bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human influence on the distribution of cacao: insights from remote sensing and biogeography
Authors: Colli-Silva Matheus , Richardson James Edward , Figueira Antonio , Pirani José Rubens 
Published: February 2024

AbstractCacao (Theobroma cacao, Malvaceae) is an important tree crop in Africa and in the Americas. Current genomic evidence suggests that its original range in Tropical Americas was smaller than its current distribution and that human-mediated dispersal occurred before European colonization. This includes regions like Mesoamerica and Eastern Amazonia where cacao is supposedly naturally occurring. In this study, we utilize remote sensing and land use data to examine the influence of human activities on cacao-growing regions and explore patterns between cacao distribution and anthropized areas. By evaluating nearly nine thousand preserved specimen collections, we worked with a comprehensive occurrence dataset that considers taxonomy and distribution. We then analyzed remote sensing images of specimen locations and compared land use profiles of regions into which cacao was introduced with documented native areas. Our findings revealed a clear association between anthropized areas and cacao specimens, with the majority located in areas strongly affected by human activities. Conversely, regions closer to the proposed native range of cacao exhibit less human impact. These results, while accounting for sampling bias, reinforce the idea that humans may have played a significant role in cacao’s dispersal, even in parts of the Amazon where its native status remains uncertain. The discussion on cacao’s native range and identification of introduced areas hold implications for jurisdiction, access to genetic resources, and conservation efforts. Additionally, it is relevant to debates surrounding the repatriation of genetic data of economically important crops. Understanding the historical human influence on cacao’s distribution is crucial for addressing issues of crop improvement, conservation, and sustainable use.
Thermal mechanism-driven microlens formation in Ge–Sb–S glasses by direct laser writing: composition dependent insight
Authors: Smolík J. , Knotek P. , Černošková E. , Kutálek P. , Samsonova E. , Schwarz J. , Kašparová J. , Tichý L. 
Published: February 2024

AbstractMicrolenses were fabricated through a thermal process using laser-induced localized overheating on the surfaces of various bulk Ge–Sb–S glasses. These glasses spanned three distinct groups: (a) stoichiometric (GeS2)1−x(Sb2S3)x glasses with x = 0–0.88; (b) a series with a constant Sb content represented as GexSb0.17S0.83−x, x = 0.13–0.24, and (c) glasses with a constant Ge content denoted by Ge0.18SbxS0.82−x, x = 0.03–0.10. A continuous-wave laser emitting at 532 nm was used in the fabrication process. Both the photo-induced microlenses and the non-illuminated surfaces underwent characterization to determine their topography (via digital holographic microscopy), chemical composition (using EDX analysis), structure (through Raman spectroscopy), and mechanical properties (assessed by Nanoindentation). The influence of the chemical composition was studied to identify parameters that described the characteristics of the formed microlenses, such as the maximum achieved height and the threshold power density for microlens formation. For (GeS2)0.66(Sb2S3)0.34 glass, the effective focal length of the produced microlenses was calculated to be approximately 145–190 µm, potentially aiding in the miniaturization of optical devices that, in the context of Ge–Sb–S, working primarily in the near and/or mid-IR region.
Graphical abstract
Portuguese adaptation of the Behavioral Inhibition Questionnaire (BIQ)
Authors: Fernandes Catarina Cova , Martins Ana Teresa , Faísca Luís 
Published: February 2024

AbstractBehavioral inhibition (BI) is a temperamental trait characterized by a consistent pattern of behavioral and emotional responses to unfamiliar people and novel situations. BI exhibits moderate stability across development and is considered a risk factor for the emergence of emotional disorders in adolescence and adulthood, making early assessment crucial for understanding this behavioral pattern. In the Portuguese context, there is a paucity of measures specifically designed to assess BI in childhood. To address this gap, we aimed to adapt and examine the psychometric properties of the Behavioral Inhibition Questionnaire (BIQ) in Portuguese children. We obtained BIQ ratings from both parents and teachers of 435 children (average age = 57.1 months; SD = 13.8). Confirmatory factor analysis supported a six-factor measurement model, each factor loading onto a higher-order BI factor. Additionally, we included a method factor to account for reversed items. Full scalar measurement invariance across gender and age groups was supported. Parents and teachers showed good agreement in their ratings of BIQ scores. Comparisons with other instruments revealed good convergent validity. The BIQ scores showed acceptable reliability, although the subscales exhibited limited sensitivity in discriminating between different contexts where BI may manifest. This study suggests that the BIQ is a reliable and valid tool for assessing BI in Portuguese children.
Cloud-based disaster management architecture using hybrid machine learning approach in IoT
Authors: Özen Figen , Souri Alireza 
Published: February 2024

