bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracture toughness of dried oil paints
Authors: Janas Arkadiusz , Avgerou Nefeli , Charalambides Maria , Fuster-López Laura , Bratasz Łukasz 
Published: January 2024

Abstract
The fracture toughness in opening mode for selected dried oil paints was determined filling in this way a critical gap in the knowledge required for the analysis of fracturing processes in paintings. The experimental work was made possible by the adaptation of the ASTM D5528–13 standard method to the size of available oil paints for a selection of oil paints from Mecklenburg's Paint Reference Collection now after approximately 30 years of drying. The careful regluing procedure allowed to improve the statistics of the measurements but also to get insight into in-depth profiles of the fracture toughness of some paints. The fracture toughness GIC values are the lowest for zinc white, raw umber and lead white with litharge, approximately 15 J/m2 and the highest for sap green. The variable homogeneity of some tested paints – lead white, zinc white, sap green and verdigris – is reflected in the large range of the values obtained. This study demonstrated that some paints were more brittle than glue-based ground layers and as a consequence more vulnerable to cracking
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
Authors: Liu Shun , Wu Kexin , Jiang Chufeng , Huang Bin , Ma Danqing 
Published: January 2024

Abstract
In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.
Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians
Authors: Omori Motonobu , Yoshitake Hiroshi , Shino Motoki 
Published: January 2024

Abstract

There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both efficiency and comfort is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient and comfortable autonomous navigation method considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method can achieve efficient and comfortable autonomous navigation, especially in crowded environments.
Full Immersive Telepresence Through Integration of a VR-Headset with a Teleoperated Humanoid Robotic Head Featuring Real-Time Stereoscopic Vision and Binaural Auditory Perceptions
Authors: Dugan Anthony H , Sadykov Rustem , Roozbahani Daniel , Alizadeh Marjan , Handroos Heikki 
Published: January 2024

Abstract
The rapid evolution of technology has created new opportunities for teleoperation, especially with the emergence of affordable Virtual Reality (VR) systems originally designed for entertainment and gaming. Teleoperation in robotics has a long history, however, its integration with VR for controlling robotic systems is currently gaining significant attention. In this study, a novel real-time platform is developed that integrates teleoperation with a VR system, providing immersive control of a biomimetic robotic head. A 3D model of the robotic head was designed and built to mimic the movements of the operator's head in real-time using the operator’s kinematics. The fabricated robotic head is equipped with a pair of cameras and a binaural microphone to provide vision and audio perception. Additionally, two servomotors are employed to track the motion of the operator’s neck. To control the biomimetic robotic head, a game was developed in Unity to deliver 3D visual and stereophonic audio inputs to the headset, which ensures a smooth viewing and listening experience for the operator. The designed platform is operated through a custom-written C++ program that facilitates continuous tracking of the headset and establishes constant communication with the servomotors. High precision in the positional data and accurate tracking of the head-mounted display (HMD), along with efficient communication with the motors, were achieved. The integration of the VR system and teleoperation developed in this study advances VR technology beyond traditional entertainment-oriented applications and makes it applicable in diverse industrial fields, simultaneously elevating teleoperation in human-robot interactions to new levels of productivity.
Measuring Post-Disaster Accessibility to Essential Goods and Services: Proximity, Availability, Adequacy, and Acceptability Dimensions
Authors: Enderami S. Amin , Sutley Elaina , Helgeson Jennifer , Dueñas-Osorio Leonardo , Watson Maria , de Lindt John W. van 
Published: January 2024

Abstract

Rapid restoration of access to essential goods and services has long been regarded as paramount for community recovery. Yet, there remains ambiguity in how access should be defined, measured, or operationalized. Defining accessibility as the ability to use available goods and services with a reasonable effort and cost requires evaluating it across six dimensions (proximity, availability, adequacy, acceptability, affordability, and awareness) while considering the perspective of both users and providers in the evaluation. But common distance-based metrics that focus solely on physical access and travel time often fall short of fully capturing these requirements, overlooking the user's perception. This paper introduces a new spatio-temporal accessibility metric that combines four out of these six dimensions, including proximity, acceptability, adequacy, and availability. The metric considers uncertainty in measuring each dimension and addresses both user and provider perspectives in measuring the acceptability and adequacy dimensions. The variation in the metric across the disaster timeline serves as a proxy for community recovery. The metric aligns with common engineering-oriented functionality-based resilience frameworks as the functionality level of the providers has been incorporated in its development. Operating at the household level, the metric determines the ratio of post-disruption access time to the intended good or service against its pre-disruption access time and yields a unitless ratio between zero and one, with zero expressing a total loss in accessibility and one signifying the same level of accessibility as pre-disruption. The proposed metric, while being scientifically principled, is a practical tool whose output is easily understood even by non-expert individuals. The metric is illustrated for schools and pharmacies using the Lumberton Testbed and data collected following the 2016 flood in Lumberton, North Carolina after Hurricane Matthew. Findings provide new insight into recovery plan prioritization and can be used to trigger protective actions. The paper concludes by discussing issues and barriers related to developing and validating accessibility metrics while highlighting areas for future research.
Molecular detection of tick-borne pathogens from ticks infesting domestic ruminants in Malawi
Authors: Chikufenji Boniface , Chatanga Elisha , Mohanta Uday Kumar , Nkhata Madalitso , Stopher Dallion , Magombo Joe , Ceylan Onur , Umemiya-Shirafuji Rika , Thekisoe Oriel , Xuan Xuenan 
Published: January 2024

