bluuebunny/crossref_metadata_2025 huggingface dataset:
KIAA1429-mediated RXFP1 attenuates non-small cell lung cancer tumorigenesis via N6-methyladenosine modification
Authors: Zhang Zhixiang , Guo Jipeng , Gong Chongwen , Wu Sai , Sun Yanlei
Published: March 2024

BACKGROUND: N6-methyladenosine (m6A) modification has been associated with non-small cell lung cancer (NSCLC) tumorigenesis. OBJECTIVES: This study aimed to determine the functions of Vir-like m6A methyltransferase-associated (KIAA1429) and relaxin family peptide receptor 1 (RXFP1) in NSCLC. METHODS: A quantitative real-time polymerase chain reaction was used to analyze the mRNA levels of KIAA1429 and RXFP1 in NSCLC. After silencing KIAA1429 or RXFP1 in NSCLC cells, changes in the malignant phenotypes of NSCLC cells were assessed using cell counting kit-8, colony formation, and transwell assays. Finally, the m6A modification of RXFP1 mediated by KIAA1429 was confirmed using luciferase, methylated RNA immunoprecipitation, and western blot assays. RESULTS: KIAA1429 and RXFP1 were upregulated and downregulated in NSCLC, respectively. Silencing of KIAA1429 attenuated the viability, migration, and invasion of NSCLC cells, whereas silencing of RXFP1 showed the opposite function in NSCLC cells. Moreover, RXFP1 expression was inhibited by KIAA1429 via m6A-modification. Therefore, silencing RXFP1 reversed the inhibitory effect of KIAA1429 knockdown in NSCLC cells. CONCLUSION: Our findings confirmed that the KIAA1429/RXFP1 axis promotes NSCLC tumorigenesis. This is the first study to reveal the inhibitory function of RXFP1 in NSCLC via KIAA1429-mediated m6A-modification. These findings may help identify new biomarkers for targeted NSCLC therapy.
Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?
Authors: Kim Roger Y.
Published: March 2024

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.
Heuristics for the two-dimensional cutting stock problem with usable leftover
Authors: Chen Qiulian , Chen Yan
Published: March 2024

Utilization of residue is a challenge in engineering practice, because unreasonable cutting causes excess materials wasted and increases the production cost. This work considers the residual two-dimensional cutting stock problem with usable leftover in which unused parts of cutting patterns can be used for future orders. We propose an algorithm that combines the iterative sequential value correction heuristic with the beam search heuristic, considering both the accumulation and the reusability of leftovers to reduce the material consumption. Cutting plans are constructed iteratively and the best one are chosen as the solution. Cutting patterns in the cutting plan are generated sequentially by recursive techniques, and potentially usable leftover are accumulated by beam search heuristic. Item values are corrected after each pattern to diversify cutting plans. Three sets of simulations under different number of periods, over medium and large instances from the literature, are used to demonstrate the effectiveness of the heuristics. Computational results show that the algorithm provides better solutions, which can save a considerable amount of plate in a long-term production period. The utilization of wastages can save a considerable amount of stock plate and contract the production cost of enterprises in the long-term production period.
Ultrasound breast images denoising using generative adversarial networks (GANs)
Authors: Jiménez-Gaona Yuliana , Rodríguez-Alvarez María José , Escudero Líder , Sandoval Carlos , Lakshminarayanan Vasudevan
Published: March 2024

INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.
Unlocking the Potential of Repetitive Transcranial Magnetic Stimulation in Alzheimer’s Disease: A Meta-Analysis of Randomized Clinical Trials to Optimize Intervention Strategies
Authors: Li Sha , Lan Xiaoyong , Liu Yumei , Zhou Junhong , Pei Zian , Su Xiaolin , Guo Yi
Published: March 2024

