bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System
Authors: Zhang Dongsong , Chen Tianhua 
Published: June 2024

Abstract
The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.
Drivers of firms’ digital activities in response to the Covid-19 pandemic
Authors: Franco Chiara , Pietrovito Filomena 
Published: June 2024

AbstractThe aim of the paper is to analyse the main internal drivers of the increase and adoption of online activities carried out by firms in reaction to the Covid-19 pandemic. While the impact of Covid-19 pandemic on several measures of firm-level performance has been debated in many papers, not enough effort has been devoted to investigating its digitalization impact, especially with respect to the drivers of firms operating in transition countries. To this end, we explore a very detailed firm-level dataset, drawn from the World Bank Enterprise Survey (WBES) combined with the Covid-19-ES Follow-up Survey, for 22 Eastern European and Central-Eastern Asian countries. Our findings reveal that (i) higher online activity is associated with higher digital and technological endowment of the firm and (ii) this relationship is shaped by external factors, such as country-level digital infrastructure.
Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses
Authors: Magazzino Cosimo , Gattone Tulia , Giolli Lorenzo 
Published: June 2024

AbstractThis study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises.
Tax Loss Carryforward Disclosure
Authors: Flagmeier Vanessa , Müller Jens 
Published: June 2024

Abstract
This study examines firms’ voluntary disclosure of tax loss carryforward (TLCF) information. We measure the content and presentation of TLCF information by a disclosure score based on hand collected data from annual reports. Our identification strategy employs new proprietary data to control for overall reporting quality. We argue and find that uncertainty about the usability of TLCF is a key driver of voluntary TLCF disclosure. The disclosure score is on average 3.86 points higher for firms with a strong loss history vis-à-vis firms without such a loss history. This positive association is economically meaningful, since the sample mean for the disclosure score is 8.88 (median 7.5). We also find that the content and type of disclosure vary systematically with the signal of uncertainty. In instances of historic uncertainty, firms exhibit increased reporting on the reasons and mechanisms behind changes in TLCF. Conversely, in the case of future uncertainty, they increase disclosure related to valuation allowance information. Our study provides detailed and unique insights into TLCF disclosure, suggesting that managers enrich the information environment with voluntary disclosure that caters to expected investors’ needs.
Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco
Authors: Bargam Bouchra , Boudhar Abdelghani , Kinnard Christophe , Bouamri Hafsa , Nifa Karima , Chehbouni Abdelghani 
Published: June 2024

AbstractStreamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) and Random Forest (RF) against the multiple linear regression (MLR) for daily streamflow forecasting in the mountainous sub-basin of Rheraya between 2003 and 2016. The results show that SVR performed best, followed by RF and MLR. In measurable terms and regarding mean performance, SVR exhibited the higher Nash–Sutcliffe efficiency score (NSE = 0.59) and a lower root mean squared error (RMSE = 1.18 $$\text {m}^3\,\text {s}^{-1}$$m3s-1) compared to RF (NSE = 0.53, RMSE = 1.18 $$\text {m}^3\,\text {s}^{-1}$$m3s-1) and MLR (NSE = 0.54, RMSE = 1.01 $$\text {m}^3\,\text {s}^{-1}$$m3s-1). Furthermore,the available time series was too short to properly capture the full range of streamflow variability, which reduced the prediction performance outside of the calibration conditions. These findings suggest that ML algorithms, particularly SVR, can provide accurate streamflow estimation useful for water resources management when trained on a representative period. The results highlight the capacity of Machine Learning algorithms, specifically SVR, to augment streamflow prediction for enhanced water resource management in arid regions.
An assessment of using eco-friendly crop production practices by the project beneficiaries and non-beneficiaries in Bangladesh
Authors: Hasan Mohammad Maruf , Farouque Md. Golam , Sarker Md. Asaduzzaman 
Published: June 2024

