bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personality Types of Medical Students in Terms of Their Choice of Medical Specialty: Cross-Sectional Study
Authors: Tobiaszewska Małgorzata , Koweszko Tytus , Jurek Jonasz , Mikołap Karolina , Gierus Jacek , Mikulski Jantoni , Waszkiewicz Napoleon 
Published: October 2024

Background
Research on personality types among doctors reveals its impact on medical specialty choices, suggesting that considering personality in career planning may enhance work satisfaction and reduce burnout risks.

Objective
This study, encompassing 2104 medical students, explores how personality types, traits, and gender relate to specialty preferences.

Methods
Participants of this study were medical students from various universities in Poland. The study surveyed 2104 participants. Each participant completed a general questionnaire and a NERIS Type Explorer personality test, based on the Myers-Briggs Type Indicator inventory and the “Big Five” personality traits concept. The questionnaire was distributed on social media groups for medical students from all Polish universities. An exploratory statistical analysis was performed to find relationships. For each tested relationship a Fisher exact test was conducted and the significance level was P<.05. Each test resulted in a P value and odds ratio (OR) with a CI. To ensure we included undecided students and obtained meaningful data, we allowed participants to select up to three medical specialties from the 77 available in Poland at the time of the study.

Results
The findings unveil significant relationships between gender, personality types, traits, and specialty preferences. Women tended to favor Neonatology (OR 9.15, 95% CI 3.02-45.46), while men leaned toward Orthopedics and traumatology of the locomotor system (OR 7.53, 95% CI 4.87-11.94). Extroverted, Intuitive, Feeling, Prospecting, and Turbulent students showed a heightened interest in Psychiatry (OR 2.23, 95% CI 1.64-3.01), whereas Introverted, Observant, Feeling, Judging, and Turbulent types favored Family Medicine (OR 2.98, 95% CI 2.08-4.24) and Pediatrics (OR 2.13, 95% CI 1.51-2.99).

Conclusions
In conclusion, this research establishes a link between personality and medical specialty selection. Taking into account the significant role of personality traits, it should be considered to integrate them into the process of selecting a medical career or designing a medical curriculum. This approach may allow for the customization of programs to match students’ traits, thereby cultivating improved clinical communication skills, fostering interprofessional collaboration and ultimately enhancing treatment outcomes and professional fulfillment among physicians. The main limitation of this study is that it was conducted on medical students, who lack the full knowledge of the work as a specific specialist. A study surveying medical doctors with longer internships across different wards could be conducted to check for any variabilities. Moreover, there are other significant factors that influence one’s medical specialty choice. Certainly, this area could be further explored.
Risk factors of cholangiocarcinoma in areas not endemic for liver fluke infection
Authors: Teerasarntipan Tongluk , Phuensan Pawat , Phathong Chonlada , Pinlaor Somchai , Mekaroonkamol Parit , Chaiteerakij Roongruedee 
Published: October 2024

Abstract

Background
Thailand has the world’s highest prevalence of cholangiocarcinoma (CCA), especially in the endemic area of liver fluke Opisthorchis viverrini infection. However, other regions of Thailand still have relatively high CCA prevalence.

Objectives
We aimed to determine CCA risk factors in areas not endemic for OV infection.

Methods
A case-–control study was performed at a referral center during December 2016–December 2017. We collected blood samples and information from CCA patients and identified them as cases. The control group comprised patients who visited a gastrointestinal clinic for colorectal cancer screening colonoscopy. Logistic regression analysis was used to determine risk factors for CCA.

Results
Of 138 participants, O. viverrini infection rate was higher in the case than in the control group (57.1% vs. 36.1%, P = 0.023). Male, O. viverrini infection, smoking, alcohol consumption, and biliary tract diseases were independent risk factors, whereas diabetes, obesity, and cirrhosis were not associated with CCA. By age and sex-adjusted analysis, chronic biliary tract diseases, especially choledochal cysts and smoking, were risk factors for CCA, with adjusted odds ratio (aOR) of 12.7 (95% confidence interval [CI]: 1.4–116.9) and 3.8 (95% CI: 1.3–11.8), respectively, while O. viverrini infection became insignificant risk for CCA (aOR 1.8, 95% CI: 0.8–4.1).

