bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing Ground-Based Lightning Detection Networks Near Wildfire Points-of-Origin
Authors: Hatchett Benjamin , Nauslar Nick , Brown Timothy 
Published: March 2024

Abstract

Lightning detection and attribution to wildfire ignitions is a critical component of fire management worldwide to both reduce hazards of wildfire to exposed values-at-risk and to enhance the potential for wildland fire to provide resource benefits in fire-adapted ecosystems.We compared two operational ground-based lightning detection networks used by fire managers to identify cloud-to-ground strokes within operationally-relevant distances (1.6 km) of the origins of 4,408 western United States lightning-ignited wildfires spanning May-September 2020. Applying two sets of constraints--varying holdover time and applying a quality control measure--we found strokes were co-detected near 55--65% of fires, increasing to 65--79% for detection by at least one network, with neither network detecting lightning near 1,024--1,666 fires.Because each network detected strokes near 136--376 unique fires, use of both networks is suggested to increase the probability of identifying potential fire starts. Given the number of fires with network-unique detections and no detections be either network, improvements in lightning detection networks are recommended given increasing fire hazard.
Water Absorption Reduction of Biodegradable Plastic Made From Sugar Cane Molasses by the Synergistic Effect of Sisal Fiber Reinforcement and Succinic Acid Crosslinking
Authors: Gerbie Birhan , Alemayehu Atalay , Belay Mezigebu , Ewunetu Meseret 
Published: March 2024

Abstract
In our daily lives, plastic has always played a significant role. Sugarcane molasses is a by-product of sugar production, and a potential raw material to develop biodegradable plastic which neither causes deforestation nor affects food supply. To address this issue, molasses-based plastic was cross-linked with a succinic acid cross-linker chemical, and locally available sisal fiber was used as reinforcement to improve the plastic's properties and reduce water absorption. The sisal fiber was manually extracted with a sharp knife and alkaline treated with an 8% NaOH solution before being utilized as a reinforcement. Additionally, acid hydrolysis methods were used to pre-treat crude raw cane molasses. After treating the molasses, the lactic acid was recovered and purified by adding cultured yogurt lactic acid to the treated molasses, and then the lactic acid was converted to PLA utilizing direct solution polymerization and condensation techniques in this experiential work. Using the solvent casting process, four (4) bioplastic film samples were made after each extraction and treatment of raw materials. Using a digital microbalance to establish the initial weight of a dry sample and immersing the film sections in distilled water, the swelling experiment was carried out to identify the best
Vulnerability to Climate Change: Evidence from a Dynamic Factor Model
Authors: Marotta Fulvia , Mumtaz Haroon 
Published: March 2024

Abstract

Using a dynamic factor model with stochastic volatility, we examine the synchronization of temperature and precipitation changes across 166 countries and regions. Firstly, the model allows us to quantify the extent of the increase in global tempera- tures. Secondly, it offers a unique measure of how each country’s temperatures align with this common global movement, translating into a robust indicator of regional exposure to global warming. Our findings reveal that a common factor explains a significant portion of temperature variance globally, with the largest contribution observed in sub-Saharan Africa, Latin America, and Asia. Additionally, the com- mon factor accounts for the increase in temperature levels across these regions. In contrast, precipitation fluctuations exhibit more localized patterns. We find that countries with higher GDP per-capita have lower exposure to global temperature changes.
PAGE2B is a prognostic marker for colorectal adenocarcinoma: bioinformatics analysis after correction for thyroid papillary carcinoma
Authors: Zhang Yanyan , Li Li , Zheng Huihui , Zheng Tiantian , Xu Liping , Li Changchun 
Published: March 2024

