bluuebunny/crossref_metadata_2025 huggingface dataset:
An automatic measurement method for the response of Caenorhabditis elegans to chemicals
Authors: Zhang Nan , Nie Yanmin , Dong Bingyue , Zhang Da , Li Guojun , Ning Junyu , Xian Bo , Chen Weiyang , Gao Shan
Published: May 2024

BACKGROUND: Caenorhabditis elegans is a widely used model animal. Chemotaxis assay is one of the experiments that study the effects of different chemicals on nematodes. It is mainly used to study the effects of different chemicals on the perception behavior of nematodes. By conducting this experiment, not only can the neurotoxicity of chemicals be reflected, but also the impact of chemicals on physiological functions regulated by the nervous system, such as nematode feeding behavior and basic motor ability. OBJECTIVE: The experiment of detecting the response of nematode to chemicals is also a common method of chemical toxicity testing based on nematode models. In the analysis of worm tendency behavior, manual operations are generally used. Manually processing a large number of worms under a microscope is very time-consuming and labor-intensive. The current quantitative methods for nematode chemotaxis experiments are not only time-consuming and labor-intensive, but also biased in experimental results due to differences in judgment standards among experimenters. The automatic and efficient quantification method for nematode chemotaxis experiments is a very important technical difficulty in the field of nematode experiments. METHODS: Here, we have designed an automatic quantification method for nematode chemotaxis experiments by incorporating image acquisition and processing techniques into the nematode experiment. RESULTS: The experimental results show that the Pearson correlation coefficient between manual and automatic counting results is 0.978. CONCLUSION: This proves the effectiveness of our method. Applying the automatic measurement method to replace manual counting by the experimenter can improve work efficiency, and reduce errors in human counting operations.
Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning
Authors: Zhou Xuezhong , Li Chenxi , Su Hai , Tang Zhixian , Li Ping , Li Yijie , Hou Jiawei , Sun Xun
Published: May 2024

BACKGROUND: Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. OBJECTIVE: This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. METHODS: We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. RESULTS: Experimental results show that the model’s quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. CONCLUSIONS: Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.
Antigout effects and mechanisms of total flavonoids from prunus tomentosa
Authors: Jiang Yanan , Zhang Chengyi , Zhang Xinyue , Lan Na , Zhao Zihan , Xv Yawei , Wang Qi , Wang Siwei , Chen Baifeng , Chen Xi , Wang Yilin
Published: May 2024

BACKGROUND: In recent years, hyperuricemia and acute gouty arthritis have become increasingly common, posing a serious threat to public health. Current treatments primarily involve Western medicines with associated toxic side effects. OBJECTIVE: This study aims to investigate the therapeutic effects of total flavones from Prunus tomentosa (PTTF) on a rat model of gout and explore the mechanism of PTTF’s anti-gout action through the TLR4/NF-κB signaling pathway. METHODS: We measured serum uric acid (UA), creatinine (Cr), blood urea nitrogen (BUN), tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6) levels using an enzyme-linked immunosorbent assay (ELISA). Histopathological changes were observed using HE staining, and the expression levels of relevant proteins were detected through Western blotting. RESULTS: After PTTF treatment, all indicators improved significantly. PTTF reduced blood levels of UA, Cr, BUN, IL-1β, IL-6, and TNF-α, and decreased ankle swelling. CONCLUSIONS: PTTF may have a therapeutic effect on animal models of hyperuricemia and acute gouty arthritis by reducing serum UA levels, improving ankle swelling, and inhibiting inflammation. The primary mechanism involves the regulation of the TLR4/NF-κB signaling pathway to alleviate inflammation. Further research is needed to explore deeper mechanisms.
Clustering algorithm based on DINNSM and its application in gene expression data analysis
Authors: Li Zongjin , Song Changxin , Yang Jiyu , Jia Zeyu , Chen Dongzhen , Yan Chengying , Tian Liqin , Wu Xiaoming
Published: May 2024

BACKGROUND: Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. OBJECTIVE: This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. METHODS: A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson’s or Spearman’s correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. RESULTS: Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. CONCLUSIONS: DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.
Personalized prediction of diabetic foot ulcer recurrence in elderly individuals using machine learning paradigms
Authors: Hong Shichai , Chen Yihui , Lin Yue , Xie Xinsheng , Chen Gang , Xie Hefu , Lu Weifeng
Published: May 2024