AbstractThe purposes of this study were to determine the present state of use of eco-friendly crop production practices by the beneficiaries of “safe crop production through eco-friendly approaches” project and non-beneficiary smallholder farmers and to explore the factors that may influence their use. The study’s population was the smallholder farmers of 2 villages (Darirampur and Birrampur Uzanpara) of Rampur union under Trishal upazila (sub-district) of Bangladesh. 60 beneficiaries and 60 non-beneficiaries were randomly selected as samples from a population of 270 beneficiaries and 585 non-beneficiaries. Thus, the total sample was 120 smallholder farmers. Data were collected using a structured questionnaire. The respondents were asked to give responses regarding their use of 21 practices following a 4-point rating scale. The highest proportion (95%) of the beneficiaries had medium extent of use, whereas highest proportion (85%) of the non-beneficiaries had low extent of use. The multiple linear regression analysis showed educational level, annual income, training experience, extension media contact, knowledge, and attitude toward eco-friendly crop production practices were the influential variables for the beneficiaries explaining 81.8% variation. However, for the non-beneficiaries, annual income, training experience, organizational participation, and credit received were the influential variables explaining 78.1% variation. The beneficiaries are more environmentally conscious than the non-beneficiaries, which highlights the need for authorities to involve more farmers in future projects to ensure safe crop production and better environmental health.
SeaOrbiter, the first International Oceanic Station
Authors: Rougerie Jacques , Vallat Francis , Fuchs Ariel 
Published: June 2024

AbstractSeaOrbiter’s vision is to become the first International Space Station of the ocean, a symbol of humanity's capacity to explore and understand the most uncharted and critical part of our planet. SeaOrbiter will bring together scientists, explorers, and innovators from around the world to conduct ground-breaking research, notably in the context of global change, explore the unknown, and develop new technologies to better understand our ocean, its fine relationship with the atmosphere and its complex ecosystems. With its unique design and innovative operational mode–a quiescent slow drifting pace–SeaOrbiter aims to become an iconic symbol of human’s commitment to protecting our planet's most valuable resource: the ocean.
Hume and the Cognitive Phenomenology of Belief
Authors: Miyazono Kengo 
Published: June 2024

AbstractThis article argues that Hume is committed to the cognitive phenomenology of believing. For Hume, beliefs have some distinctively cognitive phenomenology, which is different in kind from sensory phenomenology. I call this interpretation the “cognitive phenomenal interpretation” (“CPI”) of Hume. CPI is coherent with, and supported by, the textual evidence from A Treatise of Human Nature as well as An Enquiry Concerning Human Understanding. In both texts, Hume talks about the distinctive “manner” of believing, and CPI provides us with the best explanation of Hume’s remarks on this distinctive “manner.”
Clinical Predictors of Acute Ischemia in Patients with Low-Risk Neurological Deficits
Authors: Marko Martha , Moreau Francois , Boulanger Jean-Martin , Camden Marie-Christine , Campbell Bruce C.V. , Field Thalia S. , Krause Martin , Mikulik Robert , Penn Andrew M. , Swartz Richard H. , Hill Michael D. , Coutts Shelagh B. 
Published: June 2024

ABSTRACT:Background:Diagnosis of acute ischemia typically relies on evidence of ischemic lesions on magnetic resonance imaging (MRI), a limited diagnostic resource. We aimed to determine associations of clinical variables and acute infarcts on MRI in patients with suspected low-risk transient ischemic attack (TIA) and minor stroke and to assess their predictive ability.Methods:We conducted a post-hoc analysis of the Diagnosis of Uncertain-Origin Benign Transient Neurological Symptoms (DOUBT) study, a prospective, multicenter cohort study investigating the frequency of acute infarcts in patients with low-risk neurological symptoms. Primary outcome parameter was defined as diffusion-weighted imaging (DWI)-positive lesions on MRI. Logistic regression analysis was performed to evaluate associations of clinical characteristics with MRI-DWI-positivity. Model performance was evaluated by Harrel’s c-statistic.Results:In 1028 patients, age (Odds Ratio (OR) 1.03, 95% Confidence Interval (CI) 1.01–1.05), motor (OR 2.18, 95%CI 1.27–3.65) or speech symptoms (OR 2.53, 95%CI 1.28–4.80), and no previous identical event (OR 1.75, 95%CI 1.07–2.99) were positively associated with MRI-DWI-positivity. Female sex (OR 0.47, 95%CI 0.32–0.68), dizziness and gait instability (OR 0.34, 95%CI 0.14–0.69), normal exam (OR 0.55, 95%CI 0.35–0.85) and resolved symptoms (OR 0.49, 95%CI 0.30–0.78) were negatively associated. Symptom duration and any additional symptoms/symptom combinations were not associated. Predictive ability of the model was moderate (c-statistic 0.72, 95%CI 0.69–0.77).Conclusion:Detailed clinical information is helpful in assessing the risk of ischemia in patients with low-risk neurological events, but a predictive model had only moderate discriminative ability. Patients with clinically suspected low-risk TIA or minor stroke require MRI to confirm the diagnosis of cerebral ischemia.
Variation in the structural order of kaolinite in regolith as an effective indicator of REE mineralization
Authors: Luo Lianying , Tan Wei , Qin Xiaorong , Ji Shichao , Liang Xiaoliang , He Hongping 
Published: June 2024