Background: Repetitive transcranial magnetic stimulation (rTMS) is an advanced and noninvasive technology that uses pulse stimulation to treat cognitive impairment. However, its specific effects have always been mixed with those of cognitive training, and the optimal parameter for Alzheimer’s disease (AD) intervention is still ambiguous. Objective: This study aimed to summarize the therapeutic effects of pure rTMS on AD, excluding the influence of cognitive training, and to develop a preliminary rTMS treatment plan. Methods: Between 1 January 2010 and 28 February 2023, we screened randomized controlled clinical trials from five databases (PubMed, Web of Science, Embase, Cochrane, and ClinicalTrials. gov). We conducted a meta-analysis and systematic review of treatment outcomes and rTMS treatment parameters. Result: A total of 4,606 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles, comprising 655 participants (308 males and 337 females), were included in the final analysis. The findings revealed that rTMS significantly enhances both global cognitive ability (p = 0.0002, SMD = 0.43, 95% CI = 0.20–0.66) and memory (p = 0.009, SMD = 0.37, 95% CI = 0.09–0.65). Based on follow-up periods of at least 6 weeks, the following stimulation protocols have demonstrated efficacy for AD: stimulation sites (single or multiple targets), frequency (20 Hz), stimulation time (1–2 s), interval (20–30 s), single pulses (≤2500), total pulses (>20000), duration (≥3 weeks), and sessions (≥20). Conclusions: This study suggests that rTMS may be an effective treatment option for patients with AD, and its potential therapeutic capabilities should be further developed in the future.
Multidimensional clustering analysis of mathematical knowledge difficulty based on Gspan
Authors: Pan Hongyan
Published: March 2024

In addition to providing learners with a large amount of teaching resources, online teaching platforms can also provide learning resources and channels such as video courseware, Q&A tutoring groups, and forums. However, currently, there are still shortcomings in depth and dimensionality in mining student learning behavior data on the platform. In view of this situation, based on the learning interaction behavior, this study established the difficulty similarity model of knowledge points, and used spectral clustering to classify their difficulty. In addition, the study intended to use the maximum frequent subgraph under the Gspan framework to characterize learners’ implicit learning patterns. The outcomes expressed that the algorithm put forward in the study achieved the highest accuracy index of 98.8%, which was 1.4%, 4.0%, and 8.6% higher than Apriori-based graph mining algorithms, K-means, and frequent subgraph discovery algorithms. In terms of F1 index, the convergence value of the algorithm proposed in the study was 95.5%, which was about 2.5% higher than the last three algorithms. In addition, learners of all three cognitive levels had the highest maximum number of frequent subgraphs with sizes above 100 when the minSup value was 60%. And when the number of clusters was 3, the clustering accuracy of the three learners was the highest. In similarity calculation, the calculation method used in the study was at the minimum in terms of root mean square error and absolute error average index, which were 0.048% and 0.01% respectively. This indicated that the model proposed by the research had better classification effect on the difficulty of knowledge points for learners of different cognitive levels, and had certain application potential.
RETRACTED: AOPPI: An adaptive obstruction perception pedestrian identifier for complex environments
Authors: Zhang Yongzhi , He Keren , Ge Jue
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Autonomous traffic sign detection for self-driving car system using convolutional neural network algorithm
Authors: Yu Zhao , Ye Ting
Published: March 2024

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data.
Efficient disease identification using symptom-based ensemble models and bayes-search optimization
Authors: Indupalli Manjula Rani , Pradeepini Gera
Published: March 2024

Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable amount of time using an advanced tuning technique known as “Bayes-Search” on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., (< (n-k)!). The proposed model defines the objective function with a minimum error rate and predicts the error rate with the selected estimators for different distributions. The model depending on the predicted value decides whether to store the value or to return the value to the optimizer. The Bayes search optimization has achieved +9.21% accuracy than the grid search approach. Among the two approaches random forest has achieved good accuracy and less loss using Bayes search with cross-validation.
T-Bipolar soft groups and their fundamental laws
Authors: Mahmood Tahir , Hussain Kashif , Ahmmad Jabbar , Shahab Sana , ur Rehman Ubaid , Anjum Mohd
Published: March 2024