Conclusions
In contrast with endemic areas for O. viverrini infection, chronic biliary tract diseases and smoking are major risk factors, whereas O. viverrini infection has trivial contribution to the development of CCA.
Interrelationship Between Emergence and Yield Parameters in Mung Bean: Implication for Selection
Authors: Oloyede-Kamiyo Qudrah Olaitan , Adewumi Adedotun Daniel , Ukachukwu Paul Chiedozie , Oladipo Mayowa Segun 
Published: October 2024

Abstract
Rapid and uniform seed germination and seedling emergence have been associated with grain yield under diverse environmental conditions. Twenty-one (21) mung bean accessions were evaluated at Ile-Ife and Kishi out-stations of the Institute of Agricultural Research and Training (IAR & T), Nigeria, to identify the emergence parameter(s) that could be selected for improvement of mung bean for yield. The experiment was laid out in a randomized complete block design (RCBD) in three replications. Data were collected on emergence and yield parameters and analysed using the Statistical Analysis Software (SAS). Mean, heritability, and correlation coefficient were estimated. Path coefficient analysis was used to partition correlations into direct and indirect effects using seed yield as the dependent variable. Heritability estimate was moderate to high for most of the traits. Accessions 3, 6, 14 and 15 with high pod and seed yield had also high values of coefficient of velocity of emergence (CVE) and emergence percentage (E%). All emergence parameters except emergence energy had significant correlation with yield traits. CVE and Emergence Index (EI) had strong positive correlation with number of seeds.pod-1 and pod yield. When only the emergence parameters were considered, CVE had highest direct effect (0.74) on seed yield followed by E% (-0.73) and EI (0.70). Total indirect effects of EI and CVE accounted for only 6.06% and 11.9% of the total correlation, respectively. It therefore suggests that EI and CVE are emergence parameters that could be selected for at early stage in improvement of mung bean for yield.
Potential Use of Algae and Seaweed Extracts as Protection Against Peach Leaf Curl
Authors: Čmiková Natália , Paulen Oleg , Mankovecký Jakub , Kačániová Miroslava 
Published: October 2024

Abstract
Plant pathogenic fungi are a major cause of plant diseases, contributing significantly to the global decline in food production. Their proliferation results in substantial crop losses, fruit and vegetable deterioration, reduced food accessibility, and billions of dollars in economic losses annually. While synthetic chemical fungicides are commonly used to combat fungal diseases, their environmental impact is concerning. Marine algae provide a sustainable alternative, offering diverse compounds with industrial applications and presenting an eco-friendly solution. Taphrina deformans is responsible for peach leaf curl, a widespread disease in peach trees that can severely impact crop productivity and tree lifespan. Seaweeds and algae, encountering various biological interactions and extreme abiotic conditions, have evolved defense mechanisms, including the production of biologically active substances. In our study, we investigated the efficacy of water extracts from six algae (Nannochloropsis sp., Tetraselmis chuii, Chaetoceros muelleri, Thalassiosira weissflogii, Tisochrysis lutea, Chlorella vulgaris), four seaweeds (Palmaria palmata, Chondrus crispus, Ascophyllum nodosum), and one cyanobacterium (Arthrospira platensis) at two different concentrations (1% and 3%) in suppressing the outbreak of peach leaf curl disease in vivo on peach trees. The spray was applied twice in spring just before budbreak. Algae and seaweed extracts showed significant suppression of peach leaf curl outbreaks on peach trees compared to controls. Following the application of water extract of Chlorella vulgaris at both concentrations, the lowest incidences of symptomatic disease expression were observed (1.00 ±0.00% and 0.25 ±0.50%). Algae and seaweed extracts demonstrate potential in controlling leaf curl disease in peach trees.
Enhanced Broadband Tri-Stable Energy Harvesting System by Adapting Potential Energy – Experimental Study
Authors: Koszewnik Andrzej , Ambrożkiewicz Bartłomiej 
Published: October 2024