Abstract
This study examined ticks infesting ruminants and tick-borne pathogens (TBPs) they are carrying using polymerase chain reaction (PCR) and sequencing analysis. A total of 964 ticks were collected from cattle (n=202), goats (n=63) and sheep (n=16) in 11 districts of Malawi. Stereomicroscope and taxonomical keys were used to morphologically identify the ticks to species level, and PCR by amplifying and sequencing 12S rRNA and cytochrome c oxidase subunit I (COI) genes were used to confirm the species. PCR assays with species-specific primers were used to screen TBPs. The identified tick species were Rhipicephalus microplus (30.5%), R. appendiculatus (23.3%), R. decoloratus (13.2%), R. evertsi(9.8%), Hyalomma rufipes (7.5%), Amblyomma variegatum (6.3%), R. sanguineus (3.6%), H. truncatum (2.8%), R. simus (2.0%), R. pravus (0.6%), and R. annulatus (0.4%). Out of the total ticks, 37.0% were infected with at least one TBP, with Theileria parva making the majority (34.7%), followed by Anaplasma marginale (17.4%), Babesia bigemina (14.9%), A. ovis (11.2%), Ehrlichia ruminantium (9.2%), T. mutans (8.4%), B. bovis (2.2%) and A. bovis (2.0%). The present study reveals critical data on the distribution of tick species infesting ruminants in Malawi and TBPs they are carrying. Moreover, this study has pioneered genetic characterization of ruminant ticks in Malawi and overall data will contribute to formulation of improved ticks and TBPs control approaches.
A Bibliometric Analysis of Virtual Reality Applications in Anthropology between 2010 and 2023
Authors: Butilă Eugen Valentin , Burlacu Mihai , Boboc Răzvan Gabriel 
Published: January 2024

Abstract
As a relatively new technology that has gone through several iterations in the last decade, virtual reality (VR) applications have been used in a plethora of activities pertaining to various sciences, including anthropology. In this paper, we expound a bibliometric analysis of the reviews and research articles regarding the use of VR applications in anthropology between 2010 and 2023. The analysed publications were obtained from the Scopus database, and Microsoft Excel and VOSViewer were used to analyse the data. Utilizing bibliometric methods, the analysis encompasses a thorough examination of scholarly publications, identifying and scrutinizing prominent journals, prolific authors, affiliated institutions, and key research themes within the realm of VR applications in anthropology. The objective is to provide a systematic and insightful overview of the evolution, current state, and emerging trends in the integration of VR within the anthropological discourse, shedding light on the interdisciplinary nature and impact of this innovative technology on anthropological research and practice.
A new statistical training algorithm for single multiplicative neuron model artificial neural network
Authors: Gul Hasan Huseyin , Egrioglu Erol , Bas Eren 
Published: January 2024

Abstract
The single multiplicative neuron model has been frequently used by researchers in recent years, as it does not have a complex structure and does not include the hidden layer unit number problem, unlike many feed-forward artificial neural network models. The model of single multiplicative neuron model artificial neural networks does not have statistical assumptions just like in many artificial neural network models. Random error term is not used in the mathematical model of single multiplicative neuron model artificial neural networks. This situation is not acceptable considering that artificial neural networks work with random samples. Based on this idea, for the first time, by including a random error term in the single multiplicative neuron model artificial neural network model, mathematical equations of likelihood functions are given for Normal, Cauchy, Logistic, Gumbel and Laplace distributions. A new statistical training algorithm is proposed to obtain optimal weights and bias values of the network. In the new training algorithm, particle swarm optimization proposed by Kennedy and Eberhart (1995) is used in maximizing likelihood functions. In the performance evaluation of the proposed method, Nasdaq and S&P500 time series in different years are analyzed and the analysis results are compared with many artificial neural network models in the literature. Finally, it is concluded that the proposed method produces very successful forecasting results.
Cosine-Gaussian breathers controlled by the initial conditions in highly nonlocal media
Authors: Pan Peng , Xu Yun-Shi , Dai Zhi-Ping 
Published: January 2024