The notion of a T-bipolar soft set (T - BSftS) is the structure that has the ability to discuss the two-sided aspects of certain situations like the effects and side effects of a medicine. Moreover, T - BSftS has the ability to discuss the parametrization tool as well. Also, notice that a group is an algebraic structure that is the key tool in many branches of mathematics. In many decision-making situations, we have to discuss the two-sided aspects of a certain situation and we can see that T - BSftS is the only structure that can handle it. So based on a characteristic of T - BSftS and groups theory there is a need to define the combined notion of T - BSftS and group. So, based on these valuable structures, in this manuscript, we aim to introduce the notion of T-bipolar soft groups by generalizing T-bipolar soft sets. Based on this newly defined structure, we have defined the basic operational laws like extended union, extended intersection, restricted union, restricted intersection, AND product, and OR product for T-bipolar soft groups. Moreover, we have observed the impact of these newly defined notions on T-bipolar soft groups.
A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data
Authors: Chen Liang-Ching , Chang Kuei-Hu , Wu Chia-Heng , Chen Shin-Chi
Published: March 2024

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.
Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods
Authors: Shen Xiaohui
Published: March 2024

Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.
Integrating 5G and machine learning technologies for advanced PDM in smart farming
Authors: Zhang Weidong , Tan Huadi
Published: March 2024

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation.
Design of emergency evacuation route for large buildings based on cloud computing and GIS big data
Authors: Ma Xiyin , Li Jian
Published: March 2024

In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings.
RETRACTED: On modularity of hyperlattices
Authors: Amiri-Bideshki M. , Hoskova-Mayerova S. , Ameri R.
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Pharmacotherapy for Disease Modification in Early Parkinson’s Disease: How Early Should We Be?
Authors: Mahlknecht Philipp , Poewe Werner
Published: March 2024

Slowing or halting progression continues to be a major unmet medical need in Parkinson’s disease (PD). Numerous trials over the past decades have tested a broad range of interventions without ultimate success. There are many potential reasons for this failure and much debate has focused on the need to test ‘disease-modifying’ candidate drugs in the earliest stages of disease. While generally accepted as a rational approach, it is also associated with significant challenges around the selection of trial populations as well as trial outcomes and durations. From a health care perspective, intervening even earlier and before at-risk subjects have gone on to develop overt clinical disease is at the heart of preventive medicine. Recent attempts to develop a framework for a biological definition of PD are aiming to enable ‘preclinical’ and subtype-specific diagnostic approaches. The present review addresses past efforts towards disease-modification, including drug targets and reasons for failure, as well as novel targets that are currently being explored in disease-modification trials in early established PD. The new biological definitions of PD may offer new opportunities to intervene even earlier. We critically discuss the potential and challenges around planning ‘disease-prevention’ trials in subjects with biologically defined ‘preclinical’ or prodromal PD.
Modeling and prediction of tennis matches at Grand Slam tournaments
Authors: Buhamra N. , Groll A. , Brunner S.
Published: March 2024

In this manuscript, different approaches for modeling and prediction of tennis matches in Grand Slam tournaments are proposed. The data used here contain information on 5,013 matches in men’s Grand Slam tournaments from the years 2011–2022. All regarded approaches are based on regression models, modeling the probability of the first-named player winning. Several potential covariates are considered including the players’ age, the ATP ranking and points, odds, elo rating as well as two additional age variables, which take into account that the optimal age of a tennis player is between 28 and 32 years. We compare the different regression model approaches with respect to three performance measures, namely classification rate, predictive Bernoulli likelihood, and Brier score in a 43-fold cross-validation-type approach for the matches of the years 2011 to 2021. The top five optimal models with highest average ranks are then selected. In order to predict and compare the results of the tournaments in 2022 with the actual results, a comparison over a continuously updating data set via a “rolling window” strategy is used. Also, again the previously mentioned performance measures are calculated. Additionally, we examine whether the assumption of non-linear effects or additional court- and player-specific abilities is reasonable.
Comparison of Oncological and Functional Outcomes of Radical Versus Partial Nephrectomy for cT1b Renal Cell Carcinoma: A Two-Centre, Matched Analysis
Authors: van den Brink Luna , van den Kroonenberg Daniel L. , Graafland Niels M. , Bex Axel , Beerlage Harrie P. , van Moorselaar Jeroen R.A. , Zondervan Patricia J.
Published: March 2024

BACKGROUND:

It remains unclear which patients with cT1b renal cell carcinoma (RCC) benefit most from partial nephrectomy (PN) versus radical nephrectomy (RN) considering oncological outcomes and renal function.