BACKGROUND: This study utilizes machine learning to analyze the recurrence risk of diabetic foot ulcers (DFUs) in elderly diabetic patients, aiming to enhance prevention and intervention efforts. OBJECTIVE: The goal is to construct accurate predictive models for assessing the recurrence risk of DFUs based on high-risk factors, such as age, blood sugar control, alcohol consumption, and smoking, in elderly diabetic patients. METHODS: Data from 138 elderly diabetic patients were collected, and after data cleaning, outlier screening, and feature integration, machine learning models were constructed. Support Vector Machine (SVM) was employed, achieving an accuracy rate of 93%. RESULTS: Experimental results demonstrate the effectiveness of SVM in predicting the recurrence risk of DFUs in elderly diabetic patients, providing clinicians with a more accurate tool for assessment. CONCLUSIONS: The study highlights the significance of machine learning in managing foot ulcers in elderly diabetic patients, particularly in predicting recurrence risk. This approach facilitates timely intervention, reducing the likelihood of patient recurrence, and introduces computer-assisted medical strategies in elderly diabetes management.
Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation
Authors: Gai Xinghui , Cai Huifang , Wang Junying , Li Xinyue , Sui Yan , Liu Kang , Yang Dewu
Published: May 2024

BACKGROUND: Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. OBJECTIVE: This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). METHODS: The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. RESULTS: The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. CONCLUSIONS: The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis.
Plane-wave medical image reconstruction based on dynamic Criss-Cross attention and multi-scale convolution
Authors: Yang Cuiyun , Bian Taicheng , Yang Jin , Hou Junyi , Cao Yiliang , Han Zhihui , Zhao Xiaoyan , Wen Weijun , Zhu Xijun
Published: May 2024

BACKGROUND: Plane-wave imaging is widely employed in medical imaging due to its ultra-fast imaging speed. However, the image quality is compromised. Existing techniques to enhance image quality tend to sacrifice the imaging frame rate. OBJECTIVE: The study aims to reconstruct high-quality plane-wave images while maintaining the imaging frame rate. METHODS: The proposed method utilizes a U-Net-based generator incorporating a multi-scale convolution module in the encoder to extract information at different levels. Additionally, a Dynamic Criss-Cross Attention (DCCA) mechanism is proposed in the decoder of the U-Net-based generator to extract both local and global features of plane-wave images while avoiding interference caused by irrelevant regions. RESULTS: In the reconstruction of point targets, the experimental images achieved a reduction in Full Width at Half Maximum (FWHM) of 0.0499 mm, compared to the Coherent Plane-Wave Compounding (CPWC) method using 75-beam plane waves. For the reconstruction of cyst targets, the simulated image achieved a 3.78% improvement in Contrast Ratio (CR) compared to CPWC. CONCLUSIONS: The proposed model effectively addresses the issue of unclear lesion sites in plane-wave images.
Applications of deep learning models in precision prediction of survival rates for heart failure patients
Authors: Zhang Qiaohui , Xu Demin
Published: May 2024

BACKGROUND: Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction. OBJECTIVE: The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods. METHODS: The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates. RESULTS: The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans. CONCLUSIONS: This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.
Differences of protein expression in enterococcus faecalis biofilm during resistance to environmental pressures
Authors: Jiang Wei , Zhang Youmeng , Yan Jie , He Zhiyan , Chen Weixu
Published: May 2024