Abstract
This paper presents the process optimization of some key parameters, such as the size of the air gap and distance between fixed neodymium magnets to enhance the vibration-based energy harvesting effect in the tri-stable energy harvesting systems and the improved tri-stable energy harvesting system being the proposed solution under weak excitation. In order to do it, firstly the distributed parameters model of the magnetic coupling energy harvesting system, including macro fiber composites of the 8514 P2 with a homogenous material in the piezoelectric fiber layer and nonlinear magnetic force, was determined. The performed numerical analysis of the conventional and the improved tri-stable energy harvesting system indicated that introducing an additional magnet to the tri-stable system leads to the shallowing of the depth of a potential well by decreasing the air gap between magnets and consequently generating higher power output and improving the effectiveness of the proposed improved tri-stable energy harvesting system. Experiments carried out on the laboratory stand allowed us to verify the numerical results as well as determine the optimal parameters of the magnetic coupling system. Due to it, the effectiveness of the proposed system versus the conventional tri-stable energy harvesting system is most enhanced.
Novel Pla Composites Modified with Steel Fibres and (3-Thiopropyl) Polysilsesquioxane Derivatives
Authors: Pakuła Daria , Sztorch Bogna , Romańczuk-Ruszuk Eliza , Marciniec Bogdan , Przekop Robert E. 
Published: October 2024

Abstract
In recent years, a significant increase in the development of new composite materials with desirable mechanical, thermal or surface properties has been observed. One of the popular polymers on the market is polylactide. This article explores how to modify the polymer using steel fibres and organosilicon compounds (SSQ-SH, SSQ-SH-OCT, and SSQ-SH-OFP) to enhance its properties. Test samples were obtained by injection molding with varying concentrations of 0.5%, 1%, 1.5%, 2.5%, and 5% of steel fibres. Mechanical tests, including tensile strength, elongation at break, and impact strength, were conducted, along with an analysis of the contact angle. The modified samples showed higher impact strength values, with the PLA /steel fibres /SSQ-SH sample seeing an increase of 12%. The addition of modifiers with fluoroalkyl groups led to a contact angle increase of 8.5% compared to neat PLA. Thermal tests (TGA) were also carried out to determine the influence of fibres and organosilicon compounds on decomposition.
Research on Social Network Data Analysis and Criminal Network Identification in Criminal Law
Authors: Yang Zhaoyu 
Published: October 2024

Abstract
The public security system is increasingly utilizing technology and big data for criminal network identification, making the identification of criminal suspects through social data a crucial aspect of current criminal network identification. Based on our research into the verbal features of networks used to portray criminal suspects, we constructed a criminal network identification system using social networks and complex network theory. We then designed the overall framework and main functional modules. We established the case data warehouse of public security agencies and built the social relationship network of criminal suspects. This paper compares the effectiveness of the complex network (CN) algorithm with other community delineation algorithms and utilizes the criminal network identification system it constructs for case data mining and analysis. The extended modularity values of the CN algorithm proposed in this paper on four real datasets are the largest among all algorithms, and the community delineation effect is optimal. In the mining of criminal gangs, a1, a3, and a15 represent the heads of the gangs, while a56 shares similar duty content with a1, a3, and a15, albeit at a lower rank. A65 is in charge of connecting and communicating with gang members.
A Study on the Relationship between Multiple Regression Analysis and Students’ Achievement Improvement in Higher Education English Teaching
Authors: Li Yi 
Published: October 2024

