bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adapting the Eysenck Personality Questionnaire-Revised Neuroticism Scale for Use in Epidemiologic Studies: A Psychometric Evaluation Using Item Response Theory in the UK Biobank
Authors: Pflanz Chris Patrick , Gallacher John , Bauermeister Sarah 
Published: April 2024

Abstract
Neuroticism has been described as a personality trait measuring components of mood instability, e.g. worry, anxiety, irritability, and sadness. Increased neuroticism places an individual vulnerable to mood disorders and is therefore highly relevant in epidemiologic research. However, the measurement of neuroticism remains challenging. We aimed to optimize the 12-item Eysenck Personality Questionnaire-Revised Neuroticism (EPQ-RN) scale for use in epidemiologic studies by identifying psychometrically efficient items using item-response theory (IRT). The 12-item EPQ-RN scale was evaluated by estimating an IRT model on data from 401,527 UK Biobank participants aged 39 to 73 years (M = 56.41 years; SD = 8.06), 53.68% female, 95.57% European. The IRT model yielded two item characteristics: item discrimination, an indicator of how well an item differentiates between respondents, and item difficulty, an indicator of the amount of the latent construct (neuroticism) needed to endorse an item. The EPQ-RN exhibited psychometric inefficiency with poor discrimination at extremes of the scale range. High and low scores are relatively poorly represented and uninformative suggesting that high neuroticism scores derived from the scale are a function of cumulative mid-range values. Following systematic item deletion, a 3-item scale was found to have high levels of discrimination, but offered a narrow range of difficulty i.e. was not sensitive to low levels of neuroticism. A 7-item scale was found to be most informative, providing high levels of discrimination across the range of neuroticism scores. In conclusion, the 12-item EPQ-RN scale could benefit from item deletions to reduce the participant’s burden in epidemiological settings.
Usefulness of an early sarcopenia screening in predicting short-term mortality in older patients discharged for acute heart failure .
Authors: Okoye Chukwuma , Morelli Virginia , Franchi Riccardo , Mazzarone Tessa , Guarino Daniela , Maccioni Lorenzo , Cargiolli Cristina , Calsolaro Valeria , Niccolai Filippo , Virdis Agostino 
Published: April 2024

Abstract
Purpose Sarcopenia is a potentially reversible syndrome is associated with an increased risk of cardiogenic cachexia and adverse outcomes in older patients with HF. Despite its significance, sarcopenia is often underdiagnosed due to the complexity of a thorough assessment in patients with acute heart failure. The purpose of this study was to evaluate whether early sarcopenia screening can predict the short-term prognostic risk in very old patients recently discharge for Acutely Decompensated Heart Failure (ADHF).Methods We consecutively enrolled patients aged 75 years or older hospitalized with acutely DHF in the Geriatrics Unit of a tertiary care hospital. All patients underwent physical examination, complete blood tests, point-of-care ultrasound, and a comprehensive geriatric assessment, including physical performance through SARC-F and Hand Grip Strength test (HGS). The thirty-day post-discharge mortality rate was assessed by phone interview.Results Out of 184 patients hospitalized with ADHF enrolled in the study (mean [SD], 86.8 [5.9] years, 60.3% female), 47 died within 30 days after discharge. By multivariate logistic analysis, HGS (β = -0.73 ± 0.03, p = 0.008) and SARC-F [adjusted OR = 1.18 (CI95% 1.03–1.33), p = 0.003] resulted independently associated with mortality. Furthermore, two SARC-F sub-items, namely, limitation in rising from a chair and history of falls [aOR: 3.26 (CI95% 1.27–8.34), p = 0.008; aOR: 3.30 (CI95% 1.28–8.49), p = 0.01; respectively] emerged as determinants of 30-days mortality.Conclusion An early sarcopenia assessment comprising SARC-F and HGS test independently predicts 30-day post-discharge mortality in oldest-old patients hospitalized for acute HF.
Low autumn activity of bats results in small potential of pest reduction
Authors: Blažek Ján , Konečný Adam , Bartonička Tomáš 
Published: April 2024

Abstract
In recent years, in combination with climate change and dry summers, geometrids have caused damage to economically important forest trees, particularly oak. We focused on their co-occurring predators, the forest-dwelling bats, and their potential to reduce the number of pest geometrids. To quantify the aggregation response of bats, we used acoustic monitoring of flight activity, availability of moth imagos, and genetic analysis of their faeces. Our results show that even in the presence of massive outbreaks of geometrids, bat foraging activity decreases as winter approaches. Furthermore, our results show that for those bats that were active, the overabundant geometrids were not their main prey. Drymonia ruficornis, Conistra vaccinii and Conistra erythrocephala (Noctuidae) were foraged as often or even more often than overabundant geometrids. Our results show that opportunistic behaviour of temperate bats is not evident at lower temperatures and that reasons other than abundance and accessibility of prey determine their prey preferences. Therefore, season and predator preferences may significantly affect the ecological services provided by bats.
Constructing a Prognostic Model for Colon Cancer: Insights from Immunity-related Genes
Authors: Li Ansu , Li Qi , Wang Chaoshan , Bao Xue , Sun Feng , Qian Xiaoping , Sun Wu 
Published: April 2024