Abstract

Globally, Colon cancer has a high incidence and mortality rate, adenocarcinoma s is the most common pathological type. In this study, our objective was to explore a new biomarker for early diagnosis and prognosis in colorectal adenocarcinoma (COAD) patients. We downloaded mRNA data related to colorectal adenocarcinoma and thyroid papillary carcinoma through The Cancer Genome Atlas (TCGA) database, and used PTC as the mediator to explore the key genes causing the adverse prognosis of COAD. Univariate and multivariate Cox regression models were used, along with clinical information from COAD patients, to analyze the prognostic value of the target gene in COAD patients. Functional enrichment analysis was subsequently performed to elucidate the molecular mechanisms underlying the regulatory effects of the target gene on colorectal adenocarcinoma. We explore whether methylation is one of the causes of the high expression of target genes. Additionally, we calculated the percentage of infiltrating immune cells in COAD patients and evaluated their correlation with target gene expression. The potential sensitive drugs of target genes were explored by drug sensitivity analysis. Compared with those normal tissue, the gene expression levels of PAGE2B were found to be elevated in Compared with those colorectal adenocarcinoma tissues. Furthermore, the expression of PAGE2B in colorectal adenocarcinoma exhibited significant correlations with M stage. The results of functional enrichment analysis suggested the potential involvement of PAGE2B in the regulation of DAN methylation and cell cycle. Additionally, there were significant correlations between the expression level of the PAGE2B gene and the infiltration of various immune cells within the colon tumor microenvironment. PAGE2B can be used as a prognostic biomarker in patients with COAD could potentially be therapeutic target for COAD.
Development of virtual control terminal for high-horsepower tractor control system based on ISO-11783 protocol
Authors: Huang Ping , Bai Wenxu , Qiu Zhichuan , Zheng Fuyin , Zhang Xiaodan , Bai Shi 
Published: March 2024

Abstract

Precision control is a crucial aspect of fine agriculture. Enhancing the control performance of agricultural and forestry vehicles can reduce driver fatigue, improve productivity, and enhance operational accuracy. This study focuses on the ISO-11783 protocol of the serial communication network of a high-horsepower tractor control system. It combines the information frame of the controller area network (CAN) arbitration field and designs the CAN communication protocol network topology for the high-horsepower tractor control system. Based on the time-hierarchical controller area network protocol, the CAN communication protocol of the high-powered tractor control system is optimized to achieve dynamic scheduling of telegram messages. To evaluate the effectiveness of the virtual control terminal of the high-powered tractor control system designed in this paper, simulation tests and analyses are conducted. The results demonstrate that when the tractor speed drops below 15 km/h, the high-powered tractor engages the four-wheel-drive state, and when the tractor engine enters the idling state at 30.2 seconds, the tractor speed decreases considerably. Meanwhile, the CAN message update period obtained using the time-hierarchical controller area network is 86.5 ms, and the maximum transmission delay time of CAN message is 0.92 ms. The virtual terminal of the control system of the high-powered tractor designed with the ISO-11783 protocol can effectively carry out the state control of the high-powered tractor. It meets the requirements of real-time communication, ensuring the reliability of CAN communication and enabling the high-powered tractor to operate safely according to the driver's intention.
Predict the College Sports Scores using a Weighted BP-SVR Model
Authors: Zhu Quanying , Miao Jiani , Liu JinRong , Huang Lina 
Published: March 2024

Abstract
A key determinant of college students' physical health is their level of physical accomplishment. As a result, assessment and forecasting of physical accomplishment have grown in significance for both society and university lecturers. Research predicting collegiate athletes' success in sports is currently lacking, and the majority of forecasts were based on imprecise observations. Thus, our goal is to create a model that uses theoretical techniques to forecast the kids' academic success. The Central University of Finance and Economics (CUFE) student basketball performance data was utilized in this study to forecast performance using the back propagation (BP) neural network regression model and the support vector machine regression model (SVR). We thought about integrating the two machine learning prediction techniques to increase accuracy. We created a model using the gradient descent approach, with the real scores acting as dependent factors and the BP and SVR prediction results acting as independent variables, in order to determine the link between the predicted scores of the two models and the real scores. Our methodology, which we call the "BP-SVR weighted prediction model," may be used to predict performance for sports courses other than basketball and is more accurate than only using BP or SVR.
Assessing Landslide Susceptibility through Rock Cut Analysis: A Case Study of National Highway NH-919, India
Authors: Ahmad Bilal , Alam Mohd. Masroor , Sadique Md. Rehan , Faiz Mohammed 
Published: March 2024