Abstract
Students’ English learning achievement is affected by many factors, and the study found that there is a significant correlation between the English teaching process on the improvement of students’ English achievement, especially the teaching design. Based on this, this paper analyzes the correlation between English teaching design and students’ academic performance improvement in colleges and universities using the multiple linear regression method. Based on the multiple covariance problem in the multiple linear regression model gives the two-parameter algorithm optimization at the α level, and finally applies the optimized multiple linear regression model by example, and gets the conclusion that the optimized multiple linear regression model with the two-parameter algorithm optimization at the α level is feasible. The main research results are as follows: after this paper improves the parameter algorithm of the model on the α level, it is found that the fit of the model also reaches 85% under the influence of multifactor (more than 5), which proves that this paper is feasible for the improvement of the two parameters on the α level. The t-values of the independent test results for the five dimensions of teaching objectives and content are 6.765, 4.792, 3.163, 5.226, and 4.123, respectively, and their significance is less than 0.01, which proves that there is a significant correlation between the five dimensions selected in this paper about the design of English language teaching and the improvement of the English language learning achievement. The predictor variables chosen for the regression process are able to explain the response variables to a large extent, so the model can be used to predict improvement in English achievement.
AIGC enables vocational education teaching-take the course of Audio and video editing processing as an example
Authors: Cao Ling , Hao Gang , Wu Houmin 
Published: October 2024

Abstract
The study integrates and models the PLS method in traditional structural equations, applies it to vocational education research, and constructs a vocational education model based on PLS-SEM. The model examines the factors that influence teaching quality in vocational education. Therefore, an intelligent teaching model for vocational education based on AIGC is proposed, and teaching experiments are conducted to explore the practical effects of the teaching model. The structural equation model of vocational education constructed in this paper has good reliability, validity, and predictive ability. The analysis found that vocational education has a positive effect on emotion and motivation and learning performance, and emotion and motivation have a positive effect on learning performance. The increases in the four dimensions and the overall level of the pre-and posttests of the control group were 0.10, 0.67, 0.31, −0.12, and 0.97, respectively, and the increases in the experimental group were 2.00, 3.79, 3.24, 4.25, and 15.28, respectively. The significance of each dimension of the posttest data of both groups is less than 0.05, indicating that the intelligent teaching model of vocational education based on AIGC has a significant positive effect on students’ audio-video editing and processing ability, and the teaching effect is significantly better than that of the conventional teaching mode.
Visualization Analysis of Research Related to Stress Urinary Incontinence
Authors: Zhang Ziwei , Wang Yan , Wu Shanhong 
Published: October 2024

Abstract
Objective: To analyze the research status of stress urinary incontinence in the past five years and predict future research hotspots. Methods: The Web of Science database was searched for relevant Literature on stress urinary incontinence. Citespace.5.8. R3 software was used to visualize the study with authors, cited authors cited journals, institutions, countries, Literature, and keywords as nodes. Results & Conclusions: The published papers increased steadily from 2017 to 2021. It is expected to continue growing in 2022. MBADER and HAYLEN BT represent the field. INTUROGYNECOLJ was the most frequently referenced journal. The University of Michigan and the University of Washington are significant institutions in this field. The United States is in the leading position in the international field. The current research hotspots include the evaluation of SUI and standardization of surgical treatment, comparing the advantages and disadvantages of various surgical methods, and the innovative research of surgical methods. It is predicted that the research frontiers in the next few years are electroacupuncture, pelvic floor muscle training, and stem cell injection for stress urinary incontinence, and pregnant and menopausal women will be the focus groups.
IOS application-side secure packet decryption of the mobile framework
Authors: Hou Shuangshuang , Jiang Jincheng , Wang Xibao , Gao Chengcheng , Xu Jiali 
Published: October 2024

Abstract
In transferring the data to the iOS client, the data needs to go through a communication tunnel and be decoded as plaintext information within the client. As a powerful dynamic pile inserter, Frida has played a significant role in decryption and reverse engineering for iOS applications. An attacker can use Frida to decrypt the iOS client for malicious actions like data tampering and destruction. This paper aims to explore client decryption methods for mPaas and BonreeAgent.
Application of Power Data Mining Analysis in Fault Diagnosis and Preventive Maintenance
Authors: Jiang Dan , He Yue , Wang Yuzhen , Chen Xi , Li Tao 
Published: October 2024