Abstract
BackgroundColon cancer (CC) is a malignancy associated with significant morbidity and mortality within the gastrointestinal tract. Recurrence and metastasis are the main factors affecting the prognosis of CC patients undergoing radical surgery; consequently, we attempted to determine the impact of immunity-related genes.
Result We constructed a CC risk model based on ZG16, MPC1, RBM47, SMOX, CPM, and DNASE1L3. Consistently, we found that a significant association was found between the expression of most characteristic genes and TMB, MSI, and NEO. Additionally, a notable decrease in RBM47 expression was observed in CC tissues compared with that in normal tissues. Moreover, RBM47 expression was correlated with clinicopathological characteristics and improved DFS and OS among patients with CC. Lastly, immunohistochemistry and co-immunofluorescence staining revealed a clear positive correlation between RBM47 and CXCL13 in mature TLS region.
Conclusion We conclude that RBM47 was identified as a prognostic-related gene, which was of great significance to the prognosis evaluation of patients with CC and was correlated with CXCL13 in the TLS region.
The Effects of Disturbance on Woody Species Composition, Structure and Regeneration Status In Ura Natural Forest, Guangua district, Awi Zone, Ethiopia
Authors: Kassahun Achamyeleh Anteneh , Abate Dagim Fikir , Belete Dessie Assefa 
Published: April 2024

Abstract

Ethiopia had diverse forest resources. However, it is decreasing over time and leaving the country’s forest with only remnant patches. So, the current study was conducted to examine the species composition, structure, and regeneration status of woody plant species along the disturbance gradients. In order to collect data, the forest was first stratified into Low Disturbed (LD), Moderately Disturbed (MD, and Heavily Disturbed (HD) based on information from a reconnaissance site visit and consultation with key informants. The three transect lines were laid systematically, 200 m apart, and rectangular plots of 10 m by 20 m (200 m2) were laid at a 50 m distance interval along each transect, making up a total of 27 plots. The DBH and height of all woody individuals with ≥ 5 cm and ≥ 2 m were measured, respectively. Seedling and sapling data were recorded in four subplots of 2 m*5 m (10 m2) in the opposite corner of the main plot. Woody species density, richness, evenness, diversity, frequency, and IVI were determined for each disturbance class, and one-way ANOVA and mean comparison tests were used to examine significant differences among disturbance classes. A total of 37 woody species and 27 families were recorded. Based on structural analysis, only Croton macrostachyus has an inverted J shape. It is concluded that woody species was affected by human disturbance. So that a concentrated form of forest conservation should be given to protect from excessive human disturbance.
Renewable Energy MicroGrid Power Forecasting: AI Techniques with Environmental Perspective
Authors: Islam Amanul , Othman Fazidah 
Published: April 2024

Abstract

This research focuses on the accurate prediction of renewable energy generation in microgrid systems using artificial intelligence (AI) techniques. The study compares and evaluates different AI models, including Artificial Neural Networks (ANN), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), for forecasting solar and wind power output. The models are trained and tested using real-world data, and their performance is assessed using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Sum of Squared Errors (SSE). The results demonstrate that the ANN model achieves the lowest RMSE and MAPE values for wind power prediction, while the Fuzzy Logic model performs well in predicting solar power generation. These findings indicate the effectiveness of AI techniques in accurately forecasting renewable energy output in microgrid systems. The proposed approach has implications for optimizing the utilization and integration of renewable energy sources, leading to more efficient and sustainable microgrid operations. Future research directions may involve exploring advanced deep-learning models and incorporating additional environmental factors to further enhance the accuracy and reliability of renewable energy power forecasting in microgrids.
ETPSR: Employee Turnover Prediction with Security Recommendations
Authors: Chandgadkar Vaishnavi , Pathak Prachi , Solanki Aditya , Shrivastava Aryansh , Maktum Tabassum , Pulgam Namita 
Published: April 2024

