bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamique spatio-temporelle du couvert forestier dans le bassin de Kara au Nord Togo (Afrique de l’Ouest)
Authors: Hlovor Atsu K. Dogbeda , Adjonou Kossi , Segla Kossi , Akoete Katché Komlanvi , Kokou Kouami 
Published: December 2024

Dans le contexte de la dégradation des écosystèmes forestiers, cette étude vise à approfondir les connaissances sur la dynamique forestière dans le bassin de la Kara. Elle se concentre sur les changements d'occupation des sols sur une période de 50 ans, de 1972 à 2021. Les étapes incluent la cartographie des occupations du sol pour les années 1972, 1987, 2005 et 2021, en utilisant des images satellites Landsat, ainsi qu'une projection pour 2050 basée sur un scénario tendanciel. L’ Indice de Santé de la Végétation (VHI) a été calculé pour suivre l’état de la végétation et sa corrélation avec les paramètres météorologiques. Les résultats révèlent une réduction des surfaces forestières entre 1972 et 2005, suivie d'une légère augmentation jusqu'en 2021, ce qui suggère une tendance favorable à la conservation. Cependant, la conversion des savanes en terres agricoles est préoccupante, avec une dégradation rapide de 9,22 % entre 2005 et 2021. L'analyse de la matrice de Markov a permis d'évaluer les probabilités de transition entre les classes d'occupation du sol pour la période 2021-2050, mettant en évidence une tendance à la conversion des savanes en forêts. De plus, l'étude du VHI montre une variabilité interannuelle de l'état de la végétation, alternant entre des périodes humides et sèches entre 1972 et 2005, avec une variabilité moindre entre 2005 et 2021. Ces résultats constituent une base pour comprendre et anticiper l'évolution des paysages en réponse au développement agricole.

In the context of forest ecosystem degradation, this study aims to deepen knowledge of forest dynamics in the Kara basin. It focuses on land-use changes over a 50-year period, from 1972 to 2021. Stages include land-use mapping for the years 1972, 1987, 2005, and 2021, using Landsat satellite images, and a projection for 2050 based on a trend scenario. The Vegetation Health Index (VHI) was calculated to monitor vegetation condition and its correlation with meteorological parameters. The results show a reduction in forest area between 1972 and 2005, followed by a slight increase up to 2021, suggesting a favorable conservation trend. However, the conversion of savannah to farmland is a cause for concern, with a rapid degradation of 9.22% between 2005 and 2021. Markov matrix analysis was used to assess the transition probabilities between land-use classes for the period 2021-2050, highlighting a trend towards the conversion of savannah to forest. In addition, the VHI study shows inter-annual variability in vegetation conditions, alternating between wet and dry periods between 1972 and 2005, with less variability between 2005 and 2021. These results provide a basis for understanding and anticipating the evolution of landscapes in response to agricultural development.
Dynamique de la Population de Sarda sarda (Bloch, 1793) du Golfe de Guinée, Côte d’Ivoire
Authors: Jean Paul Angui Kouamé , Kouamé Affourmou , N’dri Florentine Assan , N’guessan Diaha Constance , Kouamé Konan Jacques , N’guessan Yao , Kouakou Yao 
Published: December 2024

Cette étude a été entreprise de janvier 2015 à décembre 2015 sur des individus capturés au filet maillant par les pêcheurs artisans exerçant dans le golfe de guinée. Des prélèvements mensuels de poissons ont été effectués au quai piroguier d’Abobo-doumé et de Zimbabwé. Les données mensuelles de fréquence de tailles obtenues ont été ensuite analysées par le logiciel FiSAT II pour l’estimation de quelques paramètres de croissance et d’exploitation de Sarda sarda du Golfe de Guinée afin d’évaluer l’état du stock. Au total, 635 spécimens de Sarda sarda de longueur à la fourche comprise entre 33 et 57 cm ont été examinés. La longueur asymptotique était de 59,85 cm avec un taux de croissance spécifique (K) de 0,57/an. L'indice de performance de croissance était de 3,31. Le modèle de recrutement a été continu toute l'année avec deux pics de recrutement d’importance inégale : le majeur apparaît en mars tandis que le mineur est observé de juillet. Les taux de mortalité totale (Z), naturelle (M) et par pêche (F) sont de 1,13/an, de 0,99/an et de 0,14/an respectivement. Le niveau d’exploitation (E) était de 0,12/an avec une taille de première capture (37,76 cm) inférieure à celle de la première maturité. Ainsi, la pérennité du stock dans le Golfe de la Guinée est menacée. Pour assurer cette pérennité et garantir des stocks permanents des ressources halieutiques du golfe de Guinée, une augmentation de la taille des mailles des filets est recommandée.
This study was undertaken from January 2015 to December 2015 on individuals caught with gillnets by artisanal fishermen operating in the Gulf of Guinea. Monthly fish samples were taken at the Abobo-doumé and Zimbabwe pirogue docks. The monthly size-frequency data obtained were then analyzed using FiSAT II software to estimate a number of growth and exploitation parameters for Sarda sarda in the Gulf of Guinea, in order to assess stock status. A total of 635 Sarda sarda specimens with fork lengths ranging from 33 to 57 cm were examined. The asymptotic length was 59.85 cm with a specific growth rate (K) of 0.57/year. The growth performance index was 3.31. The recruitment pattern was continuous throughout the year, with two recruitment peaks of unequal importance: the major peak appeared in March, while the minor peak was observed from July onwards. Total (Z), natural (M) and fishing (F) mortality rates were 1.13/year, 0.99/year, and 0.14/year respectively. The exploitation level (E) was 0.12/year, with a first-capture size (37.76 cm) below that of the first maturity. As a result, the sustainability of the stock in the Gulf of Guinea is under threat. To ensure this sustainability and guarantee permanent stocks of fishery resources in the Gulf of Guinea, an increase in net mesh size is recommended.
The Effect of Blended PBL Assisted with Advance Organizer and Thinking Style on Understanding and Application of Digital Electronics Concepts
Authors: Mige Godlief Erwin Semuel , Setyosari Punaji , Ulfa Saida , Praherdiono Henry 
Published: December 2024