OBJECTIVE:

To compare oncological and functional outcomes of RN with PN for cT1b RCC.

METHODS:

This is a retrospective analysis of patients who underwent RN or PN for cT1b between 2010 and 2022 ( n = 241). Patients were grouped by RN or PN and matched by age, sex, Charlson Comorbidity Index, BMI, PADUA score, RENAL score, ASA score, and preoperative kidney function (eGFR) using propensity score matching. The 10-year overall survival (OS), 10-year cancer-specific survival (CSS), and 10-year recurrence-free survival (RFS) were compared. Change in eGFR from baseline to 5-year follow-up was assessed.

RESULTS:

After matching, 100 patients remained in each group for analysis. The 10-year OS, CSS, and RFS rates were similar between groups. For patients classified as low risk, the PN group displayed a higher recurrence rate compared to RN (7 vs. 0, p = 0.01). Patients who underwent RN had worse 1-year postoperative eGFR than PN (RN: 57 [44–65], PN: 73 [60–87], p < 0.001). RN was more likely to induce new-onset chronic kidney disease (CKD) stage ≥3b compared to PN ( p < 0.001). Complication rate after PN was significantly higher ( p = 0.003).

CONCLUSION:

10-year survival rates were similar, despite more recurrences in the PN group. Our data shows that post-surgical renal function is superior for PN. Nevertheless, RN is a reliable treatment option when preservation of renal function is not a priority.
Effectiveness of simple and basic home-based exercise programs including pediatric massage executed by caregivers at their homes in the management of children with spastic cerebral palsy: A randomized controlled trial
Authors: Mahmood Qamar , Habibullah Shaista , Aurakzai Hazrat Ullah
Published: March 2024

PURPOSE: This study aimed to assess the effectiveness of simple and basic home-based exercise programs (HEPs), including pediatric massage (PM), executed by caregivers at their homes in the management of children with spastic cerebral palsy (CP). METHODS: Sixty-eight children with spastic CP (diplegia) aged 4–12 years were randomly assigned to PM and HEP groups for a randomized controlled trial continuing from November 01, 2021 to June 2022. Parents provided home-based exercises to both groups, five times a week for 12 weeks. However, the PM group was additionally provided with PM. Modified Ashworth Scale (MAS), Gross Motor Function Measure (GMFM-88) and Gross Motor Function Classification System (GMFCS) were used for evaluation of spasticity and gross motor activity at baseline as well as after six and 12 weeks of intervention. Comparative analysis of data was carried out with SPSS-20. RESULTS: Mean age in HEP and PM groups was 6.65±2.12 and 7.09±2.22 years respectively. Data revealed homogeneity of both groups at the beginning of study. The PM group showed a statistically significant decrease in MAS scores after six and 12 weeks of intervention (p < 0.05) when compared with the HEP group, but similar changes did not happen in GMFM scores and GMFCS levels. However, comparative analysis revealed statistically significant change in GMFM scores and GMFCS levels (p < 0.05) when compared from baseline to 12 weeks of intervention in both groups. CONCLUSION: PM along with HEPs can be used effectively to reduce spasticity and to improve gross motor ability if performed for a period of at least six and 12 weeks respectively. In conjunction with HEPs, PM has better outcomes in the management of tone and movement disorders of spastic CP than HEPs alone
Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology
Authors: Gerard Napthaline , Ben Othman Sarah , Rangandin Pajanivel , Broucqsault Marc , Hammadi Slim
Published: March 2024