Abstract

In today’s dynamic business environment, employee turnover poses a significantchallenge for organizations due to the competitive job market. Departing employ-ees may pose cybersecurity risks by retaining access to sensitive data and criticalsystems. This necessitates a focus on data security during the offboarding process.Traditional approaches lack proactive measures, relying on manual interventionsand potentially leaving gaps in data protection. To address this, the research pro-poses integrating a Cloud Services Security Recommendation (CSSR) model intothe prediction model. Leveraging machine learning and cybersecurity principles,the CSSR model identifies likely switchers and provides personalized security rec-ommendations. The research comprises of four components: a predictive model foridentifying potential switchers, an Explainable AI approach for behavior profilingand risk assessment, and integration with existing cybersecurity infrastructure toprovide tailored security recommendations. The CSSR model offers a proactivesolution to employee turnover and data security issues, enabling organizations tofortify their cybersecurity posture and protect sensitive information. The system employed in this study, the Employee Transition Prediction and Security Recom-mendations (ETPSR), integrates advanced analytics and cybersecurity principlesto offer tailored security measures during employee offboarding, ensuring dataintegrity and confidentiality throughout the transition process.
Implementation of a Full-Duplex Visible Light Communication System
Authors: Fotso S. B. Nono , Mbe Jimmi Hervé Talla , Ndjiongue Alain Richard , Nzeukou Armand C. 
Published: April 2024

Abstract
In telecommunication research, most outputs are based on simulation and/or analysis. Practical implementations are scarce. This is the case with visible light communication systems. With the knowledge that there is a gap between theoretical and practical implementation, we propose in this paper a two-way light communication system that provides efficient transmitting and receiving of files between two nodes. First, we develop hardware consisting of an emitting antenna, a receiving antenna, and a microcontroller. In order to ensure file transmission, we also develop software using the MATLAB programming language. Experimental results indicate that the bit error rate (BER) of the system increases with the transmission range, the data rate, and the angle of incidence. Accordingly, the recovered files match the transmitted files without any error correction scheme. An appropriate choice of system parameters can allow the BER to reach zero. An effective data rate of 1 Mbps is obtained with a BER of $6\times10^{-6}$. In addition, when data communication is not taking place, the designed modems provide illumination to the environment to meet the other requirements of visible light communication. Finally, the designed light-communication system proves cost-effective and easy to implement.
Joint Optimization of User Association and Resource Allocation in HetNets: A parameterized-DRL Approach
Authors: Fu Pingbo , Li Hui , Tao Xu , Wang Ziwei 
Published: April 2024

Abstract
Facing the problem of resources utilization in of multiple wireless communication systems with multiple coverage, convergence of heterogeneous network (HetNets) can reduce the burden and decrease deployment costs of a single communication network. Due to the non-convexity of joint optimization and the difficulty to obtain the feasible solution of the mixed operation space (i.e. discrete operation versus continuous operation) of HetNets, so it is a formidable challenge to achieve global optimization of energy efficiency (EE) and spectral efficiency (SE) simultaneously when facing user association (discrete) together with power allocation (continuous). Unlike the method of deep reinforcement learning (DRL) by discretizing continuous space directly, we proposed a novel parameterized-DRL that maximizes the performance of joint EE-SE while ensuring the quality of service (QoS) of downlink user devices (UEs) in HetNets. In addition, to solve the computationally intensive problem in the state-action space, an algorithm of parameterized-experience-replay dueling double DQN with multi-agent priority (P-MAPD3QN) is introduced to obtain an almost optimal QoS. Simulation results show that this algorithm improves the effectiveness of the system by 4.9% over traditional D3QN algorithm in terms of system capacity and 13.1% in terms of joint EE-SE performance.
Flapping Wing Energy Harvesting, Experiences with Modified Time Variations at high Re-Number
Authors: Geißler Wolfgang 
Published: April 2024

Abstract

Wind energy converters with rotating blades are most common for energy harvesting. In special cases a system of flapping wings may be of interest where the wings are oscillating in combined flapping (plunging) and pitching modes respectively. Then the ratio of plunging and pitching amplitudes is of main concern: for animal flight the thrust production and its optimization as well as the sufficient lift force are important, for energy harvesting the maximum energy extraction from the oncoming flow and a minimized energy input into the system are of main importance.
If one looks into the details of energy flows it is obvious that for animal flight the energy input via muscles is dominating whereas for energy harvesting main energy flow is transferred from the fluid into the oscillating system i.e., the direction of energy flow is reversed in both cases.
With these fundamental aspects in mind, it is of interest to increase and optimize the energy input from the fluid. One important measure may be the modification of the time variation: usually the time varies harmonically within one period of oscillation. In the present paper modified time variations are investigated in detail. Phase variation between modes as well as dynamic deformation of the airfoil are of further concern and will be discussed in the present paper.
The serine protease Brachyuran is highly expressed in the posterior midgut of the black soldier fly, Hermetia illucens, during the processing of horse droppings
Authors: Wakuda Megumi , Sakamoto Takuma , Tanaka Akane , Sugimura Satoshi , Higashiura Yuki , Nakazato Takeru , Bono Hidemasa , Tabunoki Hiroko 
Published: April 2024