BACKGROUND: Enterococcus faecalis biofilm was frequently found on the failed treated root canal wall, which survived by resisting disinfectant during endodontic treatment.Many researches have been conducted to explore the mechanisms of persistence of this pathogen in unfavorable conditions. However, no comprehensive proteomics studies have been conducted to investigate stress response in Enterococcus faecalis caused by alkali and NaOCl. OBJECTIVE: Enterococcus faecalis (E.f) has been recognized as a main pathogen of refractory apical periodontitis, its ability to withstand environmental pressure is the key to grow in the environment of high alkaline and anti-bacterial drug that causes chronic infection in the root canal. This study aims to focus on the protein expression patterns of E.f biofilm under extreme pressure environment”. METHODS: Enterococcus faecalis biofilm model was established in vitro. Liquid Chromatograph-Mass Spectrometer (LC-MS/MS)-based label free quantitative proteomics approach was applied to compare differential protein expression under different environmental pressures (pH 10 and 5% sodium hypochlorite (NaOCl)). And then qPCR and Parallel Reaction Monitoring Verification (PRM) were utilized to verify the consequence of proteomics. RESULTS: The number of taxa in this study was higher than those in previous studies, demonstrating the presence of a remarkable number of proteins in the groups of high alkaline and NaOCl. Proteins involved in ATP-binding cassette (ABC) transporter were significantly enriched in experimental samples. We identified a total of 15 highly expressed ABC transporters in the high alkaline environment pressure group, with 7 proteins greater than 1.5 times. CONCLUSIONS: This study revealed considerable changes in expression of proteins in E.f biofilm during resistance to environmental pressures. The findings enriched our understanding of association between the differential expression proteins and environmental pressures.
Effectiveness and safety of thoracic manipulation in the treatment of neck pain: An updated systematic review and meta-analysis
Authors: Yang Jiao , Zhao Sha , Zhang Rui , Huang Cheng , Huang Ke-Yao , Cheng Ying , He Cheng-Qi , Li Ling-Xin
Published: May 2024

BACKGROUND: The purpose of this meta-analysis was to evaluate the effectiveness and safety of thoracic manipulation (TM) in patients with neck pain (NP). OBJECTIVE: The purpose of this meta-analysis was to evaluate the effectiveness and safety of thoracic manipulation (TM) in patients with neck pain (NP). METHODS: Seven electronic databases were searched from their inception through October 2023 by two authors. The methodological quality assessments were performed with the Physiotherapy Evidence Database (PEDro) scale. Pain, cervical range of motion (ROM), disability, and quality of life (QOL) were estimated for TM treatment in patients with NP. RESULTS: Eighteen randomized controlled trials (RCTs) with 914 patients were included with a PEDro score of 6.923 ± 3.120. Pooled effect sizes of pain (SMD =-0.481, 95% CI -0.653 to -0.309, P= 0.000), disability (SMD =-1.435, 95% CI -2.480 to -0.390, P= 0.007), QOL-physical component score (PCS) (SMD = 0.658, 95% CI 0.290 to 1.025, P= 0.000), ROM of flexion (SMD = 0.921, 95% CI 0.287 to 1.555, P= 0.000), ROM of extension (SMD = 0.572, 95% CI 0.321 to 0.822, P= 0.000), ROM of left lateral flexion (SMD = 0.593, 95% CI 0.075 to 1.112, P= 0.025) and ROM of left rotation (SMD = 0.230, 95% CI 0.010 to 0.450, P= 0.04) were favored by the TM group. CONCLUSIONS: TM provides short-term effect on relieving neck pain, increasing cervical ROM, and disability in patients with NP without serious side effects. Continuous therapy and distraction therapy are recommended as optimal choice on reducing pain and improving cervical ROM, especially in patients with chronic NP (> 3 months). The TM-induced improvements in the QOL of patients with NP should be verified by more further high-quality RCTs.
Optimizing cardiovascular image segmentation through integrated hierarchical features and attention mechanisms
Authors: Liao Shijia , Wang Bin , Lin Shiming
Published: May 2024

BACKGROUND: Cardiovascular diseases are the top cause of death in China. Manual segmentation of cardiovascular images, prone to errors, demands an automated, rapid, and precise solution for clinical diagnosis. OBJECTIVE: The paper highlights deep learning in automatic cardiovascular image segmentation, efficiently identifying pixel regions of interest for auxiliary diagnosis and research in cardiovascular diseases. METHODS: In our study, we introduce innovative Region Weighted Fusion (RWF) and Shape Feature Refinement (SFR) modules, utilizing polarized self-attention for significant performance improvement in multiscale feature integration and shape fine-tuning. The RWF module includes reshaping, weight computation, and feature fusion, enhancing high-resolution attention computation and reducing information loss. Model optimization through loss functions offers a more reliable solution for cardiovascular medical image processing. RESULTS: Our method excels in segmentation accuracy, emphasizing the vital role of the RWF module. It demonstrates outstanding performance in cardiovascular image segmentation, potentially raising clinical practice standards. CONCLUSIONS: Our method ensures reliable medical image processing, guiding cardiovascular segmentation for future advancements in practical healthcare and contributing scientifically to enhanced disease diagnosis and treatment.
The effectiveness of core stabilization exercise using ultrasound biofeedback on motor function, balance control, gait speed and activities of daily living in stroke patients
Authors: Park Chanhee , Yoon Hyunsik
Published: May 2024