Abstract
Regolith-hosted rare earth element (REE) deposits hosted by the granitic regolith in South China provide >90% of the world’s heavy REEs. Kaolinite is one of the major carriers of REE ions in regolith. The formation and transformation of kaolinite can be affected by chemical weathering and hydrodynamic conditions, but the contribution of each factor has not been evaluated. This study systematically investigated the variation in abundance of phyllosilicate minerals and structural order of kaolinite in the Renju regolith-hosted REE deposit. The total abundance of 1:1 phyllosilicate minerals increased upwards along the profile from Section I to Section III. However, semi-quantitative analyses indicated that Section III-1 (depth at 10–16 m) featured an evident decrease in both abundance and structural order of kaolinite upward along the profile. The morphological feature and abundance of kaolinite revealed intensive kaolinite-to-halloysite transformation and kaolinite dissolution in Section III-1. This suggests that the alternating wetting and drying zone in Section III-1 provided a favorable kinetic environment for the entry of water molecules into the kaolinite interlayer and the kaolinite-to-halloysite transformation, resulting in both lower structural order and abundance of kaolinite in Section III-1. Moreover, REE ions started to be enriched from the alternating wetting and drying zone and formed high-grade ores at the lower part of the water table, due to a significant increase in pore water and decrease in the seepage velocity. Therefore, the abundance and structural order of secondary kaolinite can serve as important indicators of hydrodynamic changes in regolith, as well as the mineralization of regolith-hosted REEs.
The influence of space environmental factors on the laser-induced damage thresholds in optical components
Authors: Ma Bin , Guan Shuang , Yan Dongyue , Pan Qiaofei , Hou Zhiqiang , Wang Ke , Han Jiaqi 
Published: June 2024

Abstract
This paper systematically investigated the impact mechanisms of proton irradiation, atomic oxygen irradiation and space debris collision, both individually and in combination, on the laser damage threshold and damage evolution characteristics of HfO2/SiO2 triple-band high-reflection films and fused silica substrates using a simulated near-Earth space radiation experimental system. For the high-reflection film samples, the damage thresholds decreased by 15.38%, 13.12% and 46.80% after proton, atomic oxygen and simulated space debris (penetration) irradiation, respectively. The coupling irradiation of the first two factors resulted in a decrease of 26.93%, while the combined effect of all the three factors led to a reduction of 63.19%. Similarly, the fused silica substrates exhibited the same pattern of laser damage performance degradation. Notably, the study employed high-precision fixed-point in situ measurement techniques to track in detail the microstructural changes, surface roughness and optical-thermal absorption intensity before and after proton and atomic oxygen irradiation at the same location, thus providing a more accurate and comprehensive analysis of the damage mechanisms. In addition, simulations were conducted to quantitatively analyze the transmission trajectories and concentration distribution lines of protons and atomic oxygen incident at specific angles into the target material. The research findings contribute to elucidating the laser damage performance degradation mechanism of transmissive elements in near-Earth space environments and provide technical support for the development of high-damage-threshold optical components resistant to space radiation.
Diagnosing the fast-heating process of the double-cone ignition scheme with X-ray spectroscopy
Authors: Dai Yu , Gu Haochen , Fang Ke , Zhang Yihang , Zhang Chenglong , Dong Yufeng , Zhang Zhe , Yuan Xiaohui , Li Yutong , Zhang Jie 
Published: June 2024

Abstract
In the double-cone ignition scheme of inertial confinement fusion, the head-on collision of two compressed fuel jets from the cone-tips forms an isochoric plasma, which is then heated suddenly by a MeV relativistic electron beam produced by ultra-intense picosecond laser pulses. This fast-heating process was studied experimentally at the Shenguang II upgrade laser facility. By observing temporal-resolved X-ray emission and the spatial-resolved X-ray spectrum, the colliding process and heating process are carefully studied. The colliding plasma was imaged to have dimensions of approximately 86 μm in the implosion direction and approximately 120 μm in the heating direction. By comparing the simulated plasma X-ray spectrum with experimental data, the electron temperature of the heated plasma was found to rapidly increase to 600 ± 50 eV, almost doubling the temperature achieved before the heating laser incidence.
Part-of-speech tagger for Bodo language using deep learning approach
Authors: Pathak Dhrubajyoti , Narzary Sanjib , Nandi Sukumar , Som Bidisha 
Published: June 2024