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.
Ontology of active and passive environmental exposure
Authors: Vámos Csilla , Scheider Simon , Sonnenschein Tabea , Vermeulen Roel
Published: March 2024

Exposure is a central concept of the health and behavioural sciences needed to study the influence of the environment on the health and behaviour of people within a spatial context. While an increasing number of studies measure different forms of exposure, including the influence of air quality, noise, and crime, the influence of land cover on physical activity, or of the urban environment on food intake, we lack a common conceptual model of environmental exposure that captures its main structure across all this variety. Against the background of such a model, it becomes possible not only to systematically compare different methodological approaches but also to better link and align the content of the vast amount of scientific publications on this topic in a systematic way. For example, an important methodical distinction is between studies that model exposure as an exclusive outcome of some activity versus ones where the environment acts as a direct independent cause (active vs. passive exposure). Here, we propose an information ontology design pattern that can be used to define exposure and to model its variants. It is built around causal relations between concepts including persons, activities, concentrations, exposures, environments and health risks. We formally define environmental stressors and variants of exposure using Description Logic (DL), which allows automatic inference from the RDF-encoded content of a paper. Furthermore, concepts can be linked with data models and modelling methods used in a study. To test the pattern, we translated competency questions into SPARQL queries and ran them over RDF-encoded content. Results show how study characteristics can be classified and summarized in a manner that reflects important methodical differences.
Enhanced CT imaging artificial neural network coronary artery calcification score assisted diagnosis
Authors: Wang Zhen , Zhu Darong , Hu Guangzhu , Shi Xiaobei
Published: March 2024

BACKGROUND: The study of coronary artery calcification (CAC) may assist in identifying additional coronary artery problem protective factors. On the contrary side, due to the wide variety of CAC as individuals, CAC research is difficult. Due to this, evaluating data for investigation is becoming complicated. OBJECTIVE: To use a multi-layer perceptron, we investigated the accuracy and reliability of synthetic CAC coursework or hazard classification in pre or alors chest computerized tomography (CT) of arrangements resolutions in this analysis. method: Photographs of the chest from similar individuals as well as calcium-just and non-gated pictures were incorporated. This cut thickness ordered CT pictures (bunch A: 1 mm; bunch B: 3 mm). The CAC rating was determined utilizing calcification score picture information, and became standard for tests. While the control treatment’s machine learning program was created using 170 computed tomography pictures and evaluated using 144 scans, group A’s machine learning algorithm was created using 150 chest CT diagnostic tests. RESULTS: 334 external related pictures (100 μm: 117; 0.5 mm x: 117) of 117 individuals and 612 inside design organizing (1 mm: 294; mm3: 314) of 406 patients were surveyed. Pack B had 0.94, however, tests An and b had 0.90 (95% CI: 0.85–0.93) ICCs between significant learning and gold expenses (0.92–0.96). Dull Altman plots agreed well. A machine teaching approach successfully identified 71% of cases in category A is 81% of patients in section B again for cardiac risk class. CONCLUSION: Regression risk evaluation algorithms could assist in categorizing cardiorespiratory individuals into distinct risk groups and conveniently personalize the treatments to the patient’s circumstances. The models would be based on information gathered through CAC. On both 1 and 3-mm scanners, the automatic determination of a CAC value and cardiovascular events categorization that used a depth teaching approach was reliable and precise. The layer thickness of 0.5 mm on chest CT was slightly less accurate in CAC detection and risk evaluation.
The association between the work environment and the fear of COVID-19 experienced by nurses
Authors: Dost Ayse , Sezgin Duygu , Esin Melek Nihal
Published: March 2024