BACKGROUND: Patients with hemiparetic stroke experience diminished motor function, dynamic balance, and gait speed, which influence their activities of daily living (ADL). OBJECTIVE: This study aimed to determine the therapeutic effects of ultrasound biofeedback core exercise (UBCE) on Fugl-Meyer assessment (FMA), Time up and go (TUG), 10-meter walking test (10MWT) and functional independent measure (FIM) in participants with stroke. METHODS: Twenty-four stroke survivors consistently underwent UBCE or abdominal draw-in maneuver (ADIM) for 30 min/session, 3 days a week for 4 weeks. Clinical outcome measurements – the FMA, TUG, 10MWT, and FIM – were observed pre-and post-intervention. RESULTS: We detected significant changes in the FMA-lower extremities, TUG, 10MWT, and FIM scores between the UBCE and ADIM groups. UBCE and ADIM showed significant improvements in FMA-lower extremities, TUG, 10MWT, and FIM scores. However, UBCE showed more favorable results than ADIM in patients with stroke. CONCLUSIONS: Our research provides novel therapeutic suggestion of neurorehabilitation in stroke patients.
Exploring active ingredients and mechanisms of Coptidis Rhizoma-ginger against colon cancer using network pharmacology and molecular docking
Authors: Zeng Ting , Ling Caijin , Liang Yong
Published: May 2024

BACKGROUND: Colon cancer is the most prevalent and rapidly increasing malignancy globally. It has been suggested that some of the ingredients in the herb pair of Coptidis Rhizoma and ginger (Zingiber officinale), a traditional Chinese medicine, have potential anti-colon cancer properties. OBJECTIVE: This study aimed to investigate the molecular mechanisms underlying the effects of the Coptidis Rhizoma-ginger herb pair in treating colon cancer, using an integrated approach combining network pharmacology and molecular docking. METHODS: The ingredients of the herb pair Coptidis Rhizoma-ginger, along with their corresponding protein targets, were obtained from the Traditional Chinese Medicine System Pharmacology and Swiss Target Prediction databases. Target genes associated with colon cancer were retrieved from the GeneCards and OMIM databases. Then, the protein targets of the active ingredients in the herb pair were identified, and the disease-related overlapping targets were determined using the Venn online tool. The protein-protein interaction (PPI) network was constructed using STRING database and analyzed using Cytoscape 3.9.1 to identify key targets. Then, a compound-target-disease-pathway network map was constructed. The intersecting target genes were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for colon cancer treatment. Molecular docking was performed using the Molecular Operating Environment (MOE) software to predict the binding affinity between the key targets and active compounds. RESULTS: Besides 1922 disease-related targets, 630 targets associated with 20 potential active compounds of the herb pair Coptidis Rhizoma-ginger were collected. Of these, 229 intersection targets were obtained. Forty key targets, including STAT3, Akt1, SRC, and HSP90AA1, were further analyzed using the ClueGO plugin in Cytoscape. These targets are involved in biological processes such as miRNA-mediated gene silencing, phosphatidylinositol 3-kinase (PI3K) signaling, and telomerase activity. KEGG enrichment analysis showed that PI3K-Akt and hypoxia-inducible factor 1 (HIF-1) signaling pathways were closely related to colon cancer prevention by the herb pair Coptidis Rhizoma-ginger. Ten genes (Akt1, TP53, STAT3, SRC, HSP90AA1, JAK2, CASP3, PTGS2, BCl2, and ESR1) were identified as key genes for validation through molecular docking simulation. CONCLUSIONS: This study demonstrated that the herb pair Coptidis Rhizoma-ginger exerted preventive effects against colon cancer by targeting multiple genes, utilizing various active compounds, and modulating multiple pathways. These findings might provide the basis for further investigations into the molecular mechanisms underlying the therapeutic effects of Coptidis Rhizoma-ginger in colon cancer treatment, potentially leading to the development of novel drugs for combating this disease.
A computer-aid speech rehabilitation system with mirrored video generating
Authors: Cao Yang , Fan Chunjiang , Li Gang , Huang Jian , Zhang Jinli
Published: May 2024