Abstract

In the recent past, slope failures along highways have resulted in the destruction of infrastructure and loss of lives and properties throughout the world. Many issues of landslips, debris flow, and rockfalls in inhabited areas along road networks in hilly states of India, the USA, Australia, China, Canada, Brazil, etc., are testimony to this. In the present study, the slope stability of an overpass of National Highway NH-919 in the NCR region, India, has been analyzed. Field observations and petrographic analysis identified Quartzite with localized Schist presence. This study aims to identify the vulnerability of an existing road to landslide hazards and for conditional monitoring of roads for their stability in a present-day scenario of increasing vibrations of heavy vehicles and continuous seismicity in North-Western India, which are two important factors destabilizing the slopes along roads. During field assessment, a few major discontinuities were observed while collecting data at three probable failure locations. The hazard potential has been worked out by incorporating Landslide Susceptibility Score [LSS] and Landslide Hazard Evaluation Factor [LHEF], and the Slope Mass Rating [SMR] system has been used to identify the stability class. The DEM file, combined with the USGS DEM dataset, was used to generate thematic maps. Given the changing rainfall patterns and the heavy precipitation during the monsoon season, the chances of failure increase even more. The study also recommends applying simple mitigating measures to improve road stability and guarantee the long-term safety of residents and users.
Numerical Study on Heat Transfer in Carreau Fluids with Nanofluid Flow over a Curved Stretched Surface
Authors: Ullah Fahim , Ashraf Muhammad Bilal 
Published: March 2024

Abstract

This research aims to understand how fluids behave while flowing through curved stretched surfaces. The study covers nonlinear mixed convection, viscous dissipation, heat transfer via nonlinear thermal radiation, thermophoresis, Brownian diffusion, magnetic fields, and complicated nonlinear chemical reactions. Similarity variables are used to translate the governing nonlinear PDEs into nonlinear ODEs. Calculated adjustment simplifies equations' analytical treatment and improves tractability. The Shooting computational framework generates precise numerical solutions that are then persuasively presented. This academic study shows how flow parameter changes affect the geographical distribution of critical flow characteristics, such as velocity, temperature, and concentration profiles in Carreau fluid. The investigation also examines flow parameters, including the skin friction coefficient, Nusselt number, and Sherwood number throughout parameter regimes. Tabular results are tidy. This study adds to our understanding of the intricate interaction between flow characteristics and heat transfer mechanisms in a Carreau fluid across a curved and stretched surface.
Global Population: from Super-Malthus behavior to Doomsday Criticality
Authors: Drozd-Rzoska Aleksandra , Sojecka Agata 
Published: March 2024

Abstract

The report discusses global population changes from the Holocene beginning to 2023, via two Super Malthus (SM) scaling equations. SM-1 is the empowered exponential dependence:\(P\left(t\right)={P}_{0}exp{\left[\pm \left(t/\right)\right]}^{}\), and SM-2 is the Malthus-type relation with the time-dependent growth rate \(r\left(t\right)\) or relaxation time \(\left(t\right)=1/r\left(t\right)\):\(P\left(t\right)={P}_{0}exp\left(r\left(t\right)\times t\right)={P}_{0}exp\left[/\left(t\right)\right]\). Population data from a few sources were numerically filtered to obtain a 'smooth' dataset, allowing the distortions-sensitive and derivative-based analysis. The test recalling SM-1 equation revealed the essential transition near the year 1970 (population: ~3 billion): from the compressed exponential behavior (\(>1)\) to the stretched exponential one (\(<1\)). For SM-2 dependence, linear changes of \(\left(T\right)\) during the Industrial Revolutions period, since ~ 1700, led to the constrained critical behavior \(P\left(t\right)={P}_{0}exp\left[b{\prime }t/\left({T}_{C}-t\right)\right]\), where \({T}_{C}\approx 2216\) is the extrapolated year of the infinite population. The link to the 'hyperbolic' von Foerster Doomsday equation is shown. Results are discussed in the context of complex systems physics, the Weibull distribution in extreme value theory, and significant historic and prehistoric issues revealed by the distortions-sensitive analysis.
The Mediating Effect of Dark Future between Personality Traits and Fear of Artificial Intelligence in Arab Young Adults
Authors: Hemade Ali , Hallit Rabih , Malaeb Diana , Sakr Fouad , Dabbous Mariam , Merdad Nisma , Rashid Tabassum , Amin Rizwana , Jebreen Kamel , Zarrouq Btissame , Alhuwailah Amthal , Shuwiekh Hanaa Ahmed Mohamed , Fekih-Romdhane Feten , Hallit Souheil , Obeid Sahar 
Published: March 2024

