bluuebunny/crossref_metadata_2025_split huggingface dataset:
Management and Coping with Care for Older Adults: Tales of Family Caregivers in Enugu State, Nigeria
Authors: Ene Jacinta , Onyishi Nkechinyere , Lum Tanyi 
Published: July 2024

Abstract

Background: Often, ageing is associated with a decline in health conditions and changes in the dietary needs of older adults. It then follows that the most essential line of action is caregiving. This paper examines how family caregivers manage and cope with the caregiving roles for older adults in south-east Nigeria.
Method: We sought to adopt a qualitative research approach. Focus group discussions and in-depth interviews were utilized in collecting data from 54 participants aged 15-59 years who provide caregiving roles to older adults aged 60 years and above. Data were analyzed thematically.
Results: We found that participants experience challenges managing care for older adults. This was owing to older adults' age, health status, attitude, financial incapacitation and educational status of the family caregivers. Most of the participants cope through human and non-human relationships. Findings show that communal relationships in rural communities is gradually declining. It was found that participants who cope through non-human relationships adopt strategies including maintaining a positive outlook of life, observing spiritual relationship with God and spending time playing games and music. Few participants narrated how they cope through adequate ageing nutrition and unsafe hygiene practices. We observed that family caregivers are in dire need of assistance in care management for older adults. Participants showed little knowledge of social work engagement in care management for older adults.
Conclusion: We recommend that the Nigerian government should assist with health insurance schemes, security grants and payment to registered family caregivers. International organizations, indigenes in the diaspora and local humanitarian groups in collaboration with social workers can help develop care aid programs for older adults’ wellbeing in low-resource countries.
A cross-sectional investigation of a mobile health clinic run by undergraduate pharmacy students providing services to underserved communities
Authors: Maughan Emily , Richardson Charlotte , Nazar Hamde 
Published: July 2024

Abstract

Background
Mobile health clinics have been used to provide healthcare to underserved communities, especially during the COVID-19 pandemic. Student-led clinics, operated by undergraduate health students, offer valuable training while serving these populations.
Aim
This cross-sectional study investigates the demographic characteristics and services provided by a mobile health clinic run by undergraduate pharmacy students, assessing its impact on underserved communities.
Methods
The mobile health clinic operated from October 2023 to April 2024, staffed by 36 fourth-year pharmacy students. Services included cardiovascular disease risk screening and lifestyle advice. Data were collected using an electronic primary care system and analysed with descriptive statistics.
Results
The clinic served 716 users, with a demographic breakdown of 53.2% female and 46.8% male, predominantly aged 31–60 years. Users were ethnically diverse. Services provided included blood pressure (91.3%), BMI (91.3%), and diabetes risk assessments (54.9%). Many users reported low risk for smoking and alcohol consumption, but varied levels of physical activity. Referrals were made for cardiovascular disease risk and lifestyle support.
Conclusions
The mobile health clinic effectively reached a diverse, underserved population, providing essential health services and facilitating student training. Further research is needed to evaluate the long-term impact and cost-effectiveness of such clinics, and the follow-up care for referred patients.
Evaluation of the effects of gradual increases in intra-abdominal pressure on blood pressure in a patient undergoing laparoscopic cholecystectomy.
Authors: Abedinzade Mohamadreza , Taghavi Negin , Farsani Nafise Mohamadi 
Published: July 2024

Abstract

Backgrounds
Gallbladder stones are a common health problem in developed countries, and the laparoscopic surgical method is widely used for treatment. It is important to identify surgical techniques that can reduce complications during and after surgery.
Methods
This study included 100 patients, with 50 in the intervention group who underwent surgery with stepwise increased intra-abdominal pressure, and 50 in the control group who had surgery with 15 mmHg pressure. Demographic information was recorded, and hemodynamic changes during surgery and postoperative complications were documented. Data was compared using independent t-tests, ANOVA, and Chi-square tests.
Results
The results of our study showed that the intervention group had significantly more stable mean heart rate and blood pressure (systolic, diastolic, and mean arterial pressure) during surgery compared to the control group. The intervention group also had significantly lower frequency of nausea, mean pain, and administration of the analgesic Pethidine during recovery. However, there were no significant differences between the two study groups in mean arterial oxygen saturation during surgery, duration of hospital stay in recovery, frequency of vomiting, and headache.
Conclusion
Based on our findings, it is recommended to increase intra-abdominal pressure using the stepwise method employed in our study for all patients undergoing cholecystectomy. This can help achieve hemodynamic stability during surgery, reduce pain in patients, and improve the overall quality of treatment.
Deep Transfer Learning Hybrid Techniques for Precision in Breast Cancer Tumor Histopathology Classification
Authors: Gupta Muniraj , Verma Nidhi , Sharma Naveen , Singh Satyendra Narayan , Singh R. K. Brojen , Sharma Saurabh Kumar 
Published: July 2024