BACKGROUND: Aphasia is a communication disorder that affects the ability to process and produce language, which severely impacting their lives. Computer-aid exercise rehabilitation has shown to be highly effective for these patients. OBJECTIVE: In our study, we proposed a speech rehabilitation system with mirrored therapy. The study goal is to construct a effective rehabilitation software for aphasia patients. METHODS: This system collects patients’ facial photos for mirrored video generation and speech synthesis. The visual feedback provided by the mirror creates an engaging and motivating experience for patients. And the evaluation platform employs machine learning technologies for assessing speech similarity. RESULTS: The sophisticated task-oriented rehabilitation training with mirror therapy is also presented for experiments performing. The performance of three tasks reaches the average scores of 83.9% for vowel exercises, 74.3% for word exercies and 77.8% for sentence training in real time. CONCLUSIONS: The user-friendly application system allows patients to carry out daily training tasks instructed by the therapists or the prompt information of menu. Our work demonstrated a promising intelligent mirror software system for reading-based aphasia rehabilitation.
A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks
Authors: Xie Hefu , Wang Bingbing , Hong Yuanzhen
Published: May 2024

BACKGROUND: Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease. OBJECTIVE: This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization. METHODS: The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF. RESULTS: The model’s effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities. CONCLUSIONS: The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.
The Maslach Burnout Inventory is not a measure of burnout
Authors: Bianchi Renzo , Swingler Gail , Schonfeld Irvin Sam
Published: May 2024

For decades, the Maslach Burnout Inventory (MBI) has been regarded as the “gold standard” for the assessment of burnout. The present paper demonstrates that the MBI fails to measure the construct it is purported to measure. On a deeper level, the problems affecting the MBI question the very idea of burnout. These problems may be unsurprising in light of the genesis of the burnout construct. Burnout emerged in the mid-1970s as a largely predefined entity. Burnout’s definition was not predicated on robust empirical investigations or sound theorizing, nor was it anchored in a systematic review of the literature on stress and health. Interestingly, other measures deemed to assess burnout, such as the Oldenburg Burnout Inventory, similarly fail to measure what they are claimed to measure. Despite far-reaching implications for our ability to study and promote occupational health, the flaws that undermine the conceptualization and measurement of burnout remain widely underappreciated. We hope this paper will help raise awareness of these flaws and correct current practices in research on job-related distress. Alternatives to burnout do exist and may enable us to support our workforce more effectively.
Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive
Authors: Gundala Srinivasa Rao , Anuradha Devi Tellapati , Sarada K. , Bharathi M. , Srikanth Goud B. , Neelima K. , Bhargavi K. S.
Published: May 2024

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.
Improving time intervals of blood glucose levels and insulin : the best practice bundle
Authors:   , Coyle Taylor N.
Published: May 2024

Nature and scope of the project: Proper diabetes management for patients in a hospital setting is critical due to their high propensity for co-morbidities (Kansas Department of Health and Environment, 2019). Dosing insulin off real-time blood glucose levels prevents hypo and hyperglycemic events (ADA, 2024). The purpose of this project is to decrease the time interval between the point-of-care blood glucose (POC-BG) and insulin administration on hospitalized patients by following a nursing bundle. The primary outcome variable was insulin administrations ?15 minutes from the POC-BG. Synthesis and analysis of supporting literature: The American Diabetes Association recommends hospitalized receive their blood glucose check, insulin administration, and coordinated with their meal when applicable. A nurse driven bundle was developed to improve the timing between the POC-BG and insulin administration. Project implementation: To increase compliance with the best practice bundle, the existing bundle was modified and reintroduced in December 2023 and POC-BG to insulin administration time intervals were measured in T1- January 2024 and T2- February 2024. Evaluation criteria: Pre-intervention compliance rates were measured, interventions were completed, and evaluation of the POC-BG to insulin administration times were measured with a goal of 75 percent being ?15 minutes in T1 and T2 on two separate units. Outcomes: There were 446 instances measured from 146 patients on the two units over T1 and T2. The CCU increased their compliance from pre-intervention level of 72.6 percent to T1-82.1 percent, and T2-86.2 percent. The primary objective was achieved on this unit. The HNVU increased their compliance from a pre-intervention level of 34.4 percent to T1-52.3 percent and T2-43.1 percent. Although their compliance has improved from baseline, they did not achieve the primary objective of 75 percent. Recommendations: This pilot project will continue at this site by expanding to other units. In the expansion, the data from this project will help guide the implementation of this bundle by recognizing target areas that are prone to not meeting goals. Research should also continue, when possible, in evaluating the number of hypo and hyperglycemic events in the pre and post pilot periods to measure if the bundle does affect glucose variability.
Leadership walkrounds and the effect on burnout
Authors:   , Sanders Jill
Published: May 2024

