bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcranial alternating current stimulation improves quality of life in Parkinson’s disease: study protocol for a randomized, double-blind, controlled trial
Authors: Zhang Hong-yu , Hou Ting-ting , Jin Zhao-hui , Zhang Tian , Wang Yi-heng , Cheng Zi-hao , Liu Yong-hong , Fang Jin-ping , Yan Hong-jiao , Zhen Yi , An Xia , Du Jia , Chen Ke-ke , Li Zhen-zhen , Li Qing , Wen Qi-ping , Fang Bo-yan 
Published: March 2024

Abstract
Background
The neural cells in the brains of patients with Parkinson’s disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson’s disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson’s disease based on the central-peripheral-central theory.

Methods
The present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson’s disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes.

Discussion
The objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP.

Trial registration
Chinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.
On the maximum principle for relaxed control problems of nonlinear stochastic systems
Authors: Mezerdi Meriem , Mezerdi Brahim 
Published: March 2024

AbstractWe consider optimal control problems for a system governed by a stochastic differential equation driven by a d-dimensional Brownian motion where both the drift and the diffusion coefficient are controlled. It is well known that without additional convexity conditions the strict control problem does not admit an optimal control. To overcome this difficulty, we consider the relaxed model, in which admissible controls are measure-valued processes and the relaxed state process is governed by a stochastic differential equation driven by a continuous orthogonal martingale measure. This relaxed model admits an optimal control that can be approximated by a sequence of strict controls by the so-called chattering lemma. We establish optimality necessary conditions, in terms of two adjoint processes, extending Peng’s maximum principle to relaxed control problems. We show that relaxing the drift and diffusion martingale parts directly as in deterministic control does not lead to a true relaxed model as the obtained controlled dynamics is not continuous in the control variable.
Primary care involvement in clinical research – prerequisites, motivators, and barriers: results from a study series
Authors: Wangler Julian , Jansky Michael 
Published: March 2024

Abstract
Background
Long-term reinforcement in the role of primary care and improvement the healthcare system as a whole requires the involvement of GPs in clinical research processes. However, many clinical studies fail due to failure to achieve sample population targets amongst GPs and their patients. This issue has been identified and discussed, but effective strategies to overcome it are still lacking. One of the reasons is that the positions, requirements, and experiences of GPs on participating in clinical research have hardly been examined up to now.

Methods
The years 2021 and 2022 saw three quantitative and qualitative surveys amongst GPs in Germany with the aim of shedding light on the attitudes, experiences, and potential issues regarding the involvement of primary care in clinical research projects and participation in cluster-randomised controlled trials (cRCTs) in a general sense. This overview summarises and abstracts conclusions gained from the exploratory series of studies and compares the results with the current research situation. From here, this contribution will then develop an approach towards optimising the integration of GPs into clinical research.

Results
Most of the GPs asked associated clinical research with opportunities and potential such as closing gaps in healthcare, using evidence-based instruments, optimising diagnostic and therapeutic management, and reinforcement of multiprofessional healthcare. Even so, many GPs unsure as to how far primary care in particular would stand to benefit from studies of this type in the long term. Respondents were also divided on willingness to participate in clinical research. GPs having already participated in Innovation Fund projects generally saw a benefit regarding intervention and cost–benefit relationship. However, some also reported major hurdles and stress factors such as excessive documentation and enrolment requirements, greater interference in practice routines, and sometimes poor integration into project processes such as in communication and opportunities to play an active role in the project.

Conclusions
Results from the studies presented provide indications as to how GPs perceive clinical research projects and cRCTs as a whole and from their existing project experience, and on the requirements that studies would have to meet for GPs to be willing to participate. In particular, making sure that clinical studies fully conform with GPs would play a major role; this especially applies to freedom to make medical decisions, limitation of documentation obligations, interference in regular practice routine, greater involvement in research planning, and long-term reinforcement in the role of primary care. Clinical research projects and cRCTs should be planned, designed, and communicated for clear and visible relevance to everyday primary care.
Under the surface of teacher occupational wellness and effectiveness in higher education: a look into the mediator roles of work passion and emotion regulation via SEM analysis
Authors: Yu Xiumin , Ying Tongxiu 
Published: March 2024

