression of PANoptosis hub genes were validated by qRT-PCR in AD transgenic mice. Results: Our study utilized tissue expression profile data from AD patients to construct three distinct PANoptosis patterns, each with unique molecular and clinical characteristics. We have created a risk scoring system called PANscore, which can analyze patterns specific for each AD patient. Additionally, we observed significantly lower levels of follicular helper T (Tfh) cells in the high PANscore and AD patients. Further analysis revealed a significant negative correlation of Tfh with GSDMD and MLKL. Conclusions: These findings provide a roadmap for personalized patient stratification, enabling clinicians to develop personalized treatment plans for AD patients and advance the field of precision medicine.
Comparative vector control study on speed of PMSM drive using sensorless and machine learning techniques: review
Authors: Nippatla V. Ramanaiah , Mandava Srihari
Published: December 2023

The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers.
Backstepping method tracking control for underactuated AUV with unknown dynamics based on action-critic networks based ADP
Authors: Che Gaofeng , Yu Zhen
Published: December 2023

This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme.
Using radial basis function for estimating the high-performance concrete compressive strength with optimizing AVOA and SSA
Authors: Zhang Yongcun , Bai Zhe
Published: December 2023

Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.
A novel threat assessment method for maritime defense integrating improved IT2FBWM, prospect theory and VIKOR under hybrid fuzzy environment
Authors: Ma Junwen , Bi Wenhao , Mao Zeming , Zhang An , Tang Changhong
Published: December 2023

The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism.
ATBBC: Named entity recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training
Authors: Cai Buqing , Tian Shengwei , Yu Long , Long Jun , Zhou Tiejun , Wang Bo
Published: December 2023

With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model.
Hybrid whale optimized crow search algorithm and multi-SVM classifier for effective system level test case selection
Authors: Tamizharasi A. , Ezhumalai P.
Published: December 2023

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost.
A Lightweight convolutional medical segmentation algorithm based on ConvNeXt to improve UNet
Authors: Wang Chuantao , Wang Xiumin , Zhai Jiliang , Shao Shuo
Published: December 2023

In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet’s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU.
Active contrastive coding reducing label effort for sensor-based human activity recognition
Authors: Li Zhixin , Liu Hao , Huan Zhan , Liang Jiuzhen
Published: December 2023

Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively.
An entropy weight variable fuzzy recognition and 1D-CNN deep learning method for general aviation fleet reliability evaluation
Authors: Chen Nongtian , Chen Kai , Sun Youchao
Published: December 2023

The reliability level of general aviation fleet system directly affects the economic benefits and safe operation of general aviation fleet. In order to effectively evaluate the reliability level of general aviation fleet, using the entropy weight variable fuzzy recognition and 1D-CNN depth learning reliability evaluation method. Firstly, taking the Cessna 172 general aviation fleet as the research object, refers to the maintenance statistical analysis of general aviation fleet reliability data, and classifies the fleet reliability evaluation indexes according to the ATA100 chapter standard. Combined with index importance analysis and Delphi expert investigation, 14 key items are extracted as reliability evaluation indexes of general aviation fleet. Secondly, using entropy weight method to obtain indexes weight objectively, and the evaluation level membership function is constructed based on variable fuzzy recognition method. Finally, a reliability evaluation model based on 1D-CNN deep learning method was established. Through training and testing the reliability data evaluation model of general aviation fleet, and comparing with the results of evaluation methods such as support vector machines. The results show that the recognition rate of the 1D-CNN deep learning method based on entropy weight variable fuzzy recognition can reach 91.95%, verifying the objective effectiveness of the evaluation method.
Diverse ensemble classifier driven Email spam classification using multiple word embedding’s with COCOB optimizer
Authors: Sri Vinitha V. , Renuka D. Karthika
Published: December 2023

Spam Email is a serious concern which can steal user’s personal information and cause huge financial loss due to the increasing rate of internet users. Therefore, the demand for accurate spam filtering has become more sophisticated for the Email spam detection. In the existing techniques, it is difficult to intricate the relationship between words in the Email using certain word embedding techniques and learning rate tuning is one of the greatest challenges of stochastic optimization. To overcome this difficulty, the proposed framework uses diverse ensemble based Email spam classification by incorporating multiple word embedding’s with Continuous Coin Betting optimizer. Word2Vec is used to produce the first set of 200D, next set of 200D word embedding is produced by Glove and 768D is produced by using Bidirectional Encoder Representations from Transformers (BERT) respectively. After generating word embedding, then it is classified through diverse ensemble based classifier with base level classifier consists of Long Short Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) and Bi-directional Gated Recurrent Unit (Bi-GRU) and LSTM as Meta-classifier using COCOB optimizer. Experiments were conducted on 3 benchmark Email dataset and result shows that the proposed system outperforms well with a low false positive rate.
Integrating interval-valued intuitionistic fuzzy multiple-attribute decision-making for teaching quality evaluation of visual communication design
Authors: Zeng Zengpei
Published: December 2023

Visual communication design, as a type of artistic and three-dimensional design behavior, helps to spread visual behavior by designing it. The rapid development of new media technology has provided rich channels and vast space for visual communication design, and the elements and modes of visual communication design are constantly being updated, better promoting the development of visual communication technology. The Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities based on the cultivation of innovative and creative abilities is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed based on the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (IVIFHIHWA) operator and induced ordered weighted averaging (I-OWA) operator. Meanwhile, some ideal properties of I-IVIFHIHWA operator are studied. Then, the I-IVIFHIHWA operator is employed to cope with the MADM under IVIFSs. Finally, an example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities is employed to test the I-IVIFHIHWA operator. Thus, the main research aim of this paper is concluded as follows: [1] the I-IVIFHIHWA operator is constructed based on classical IOWA operator; [2] the I-IVIFHIHWA operator is put forward to cope with the MADM under IVIFSs; [3] an empirical example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities has been put forward to show the I-IVIFHIHWA operator.
Cricket data analytics: Forecasting T20 match winners through machine learning
Authors: Chakraborty Sanjay , Mondal Arnab , Bhattacharjee Aritra , Mallick Ankush , Santra Riju , Maity Saikat , Dey Lopamudra
Published: December 2023

