bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Data Set of Synthetic Utterances for Computational Personality Analysis
Authors: Neuman Yair , Cohen Yochai 
Published: June 2024

AbstractThe computational analysis of human personality has mainly focused on the Big Five personality theory, and the psychodynamic approach is almost nonexistent despite its rich theoretical grounding and relevance to various tasks. Here, we provide a data set of 4972 synthetic utterances corresponding with five personality dimensions described by the psychodynamic approach: depressive, obsessive, paranoid, narcissistic, and anti-social psychopathic. The utterances have been generated through AI with a deep theoretical orientation that motivated the design of prompts for GPT-4. The dataset has been validated through 14 tests, and it may be relevant for the computational study of human personality and the design of authentic persona in digital domains, from gaming to the artistic generation of movie characters.
Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence
Authors: Wefki Hossam , Khallaf Rana , Ebid Ahmed M. 
Published: June 2024

AbstractThis research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.
Shrinking sizes of trout and salamanders are unexplained by climate warming alone
Authors: Arismendi Ivan , Gregory Stanley V. , Bateman Douglas S. , Penaluna Brooke E. 
Published: June 2024

AbstractDecreases in body sizes of animals related to recent climate warming can affect population persistence and stability. However, direct observations of average sizes over time and their interrelationships with underlying density-dependent and density-independent processes remain poorly understood owing to the lack of appropriate long-term datasets. We measured body size of two species common to headwater streams in coastal and Cascades ecoregions of the Pacific Northwest of North America over multiple decades, comparing old-growth and managed forests. We found consistent decreases in median length of Coastal Cutthroat Trout Oncorhynchus clarkii clarkii, but a coexisting species, the Coastal Giant Salamander Dicamptodon tenebrosus, appears to be more resilient to size changes over time. Based on observed trends, adult trout have decreased in length by 6–13% over the last 30 years. Length decreased more in larger compared to smaller animals, suggesting that these effects reflect changes in growth trajectories. Results from a model-selection approach that included hydroclimatic and biological information as covariates in one of our study ecoregions demonstrated that stream temperature alone did not explain observed length reductions. Rather, a combination of density-dependent (animal abundances) and local density-independent factors (temperature, habitat, and streamflow) explained observed patterns of size. Continued decreases in size could lead to trophic cascades, biodiversity loss, or in extreme cases, species extirpation. However, the intricate links between density-independent and density-dependent factors in controlling population-level processes in streams need further attention.
Cervical cancer screening and vaccination acceptability and attitudes among Arab American women in Southeastern Michigan: a qualitative study
Authors: Khoja Lilah , Torres Heidi G. , Hak Layla , Aljebori Manar , Phung Minh Tung , Sakleh Andrea , Stiffler Matthew , Tariq Madiha , Pearce Celeste Leigh 
Published: June 2024

AbstractAmong Arab-American women in Michigan, rates of cervical cancer screening are lower than those in non-Hispanic White and Black women in the state. A deep understanding of the Arab community’s perspective on cervical cancer screening is needed to address the disparity in rates across populations in Michigan. Arab and Chaldean women across Michigan were invited to participate in Zoom-based focus groups to understand the attitudes, acceptability, and barriers of cervical cancer screening among this population. Four focus groups with a total of 19 women aged 30 to 61 were conducted. The focus groups were conducted in English, Arabic, or both languages. The guided discussion was focused on knowledge of cervical cancer and Human papillomavirus (HPV) and its transmission, attitudes towards HPV vaccination, and attitudes towards cervical cancer screening. HPV self-sampling as an alternative to traditional provider-based screening was specifically discussed as this has been proposed as a way to increase screening in hard-to-reach populations. The conversations revealed insights related to barriers at the individual and community levels for screening and vaccination, attitudes towards preventive health care including screening, a need for accessible women’s health literature, and health education. The women also discussed vaccine hesitancy related to HPV and COVID-19, suggesting a need for targeted community interventions.
Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
Authors: Javed Muhammad Faisal , Shahab Muhammad Zubair , Asif Usama , Najeh Taoufik , Aslam Fahid , Ali Mujahid , Khan Inamullah 
Published: June 2024

AbstractThe escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.
SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer’s disease
Authors: Zhuang Xianbo , Xia Yitong , Liu Yingli , Guo Tingting , Xia Zhangyong , Wang Zheng , Zhang Guifeng 
Published: June 2024

AbstractThe slow-developing neurological disorder Alzheimer’s disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.
A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models
Authors: Xu Fanghao , Wang Chao , Liang Jiakai , Zuo Chenyang , Yue Keqiang , Li Wenjun 
Published: June 2024

AbstractAutomatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios.
Biomechanical Effects of Stem Extension of Tibial Components for Medial Tibial Bone Defects in Total Knee Arthroplasty: A Finite Element Study
Authors: Park Kwan Kyu , Kang Kyoung-Tak , Kwon Hyuck Min , Hong Hyoung-Taek , Kim Inuk , Cho Byung Woo , Koh Yong-Gon 
Published: June 2024