AbstractNatural disasters are becoming more frequent and more severe as a result of global warming. It is critical to take precautions before disasters, to gather and analyze information simultaneously while they are happening, and to make accurate assessments after them given that the deaths and injuries brought on by such disasters both leave lasting traumas in the life of society and damage the economy. Internet of Things (IoT) technology, is a young field that can assist intelligent safety-critical systems with data collection, processing in cloud edge data centers, and application of prediction methodologies for discovering key points and unexpected patterns using 5G technology. With the use of a cloud-based prediction algorithm for disaster management in the IoT environment, this study seeks to quickly process the data that is gathered during disasters and to speed up the analysis that will be done both during and after the disasters. An Optimized Ensemble Bagged Tree (OEBT) algorithm with ANOVA-based feature selection is developed for this aim. The experimental results show that accuracy, F1-Score, precision, and recall of the proposed OEBT algorithm utilizing the US Natural Disasters Dataset are 97.9%, 78.3%, 98.7%, and 78.9%, respectively. Comparisons with decision tree, logistic regression, and the traditional ensemble techniques are made. The suggested algorithm outperforms them all in terms of success rates.
Large neighborhood local search method with MIP techniques for large-scale machining scheduling with many constraints
Authors: Matsuzaki Jin , Sakakibara Kazutoshi , Nakamura Masaki , Watanabe Shinya 
Published: February 2024

AbstractThis study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method.
Guinea pig herpes like virus is a gamma herpesvirus
Authors: Stanfield Brent A. , Ruiz Emmanuelle , Chouljenko Vladimir N. , Kousoulas Konstantin G. 
Published: February 2024

AbstractGuinea Pig Herpes-Like Virus (GPHLV) is a virus isolated from leukemic guinea pigs with herpes virus-like morphology described by Hsiung and Kaplow in 1969. GPHLV transformed embryonic cells from Syrian hamsters or rats, which were tumorigenic in adult animals. Herein, we present the genomic sequence of GPHLV strain LK40 as a reference for future molecular analysis. GPHLV has a broad host tropism and replicates efficiently in Guinea pig, Cat, and Green African Monkey-derived cell lines. GPHLV has a GC content of 35.45%. The genome is predicted to encode at least 75 open-reading frames (ORFs) with 84% (63 ORFs) sharing homology to human Kaposi Sarcoma Associated Herpes Virus (KSHV). Importantly, GPHLV encodes homologues of the KSHV oncogenes, vBCL2 (ORF16), vPK (ORF36), viral cyclin (v-cyclin, ORF72), the latency associated nuclear antigen (LANA, ORF73), and vGPCR (ORF74). GPHLV is a Rhadinovirus of Cavia porcellus, and we propose the formal name of Caviid gamma herpesvirus 1 (CaGHV-1). GPHLV can be a novel small animal model of Rhadinovirus pathogenesis with broad host tropism.
The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach
Authors: Doruk Ömer Tuğsal 
Published: February 2024

AbstractThis article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.
Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging
Authors: Iqbal Talha , Khalid Aaleen , Ullah Ihsan 
Published: February 2024

AbstractIn certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis and decision-making are required, the healthcare system can leverage the power of artificial intelligence (AI) models to support decision-making and prevent complications. This paper investigates the optimization of healthcare AI models based on time complexity, hyper-parameter tuning, and XAI for a classification task. The paper highlights the significance of a lightweight convolutional neural network (CNN) for analysing and classifying Magnetic Resonance Imaging (MRI) in real-time and is compared with CNN-RandomForest (CNN-RF). The role of hyper-parameter is also examined in finding optimal configurations that enhance the model’s performance while efficiently utilizing the limited computational resources. Finally, the benefits of incorporating the XAI technique (e.g. GradCAM and Layer-wise Relevance Propagation) in providing transparency and interpretable explanations of AI model predictions, fostering trust, and error/bias detection are explored. Our inference time on a MacBook laptop for 323 test images of size 100x100 is only 2.6 sec, which is merely 8 milliseconds per image while providing comparable classification accuracy with the ensemble model of CNN-RF classifiers. Using the proposed model, clinicians/cardiologists can achieve accurate and reliable results while ensuring patients’ safety and answering questions imposed by the General Data Protection Regulation (GDPR). The proposed investigative study will advance the understanding and acceptance of AI systems in connected healthcare settings.
Modelling the industrial production of electric and gas utilities through the $$CIR^3$$ model
Authors: Ceci Claudia , Bufalo Michele , Orlando Giuseppe 
Published: February 2024