assisted with Advance Organizer (BPBL AO) model in the experimental class and Direct Learning in the control class, the moderator variable of Thinking Style (Internal and External), on the dependent variable Digital Electronics Concept Understanding and Digital Electronics Concept Application and the interaction effect of the independent variable and moderator variable on the dependent variable. This research is quantitative research with a quasi-experimental approach with a 2x2 factorial design, Multivariate Analysis of Variance (MANOVA) is used to analyze research data. The results of the study are (1) there are differences in learning outcomes between students taught with BPBL AO and students taught with direct learning on Digital Electronics Concept Understanding and Digital Electronics Concept Application, (2) there are differences in higher order thinking skills between students who have internal thinking styles and external thinking styles in the Digital Electronics Concept Understanding dimension and while they tend to be the same or there is no difference in Digital Electronics Concept Application, and (3) between the learning strategies applied and the thinking styles possessed by students on the learning outcomes of Digital Electronics Concept Understanding and Digital Electronics Concept Application, there is no interaction effect. The application of BPBL AO can improve critical thinking skills for problem solving without depending on their thinking style. This research shows that BPBL AO can stimulate students to think more critically, analyze information, link digital electronics concepts with the real world, and produce creative solutions in problem solving.
Model of The 21st Century Vocational Teacher Competency Development
Authors: Pakaja Jemmy A. , Kamdi Waras , Hadi Syamsul , Yoto  
Published: December 2024

The development of Information and Communication Technology (ICT) had changed our lives profoundly. The period known as the industrial revolution had forced various changes in all fields of knowledge. In facing the era of the industrial revolution 4.0, Human Resources development became an important factor. Human resources, mainly Vocational High Schools (Sekolah Menengah Kejuruan/SMK) graduates were one of the spearheads to fulfil the needs of the industrial worlds. To achieve this goal, it was necessary to improve the quality of education. Teachers, as one of the main components in the process of increasing the quality of human resources, were required to be professionals in their fields and possess competencies according to the needs of the times. In efforts to improve the quality of the education field, developing teacher competency is a top priority. The purpose of this study is to examine the theories and models of teacher competencies and their development, especially vocational teachers. This study used a literature review method. The initial study was conducted by examining and considering the results of previous research related to the competence of vocational teachers, then proceeded with the study of theories and models of teacher competencies and their development. The results of this literature review lead to a conceptual framework for the development of vocational teacher competencies. The conceptual framework offered can be used as a basis for reference and guidance for further studies conducted by researchers in the future.
Guest Editorial: Patience in AI Development Is a Virtue: Why 99% Correct Is 100% Wrong
Authors: McArdle Shane 
Published: December 2024

In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences.
For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards.
Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest.
Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations.
The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards.
However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value.
Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies.
While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic.
Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability.
Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance.
Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.
Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells
Authors: Sun Li , Cao Changfu , Yang Jianbo , Jin Jian 
Published: December 2024