Abstract

Breast cancer is one of the most prevalent causes of cancer-related death globally. Preliminary diagnosis of breast cancer increases the patient's chances of survival and healing. In this paper, we propose a hybrid deep transfer learning model integrating xception with support vector classifier (XSV) and xception with random forest (XRF) along with pre-processing technique to classify breast cancer as cancerous (malignant) or non-cancerous (benign) along comparative analysis of prominent machine learning classifiers, such as Random Forest Classifier (RFC), Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (K-NN), and Ada-boost. In experiment all the models are implemented on two openly accessible datasets: BreakHis and Breast Histopathology Images Database (BHID) across various metrics such as accuracy, area under the receiver operating curve, precision, recall, f1-score, Matthew's correlation coefficient, classification success index, and kappa at different magnification levels of images. Our proposed model that utilized the fine tuning of xception model in conjunction with RFC and SVC, surpass existing breast cancer classification methodologies. Specifically, the XSV that achieved accuracies of 89.26%, 85.87%, 90.17%, and 88.98%, while the XRF attained accuracies of 87.78%, 84.78%, 88.98%, and 87.61% for BreakHis at 40X, 100X, 200X, and 400X magnifications, respectively. For BHID at 40X magnification, the XSV and XRF models achieved accuracies of 87.35% and 87.29%, respectively. Employing this study will aid our medical practitioners and researchers in choosing an accurate model for tumor classification and our results will help medical professionals to classify the disease with precision.
Assessing the Impact of Human Activities on Ecosystem Asset Dynamics in the Yellow River Basin From 2001 to 2020
Authors: Ming Lei , Wang Yuandong , Liu Guangxu , Meng Lihong , Chen Xiaojie 
Published: July 2024

Abstract

The intensification of human activities in the Yellow River Basin has significantly altered its ecosystems, challenging the sustainability of the region's ecosystem assets. This study constructs an ecosystem asset index for the period from 2001 to 2020, integrating it with human footprint maps to analyze the temporal and spatial dynamics of ecosystem assets and human activities within the basin, as well as their interrelationships. Our findings reveal significant restoration of ecosystem assets, mainly attributed to the conversion of farmland back into natural habitats, resulting in a 15,994 km2 increase in ecological land use. Notably, 45.88% of the basin has experienced concurrent growth in both human activities and ecosystem assets, with ecosystem assets expanding at a faster rate (22.61%) than human activities (17.25%). Areas with high-quality ecosystem assets are expanding, in contrast to areas with intense human activities, which are facing increased fragmentation. Despite a global escalation in threats from human activities to ecosystem assets, the local threat level within the Yellow River Basin has slightly diminished, indicating a trend towards dispersion. Results highlight the critical importance of integrating spatial and quality considerations into restoration efforts to enhance the overall condition of ecosystem assets, especially under increasing human pressures. Our work assesses the impact of human activities on the dynamics of ecosystem assets in the Yellow River Basin from 2001 to 2020, offering valuable insights for quality development in the region, may provide a scientific basis for general watershed ecological protection and sustainable management in a region heavily influenced by human activity but on a path to recovery.
DSC-YOLO: Non-Destructive Defect Detection of Tire X-Ray Images Based on Dynamic Snake Convolution
Authors: Xu Guangpeng , Li Aijuan , Wang Xibo , Xu Chuanyan , Chen Jiaqi , Zheng Fei 
Published: July 2024

Abstract

Tire X-ray nondestructive testing before leaving the factory is crucial for driving safety. Given the complexity of tire structures and the diversity of defect types, traditional manual visual inspections and machine learning methods face significant challenges in terms of accuracy and efficiency. This study proposes an innovative tire X-ray image nondestructive testing technique based on the YOLOv5 model, incorporating several advanced technologies to enhance detection performance. Specifically, we introduce Dynamic Snake Convolution (DSConv), which adaptively focuses on slender and curved features within tires. Additionally, we have designed a C3 module based on DSConv, specifically targeting slender defects such as cord-overlap and cord-cracking. To improve the detection accuracy of small defects, we redesigned the neck network structure and introduced the Scale sequence feature fusion module (SSFF) and the Triple feature encoding module (TFE) to integrate multi-scale information from different network layers. Furthermore, we developed the Convolution Block Attention Module, integrated into the SSFF, which effectively reduces the interference of complex backgrounds and focuses on defect recognition. In the post-processing stage, we employed the Soft-NMS algorithm to optimize the confidence of candidate detection boxes, enhancing the accuracy of box selection. The experimental results show that compared to the YOLOv5 benchmark model, the algorithm proposed in this study achieved a 5.9 percentage point increase in mAP0.5 and a 5.7 percentage point increase in mAP0.5:0.95, demonstrating superior detection accuracy compared to current mainstream object detection algorithms and effectively completing the nondestructive testing task of tire defects.
GPT-3.5 altruistic advice is sensitive to reciprocal concerns but not to strategic risk
Authors: Schmidt Eva-Madeleine , Bonati Sara , Köbis Nils , Soraperra Ivan 
Published: July 2024