AbstractThe aim of this study was to investigate the biomechanical effects of stem extension with a medial tibial bone defect in primary total knee arthroplasty (TKA) on load distribution and stress in the proximal tibia using finite element (FE) analysis.FE simulations were performed on the tibia bone to evaluate the stress and strain on the tibia bone and bone cement. This was done to investigate the stress shielding effect, stability of the tibia plate, and the biomechanical effects in TKA models with various medial defects and different stem length models.The results demonstrated that in the bone defect model, the longer the stem, the lower the average von Mises stress on the cortical and trabecular bones. In particular, as the bone defect increased, the average von Mises stress on cortical and trabecular bones increased. The average increase in stress according to the size of the bone defect was smaller in the long stem than in the short stem. The maximal principal strain on the trabecular bone occurred mainly at the contact point on the distal end of the stem of the tibial implant. When a short stem was applied, the maximal principal strain on the trabecular bone was approximately 8% and 20% smaller than when a long stem was applied or when no stem was applied, respectively.The findings suggest that a short stem extension of the tibial component could help achieve excellent biomechanical results when performing TKA with a medial tibial bone defect.
Understanding the relationship between narrative identity, transdiagnostic factors and psychological functioning in a
young adult community sample
Authors: Corbett Monique , Reid Vincent , Bird Amy 
Published: June 2024

Abstract
Determining how narrative identity relates to psychological functioning is important for understanding how
psychopathology develops and is maintained. The present study investigated whether transdiagnostic factors mediate and/or moderate
associations of narrative identity with psychological functioning. We analysed data from n = 245 University
students who completed an online survey measuring turning point narratives, transdiagnostic factors and psychological functioning.
Results indicated that rumination and emotion dysregulation, but not overgeneral memory, mediated the relationship between lower
causal coherence and psychological functioning. Contrary to predictions, neither attachment security nor memory tone moderated any
relationships between narrative identity and psychological functioning. These pathways may be particularly important for
understanding the development and maintenance of psychopathology.
Baan Khoa Lak: A Case Study on Sustainable Rural Development through Community-Based Tourism and Natural Resource Conservation
Authors: Krittayaruangroj Krittawit , Suriyankietkaew Suparak 
Published: June 2024

Summary
Baan Khoa Lak, a small village in Trang Province, Thailand, showcases rural sustainability. With around 183 families, it is a model for successful sustainable development. A standout initiative is the community-based tourism program, promoting sustainability while generating local income. Eco-tourism offerings like water rafting, Thai cooking classes, and homestays empower residents by involving them in decisions and nurturing a sense of ownership and belonging. A community forest diversifies tourism, safeguarding trees from harm, conserving resources, and upholding heritage. Baan Khoa Lak’s achievements earned it honors, including Trang Province’s cleanest village, Southern Region’s happiness village winner, and Thailand’s 2020 Best Responsible Tourism third prize. Community-based tourism uplifts living standards, spurs social connections, and fosters pride. Baan Khoa Lak epitomizes rural development’s potential with limited means. Through environmental and cultural preservation, it attracts nature-loving tourists, enriching both economy and residents’ lives.

Information
© The Authors 2024
Dendritic tip selection during solidification of alloys: Insights from phase-field simulations
Authors: Zhang 张 Qingjie 清杰 , Xing 邢 Hui 辉 , Wang 王 Lingjie 灵杰 , Zhai 翟 Wei 薇 
Published: June 2024

Abstract
The effect of undercooling ΔT and the interface energy anisotropy parameter ε
4 on the shape of the equiaxed dendritic tip has been investigated by using a quantitative phase-field model for solidification of binary alloys. It was found that the tip radius ρ increases and the tip shape amplitude coefficient A
4 decreases with the increase of the fitting range for all cases. The dendrite tip shape selection parameter σ* decreases and then stabilizes with the increase of the fitting range, and σ* increases with the increase of ε
4. The relationship between σ* and ε
4 follows a power-law function

σ
∗

∝

ε
4
α

, and α is independent of ΔT but dependent on the fitting range. Numerical results demonstrate that the predicted σ* is consistent with the curve of microscopic solvability theory (MST) for ε
4 < 0.02, and σ* obtained from our phase-field simulations is sensitive to the undercooling when ε
4 is fixed.
Orbital angular momentum conversion of acoustic vortex beams via planar lattice coupling
Authors: Han 韩 Qingbang 庆邦 , Liu 刘 Zhipeng 志鹏 , Yin 殷 Cheng 澄 , Wu 吴 Simeng 思梦 , Luo 罗 Yinlong 寅龙 , Yang 杨 Zixin 子鑫 , Pang 庞 Xiuyang 修洋 , Wang 王 Yiqiu 溢秋 , Kan 阚 Xuefen 雪芬 , Zhang 张 Yuqiu 雨秋 , Yu 俞 Qiang 强 , Wu 吴 Jian 坚 
Published: June 2024