Abstract

PEDV is a highly contagious enteric pathogen that can lead to severe diarrhea and death in neonatal pigs. Despite extensive research, the complete pathomechanism of PEDV and the molecular mechanisms of host responses remain unclear. In this study, differentially expressed genes (DEGs), time-specific co-expression modules, and key regulatory genes associated with PEDV infection were identified. Differential analysis showed that 2,275, 1,492, and 3,409 differential genes were screened in the 12 h vs. Mock, 24 h vs. Mock, and 48 h vs. Mock, respectively. Time series analysis showed that the genes of the up-regulated module were mainly involved in antiviral pathways such as viral defense response and regulation of immune system processes. Protein interaction network analysis revealed that the top 20 core genes in the interaction network included six up-regulated genes (TFRC, SUOX, RMI1, CD74, IFIH1, CD86) and 14 down-regulated genes (FOS, CDC6, CDCA3, PIK3R2, TUFM, VARS, ASF1B, POLD1, MCM8, POLA1, CDC45, BCS1L, RAD51, RPA2). In addition, GSEA enrichment analysis showed that pathways such as DNA replication and homologous recombination were significantly inhibited during viral infection, and RAD51, CDC6, and RPA2 were involved. Our findings not only reveal dynamic changes in the transcriptome profile of PEDV-infected IPEC-J2 cells, but also provide novel insights into the mechanism of PEDV infection of the host.
Airway pressures generated by high flow nasal cannula in patients with acute hypoxemic respiratory failure: A computational study
Authors: Shamohammadi Hossein , Weaver Liam , Saffaran Sina , Tonelli Roberto , Laviola Marianna , Laffey John G. , Camporota Luigi , Scott Timothy E. , Hardman Jonathan G. , Clini Enrico , Bates Declan G. 
Published: December 2024

Abstract

Introduction and Objectives: High flow nasal cannula (HFNC) therapy is an increasingly popular mode of non-invasive respiratory support for the treatment of patients with acute hypoxemic respiratory failure (AHRF). Previous experimental studies in healthy subjects have established that HFNC generates flow-dependent positive airway pressures, but no data is available on the levels of mean airway pressure (MAP) or positive end-expiratory pressure (PEEP) generated by HFNC therapy in AHRF patients. We aimed to estimate the airway pressures generated by HFNC at different flow rates in patients with AHRF, whose functional lung volume may be significantly reduced compared to healthy subjects due to alveolar consolidation and/or collapse.
Materials and Methods: We developed a high-fidelity mechanistic computational model of the cardiopulmonary system during HFNC therapy using data from healthy subjects, and then measured the MAP and PEEP levels produced when different amounts of alveolar consolidation/collapse were incorporated into the model.
Results: When calibrated to represent normal lung physiology in healthy subjects, our model recapitulates the airway pressures produced by HFNC at different flow rates in healthy volunteers who were breathing normally, with their mouths closed or open. When different amounts of alveolar consolidation/collapse are implemented in the model to reflect the pathophysiology of AHRF, the mPaw and PEEP produced by HFNC at all flow rates increases as the functional lung volume decreases (up to a mPaw of 11.53 and a PEEP of 11.41 cmH2O at 60 L/min with the mouth closed when 50% of the model’s alveolar compartments are non-aerated). When the model was matched to individual patient data from a cohort of 58 patients with AHRF receiving HFNC at 60 L/min, the mean (standard deviation) of the mPaw / PEEP produced by HFNC in the models of these patients was 8.56 (1.50) / 8.92 (1.49) cmH2O with mouths closed, and 1.73 (0.31) / 1.36 (0.36) cmH2O with mouths open.
Conclusions: Our results suggest that the airway pressures produced by HFNC in patients with AHRF could be higher than is currently assumed based on experimental data from healthy subjects, particularly in patients whose mouths remain closed. Higher levels of PEEP could be beneficial if they lead to alveolar recruitment and improved lung compliance, but could cause alveolar overdistension if they do not, motivating the close monitoring of the effects of HFNC on lung mechanics. Further clinical studies are warranted to directly measure the airway pressures produced by HFNC in patients with different severities of AHRF.
Serial Systemic Immune Inflammation Indices: Markers of Acute Migraine Events or Indicators of Persistent Inflammatory Status?
Authors: Wijeratne Tissa , Murphy Melanie J. , Wijeratne Chanith , Martelletti Paolo , Karimi Leila , Apostolopoulos Vasso , Sales Carmela , Riddell Nina , Crewther Sheila G. 
Published: December 2024