Abstract

Pre-trained large language models (LLMs) have garnered significant attention for their ability to generate human-like text and responses across various domains. This study delves into the social and strategic behavior of the commonly used LLM GPT-3.5 by investigating its suggestions in well-established behavioral economics paradigms. Specifically, we focus on social preferences, including altruism, reciprocity, and fairness, in the context of two classic economic games: the Dictator Game (DG) and the Ultimatum Game (UG). Our research aims to answer three overarching questions: (1) To what extent do GPT-3.5 suggestions reflect human social preferences? (2) How do socio-demographic features of the advisee and (3) technical parameters of the model influence the suggestions of GPT-3.5? We present detailed empirical evidence from extensive experiments with GPT-3.5, analyzing its responses to various game scenarios while manipulating the demographics of the advisee and the model temperature. Our findings reveal that, in the DG, model suggestions are more altruistic than in humans. We further show that it also picks up on more subtle aspects of human social preferences: fairness and reciprocity. This research contributes to the ongoing exploration of AI-driven systems' alignment with human behavior and social norms, providing valuable insights into the behavior of pre-trained LLMs and their implications for human-AI interactions. Additionally, our study offers a methodological benchmark for future research examining human-like characteristics and behaviors in language models.
Cattle identification based on multiple feature decision layer fusion
Authors: Li Dongxu , Li Qi , Wang Yueming , Han Mingshuo , Yang Mei , Li Baoshan 
Published: July 2024

Abstract

In breeding farm cattle feeding and watering scenarios, the number of cattle is dense, resulting in complex scenes and spatial congestion, and traditional single-modal cattle identification methods often encounter problems such as susceptibility to occlusion and low identification accuracy. Consequently, this study proposes a decision layer fusion cattle identity recognition method with multiple features of cattle face, cattle muzzle pattern, and cattle ear tag. The image is segmented into cattle face, cattle muzzle pattern, and cattle ear tag by the SOLO algorithm. Cattle face, and cattle muzzle patterns adopt the FaceNet network model with different backbones, respectively, while the ear tag adopts the PP-OCRv4 network model. The experiment extracts the features of the three parts respectively, stores these features in the database while comparing them with the database, calculates the Euclidean distance and text similarity with the sample data, and extracts the Top 3 confidence levels. Then, it performs One-Hot encoding processing for each of these features, which are used as inputs to the decision-making layer. An integration approach is used in the decision fusion part, where different integration strategies combine multiple base classifiers and compare the best performance as the final decision fusion recognition model. The results show that using the multimodal decision fusion method makes the recognition accuracy reach 95.74%, 1.4% higher than the traditional optimal unimodal recognition accuracy. The verification rate reaches 94.72%, 10.65% higher than the traditional optimal unimodal recognition verification rate. This fusion method achieves individual recognition and accurate management of cattle in breeding farms, bringing an efficient and accurate solution for the animal husbandry industry.
Multi-ensemble Bayesian Analysis of Inclusion-based Rock-physics Models
Authors: Spikes Kyle T. , Sen Mrinal K. 
Published: July 2024

Abstract

A statistics-based approach to rock physics often includes the calculation of a series of simulations to fit data along with probability associated with the modeling to characterize uncertainty. We present a Bayesian approach to determine the most probable rock-physics model (e.g., inclusion based). Our results also feature combinations of highly probable model inputs from posterior distributions; these combinations result from using of many different sets of input values from a prior distribution where each set corresponds to an ensemble. Exhaustive sampling allows for the calculation of the full posterior distribution for each ensemble. We demonstrate this method using two inclusion-based rock-physics models, the self-consistent and differential effective medium models along with a carbonate laboratory data set. Results indicate that the latter of the two models is the most probable. Analyses of the underlying model inputs indicate multiple but distinct clusters among those inputs. The problem is computationally demanding and requires parallel computation for tractability. Results from this work are applicable to data sets with similar velocity-porosity trends. More generally, the method is applicable to any other data set and relevant rock-physics models of interest.
CiteSpace-based Knowledge Graph Visualization of Mendelian Randomization Studies in Cancer
Authors: Kaiyue Zhang , Lai Wei , Jiayu Xiu , Tingjun Wang , Yuanpeng Huang 
Published: July 2024