Abstract
In order to ensure the stable transmission of electric power, it is an effective way to diagnose and maintain the operating status of electric power equipment from the operation data of electric power equipment. This paper uses a stacked sparse autoencoder to design a training model to realize the data function operation function in the fault detection model. After collecting and classifying the power system data, the line current is standardized and transformed. Then, the processed data is input into the stacked sparse autoencoder, and the model is trained layer by layer. On this basis, the long-term memory network model is introduced to establish a fault diagnosis model. To solve the double-sample situation of power data, the maximum mean difference method must be used. A preventive maintenance strategy is constructed based on failure prediction and remaining life to optimize the implementation path. Evaluate the model’s value in terms of its performance, reliability, and economic benefits of preventive O&M methods. However, judging from the fuzzy fault degree, the electrical components with a high probability of failure are T
1, T
3, L
3, L
9, and B
1, and the fuzzy fault degrees are 0.3154, 0.2789, 0.0648, 0.2657, and 0.0678, respectively—fusion of multidimensional evidence. The components most likely to fail are T
1, T
3, and L
9. From the perspective of operation and maintenance costs, when the MMC maintenance time of Dublin Fang Electric Farm is 17 times, the lowest operation and maintenance cost is 22.8615 million yuan.
The impact of digital finance on the urban-rural income gap: an empirical analysis based on spatial econometric modeling
Authors: Tian Yanjie 
Published: October 2024

Abstract
China’s economy has stepped into the new normal development stage. Coordinating urban and rural economic development and continuously narrowing the income gap has become a more urgent strategic task, and digital finance is an important engine for economic development and transformation and upgrading, is bound to have a far-reaching impact on the narrowing of the urban-rural income gap. This paper first explores the essential relationship between digital finance and regional economic development and lists the important roles played by digital finance in regional economic development. The entropy power Topsis method is used to select variable indicators and construct the index system. Combining the Moran index, spatial spillover effect, and spatial panel regression model, the regional economic distance is used as the spatial weight matrix, and the corresponding elemental expressions are derived to construct the spatial econometric model of the urban-rural income gap by digital finance, and the model parameters are estimated by the great likelihood method. The model is applied to 31 provinces in China to measure their relevant variables from 2012 to 2022 and analyze the impact of digital finance on the urban-rural income gap from the perspectives of spatial autocorrelation and threshold regression. Through the experiment, it can be seen that the digital finance indexes of the eastern, central, and western regions are all negative, and they are significant at the 5%, 10%, and 10% levels, respectively, which indicates that the digital finance in these three regions stimulates the development of the financial industry while narrowing the urban-rural income gap. When the level of economic development crosses the first threshold value of 10.4365, the regression coefficient of digital finance decreases from −0.0348 to −0.0487, which again confirms that digital finance can reduce the urban-rural income gap.
An Intelligent Obstacle Avoidance Method for Six Degree of Freedom Robotic Arm Based on Assembly Sequence Planning
Authors: Wang Wei 
Published: October 2024

Abstract
Autonomous operation planning of robotic arms under complex task constraints is still a difficult problem in the current research in this direction, and the existing operation planning methods are facing challenges and urgently need to break through the technical bottleneck. This paper proposes an improved RRT algorithm for six-degree-of-freedom robotic arms, which can be used for obstacle avoidance path planning. Based on the assembly sequence planning method, combining the advantages of RRT and RRT-connect algorithm, applying the idea of target sampling to strengthen the convergence of the algorithm to the target point search, introducing the greedy algorithm to improve the efficiency of the algorithm, combining with the five times of the B-spline interpolation on the path of the smoothing optimization, to shorten the planning path time and length. The Robotics Toolbox toolkit in MATLAB is used to count the time, number of samples, and path length required for the planning of RRT and the improved RRT algorithm, and the dynamic planning library is used to configure the algorithm and verify the path planning effect of the improved algorithm. Compared with the traditional RRT algorithm, using the proposed SDPS-RRT Connect algorithm for six-degree-of-freedom robotic arm path planning, the minimum distance of the connecting rod from the obstacle is 51.4948cm, which is greater than the standard safety distance of 45cm, which verifies the feasibility of the algorithm in practical applications.