PURPOSE STATEMENT AND PICOT: This project aimed to implement Leadership WalkRounds and evaluate its impact on burnout in a surgical services department in rural, acute care hospital in Missouri. In healthcare workers (P), how has the implementation of Leadership WalkRounds (I), when compared to absent leadership rounding (C) affected burnout (O) at two months after intervention (T)? OBJECTIVES: 1. A 2 percent decrease in burnout among healthcare workers. 2. A 5 percent increase in psychological safety among healthcare workers. 3. A 50 percent increase in local leadership behaviors.
Unveiling Interconnectedness and Volatility Transmission: A Novel GARCH Analysis of Leading Global Cryptocurrencies
Authors: Kushwah Silky Vigg , Hundal Shab , Goel Payal
Published: May 2024

The cryptocurrency industry has grown erratically and at an alarming pace during its brief life. It has received massive attention from the practitioners, academicians, and especially media since Bitcoin was introduced in 2009. The current paper investigates the volatility dynamics of five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, DASH and Ripple’s XRP. It discloses the impact of volatility in the prices of other cryptocurrencies on the Bitcoin’s price volatility. The time series of all the cryptocurrencies prices for the period 2016 to 2022 are considered and preliminary analysis highlights that there exists conditional volatility in all the cryptocurrencies which allows us to use the family of GARCH model. The research findings reveal the significant effect of asymmetric past shocks on the current volatility of Bitcoins. Further, the volatility of remaining virtual currencies are also significantly affecting the Bitcoin’s current volatility. Therefore, the present study unveils the absence of diversification benefits in the market of virtual assets as significant effect of price volatility of other digital coins has been found on the price volatility of Bitcoins. The practical implication of our study is that the findings offer new information for investors and portfolio managers, who are attracted to invest in or hedging strategies in cryptocurrencies.
The Effects of Exchange Rate Fluctuation on Bangladeshi Exports: An ARDL Bound Testing Technique
Authors: Khan Md. Monir , Ahmed Asif
Published: May 2024

The objective of this paper is to investigate the influence of fluctuations in exchange rates on the export performance of Bangladesh. The analysis is based on annual data obtained from reputable sources such as the World Development Indicators (WDI) for the period spanning from 1981 to 2022. Autoregressive Distributive Lag (ARDL) bound testing approach is used to estimate the long-term as well as short-term relationship between the independent variables and dependent variable. The exports serve as the dependent variable, while the real exchange rate, gross capital formation (as a proxy for investment), inflation, and real interest rate are considered as the independent factors. The study's findings suggest that Bangladesh's exports are negatively impacted by exchange rates over the long and short terms. However, it is noteworthy that the magnitude of this negative effect is much greater in the long run as compared to the short run. Other controlled variables such as gross capital formation and real interest rate also have negative impact on exports in Bangladesh at lag one. Finally, this study suggests the implementation of policies that will foster better exchange rate stability as well as the pursuit of a stable and sustainable exchange rate policy.
Socio-Economic Determinants of the Financial Inclusion of Women and Men in Burkina Faso: An Analysis using the Sequential Logit Model
Authors: Compaore Dimaviya Eugène , Maiga Boukaré
Published: May 2024

This article identifies and analyses the socio-economic determinants of financial inclusion. Using data from the FinScope survey (2016) in Burkina Faso, a sequential logit model was used on a sample of 5012 individuals. The results indicate that level of education, age, access to television, urban location, marital status and time to access a bank branch are the main determinants of the first phase of the financial inclusion process. In the second phase of the process, level of education, age, access to television, urban location, marital status and time spent accessing a bank branch emerged as determinants of access to savings. In the third and final phase of the process, household size, access time to a bank branch and confidence in financial institutions were the only determinants of access to credit. The results also show a difference between the factors influencing financial inclusion for men and women.
The Implication of Political Risk and Specific Macroeconomic Variables on Total Revenue in Tourism Industry
Authors: Habanabakize Thomas , Mothibi Lerato
Published: May 2024