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting.
Energy efficiency in 5G systems: A systematic literature review
Authors: Maiwada Umar Danjuma , Danyaro Kamaluddeen Usman , Sarlan Aliza , Liew M.S. , Taiwo Ayankunle , Audi Umar Ismaila
Published: December 2023

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.
Effects of enriched task-specific training on sit-to-stand tasks in individuals with chronic stroke
Authors: Vive Sara , Zügner Roland , Tranberg Roy , Bunketorp-Käll Lina
Published: December 2023

BACKGROUND: Approximately 80% of stroke survivors experience motor impairment of the contralateral limb that severely affects their activities of daily living (ADL). OBJECTIVE: To evaluate whether an enriched task-specific training (ETT) program affected the performance and kinetics of sit-to-stand (STS) tasks. METHODS: The study was part of an exploratory study with a within-subject, repeated-measure-design, with assessments before and after a three-week-long baseline period, and six months after the intervention. Forty-one participants underwent assessments of strength and endurance measured by the 30-second-chair-stand test (30sCST). The STS-kinetics, including the vertical ground reaction force (GRF) during STS, were analysed in an in-depth-subgroup of three participants, using a single-subject-experimental-design (SSED). For kinetic data, statistical significance was determined with the two-standard deviation band method (TSDB). RESULTS: After the baseline period, a small increase was seen in the 30sCST (from 5.6±4.5 to 6.1±4.9, p = 0.042). A noticeable significant change in the 30sCST was shown after the intervention (from 6.1±4.9 to 8.2±5.4, p < 0.001), maintained at six months. The in-depth kinetic analyses showed that one of three subjects had a significant increase in loading of the affected limb post-intervention. CONCLUSION: ETT can produce long-term gains in STS performance. Weight-bearing strategies could be one of several factors that contribute to improvements in STS performance in the chronic phase after stroke.
Wikidata subsetting: Approaches, tools, and evaluation
Authors: Hosseini Beghaeiraveri Seyed Amir , Labra Gayo Jose Emilio , Waagmeester Andra , Ammar Ammar , Gonzalez Carolina , Slenter Denise , Ul-Hasan Sabah , Willighagen Egon , McNeill Fiona , Gray Alasdair J.G.
Published: December 2023

Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint.
The effect of nurses’ perceived stress on their work engagement and perceived professional benefit during the routine management of the COVID-19 pandemic
Authors: Deng Kai-Qin , Chen Xin-Yuan , Yuan Xian-Ming , Ren Yan-Rui , Luo Zhong-Mei , Li Gang-Ying , Deng Xia-Kang
Published: December 2023

BACKGROUND: In a previous study, more attention has been given to the psychological state of doctors than to that of nurses although the workload, working hours, and patient contact time are generally higher for nurses than doctors. The current status of nurses’ perceived stress, work engagement, and perceived professional benefit during the routine management of the Coronavirus disease 2019 (COVID-19) pandemic and how their perceived stress affects the other two variables are topics that merit research attention. OBJECTIVE: In this study, the status of nurses’ perceived stress, work engagement, and perceived professional benefit during the routine management of the COVID-19 pandemic was investigated to explore whether their perceived stress level has any effect on the other two variables. METHODS: The convenience sampling method was adopted, and 669 nurses from the First People’s Hospital of Jingzhou were selected to participate in this study. Questionnaires on perceived stress, work engagement, and perceived professional benefit were used in the survey, and the data were processed using the SPSS 20.0 program for the descriptive statistics, independent sample t-test, analysis of variance. RESULTS: The total score of the nurses’ perceived stress was 18.58±4.37 points. The total scores of their work engagement (43.32±14.01) and perceived professional benefit (140.23±17.75). CONCLUSION: The nurses’ total perceived stress score was at an upper-middle level, and their total work engagement and perceived professional benefit scores were relatively high. Overall, perceived stress has a negative effect on nurses’ work engagement and perceived professional benefit. That is, the higher the pressure perception of nurses, the lower the degree of work engagement and perceived professional benefit.
Mapping the family incivility, dissatisfaction and organizational support after the COVID-19 outbreak
Authors: Rajak Binod , Reddy K. Vidyullatha , Singh Punam , Kumar Vimal
Published: December 2023

BACKGROUND: Stress is a pivotal aspect of organizational psychology. Although an individual’s attitude and behaviour at work have received greater investigation, scant attention has been given to family-related dynamics and their impact on work. OBJECTIVE: This study contributes to understanding work-family dynamics by investigating the connection between family incivility and job dissatisfaction during the pandemic. Based on the conservation of resources theory, the research explores the potential moderating role of perceived organizational support. METHODS: The investigation uses structural equation modelling (SEM) for data analysis while controlling for age, gender, and experience. Data has been collected with an adapted scale and time-lag technique in which the first wave encompassed independent variables and demographics. The second wave gathered moderator and dependent variables through a non-probability sampling of IT employees. RESULTS: Findings reveal a significant positive link between family incivility and job dissatisfaction, implying higher family incivility is linked to increased job dissatisfaction. Moreover, the study indicates that organizational support can mitigate the negative impact of family incivility on job dissatisfaction. CONCLUSION: This research underscores the pivotal role of organizational support in enhancing employee job outcomes. Hence, organizations should prioritize employee well-being, recognizing it as an integral aspect of their functioning.