Abstract
Orbital angular momentum (OAM) conversion is critical in understanding interactions between a structural sound field and a planar lattice. Herein, we explore the evolution of a monochromatic acoustic vortex beam (AVB) that is scattered by a phononic crystal (PnC) or a correlated random lattice. The phenomenon is ascribed to the enhanced orbit–orbit angular momentum coupling induced by the band structure. By modifying the coupling condition, accurate and continuous micro-manipulation of AVBs can be achieved, including the transverse/lateral gravity shift, the dynamics of the phase singularities, and the spatial distribution of acoustic pressure, etc. This research provides insight to the inhomogeneous coupling of AVBs with both propagating Bloch waves and localized Anderson modes, and may facilitate development of novel OAM-based acoustic devices for active sound field manipulation.
Three data-selection approaches for beam position monitor offline calibration
Authors: Kim Geunwoo , Kim Eun-San , Kwon Jang Won , Jang Siwon 
Published: June 2024

Abstract
In this study, we describe the fabrication of an
electrostatic beam position monitor (BPM) for beam diagnostic
systems. Calibration of the BPM pickup requires both sensitivity and
curve fitting owing to manufacturing tolerances following
fabrication. To calibrate the BPM, we developed a BPM calibration
system that comprises a wire-based test bench and readout
electronics. We considered three difference-over-sum (DOS) data
selection methods: DOSdiag, DOSaxis, and
DOSall, to understand their impact on calibration
accuracy. Each method selected specific DOS data, and we compared
their characteristic curves. Consequently, we analyzed the
calibration results by performing linear, 3rd-order, 5th-order, and
two-dimensional (2D) 3rd-order polynomial fitting. Each of the three
DOS data selection methods was examined to illustrate their
advantages and limitations in terms of calibration accuracy and
required amount of data. The results obtained demonstrated that
choosing the appropriate DOS data-selection approach could be
employed for the BPM offline calibrations, achieving minimized
calibration errors and optimized response characteristics with
polynomial curve fitting.
Assessment of NIR-triggered PEG-coated NaGdF4:Tm3+/Yb3+ bio-compatible upconversion nanoparticles for contrast enhancement in OCT imaging and optical thermometry
Authors: Shwetabh Kumar , Banerjee Abhishek , Poddar Raju , Kumar Kaushal 
Published: June 2024

Abstract
In this investigation, we embarked on the synthesis of polyethylene glycol coated NaGdF4:Tm3+/Yb3+ upconversion nanoparticles (UCNPs), aiming to assess their utility in enhancing image contrast within the context of swept source optical coherence tomography (OCT) and photo-thermal OCT imaging. Our research unveiled the remarkable UC emissions stemming from the transitions of Tm3+ ions, specifically the 1G4 → 3H6 transitions, yielding vibrant blue emissions at 472 nm. We delved further into the UC mechanism, meticulously scrutinizing decay times and the nanoparticles′ capacity to convert radiation into heat. Notably, these nanoparticles exhibited an impressive photo-thermal conversion efficiency of 37.5%. Furthermore, our investigations into their bio-compatibility revealed a promising outcome, with more than 90% cell survival after 24 h of incubation with HeLa cells treated with UCNPs. The nanoparticles demonstrated a notable thermal sensitivity of 4.7 × 10−3 K−1 at 300 K, signifying their potential for precise temperature monitoring at the cellular level.
Model-based agricultural landscape assessments: a review
Authors: Heidenreich Anja , Muller Adrian , Oggiano Philipp , Pfeifer Catherine , Moakes Simon , Six Johan , Stolze Mathias 
Published: June 2024

Abstract
Agricultural landscapes are multifunctional and closely connected to the much wider food system. In our review, we explore three specific aspects of modelling approaches for agricultural landscape assessments: (a) how multifunctionality is commonly analysed to support decision-making for sustainable agricultural land management; (b) how the modelling approaches relate to the wider food systems; and (c) how gaps in the existing modelling approaches might be addressed. For this, we identified key elements of agricultural landscape assessments, including ecosystem services (ESS) provided, driving factors, and linkages between crop and livestock production, and to the wider food system. We reviewed 238 publications with respect to these elements. While biodiversity and the ESS ‘water conditions’ and ‘atmospheric composition/conditions’ are widely covered, they are rarely used in combination. Other ESS, such as ‘pest and disease control’, are largely missing. Our results further indicate strong differences between individual approaches regarding model parameterisation and consideration of abiotic, biotic, and management driving factors. Our analysis also shows that the interconnectedness of crop and livestock production is rarely considered and that return flows from the food system are not considered. Furthermore, impacts from the production of external inputs and off-site effects are not considered. Consequently, existing models might overlook trade-offs and synergies between landscape functions. Failure to consider variations in relevant driving factors and food system linkages likely results in studying incorrect levers for change and failing to show decision-makers the full scope of available action. We thus suggest adopting more encompassing modelling approaches to ensure coverage of the full scope of potential actions, whilst hedging against overly costly data requirements by, e.g. employing well-designed sensitivity analyses. In this way, the most relevant components and thus the most important leverage points for interventions can be identified.