AbstractThis work aims to extend previous research on how a trifactorial stochastic model, which we call$$CIR^3$$CIR3, can be turned into a forecasting tool for energy time series. In particular, in this work, we intend to predict changes in the industrial production of electric and gas utilities. The model accounts for several stylized facts such as the mean reversion of both the process and its volatility to a short-run mean, non-normality, autocorrelation, cluster volatility and fat tails. In addition to that, we provide two theoretical results which are of particular importance in modelling and simulations. The first is the proof of existence and uniqueness of the solution to the SDEs system that describes the model. The second theoretical result is to convert, by the means of Lamperti transformations, the correlated system into an uncorrelated one. The forecasting performance is tested against an ARIMA-GARCH and a nonlinear regression model (NRM).
Abnormal Grain Growth and Pseudoelasticity of Industrially Processed Fe–Mn–Al–Ni Shape Memory Alloy Joined by Metal Inert Gas Welding
Authors: Viebranz Vincent Fabian , Bauer André , Hassel Thomas , Niendorf Thomas , Maier Hans Jürgen 
Published: February 2024

AbstractThe effect of metal inert gas welding on the microstructure, abnormal grain growth and the pseudoelastic properties of industrially processed Fe–Mn–Al–Ni shape memory alloy sheets were studied. Square-butt welds were manufactured using similar filler material. The influence of alternating mean arc linear energy on the microstructure of the individual zones is shown. A correlation between the process parameters, the associated heat input, the grain morphology and the α/γ-ratio could be deduced. As the mean arc linear energy increases, the α/γ-ratio in the fusion zone and the heat-affected zone increases. To evaluate the influence of the welding process on abnormal grain growth, a post-weld cyclic heat-treatment was carried out. Although no loss of major alloying elements in the fusion zone was observed after welding, metal inert gas welding has a significant effect on grain growth upon cyclic heat treatment. While abnormal grain growth occurred in the base material, a polycrystalline microstructure characterized by significantly smaller grain sizes was visible in the former fusion zone. Incremental strain tests revealed severe plastic deformation near the grain boundaries in the former fusion zone. However, the grain boundaries of the bamboo-like grown grains in the base material turned out to be more critical for structural failure.
Perspective on Emerging Therapies to Achieve Functional Cure of Chronic Hepatitis B
Authors: Gopalakrishna Harish , Ghany Marc G. 
Published: February 2024

Abstract
Purpose of Review
Advancements in our understanding of the hepatitis B viral (HBV) life cycle have paved the way for novel approaches to treat HBV infection. This review summarizes the various strategies being pursued to achieve a functional cure, defined as loss of hepatitis B surface antigen (HBsAg) and absence of viral replication 6 months off-therapy.

Recent Findings
Direct acting antiviral, host targeting antiviral, and immunological approaches are in various stages of development as treatment for chronic HBV infection.

Summary
Novel treatments are being developed in pursuit of a cure for HBV. Current evidence suggests a single therapeutic agent alone may be insufficient, necessitating the need for combination therapy targeting HBV and the host immune response. Ongoing research focused on identifying the best therapeutic combination holds promise in achieving functional cure for HBV.
Effects of Electronic Cigarette Vaping on Cardiac and Vascular Function, and Post-myocardial Infarction Remodeling in Rats
Authors: Dai Wangde , Shi Jianru , Siddarth Prabha , Carreno Juan , Kleinman Michael T. , Herman David A. , Arechavala Rebecca J. , Renusch Samantha , Hasen Irene , Ting Amanda , Kloner Robert A. 
Published: February 2024