Political risk is one factor that influences tourism performance as a tourist is more sensitive to the hosting country’s political environment. In addition, to the effect of country risk on the tourism industry, the global economy has recently experienced severe shocks due to high inflation rates, political risk, and currency volatility. This paper aims to investigate the implications of political risk, inflation, and exchange rate volatilities on tourism revenue in South Africa. A sample of 144 monthly data was used to assess both long-run and short-run relationships amongst the study variables using the ARDL cointegration and error correction model approaches to achieve the study objective. Results indicated that political risk, inflation rate, and exchange rate volatility influence long-term revenue changes in the tourism industry. In contrast, real effective exchange rates and inflation significantly impact total revenue in the tourism sector. The findings also indicated that political risk has no short-term effect on tourism revenue. Based on these findings, the study recommends that the country's political stability increase, increasing the number of tourist arrivals and resulting in growing revenue for the tourism industry in S.A. Additionally, the South African reserve bank should revise its exchange-rate pegging, monetary targeting, and inflation targeting to reduce the effects of inflation on tourism revenue.
Die öffentliche Sittlichkeit im Kontext der Rechtsstaatlichkeit
Authors:   , Braun Aisha Paloma
Published: May 2024

Zieht die Teilnahme am rechtlichen Diskurs eine minimale Verpflichtung zu «richtigem» Verhalten nach sich? Diese Frage wird in der Rechtswissenschaft kontrovers diskutiert und in dieser Schrift grundsätzlich aus allgemein öffentlich-rechtlicher, aber auch aus strafrechtlicher und privatrechtlicher Perspektive beleuchtet. Während der dogmatische Fokus dieser Arbeit auf dem Legalitätsprinzip liegt, konzentriert sich die wissenschaftliche Behandlung der Wertedebatte, die vorwiegend anhand der privatrechtlichen Generalklauseln erfolgt, auf die Rezeption von gesellschaftlichen Wertvorstellungen im Recht. Diese Betrachtung wird durch die massgebende Rechtsprechung im öffentlichen Recht ergänzt, aus der hervorgeht, dass ein überindividueller Menschenwürdebegriff im Zentrum der sittlichen Frage im Recht liegt. Die Autorin findet darin einen Ansatz, um den rechtsgebietsübergreifenden peripheren Gehalt und den Kernbereich des Rechtsguts der öffentlichen Sittlichkeit zu erschliessen.
Alacsony sűrűségű idomtöltetek tesztrobbantása
Authors: Ember István
Published: May 2024

A 3D nyomtatás alkalmazása a robbantástechnikában is jelentős előnyöket hordozhat. Mivel széles körben elterjedt technológia, ezért a bevezetése nem jelenthet kihívást. A lehetséges alkalmazási terület pedig a különleges formájú idomtöltetek lehetnek. Vizsgálatomban olyan töltetek robbantási hatékonyságát mutatom be, amelyek nem tartalmaznak fémet, kizárólag alacsony sűrűségű anyagból készültek. A hatékonyságot tesztrobbantással vizsgáltam, amely során a kétféle eltérő béléstest-hajlásszöggel készült verzió megfelelt az elvárásoknak. A kevesebb befektetett energiát igénylő változat teljesítménye jobb volt, meggyőző képet mutatott.
Improvement of financial management tools of agricultural enterprises
Authors: Myniv R. , Khrystenko O.
Published: May 2024