Abstract
The study delves into the behavior of cos-Gaussian breathers navigating through a highly nonlocal medium, with a spotlight on their width and intensity variations under different parameter adjustments. The mathematical formulas describing the characteristics of cos-Gaussian beam have been derived. By controlling the parameters, the transverse width of cos-Gaussian beams can exhibit various variations, reflecting various mode transformations. We scrutinized how alterations in cosine parameters, offset distance, and initial energy manifest in the breather’s width and shape during propagation. Our observations underscore the significant influence of these parameters on the dynamic evolution and morphology of cos-Gaussian breathers, thereby unveiling the potential application of using parameters to control the dynamic characteristics of cos-Gaussian beam transmission.
Screening of temperature-responsive signalling molecules during sex differentiation in Asian yellow pond turtle (Mauremys mutica)
Authors: Liu Xiaoli , Xu Haoyang , Peng Mingwei , Zhou Chenyao , Wei Chengqing , Hong Xiaoyou , Li Wei , Chen Chen , Ji Liqin , Zhu Xinping 
Published: January 2024

Abstract

Background
The Asian yellow pond turtle (Mauremys mutica) is an important commercial freshwater aquaculture species in China. The Asian yellow pond turtle is a highly sexual dimorphic species, with males growing at a faster rate than females. The Asian yellow pond turtle exhibits temperature-dependent sex determination (TSD), in which the incubation temperature during embryonic development determines the sexual fate. However, the mechanisms of the sex determination or sex differentiation of the Asian yellow pond turtle are remain a mystery.
Results
Here, we performed temperature-specific gonadal transcriptomics of the Asian yellow pond turtles during the thermosensitive period (stage 15) to identify candidate genes that initiate gonadal differentiation using RNA-seq technology. We uncover candidates that were the first to respond to temperature, they were sexually dimorphic in expression, reflecting differences in gonadal (Cirbp, Runx1) and germline differentiation (Vasa, Nanos1, Piwil2), gametogenesis (Hmgb3, Zar1, Ovoinhibitor-like, Kif4), steroid hormone biosynthesis (Hsd17b5, Hsd17b6), heat shock (Dnajb6, Hsp90b1, Hsp90aa1) and transient receptor potential channel genes (Trpm1, Trpm4, Trpm6, Trpv1).
Conclusions
Our work will provide crucial genetic information to elucidate the mechanisms of sex control in the Asian yellow pond turtles, and will contribute important genetic resources for further studies of temperature-dependent sex determination in turtles.
Inquiring of Aggression Based on Underlying Motivation and Personality Mechanisms in Aggressive Iranian Students: A Qualitative Study
Authors: Akbari Monireh , Taherifar Zahra , Farahani Hojatolah , Fallahinia Niloofar 
Published: January 2024

Abstract

Background
The term aggression, one of the persistent concerns of society, refers to any behavior seeking to harm another person, animal, or object. Violence and aggression remain pressing concerns in society, with forecasts indicating a significant rise by 2030. The purpose of the present study was to investigate aggression based on underlying motivations and personality mechanisms in aggressive Iranian students.
Methods
Eight adults (37.5% male) participated in the study. The Bass and Perry Aggression Questionnaire was completed by the participants as part of the screening process. A thematic content analysis was conducted in this study. In-depth interviews were carried out as well as open-coding inductive analysis.
Results
The underlying motivations of aggression were categorized into four major themes, including personal relationship improvement, self-stabilization, avoiding anger occurrence, and anger occurrence. Furthermore, the results revealed that five social, communicative, behavioral, cognitive, and emotional themes were recognized as personality factors playing a role in students' aggression.
Conclusions
Overall, the results obtained from the current research have led to a deeper understanding of students' aggression; Therefore, based on the identified themes, it is possible to recognize parts of underlying mechanisms of aggression and prepare appropriate interventions to reduce students' aggression. Future research should encompass a broader demographic and incorporate quantitative methods for causal exploration.
Improved 2DiffusionDet: The superior ability for sampling object detection boxes
Authors: Fei Teng , Wang Li , Li Wuzhi , Cui Huankang , Zhang Guowei 
Published: January 2024