AbstractTeacher occupational wellness and effectiveness are crucial aspects of a teacher's capacity to contribute to the advancement of excellence in education. Nevertheless, there is a dearth of considerable studies regarding the interconnections between work passion and emotion regulation in higher education. This study developed a model to demonstrate the interplay between the above-mentioned constructs to fill this research gap. To gather this information, the required scales were sent to 401 different university professors. Based on the findings of Structural Equation Modelling (SEM) and Confirmatory Factor Analysis (CFA), it is suggested that work passion and emotion regulation have the potential to enhance teacher occupational wellness and effectiveness in higher education. In the end, implications and directions for the future were presented to educators and researchers who are enthused about the potential of work passion, emotion regulation, and self-compassion for improving instructive practices.
Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets
Authors: Lin Chia-Ying , Guo Shu-Mei , Lien Jenn-Jier James , Tsai Tzung-Yi , Liu Yi-Sheng , Lai Chao-Han , Hsu I-Lin , Chang Chao-Chun , Tseng Yau-Lin 
Published: March 2024

Abstract
Background
Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images.

Methods
In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules.

Results
The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%.

Conclusion
A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.
Magnetic resonance imaging in dental implant surgery: a systematic review
Authors: Al-Haj Husain Adib , Zollinger Marina , Stadlinger Bernd , Özcan Mutlu , Winklhofer Sebastian , Al-Haj Husain Nadin , Schönegg Daphne , Piccirelli Marco , Valdec Silvio 
Published: March 2024

Abstract
Purpose
To comprehensively assess the existing literature regarding the rapidly evolving in vivo application of magnetic resonance imaging (MRI) for potential applications, benefits, and challenges in dental implant surgery.

Methods
Electronic and manual searches were conducted in PubMed MEDLINE, EMBASE, Biosis, and Cochrane databases by two reviewers following the PICOS search strategy. This involved using medical subject headings (MeSH) terms, keywords, and their combinations.

Results
Sixteen studies were included in this systematic review. Of the 16, nine studies focused on preoperative planning and follow-up phases, four evaluated image-guided implant surgery, while three examined artifact reduction techniques. The current literature highlights several MRI protocols that have recently investigated and evaluated the in vivo feasibility and accuracy, focusing on its potential to provide surgically relevant quantitative and qualitative parameters in the assessment of osseointegration, peri-implant soft tissues, surrounding anatomical structures, reduction of artifacts caused by dental implants, and geometric accuracy relevant to implant placement. Black Bone and MSVAT-SPACE MRI, acquired within a short time, demonstrate improved hard and soft tissue resolution and offer high sensitivity in detecting pathological changes, making them a valuable alternative in targeted cases where CBCT is insufficient. Given the data heterogeneity, a meta-analysis was not possible.

Conclusions
The results of this systematic review highlight the potential of dental MRI, within its indications and limitations, to provide perioperative surgically relevant parameters for accurate placement of dental implants.

Graphical Abstract
Research on cooling load estimation through optimal hybrid models based on Naive Bayes
Authors: Xu Ying 
Published: March 2024

AbstractCooling load estimation is crucial for energy conservation in cooling systems, with applications like advanced air-conditioning control and chiller optimization. Traditional methods include energy simulation and regression analysis, but artificial intelligence outperforms them. Artificial intelligence models autonomously capture complex patterns, adapt, and scale with more data. They excel at predicting cooling loads influenced by various factors, like weather, building materials, and occupancy, leading to dynamic, responsive predictions and energy optimization. Traditional methods simplify real-world complexities, highlighting artificial intelligence’s role in precise cooling load forecasting for energy-efficient building management. This study evaluates Naive Bayes-based models for estimating building cooling load consumption. These models encompass a single model, one optimized with the Mountain Gazelle Optimizer and another optimized with the horse herd optimization algorithm. The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is tested. Based on analysis through evaluation metrics, among the three candidate models, Naive Bayes optimized with the Mountain Gazelle Optimizer (NBMG) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 18% and 31% compared to the other two models (NB and NBHH) and achieving a maximum R2 value of 0.983 for cooling load prediction.
Predictors of maternal satisfaction with childbirth experience among first-time mothers in Phalombe District, Malawi
Authors: Chilikutali Andrew , Kumbani Lily , Kafulafula Ursula 
Published: March 2024