Financial management of agricultural enterprises, like any other direction of managerial activity, is a complex system consisting of many interdependent elements that collectively determine the financial stability of the subject. The authors defined the principles of financial management in the system of management activities: the principle of complexity after the adoption of management decisions, the principle of variability of approaches to the development of management decisions, the principle of dynamism of management decisions, the principle of compliance of management decisions with the strategic goals of the enterprise. It has been established that the main goal, the achievement of which is aimed at managerial decisions within the framework of financial management, is to guarantee the financial stability of agricultural enterprises based on the organization of the competent distribution of financial resources, their use and movement, and that at the current stage of the development of economic relations, an important factor in increasing the competitiveness of agricultural enterprises enterprises are using digital products that allow automating the management system of business entities. It was found that in order to increase the efficiency of the financial management system, agricultural enterprises need to improve financial resources management tools, one of which is the implementation of the Enterprise Resource Planning System (ERP-system), which automates management activities. The use of such software products will allow: at the enterprise level – to improve the general financial condition and timely neutralization of threats capable of weakening the financial stability of entities; at the level of the national economy – to guarantee the financial security of the state. Improving the financial management system of agricultural enterprises based on the implementation of the ERP software product will allow to increase the effectiveness of management decisions in the field of financial resource management and, accordingly, significantly strengthen their financial stability.
Analysis of the «veterinary drugs» segment of the international stock market using IT
Authors: Stepanyuk O. , Senyk A. , Manziy O. , Pavlyuk N. , Senyk Y.
Published: May 2024

Since the application of specific methods of stock market analysis should be based on already existing and functioning methods, the main provisions of the theories of technical, fundamental, and statistical analysis are considered in the work. A method is proposed for the effective selection of financial investment directions by visualizing data available on the Internet about the availability and value of assets. As an example of the implementation of the specified methodology, such a field of wide practical application as the "veterinary drugs" segment was chosen. The use of visualization methods built into open software allows you to present information intuitively, succinctly and to summarize statistical and analytical data. A review and comparative analysis of the leading specialized software products for data visualization and business analytics used for working with large volumes of data is carried out. The Java programming language, which is open for public use, has been chosen as the main software product for the implementation of intentions, which, when using the appropriate attached open libraries, has wide possibilities of using built-in visualization and forecasting methods. With the use of the specified software, a project was implemented to review and analyze the movement of investments in the financial market in the form of historical time series, which allows monitoring the dynamics of the movement of assets in the selected segment and diversifying the set of financial assets. It is described how even a non-professional low-budget investor using application software products and programming languages open to the public can create information systems for analysis and forecasting, while supporting dynamic diversification, of the investment process in order to obtain the optimal set of financial assets in the selected time period. For the purpose of a practical study of the situation on the market of veterinary drugs, the attention is focused on the visual presentation of the dynamics of assets using the advantages of diversification when choosing the names of drugs and their quantity. The practical approach of analyzing the dynamics of the stock price, in particular shares, proposed with the use of IT, is used to assess the possibility of short-term investment while providing appropriate advice. As a result of directly conducted market analysis, in particular, statistical analysis, the structure of the segment of veterinary drugs in the stock markets of various leading manufacturers, the dynamics of stock indices and shares of some companies is investigated.
PSYCHOLOGICAL HEALTH OF THE STAFF OF THE STATE PENITENTIARY SERVICE OF UKRAINE DURING MARTIAL LAW
Authors:   Penitentiary Academy of Ukraine,  Chernihiv, Ukraine, Danylchenko T.V.
Published: May 2024

The article analyzes the parameters of psychological health of the personnel of the State Criminal and Executive Service of Ukraine during martial law. The study was conducted in the summer of 2023. The respondents were employees of penitentiary institutions from different interregional departments (N=127). The questionnaires “Mental Health Stability – Short Form” and “Modified BBC Subjective Wellbeing Scale” were used. We consider psychological health to be a dynamic state of internal well-being of an individual that ensures its optimal functioning. It is shown that prosperity as the highest degree of psychological health is more pronounced in middle managers, and the most depressed are freelance workers. Those with junior and senior managerial status occupy an intermediate position. The highest rates of psychological health parameters were recorded for personnel of both sexes, such as a sense of meaning in life and its interest. Women have a statistically significantly higher assessment of warm, trusting relationships with others. The lowest scores were recorded by respondents of both sexes in the following parameters: faith in people, faith in improving social conditions for life, and assessment of their contribution to society. For men, the most important thing for maintaining psychological health is a sense of their own importance for social life, as well as the experience of their own competence as an ability to solve everyday problems and manifest their life position. For women, the ability to lead a life that is meaningful in their own estimation and to be satisfied with their relationships is more important for maintaining psychological health. As the length of holding a service position increases, men are characterized by an increased sense of self-worth and satisfaction with team relationships, while women are characterized by a high level of professional competence. The results can be used by psychologists to provide more effective psychological support to the personnel of the SCES of Ukraine. Key words: psychological health, subjective social well-being, subjective well-being, penitentiary institutions, martial law.