Abstract

Objective
This study aims to perform a visual analysis of the literature on Mendelian randomization in the field of cancer, summarizing its developmental trajectory, current research status, and hot topics to provide references for future research directions.
Methods
Literature on Mendelian randomization studies in the field of cancer was retrieved from the Web of Science Core Collection up until April 18, 2024. The analysis and visualization of publication trends, author and institutional collaboration networks, keyword co-occurrence, clustering, timeline graphs, and burst terms were conducted using CiteSpace6.3.R1.
Results
A total of 964 English-language articles were included, showing an increasing trend in the number of articles using MR for cancer-related research, with a sharp rise in recent years. Despite the large volume of publications, there has not yet been a large-scale formation of collaborative groups among authors and institutions. The research hotspots include cancers such as breast cancer, colorectal cancer, prostate cancer, and lung cancer. Discussions on key risk factors include macro indicators such as BMI, hematological micro indicators such as sex hormones, symptoms such as back pain, and other diseases such as obesity. Research methods are often used in conjunction with meta-analysis.
Conclusion
The field of cancer Mendelian randomization research is heating up, with research continuously deepening and improving. Future efforts should strengthen collaboration and communication between researchers and institutions, delve deeper into current research hotspots, and provide research directions and theoretical bases for disease prevention and the development of new therapies.
The Quest for Optimal Ketamine Dosing Formula in Treatment-Resistant Major Depressive Disorder
Authors: Kwaśna Julia , Cubała Wiesław Jerzy , Kwaśny Aleksander , Wilkowska Alina 
Published: July 2024

Abstract

Background
Emerging evidence indicates that intravenous ketamine is effective in managing treatment-resistant unipolar and bipolar depression. Clinical studies highlight its favorable efficacy, safety, and tolerability profile within a dosage range of 0.5-1.0 mg/kg based on actual body weight. However, data on alternative dosage calculation methods, particularly in relation to body mass index (BMI) and therapeutic outcomes, remain limited.
Methods
This retrospective analysis of an open-label study aims to evaluate various dose calculation strategies and their impact on treatment response among inpatients with treatment-resistant major depressive disorder (MDD) (n = 28). The study employed the Boer and Devine formulas to determine lean body mass (LBM) and ideal body weight (IBW), and the Mosteller formula to estimate body surface area (BSA). The calculated doses were then compared with the actual doses administered or converted to a dosage per square meter for both responders and non-responders.
Results
The findings support using actual body weight-based dose calculations for ketamine in treatment-resistant MDD.
Conclusions
This preliminary analysis does not endorse alternative ketamine dosing methods based on different body weight metrics. Incorporating body impedance analysis and waist-to-hip ratio measurements could improve the accuracy of ketamine dosing.
In-chip critical plasma seeds for laser writing of reconfigurable silicon photonics systems
Authors: Grojo David , Wang Andong , Das Amlan , Fedorov Vladimir , Sopeña Pol , Tzortzakis Stelios 
Published: July 2024

Abstract

Ultrafast laser three-dimensional (3D) writing has made breakthrough in manufacturing processes with for instance the advent of super-resolution 3D printing and perennial optical memory technologies. However, similar precision applications were inaccessible for silicon and other semiconductor technologies due to a lack of in-chip controllability of intense infrared beams caused by strong propagation nonlinearities. To solve this problem, plasma-optics concepts are promising since a single ultrashort laser pulse, even if inappropriate for direct writing, can readily inject high-density free-carriers inside semiconductors. To achieve highly localized and reliable processing, we create plasma seeds with tightly focused pre-ionizing femtosecond pulses. We show how critical density conditions can be found under damage threshold inside silicon. This is used for extremely confined energy deposition with a synchronized writing irradiation creating isotropic modifications with sub-diffraction-limit size for the first time. Drastic improvement is also found on the material change controllability leading to unique demonstrations including rewritable optical memories (>100 writing/erasure cycles) and graded-index functionalities. By solving its controllability issues with critical plasma seeds, ultrafast laser writing is poised to offer its full potential for flexible fabrication of reconfigurable monolithic silicon-based devices.