Abstract

Proposals Average Precision (AP) of DiffusionDet relies on a random coverage of the object boxes and a redundant iterative evaluation strategy. In order for diffusion model to achieve more outstanding performance in object detection tasks, we propose a simple and efficient sampling strategy for detection boxes: Only Use Dynamic Head Once. That implementation is based on we propose a Deformable Sigmoid Variance Schedule to optimize the process of adding noise and sampling and also propose an Adjustable Sampling Strategy to reduce the randomness of sampling results. Through that two methods combined we can choose to apply fewer timesteps to the process of adding noise and sampling and get the model achieves better sampling results with a shorter number of iterations, also in this way to alleviate the model's difficulty in learning to sample sparse and discrete Ground Truth(GT) boxes information. Our model gets a sizable performance improvement over DiffusionDet. For example, the same and even beyond AP was achieved by applying half the number proposals(random boxes) based on DiffusionDet in detecting the VOC dataset, meanwhile with Timesteps=50, we outperformed DiffusionDet by 6.0 AP. A 0.4 AP improvement was obtained based on the COCO dataset and in the same ablation experiments. This work which to have certain extent solved the disadvantage of the low distribution density of GT boxes information in Proposals, which makes it difficult for the model to learn to sample.
SARS-CoV-2 vaccination during pregnancy enhances hippocampal neurogenesis and working memory in offspring through IFN-gamma and CX3CR1-dependent mechanisms
Authors: Qi Fangfang , Yao Zhibin 
Published: January 2024

Abstract
The worldwide Coronavirus disease 2019 (COVID-19) pandemic affects the world, and prenatal COVID-19 infection is associated with severe adverse outcomes. Thus, both for safety and immunogenicity, the COVID-19 vaccination is strongly recommended for pregnant women. Specifically, this study examines how maternal COVID-19 vaccination affects postnatal physical development, neurogenesis, and behavior in pups. We successfully detected a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody in the plasma of vaccinated dams and their pups when inactivated COVID-19 vaccine (Vero Cell) was administered at gestational day 14.5. At the age of 1 month, pups from pregnant mice receiving the SARS-CoV-2 vaccine displayed increased proliferation, neuroblasts, neuronal stem cells, and mature neurons in the dentate gyrus (DG) and enhanced working memory. Luminex multiplex assay showed that elevated levels of hippocampal cytokines/chemokines, including interferon-γ (IFN-γ) and CX3C motif chemokine ligand 1 (CX3CL1), which are crucial for neurogenesis and memory function. Mechanistically, using conditional knockout technology, we identify microglial IFN-γ receptor 1 (IFNγR1) and CX3C motif chemokine receptor 1 (CX3CR1), as crucial intercellular participants, in the neuronal developmental process via regulating microglial activation and microglial chemotaxis, respectively. We propose that maternal SARS-CoV-2 vaccination appears to enhance hippocampal neurogenesis and working memory in offspring underlying the mechanism of microglia-proliferating neuron interaction.
A Pilot Case-Control Study: Epigenetic Age Acceleration in Psoriasis
Authors: Macit Betul , Ragi Sara D , Moseley Isabelle , Molino Janine , McGeary John E , Horvath Steve , Qureshi Abrar , Reginato Anthony , Cho Eunyoung 
Published: January 2024

Abstract
Psoriasis (PsO) is a chronic inflammatory skin condition, often accompanied by psoriatic arthritis (PsA) and linked to various comorbidities and increased mortality rates. This study aimed to explore the relationship between PsO and accelerated biological aging, specifically focusing on epigenetic DNA methylation clocks. Using a matched case-control design, 20 PsO cases were selected along with age, race, and sex-matched 20 controls without PsO from the Skin Disease Biorepository at Brown Dermatology, Inc, Providence, Rhode Island. Blood samples retrieved from both groups were analyzed for DNA methylation, and epigenetic ages were calculated using DNA methylation clocks, including Horvath, Hannum, Pheno, SkinBlood, and Grim ages. Generalized estimation equations were employed to test the differences in epigenetic and chronological ages between PsO cases and controls, as well as within various subgroups in comparison to their respective controls. There were no statistically significant differences in epigenetic ages between PsO cases and controls. However, notably, PsO cases with PsA demonstrated an accelerated PhenoAge, compared to their matched controls. This study represents a pioneering investigation into the potential link between PsO and epigenetic aging, shedding light on the possibility of accelerated epigenetic aging in PsA, possibly associated with heightened inflammatory burden. These findings emphasize the systemic impact of PsA on the aging process, prompting the need for deeper exploration into autoimmune pathways, inflammation, and epigenetic modifications underlying PsO pathogenesis and aging mechanisms. Larger-scale studies with diverse populations are imperative to discern PsO subgroups experiencing accelerated biological aging and decipher the intricate interplay between PsO, inflammation, and aging pathways.