Abstract

Background
Migraine is the most common complex neurological disorder, affecting over a billion people worldwide. Neurogenic inflammation has long been recognized as a key factor in the pathophysiology of migraine though little research has been directed to investigating whether inflammation is greatest in migraine with aura or without, and whether inflammation is a permanent state in migraine or whether is an event related transitory state. Thus, the primary aim of this single-centre, retrospective study was to explore the potential clinical utility of the systemic immune-inflammatory indices as a measure of duration and severity of inflammation derived from relative blood cell counts in migraine patients within an acute inpatient setting. Specifically, we assessed the role of serial white blood cell counts to calculate Serial Systemic Immune-Inflammatory Indices (SSIIi) (calculated using the formula: neutrophil count x platelet count/lymphocyte count) between aura (N = 67) and no-aura (N = 119) migraine patients at time of admission to a tertiary care centre in Melbourne, Australia, and at 24 hours post admission versus stable in-community migraineur controls (N = 20)
Main Body:
We conducted a retrospective analysis of medical records from patients presenting with migraine at Western Health over an 18-month period. Patients were categorized as having migraine with aura (MA) or without aura (MO) according to ICHD-3 criteria. Baseline demographics and brain imaging findings were evaluated for each group. Patients who had at least two sets of white blood cell counts drawn within 24 hours during their admission were included in the study. A mixed-design ANOVA showed a significant main effect of SSIIi between patients with migraine with aura (MA) and migraine without aura (MO) during acute inpatient presentation, in comparison to a convenience sample of outpatients with migraine (MA and MO) with similar blood measures.
Conclusion
SSIIi levels were significantly lower in patients with migraine with aura (MA), who also showed a greater, though non-significant, decrease between the two measurements compared to those with migraine without aura (MO) and controls, whose SSIIi levels remained consistently higher. The control group displayed similar findings to MO inpatients, suggesting persistent systemic inflammation in a subset of migraine patients regardless of the acuity of presentation. Further studies are needed to evaluate the role of systemic inflammation in migraine pathophysiology, chronicity, and progression.
Diagnosis of acute aortic dissection patients with normal non-enhanced computed tomography and risk factors of in-hospital mortality of type A acute aortic dissection patients
Authors: Zou Chuande , Wang Donglin 
Published: December 2024

Abstract

Background: The non-enhanced computed tomography(CT) of patients with acute aortic dissection(AAD) could be completely normal, which could lead to misdiagnosis of AAD. We aimed to evaluate the diagnosis of AAD patients with normal non-enhanced CT and risk factors of in-hospital mortality of type A AAD patients.
Methods: A total of 150 patients with chest pain, back pain, or abdominal pain in emergency center from June 2021 to June 2023 were selected in our study. AAD patients with normal non-enhanced CT were selected as AAD group(56 cases), patients with normal CT angiography were selected as non-AAD group(94 cases).Type A AAD patients in AAD group were divided into death group(14 cases) if died in the hospital before discharge and survival group(29 cases) if lived at discharge. The information of emergency patients were collected anonymously and logistic regression analysis and receiver operator characteristic curve were performed to analyze the figures.
Results: D-dimer and pain score have significance in diagnosis of AAD (OR=2.195, 95%CI 1.678 to 2.873 and OR=2.609, 95%CI 1.383 to 4.921, respectively). D-dimer has an overall sensitivity of 92.5% and a specificity of 79.3% for diagnosis of AAD. BMI and entry tear size were independent predictors of in-hospital mortality of type A AAD (OR=1.646, 95%CI 1.246 to 2.175 and OR=1.266, 95%CI 1.073 to 1.492, respectively). AUC of 0.943 combined with BMI and entry tear size were higher than that of single factor (BMI was 0.877, p<0.001 and entry tear size was 0.784, p<0.001). Patients with BMI≥28.57kg/m2 have higher in-hospital mortality compared to those with lower levels and patients with entry tear size ≥ 7.1mm have higher in-hospital mortality compared to those with lower levels.
Conclusions: D-dimer has diagnostic value for AAD patients with normal non-enhanced CT and combination of BMI and entry tear size have prognostic prediction value for in-hospital mortality of type A AAD.
Optimizing EMS Response Times with Machine Learning: A Multivariate Analysis for Enhanced Resource Allocation
Authors: Hill Peter , Lederman Jakob , Jonsson Daniel , Bolin Peter , Vicente Veronica 
Published: December 2024

Abstract

Background
This study examines the multifaceted determinants of Emergency Medical Services (EMS) response times through a machine learning approach. Using data from over one million EMS missions spanning 2017 to 2022, this research aims to understand and predict factors influencing response times, highlighting the role of machine learning in optimizing emergency services.
Methods
Advanced machine learning techniques, including Gradient Boosting models, were employed to analyze response times across various emergency scenarios. The data set was carefully curated to consider numerous factors such as urgency levels, call handling times, travel times, and geographical and temporal variables.
Results
The analysis confirms significant variability in response times influenced by factors such as the urgency of calls, weather conditions. Adverse weather and extreme temperatures were found to exacerbate response times significantly. Central urban areas showed faster response times compared to remote regions. The machine learning models effectively identified and quantified these impacts, offering a nuanced understanding that can inform more targeted EMS deployment strategies.
Conclusions
The findings advocate for the integration of real-time data on traffic, weather, and incident specifics into EMS resource allocation strategies to optimize response times dynamically. This approach underscores the importance of leveraging predictive analytics in emergency response planning, enhancing service delivery and patient outcomes. The study not only contributes to academic knowledge but also provides practical insights for emergency service administrators aiming to improve operational efficiency and response effectiveness.