Background/Aims Assessing maternal satisfaction with care improves healthcare professionals' understanding of women's needs at maternal healthcare services and can encourage future use of hospital-based intrapartum care. This study's aim was to explore predictors of maternal satisfaction with their childbirth experience at health facilities in Phalombe District, Malawi. Methods A cross-sectional study was conducted with 254 first-time mothers, recruited using systematic random sampling. Data were collected using questionnaires and analysed using bivariate and multivariate analyses to determine predictors of maternal satisfaction. Results Only half (49.2%) of participants were satisfied with their childbirth experience. Welcoming and encouraging doctors/midwives (P<0.001) and being kept informed during labour (P=0.001) were the main predictors of maternal satisfaction. Conclusions Maternal satisfaction is impacted by a woman's relationship with her care providers and the information imparted during care. To improve maternal satisfaction with care at the study site, there is a need for healthcare providers to improve their interpersonal relationships with labouring women and provide adequate information during care.
Cross-sectional study of perceptions of competence and confidence in feline handling
Authors: White Gillian L , Fontaine Samantha J 
Published: March 2024

Background: Cats may be difficult to manage in the clinical environment as a result of behavioural responses to fear and stress, which can be misinterpreted as aggression. Registered veterinary nurses require both attributes of competence and confidence when working with feline patients. Methods: 152 registered veterinary nurses working in small animal practice were recruited to this cross-sectional mixed-methods online survey study. Results: The results show that perceptions of competence and confidence are high, and both attributes are linked. Perceived competence and confidence across different scenarios were scored, with both attributes positively correlating – this was significantly higher with participant enjoyment and when handling amenable cats. Scores for handling difficult cats were significantly impacted by participant age, qualification, time qualified, ownership and previous experience. However, sample bias cannot be discounted, and the sample size was small. Conclusions: Registered veterinary nurses should be supported to develop both competence and confidence over time, especially when working with challenging patients. Overcoming negative experiences may be important for improving feline care, and further training may be one way to support this.
Extreme Learning Machine Predicts High-Frequency Stream flow and Nitrate-N Concentrations in a Karst Agricultural Watershed
Authors: McGill Timothy , Ford William Isaac 
Published: March 2024

HighlightsA novel high-frequency dataset of streamflow, nitrate-N concentration, and soil moisture at depths throughout and below the root zone highlighted tight connectivity between soil moisture dynamics and nitrate-N concentrations spanning event to seasonal timescales.A TELM model successfully predicted both flow rate and nitrate exports from these complex systems because the time lag associated with the soil conditions was represented in model training.The inclusion of soil moisture and temperature data below the effective root zone was important to accurately capture the storm event hysteresis dynamics observed in the exported nitrate signals. Abstract. Efforts to reduce nitrogen contributions from karst agroecosystems have had variable success, in part due to an incomplete understanding of nitrogen source, fate, and transport dynamics in karst watersheds. Recent advancements in environmental sensors and data-driven artificial intelligence models may be useful in improving our understanding of system behavior and the linkages between soil hydrologic processes and karst nitrate loading dynamics. We collected 35 months of high-resolution streamflow, nitrate-N concentration, soil moisture and temperature (from 10-100 cm depths), and meteorological data in a karst agricultural watershed in the Inner-Bluegrass region of Central Kentucky. Two-layer extreme learning machine (TELM) models were developed to predict nitrate-N concentrations and flow rates as a function of meteorological and soil parameter inputs. Results suggest tight linkages between soil moisture gradients at different depths and nitrate-N concentrations at the watershed outlet. TELM modeling results supported visual observations from the high-frequency data and suggest that inclusion of both soil moisture and temperature parameters at all soil depths improved predictions of both flow rate and nitrate-N concentration (with optimal NSE values of 0.93 and 0.94, respectively, when all inputs were considered). Hysteresis analysis suggested that inclusion of the deepest soil layer (100 cm) was necessary to predict hysteresis observed during storm events. The findings of the study highlight the importance of variable activation of matrix waters in preferential flows throughout events and seasons and its subsequent impacts on nitrate-N concentrations. Results suggest that management models should incorporate vertical variability in soil hydrology to accurately characterize nitrate source and transport dynamics. Further, the results of hysteresis analysis underscore the importance of inclusion of hysteresis indices, in addition to typical model evaluation statistics, to ensure accurate representation of nutrient flow pathways. Keywords: Extreme learning machine, Karst agroecosystem, Nitrate, Water resources.
The effect of malnutrition on adult Covid-19 patient’s ICU admission and mortality in Covid-19 isolation and treatment centers in Ethiopia: A prospective cohort study
Authors: Jima Lencho Mekonnen , Atomsa Gudina Egeta , Allard Johane P. , Nigatu Yakob Desalegn 
Published: March 2024