BACKGROUND: The conditions in the workplace have a critical influence on the mental health of nurses and their attitudes toward their job, which may impact patient care. OBJECTIVE: This cross-sectional study aimed to investigate the association between perceptions of the work environment and fear of COVID-19 experienced by nurses. METHODS: The data were collected using a demographic data form, the Work Environment Scale (WES), and the Fear of COVID-19 Scale. The study was completed with 183 nurses who provide care to COVID-19 patients. RESULTS: The mean scores for the WES and Fear of COVID-19 Scale were 63.59±12.35 and 21.98±8.36, respectively. There was a positive correlation between the points acquired from the “employee fears” section of the WES and the Fear of COVID-19 Scale mean ranks (r = 0.22). There was a weak negative correlation between the Fear of COVID-19 score and the WES “job satisfaction” score (r = –0.214). There was a weak negative correlation between the scores of the Fear of COVID-19 Scale and perceived support at work (r = –0.33) and between the WES scores and weekly working hours (r = –0.27). However, there was a weak to moderate positive correlation between the WES scores and number of days off per week (r = 0.45). CONCLUSIONS: Nurses experience a high fear of COVID-19, and a decrease in their attitudes of the work environment was associated with an increased fear of COVID-19. The fear of COVID-19 may be reduced by various interventions to provide support at work and increase nurses’ job satisfaction.
Physical, functional and personal variables affecting shoulder complaints in healthcare workers
Authors: Lopes Ester Rodrigues do Carmo , Macêdo Flávia Pessoni Faleiros , Fifolato Thaís Marques , Nardim Heloisa Correa Bueno , Suzuki Karen Ayumi Kawano , Fonseca Marisa de Cássia Registro
Published: March 2024

BACKGROUND: Work-related shoulder disorders adversely affect the quality of life and lead to increased costs related to decreased productivity and injury treatment. Therefore, understanding the relationship between strength, upper extremity function and work ability contributes to the development of interventions aimed to improve the well-being of healthcare workers. OBJECTIVE: Correlate shoulder abduction and handgrip strength with upper extremity function and work ability in healthcare workers with shoulder complaints. METHODS: 67 workers with shoulder pain in the last year were assessed by Disability of Arm, Shoulder and Hand (QuickDASH), Work Ability Index (WAI), isokinetic shoulder strength and isometric handgrip strength dynamometers. Data were analysed with Spearman’s Correlation Coefficient (ρ= 0.05), SPSS 20.0®. RESULTS: 52% males, mean age 48.4 years, 42% physically active, most administrative and general services workers. Low correlation between handgrip strength and QuickDASH (r=-0.359; p = 0.004); low correlation between handgrip strength and WAI (r = 0.359; p = 0.003) and between shoulder abduction strength and the QuickDASH (r = -0.267; p = 0.049); no significant correlation between shoulder abduction strength and WAI (r = 0.001; p = 0.997). CONCLUSIONS: Handgrip and shoulder abduction strength were inversely associated with upper extremity dysfunction. Shoulder abduction strength was associated with work ability. The inclusion of strength assessment in workers is important to guide strategies to prevent musculoskeletal disorders.
Patterns of Psychosocial Dynamic Factors Surrounding Suicide Notes on Facebook Platforms: A Netnographic Approach
Authors: Oguntayo Rotimi , Noibi Tajudeen
Published: March 2024

BACKGROUND: Suicide is a global public health concern with multifaceted risk factors, necessitating exploration through qualitative methodologies. Meanwhile, studies have shown that netnography offers a promising avenue for understanding suicide-related discourse on social media platforms like Facebook.

OBJECTIVE: This study aims to address the gap in literature by examining suicide-related discourse on Facebook through a netnographic approach, focusing on emotional vulnerability, communication of pain and need, and the role of online communities.

METHODS: Using content analysis of two case studies, this study explores themes emerging from suicide notes shared on Facebook. Participants were observed within relevant Facebook groups, and data were collected through direct participation, archival data, and field notes. Ethical considerations were prioritized, including informed consent and anonymity.

RESULTS: The analysis revealed themes of emotional vulnerability among respondents, expressions of suffering and necessity, and the significance of supportive responses within Facebook platform online communities. The results highlighted the importance of targeted social network support systems and the role of empathy via Facebook online platforms in suicide prevention and the alleviation of grief among the families of the affected persons.