Abstract

Background
Fear of Autonomous Robots and Artificial Intelligence is a novel concept introduced to measure how fearful individuals are about Artificial Intelligence and autonomous robots. This study aimed to measure the mediating effect of dark future, a measure of future anxiety, between personality traits and Fear of Autonomous Robots and Artificial Intelligence.
Methods
Using the snowball sampling technique, participants (n = 1839) were Arab adults who completed the Fear of Autonomous Robots and Artificial Intelligence Scale, Arabic Big Five Personality Inventory, and the Dark Future Scale.
Results
The results of the mediation analysis showed that dark future mediated the association between extroversion/neuroticism/agreeableness and fear of Artificial Intelligence. Higher extroversion was significantly associated with less dark future, whereas more dark future was significantly associated with higher Fear of Autonomous Robots and Artificial Intelligence (FARAI) scores. Extroversion was not directly associated with FARAI scores. Higher neuroticism and agreeableness were significantly associated with more dark future, whereas more dark future was significantly associated with higher FARAI scores. Neuroticism and agreeableness were significantly directly associated with higher FARAI scores.
Conclusion
This study suggests that the relationship between certain personality traits and Fear of Autonomous Robots and Artificial Intelligence can be explained by evaluating one’s Dark Future. This shall help improve research on attitudes towards Artificial Intelligence and also have clinical implications in treating cases of Artificial Intelligence fear.
Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram
Authors: Liu Ting , Wang Guimei , Chen Chunmei , He Lihe , Wang Rensheng 
Published: March 2024

Abstract
Purpose
Sarcopenia, as defined by the Global Leadership Initiative on Malnutrition (GLIM) consensus, serves as a diagnostic indicator for malnutrition and has been shown to influence cancer treatment and clinical results. However, the impact of sarcopenia on individuals diagnosed with nasopharyngeal carcinoma (NPC) remain insufficiently elucidated. The objective of this study was to investigate the prognostic significance of sarcopenia on the survival outcomes of NPC patients and to develop a nomogram.
Patients and methods:
545 patients with stage III-IVa NPC were included in this retrospective study and randomly divided into training and validation cohort (381 and 164 patients, respectively). Sarcopenia was defined using the skeletal muscle index (SMI) determined at the C3 level based on baseline MRI. The nomogram was developed utilizing a multivariable Cox model with baseline variables from the training cohort, and validated with the validation cohort. The nomogram's discriminative ability and accuracy were evaluated using the consistency index (C-index), receiver operating characteristic curve (ROC), and calibration plots, while the net benefit was assessed and compared with the TNM clinical stage through decision curve analysis (DCA).
Results
The results of the multivariate analysis revealed that higher T stage (HR = 2.15, 95% CI: 1.3–3.57, P < 0.01), higher N stage (HR = 2.15, 95% CI: 1.56–2.95, P < 0.01), sarcopenia group (HR = 2.46, 95% CI: 1.58–3.83, P < 0.01), and a history of comorbidities (HR = 1.76, 95% CI: 1.16–2.67, P = 0.01) were identified as independent risk factors that significantly impacted both overall survival (OS). The C-index (0.731 for the training cohort and 0.72 for the validation cohort indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. Moreover, nomograms also showed higher outcomes of DCA and the area under the curve (AUC) compared to TNM clinical stage.
Conclusion
Sarcopenia, T stage, N stage, and comorbidities were identified as independent prognostic factors for locally advanced NPC (laNPC). The integration of these factors into a nomogram predictive model demonstrated enhanced predictive accuracy.
Potassium Silicate improves drought tolerance in Cotton by modulating growth, gas exchange and antioxidant activities
Authors: Nazim Muhammad , Li Xiangyi , Anjum Shazia , Shahzad Khurram , Ahmad Fiaz , Ali Muqarrab , Zulfiar Usman , Muhammad Murad , Zeng Fanjiang 
Published: March 2024