Background
A new coronavirus was first identified in Wuhan, China in December 2019. Since the times of the 1918 influenza pandemic, malnutrition has been known as a risk factor for severity and mortality from viral pneumonia. Similarly, the recently identified SARS-Cov2 infection (COVID-19) and related pneumonia may be closely linked to malnutrition. Therefore, this study will contribute to new knowledge and awareness of the recording and evaluation of each COVID-19 patient’s nutritional status by assessing the effect of malnutrition on ICU admission and death of COVID-19 patients in developing countries.

Method
We conducted a prospective cohort study in adult COVID-19 patients admitted to selected COVID-19 Isolation and Treatment Centers, Addis Ababa, Ethiopia. Baseline data of the patients were collected using interviewer-administered structured questionnaire and data on the adverse outcomes of follow up were extracted from follow up chart. The main clinical outcomes (ICU admission and death) were captured every week of follow up. We ran a multivariate Cox’s regression analysis to determine the relationship between malnutrition at admission and its effect on ICU admission and death.

Results
A total of 581 COVID-19 patients were enrolled. From the total of recruited patients, 346 (59.6%) were males and 235 (40.4%) were females. The mean age of the respondents was 55 years (16.45) years. The Cox proportional hazard model controlled for sex, age group, number of co-morbidities, and number of medications found that malnutrition at admission was associated with ICU admission and death. When compared to well-nourished patients, the rate of ICU admission was significantly associated and found to be higher among underweight [(adjusted hazard ratio (AHR) = 10.02, 95% CI: (8.64–12.10)] and overweight [(AHR = 7.7, 95% CI: (6.41–9.62)] patients. The rate of survival probability was significantly associated and was found to be better among well-nourished patients (AHR = 0.06, 95% CI : (0.01–0.44) when compared with malnourished COVID-19 patients.

Conclusion
Malnutrition at the time of admission was shown to increase the risk of ICU admission and mortality among COVID-19 patients. Therefore, it is vital to evaluate patients’ nutritional condition early in their admission and provide timely intervention to minimize the effects on patients and the healthcare system.
Ways of Structuring Abstract Objects in German Engineering Discourse
Authors: Шнякина Наталья  Юрьевна 
Published: March 2024

The article is devoted to the study of the ways of structuring abstract objects in German engineering discourse. We argue the idea, that some non-material substances, described by means of language, are realized by a person as abstract objects which are described by a person using the experience of structuring geometric space. On the material of scientific texts, placed on specialized information portals, are traced the verbal regularities of abstract knowledge in the field of engineering communication through the use of direct and metaphorical spatial nominations. Using the method of conceptual modeling, it is concluded that the main ways of dividing abstract objects of science are segmentation and scaling. Segmentation implies the division of virtual space into parts (steps, phases, stages, segments), which are the stages of production or communication processes; scaling is the result of creating a system of divisions intended for determination of the manifestation level of any feature. The criteria for choosing language means of segmentation and scaling are: horizontal-vertical, static-dynamic, length-localization, part-whole, limited-unlimited.