CONCLUSION: The findings underscore the need for targeted e-intervention programs and the fostering of supportive online communities. Methodological reflections emphasize the value of netnography in understanding suicide-related discourse on social media platforms and informing future research and interventions in this critical area.
Two Intrinsic Formulae Generated by the Jones Polynomial
Authors: Wolk Brian Jonathan
Published: March 2024

This paper notes and derives two simple, intrinsic mathematical relations generated by the Jones polynomial.
Contextualizing Public Coastal Access Challenges in South Africa, a Review of Best Practices in Selected Countries
Authors: Mafumbu Luyanda
Published: March 2024

Coastal regions and populations worldwide experience the challenge of coastal access constraints. Owing to South Africa’s socio-economic background of apartheid, equitable access to coastal resources remains a challenge. Integrated coastal management has been the subject of numerous research in South Africa, nevertheless, the issues surrounding coastal access and global best practices received little attention. This study was conducted due to insufficient documentation of global practices for addressing coastal access challenges relevant to South Africa. The objective of this paper was to contextualize challenges of coastal access in South Africa and assess the best practices in selected countries to address them. This study was contextualized in terms of the established literature and previous studies. In examining the key public coastal access challenges, thematic literature review was used to systematically identify themes and gaps. The best practices for public coastal access are examined in this research using global examples. The following challenges were identified, that is, influence by private properties, apartheid spatial legacy, influence of new developments, lack of institutional capacity, policy gaps and funding constraints. The study conclude that lessons learnt from different case studies should be integrated into policy framework of South African integrated coastal management to improve public coastal access.
El trabajo por cuenta propia. Delimitación del derecho penal y administrativo sancionador en Cuba
Authors: Muñoz Alfonso Yisel , Brito Febles Osvaldo
Published: March 2024

La realidad económica cubana plantea diversos retos. Entre ellos se encuentra el desarrollo creciente de un sector emergente, como el trabajo por cuenta propia, que determinó la creación de un marco legislativo y propició, no sólo su incentivo y crecimiento progresivo, sino la regulación de un nuevo sujeto dentro de las relaciones sociales y económicas. Es escaso el tratamiento bibliográfico sobre el tema de la actividad por cuenta propia en el ámbito jurídico; ello justifica la necesidad de su estudio teórico conceptual. Las carencias cognitivas sobre este sector y la creciente proliferación legislativa sobre el mismo, a veces contradictoria y antinómica, determina la superposición del tratamiento penal y administrativo a este sujeto, que hacen necesario el estudio con claras aristas de integración interdisciplinar y hasta el presente de pobre abordaje conceptual. Es objetivo fundamental de este artículo sistematizar algunos caracteres del trabajador por cuenta propia y establecer elementos delimitadores del derecho penal y administrativo sancionador al recibir a este sujeto dentro de ellos.
Conflicto social: orientaciones colectivas y políticas
Authors: Martínez Torres Eduardo
Published: March 2024

Las preguntas que guían este texto son: ¿Cómo se define un conflicto social? ¿Qué los distingue de otro tipo de dinámicas sociales? ¿Qué se disputaría en estos? ¿Qué elemento es fundamental para explicarlos cuando adquiere formas de movilización, actores organizados y en dónde se emplean acciones colectivas transgresoras? Se argumentan tres puntos centrales. Primero: el concepto de conflicto social en una sociedad es una disputa por la orientación y control de los recursos materiales y simbólicos. Segundo: esto alude a la dimensión política de toda sociedad y de las relaciones de poder. Por último, las orientaciones colectivas son las que permiten construir procesos de movilización, identidades colectivas y polarización en procesos de conflicto social.
Experiencia, profesionalización, reelección inmediata y trabajo de los legisladores en la Cámara de Diputados (1997-2013)
Authors: Castañeda Tenorio Enrique , Hernández Salazar Julián , Tlahuel Adrián María de Lourdes
Published: March 2024

El presente artículo identifica los principales elementos que han hecho que el poder legislativo (especialmente los legisladores) no cuente con factores institucionales y políticos para fortalecer su profesionalización. Para ello, se presenta un análisis del papel que ha jugado la no reelección inmediata en la productividad legislativa y las repercusiones en la falta de experiencia del legislador.
Incidente de falta de personalidad en las sociedades mercantiles
Authors: Pérez Cázares Martín Eduardo
Published: March 2024

El presente trabajo es una reflexión respecto a las exigencias que se piden dentro de un procedimiento mercantil para acreditar la representación de una sociedad mercantil, que va mas allá de lo que la misma legislación mercantil señala y que aflora al momento de la interposición del incidente de falta de personalidad.