Abstract
Cotton is a highly sensitive crop to drought stress. Consequently, it is crucial to devise strategies that optimize crop production in conditions of limited water availability. While potassium silicate has demonstrated effectiveness in mitigating drought stress in various crops, its specific impact on different cotton cultivars under drought conditions remains not fully clarified. This research aimed to assess the efficacy of six potassium silicate levels (ranging from 0, 100, 200, 300, 400 and 500 mg L-1) on four cotton cultivars (Zong main-113, Xin Nong-525, Xin lu Zhong-55, and Xin lu Zhong-66) under two field capacity levels (80% and 50% FC) in a sand culture. The application of foliar potassium silicate significantly improved photosynthetic efficiency, shoot biomass, root biomass, and leaf area under 50% FC. The most substantial reduction in H2O2, malondialdehyde levels, and electrolyte leakage was recorded with potassium silicate applied at a rate of 400 mg L-1. This concentration effectively mitigated reactive oxygen species accumulation, safeguarding plants against oxidative damage at 50% FC. Furthermore, potassium silicate contributed to maintaining water status, resulting in increased leaf water content and elevated water-soluble proteins in cotton plants. This research recommended that the foliar application of potassium silicate at a concentration of 400 mg L-1 significantly enhances cotton's resistance to drought, offering valuable insights for sustainable cotton cultivation in water-limited environments.
CryoEM structure of a novel class of spore virulence factors on the foodborne outbreak strain Bacillus paranthracis NVH 0075-95
Authors: Sleutel Mike , Zegeye Ephrem , LLarena Ann-Katrin , Pradhan Brajabandhu , Fislage Marcus , O´Sullivan Kristin , Gerven Nani Van , Aspholm Marina , Remaut Han 
Published: March 2024

Abstract

Bacterial endospores are remarkable examples of biological resilience, representing a dormant and heavily fortified differentiation form capable of withstanding physical and chemical stressors detrimental to vegetative cells. In pathogenic Bacillota, spores also form an infectious particle and can take up a central role in the environmental persistence and dissemination of disease. A poorly understood aspect of spore-mediated infection is the fibrous structures or ‘endospore appendages’ (ENAs) that have been seen to decorate the spores of pathogenic Bacilli and Clostridia. New methodological approaches are opening an unprecedented window on these long enigmatic structures. Using cryoID, Alphafold modelling and genetic approaches we identify a novel class of ultra-robust ENAs in the foodborne outbreak strain Bacillus paranthracis NVH 0075-95. We demonstrate that L-ENA are encoded by a rare three-gene cluster (ena3) that contains all components for the self-assembly of ladder-like protein nanofibers of stacked heptameric rings, their anchoring to the exosporium, and their termination in a trimeric ‘ruffle’ made of a complement C1Q-like BclA paralogue. The role of ENA fibers in spore-spore interaction and the distribution of L-ENA operon as mobile genetic elements in predominantly pathogenic B. cereus s.l. strains, suggest that L-ENA fibers promote strain virulence by spore clustering and aggregation.
Plasma exosomes proteome profiling discovers protein markers to identify sepsis
Authors: Gong Siyin , Liu Tingting , Li Ziwei , Yang Yixuan , Yi Hong , Wang Xiaohao 
Published: March 2024

Abstract

Sepsis, a life-threatening clinical issue caused by infection, can be diagnosed early through the analysis of exosomes in plasma. Plasma exosomes were extracted and identified by group (a sepsis group, a non-sepsis group, and a healthy control group). iTRAQ was used to identify the differentially expressed proteins among the three groups. The identified proteins were measured by ELISA in plasma samples. LCN2 and MGP were differentially expressed among the three groups. The median level of LCN2 were 131.93 (96.26,183.65), 52.79 (31.39,89.05) and 33.80 (26.80,47.06) ng/ml in the sepsis, non-sepsis and healthy control group, respectively. The plasma MGP levels in these three groups were 1.16 (1.06,1.30), 0.99 (0.91,1.07) and 0.89 (0.80,1.02) ng/ml, respectively. For both biomarkers, the sepsis group had higher levels than the non-sepsis group(P < 0.05). The area under the curve values of LCN2 and MGP were 0.903 and 0.813, respectively. When combined, these two biomarkers had a sensitivity of 0.900 and a specificity of 0.900 in distinguishing sepsis from non-sepsis. Plasma exosome proteome analysis has revealed that LCN2 and MGP may serve as effective biomarkers for diagnosing sepsis, further research is necessary to validate their diagnostic efficiency.