Abstract
Objective
Livestock droppings cause some environmental problems, but they have the potential to be used as effective biomass resources. The black soldier fly (BSF), Hermetia illucens (Diptera: Stratiomyidae), is suitable for the efficient processing of livestock droppings. By using BSF larvae for the disposal of livestock droppings, we can obtain two valuable products: protein resources and organic fertilizer. However, there is not enough research on the digestive enzymes suitable for processing livestock droppings. Here, we aimed to construct an efficient BSF processing system using livestock droppings, and we explored the digestive enzymes involved in this process.
Results
First, we investigated the characteristics of transcripts expressed in the midgut of H. illucens and found that immune response-related genes were expressed in the midgut. Then, we investigated digestive enzymes and identified a novel serine protease, HiBrachyuran, whose mRNA was highly expressed in the posterior midgut when H. illucens larvae fed on horse droppings. Despite the low protein content of horse droppings, larvae that fed on horse droppings accumulated more protein than did those in the other groups. Therefore, HiBrachyuran may contribute to digestibility in the early stage of protein degradation in H. illucens larvae fed on horse droppings.
FDEM simulation study on the development of water-conducting fractures in fault-bearing roof under repeated mining of extra-thick coal seams
Authors: Longquan Mai , Hao Li , Zhiqin Kang 
Published: April 2024

Abstract

The formation of water-conducting fractures in overlying strata caused by underground coal mining not only leads to roof water inrush disasters, but also destroys the sustainability of underground aquifers. This phenomenon is particularly significant in extra-thick coal seams and fault-bearing areas. Numerical simulation is an effective method to predict the failure range of mining overburden rock with low cost and high efficiency. The key to its accuracy lies in reasonable constitutive model and simulation program. In this study, considering that the three parts of penetrating cracks, non-penetrating cracks and intact rock blocks are often formed after rock failure, the contact state criterion and shear friction relationship of discrete rock blocks and the mixed fracture displacement-damage-load relationship are established respectively. Combined with Mohr-Coulomb criterion, the constitutive model of mining rock mass deformation-discrete block motion and interaction is formed. On this basis, according to the engineering geological conditions of Yushupo Coal Mine, a numerical model for the development of water-conducting cracks in the roof with faults under repeated mining of extra-thick coal seams is established. The results show that: The constitutive relation of continuous deformation-discrete block interaction of overlying strata and the corresponding finite element-discrete element FDEM numerical program and VUSDFLD multi-coal seam continuous mining subroutine can numerically realize the formation process of faults and water flowing fractures in overlying strata under continuous mining of extra-thick multi-coal seams. The toughness of sand mudstone is low, and the fracture will be further developed under the repeated disturbance of multi-thick coal seam mining. Finally, it is stabilized at 216m-226m, and the ratio of fracture height to mining thickness is 14.1. When the working face advances to the fault, the stress concentration occurs in the fault and its overlying rock, which leads to the local fracture of the roof rock mass and the formation of cracks. The fault group makes this phenomenon more obvious. The results have been preliminarily applied and tested in Ningwu mining area, which provides theoretical support for further development of roof water disaster control under the condition of extra-thick coal seam.
Machine Learning Prediction of Intestinal α-Glucosidase Inhibitors Using a Diverse Set of Ligands: A Drug Repurposing Effort with DrugBank Database Screening
Authors: Odugbemi Adeshina I. , Nyirenda Clement , Christoffels Alan , Egieyeh Samuel A. 
Published: April 2024

Abstract
The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Glucosidase inhibitors play a crucial role in managing postprandial hyperglycemia and reducing the risk of complications in Type 2 DM. Quantitative Structure-Activity Relationship (QSAR) modeling is critical in computational drug discovery. However, many QSAR studies on α-glucosidase inhibitors often rely on limited compound series and statistical methods, restricting their applicability across wide chemical space. Integrating machine learning (ML) into QSAR offers a promising avenue for discovering novel therapeutic compounds by handling complex information from diverse compound sets. Our study aimed to develop robust predictive models for α-glucosidase inhibitors using a dataset of 1082 compounds with known activity against intestinal α-glucosidase (maltase-glucoamylase). After thorough data preparation, we employed 626 compounds to train ML models, generating different training data of three distinct molecular representations: 2D-descriptors, 3D-descriptors, and Extended-connectivity-fingerprint (ECFP4). These models, trained on random forest and support vector machine algorithms, underwent rigorous evaluation using established metrics. Subsequently, the best-performing model was utilized to screen the Drugbank database, identifying potential α-glucosidase inhibitor drugs. Drug repurposing, an expedited strategy for identifying new therapeutic uses for existing drugs, holds immense potential in this regard. Molecular docking and dynamics simulations further corroborated our predictions. Our results indicate that 2D descriptors and ECFP4 molecular representations outperform 3D descriptors. Furthermore, drug candidates identified from DrugBank screening exhibited promising binding interactions with α-glucosidase, corroborating our ML predictions and supporting their potential for drug repurposing.