            El tema refleja una importancia trascendental en materia procesal, puesto que vemos al incidente como parte de un sistema del derecho procesal mercantil, que interactúa con él.
            Se elabora un concepto de incidente desde la perspectiva procesal, desmembrándolo para llegar a su contenido; así mismo vemos el concepto de personalidad aplicado al derecho procesal.
EL RUMBO DE LOS GOBIERNOS DE COALICIÓN EN MÉXICO
Authors: Montoya Zamora Raúl , Valles Santillán Gabriela Guadalupe , Alanís Herrera María Magdalena
Published: March 2024

En este trabajo se realizarán algunas reflexiones sobre el rumbo que pueden tomar los gobiernos de coalición en México, y se delinearán algunas ideas en las que se pueden sustentar algunas propuestas para regular de la mejor forma posible la figura de gobierno de coalición en nuestro país, al ser una novedosa inclusión de rango constitucional en el sistema político mexicano, derivado de la reforma constitucional político-electoral publicada en el Diario Oficial de la Federación el pasado 10 de febrero de 2014.

 La metodología en la que se abordará la temática expuesta será la siguiente: en primer término, se realizará un planteamiento introductorio, para posteriormente detallar un breve marco teórico que permita al lector distinguir la figura de gobierno de coalición de entre diversos tipos de alianzas partidistas que pueden suscitarse en el escenario político-electoral mexicano. Una vez hecho lo anterior, se hará alusión a la regulación vigente de los gobiernos de coalición, para luego continuar con la perspectiva y retos que se consideran respecto a éstos -incluyendo su incidencia en el ámbito de los gobiernos locales, en específico el de Durango- de cara a futuros escenarios a partir de 2018, finalizando con la correspondiente formulación de propuestas y las conclusiones.
MANAJEMEN PEMELIHARAAN SAPI PERAH LAKTASI DI KOPERASI LARAS ATI
Authors: Nuwafwahyudin Seftian , Widyani Retno , Nilamcaya Mus , Perwitasari Fitri dian
Published: March 2024

Manajemen dalam usaha sapi perah akan menentukan tingkat keberhasilan dari suatu usaha ataupun kerugian dari suatu usaha. Manajemen sebagai kunci kegiatan yang sepenuhnya bergantung kepada kemampuan manusianya sebagai peran utama dalam penggelolaan kegiatan usaha. Tujuan Penelitian ini adalah untuk mengetahui managemen pemeliharaan di Koperasi Laras Ati. Waktu dan tempat pelaksanaan kegiatan tanggal 11 September- 11 Oktober 2022 yang berlangsung selama satu bulan di Koperasi Laras Ati. Metode penelitian yang dilakukan adalah observasi dan kemudian hasil pengamatan di deskriptifkan. Manajemen pemliharaan yang kurang maksimal seperti pemberian pakan, proses pemarahan, dan pengolahan limbah. Pakan yang diberikan kepada sapi perah tidak disesuaikan dengan kebutuhan masing-masing ternak. Pemberian pakan kosentrat dan hijuan baiknya disesuaikan dengan kebutan pokok hidup dan kebutuhan produksi tiap masing-masing sapi. Pemerahan di Koperasi Laras Ati hanya membersihkan ambing menggunakan air biasa tanpa dilakukanya suci hama ambing menggunakan desinfektan (dipping). Hal ini bisa menyebakan sapi perah terjangkit penyakit mastitis yang akhirnya terjadi penurunan produksi susu.