AbstractThe effect of electronic cigarette (E-cig) vaping on cardiac and vascular function during the healing phase of myocardial infarction (MI), and post-MI remodeling was investigated. Sprague Dawley rats were subjected to left coronary artery ligation to induce MI. One week later, rats were randomized to receive either 12 weeks of exposure to purified air (n = 37) or E-cig vapor (15 mg/ml of nicotine) (n = 32). At 12 weeks, cardiac and vascular function, and post-MI remodeling were assessed. Baseline blood flow in the femoral artery did not differ between groups, but peak reperfusion blood flow was blunted in the E-cig group (1.59 ± 0.15 ml/min) vs. the air group (2.11 ± 0.18 ml/min; p = 0.034). Femoral artery diameter after reperfusion was narrower in the E-cig group (0.54 ± 0.02 mm) compared to the air group (0.60 ± 0.02 mm; p = 0.023). Postmortem left ventricular (LV) volumes were similar in the E-cig (0.69 ± 0.04 ml) and air groups (0.73 ± 0.04 ml; p = NS); and myocardial infarct expansion index did not differ between groups (1.4 ± 0.1 in E-cig group versus 1.3 ± 0.1 in air group; p = NS). LV fractional shortening by echo did not differ between groups at 12 weeks (E-cig at 29 ± 2% and air at 27 ± 1%; p = NS). Exposure to E-cig during the healing phase of MI was associated with altered vascular function with reduced femoral artery blood flow and diameter at reperfusion, but not with worsened LV dilation or worsened cardiac function.
The significance of an immunohistochemical marker-based panel in assisting the diagnosis of parathyroid carcinoma
Authors: Hu Ya , Mo Shengwei , Xiao Jinheng , Cui Ming , Zheng Qingyuan , Chen Tianqi , Chang Xiaoyan , Liao Quan 
Published: February 2024

Abstract
Purpose
Parathyroid carcinoma (PC) is an endocrine malignancy with a poor prognosis. However, the diagnosis of PC is still a difficult problem. A model with immunohistochemical (IHC) staining of 5 biomarkers has been reported from limited samples for the differential diagnosis of PC. In the present study, a series of IHC markers was applied in relatively large samples to optimize the diagnostic model for PC.

Methods
In this study, 44 patients with PC, 6 patients with atypical parathyroid tumors and 57 patients with parathyroid adenomas were included. IHC staining for parafibromin, Ki-67, galectin-3, protein-encoding gene product 9.5 (PGP9.5), E-cadherin, and enhancer of zeste homolog 2 (EZH2) was performed on formalin-fixed, paraffin-embedded tissue samples. The effects of clinical characteristics, surgical procedure, and IHC staining results of tumor tissues on the diagnosis and prognosis of PC were evaluated retrospectively.

Results
A logistic regression model with IHC results of parafibromin, Ki-67, and E-cadherin was created to differentiate PC with an area under the curve of 0.843. Cox proportional hazards analysis showed that negative parafibromin staining (hazard ratio: 3.26, 95% confidence interval: 1.28–8.34, P = 0.013) was related to the recurrence of PC.

Conclusion
An IHC panel of parafibromin, Ki-67 and E-cadherin may help to distinguish PC from parathyroid neoplasms. Among the 6 IHC markers and clinical features examined, the risk factor related to PC recurrence was parafibromin staining loss.
Accessing Sexual and Reproductive Health Services in Chile: Women with Disabilities and Their Experience with Gynaecological and Obstetric Violence
Authors: Yupanqui-Concha Andrea , Hichins-Arismendi Melissa , Mandiola-Godoy Daniela , Rodríguez-Garrido Pía , Rotarou Elena S. 
Published: February 2024

Abstract
Introduction
Although access to sexual and reproductive health services is a human right, violations of the rights of women with disabilities in this aspect are frequent. However, there are no studies regarding gynaecological and obstetric violence that this population subgroup may face. This qualitative study is aimed at identifying and analysing the experiences of women with disabilities with regard to gynaecological and obstetric violence in Chile.

Methods
From a feminist phenomenological paradigm of research, data were collected from March 2017 to December 2018. Women with disabilities (n = 18) were interviewed regarding their experiences. Data were analysed through thematic analysis.

Results
The experiences of women with disabilities regarding gynaecological and obstetric violence fall under three central categories: (a) health inequity in communication, (b) objectification of girls’ and women’s bodies through practices, such as forced sterilisation, and (c) intimidation and discrimination related to maternity.

Conclusions
Gynaecological and obstetric violence of women with disabilities is a present form of gender-based violence in Chile. Such instances occur systematically and persistently and lead to the dehumanisation of healthcare, an increase in health inequities, and the creation of particularly complex scenarios experienced by women with disabilities regarding access to sexual and reproductive healthcare.

Policy Implications
It is necessary to promote research on gynaecological and obstetric violence faced by women with disabilities, take action against this form of gender-based violence, and improve access to sexual and reproductive services for this population. Specific recommendations aimed at the elimination of gynaecological and obstetric violence are given.
Octonion-based transform moments for innovative stereo image classification with deep learning
Authors: Tahiri Mohamed Amine , Boudaaoua Brahim , Karmouni Hicham , Tahiri Hamza , Oufettoul Hicham , Amakdouf Hicham , Qjidaa Hassan , Abouhawwash Mohamed , Askar S. S. , Sayyouri Mhamed 
Published: February 2024

AbstractRecent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.