AbstractLanguage processing systems such as part-of-speech (POS) tagging, named entity recognition, machine translation, speech recognition, and language modeling have been well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, is either yet to commence or is in its nascent stages. Language model (LM) plays a vital role in the downstream tasks of modern natural language processing. Extensive studies are carried out on LMs for high-resource languages. However, these low-resource languages are still underreprese. In this study, we first present BodoBERT, an LM for the Bodo language. To the best of our knowledge, this work is the first such effort to develop an LM for Bodo. Second, we present an ensemble deep learning-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with conditional random field and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several LMs in the experiment to see how well they work in POS tagging tasks. The best-performing model achieves an F1 score of 0.8041. A comparative experiment was also conducted on Assamese POS taggers, considering that the language is spoken in the same region as Bodo.
A case study on decompounding in Indian language IR
Authors: Sahu Siba Sankar , Pal Sukomal 
Published: June 2024

AbstractDecompounding is an essential preprocessing step in text-processing tasks such as machine translation, speech recognition, and information retrieval (IR). Here, the IR issues are explored from five viewpoints. (A) Does word decompounding impact the Indian language IR? If yes, to what extent? (B) Can corpus-based decompounding models be used in the Indian language IR? If yes, how? (C) Can machine learning and deep learning-based decompounding models be applied in the Indian language IR? If yes, how? (D) Among the different decompounding models (corpus-based, hybrid machine learning-based, and deep learning-based), which provides the best effectiveness in the IR domain? (E) Among the different IR models, which provides the best effectiveness from the IR perspective? This study proposes different corpus-based, hybrid machine learning-based, and deep learning-based decompounding models in Indian languages (Marathi, Hindi, and Sanskrit). Moreover, we evaluate the effectiveness of each activity from an IR perspective only. It is observed that the different decompounding models improve IR effectiveness. The deep learning-based decompounding models outperform the corpus-based and hybrid machine learning-based models in Indian language IR. Among the different deep learning-based models, the Bi-LSTM-A model performs best and improves mean average precision (MAP) by 28.02% in Marathi. Similarly, the Bi-RNN-A model improves MAP by 18.18% and 6.1% in Hindi and Sanskrit, respectively. Among the retrieval models, the In_expC2 model outperforms others in Marathi and Hindi, and the BB2 model outperforms others in Sanskrit.
Association between whole-grain consumption, tryptophan metabolism and psychological distress: a secondary analysis of a randomised controlled trial
Authors: Liikonen Vilma , Näätänen Mari , Kårlund Anna , Hanhineva Kati , Karhunen Leila , Kolehmainen Marjukka 
Published: June 2024

AbstractThis study aimed to investigate whether psychological distress, whole-grain consumption and tryptophan metabolism are associated with participants undergoing weight management intervention. Seventy-nine women and men (mean age 49·7 (sd 9·0) years; BMI 34·2(sd 2·5) kg/m2) participated in a 7-week weight-loss (WL) period and in a 24-week weight maintenance (WM) intervention period. Whole-grain consumption was measured using 4 d food diaries. Psychological distress was assessed with the General Health Questionnaire-12 (GHQ), and participants were divided into three GHQ groups based on the GHQ scores before WL. Tryptophan metabolites were determined from the participants’ fasting plasma using liquid chromatography-MS. GHQ scores were not associated with the whole-grain consumption. A positive association was observed between the whole-grain consumption and indole propionic acid (IPA) during the WM (P = 0·033). Serotonin levels were higher after the WL in the lowest GHQ tertile (P = 0·033), while the level at the end of the WM was higher compared with other timepoints in the highest GHQ tertile (P = 0·015 and P = 0·001). This difference between groups was not statistically significant. Furthermore, levels of several tryptophan metabolites changed within the groups during the study. Tryptophan metabolism changed during the study in the whole study group, independently from the level of psychological distress. The association between whole-grain consumption and IPA is possibly explained by the effects of dietary fibre on gut microbiota. This broadens the understanding of the pathways behind the health benefits associated with the intake of whole grains.