Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE ( Digital Academic Repository ) Variable wind directions and anemotactic strategies of searching for an odour plume

This paper deals with anemotact ic strategies of searching for odour plumes under condit ions of var ia t ion in wind direction and equal energy costs involved in moving a unit pa th length in any direction in the x, y plane. I t is generally accepted that cross-wind searching is the best searching strategy to acquire a maximum amount of information. This is of course true, if the wind is unidirectional. A geometrical model of anemotact ic searching is presented that takes into account var ia t ion in wind direction. I t is shown, that, if wind direct ion fluctuates over a range larger than 30 degrees from the mean wind direction, then upwind or downwind searching is the best strategy and cross-wind searching is the worst of all possibilities. Odour plumes can play a major role in animals locating mates or food. Once an animal enters the odour plume, it will find the odour source by performing a complex repertoire of behavioural responses to the odour gradients and the wind directions. This aspect has been subject to detailed investigations (Kennedy 1977, 1978, 1983; Bell and Tobin 1982). Much less a t tent ion has been paid to the problem of how an animal finds an odour plume. It is generally accepted that cross-wind searching is the best strategy to acquire the maximum of informat ion (e.g. Linsenmair 1969, 1973). Of course, this is true if the wind is unidirectional , but does this hypothesis also hold when wind direct ion varies, a s i tuat ion more likely to prevail under natura l condi t ions? This question can be answered by considering the size of the area from which the searching animal receives informat ion by scent, further referred to as the inspected area. We will show by simple geometry that the best anemotact ic strategy depends on the range of var ia t ion in wind direction. Geometry o f the inspected area F o r reasons of simplicity the searching animal is represented by a poin t and the odour source by a poin t source. The odour is assumed to be displaced only in the wind direction prevail ing at that moment . Moreover , it is assumed that the searching animal will find the odour source if downwind within a distance r from its target source. The final most critical assumpt ion is that the animal can estimate the range of var ia t ion in wind direction (=2c~; 0~ 180~ Hence, if A in Fig. 1 is the start ing posi t ion Offprint requests to. " M.W. Sabelis b D d cC>
Therapeutic efficacy of tylvalosin combined with Poria cocos polysaccharides against porcine reproductive and respiratory syndrome

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically important infectious diseases of pigs worldwide. Vaccination and various management measures have been implemented to control PRRS. However, due to high genetic diversity and insufficient understanding of the pathogenesis and immunological mechanisms, PRRS is still a challenge to the pig industry. Therefore, it is important to develop novel strategies to combat PRRS virus (PRRSV) infection. In this study, our data show that tylvalosin, a third-generation animal-specific macrolide, could inhibit PRRSV replication in MARC-145 cells, and suppress the PRRSV-induced NF-κB activation and cytokines expression. The pig infection experiment further demonstrated that tylvalosin could significantly reduce the virus loads in serum and tissues, and alleviate lung lesions of pigs infected with highly pathogenic PRRSV strains. The fever and loss of daily gain (LoDG) of the pigs were decreased as well. Considering the feature of immune suppression of PRRSV, a combination of tylvalosin with the immunopotentiator Poria cocos polysaccharides (PCP) was developed. Pig experiment showed this combination had a better therapeutic efficacy against PRRSV infection than tylvalosin and PCP alone in attenuating lung lesions, alleviating fever, and suppressing cytokines production. This study suggests that tylvalosin has significant antiviral and anti-inflammatory effects against PRRSV infection, and the combination of tylvalosin and PCP provides a promising strategy for PRRS treatment.
Velocity-Adaptive Irregular Point Spread Function Deconvolution Imaging Using X-Shaped Denoising Diffusion Filtering

Most least-squares migration (LSM) imaging methods use iterative gradient update methods to approximate the inverse of the Hessian matrix, but such approximations are not accurate and require a large amount of computational time. The point spread function (PSF) is an optical imaging method, defined as the response of an imaging system to a point source of light. In optics, the image of a complex object is considered to be a degenerate result obtained by convolving the PSF with the real object, which is analogous to the definition of seismic imaging using the Hessian operator. To accurately calculate the inverse of the Hessian matrix, solve the PSF sparsity selection problem, and eliminate migration noise, we propose a velocity-adaptive irregular PSF deconvolution imaging using a Gaussian smoothing X-shaped denoising diffusion filtering operator. The velocity-adaptive irregular grid selection strategy allows for adaptive selection of the grid size based on the velocity of the different layers, ensuring maximum imaging accuracy and avoiding the risk of interference among PSFs. The Gaussian smoothing X-shaped denoising diffusion filtering operator eliminates migration noise and also prevents the introduction of deconvolution noise. We use the Marmousi model and a field dataset to compare the performance of our proposed method with other common methods, demonstrating that our proposed method has clearer seismic events, more balanced amplitude, higher resolution, and higher signal-to-noise ratio than others.
Genetic association of lipids and lipid-lowering drugs with sepsis: a Mendelian randomization and mediation analysis

Background The impact of lipid-lowering medications on sepsis is still not well defined. A Mendelian randomization (MR) study was carried out to probe the causal connections between genetically determined lipids, lipid-reducing drugs, and the risk of sepsis. Materials and methods Data on total serum cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and triglycerides (TG) were retrieved from the MR-Base platform and the Global Lipids Genetics Consortium in 2021 (GLGC2021). Our study categorized sepsis into two groups: total sepsis and 28-day mortality of sepsis patients (sepsis28). The inverse-variance weighted (IVW) method was the primary method used in MR analysis. Cochran's Q test and the MR-Egger intercept method were used to assess the heterogeneity and pleiotropy. Results In the MR analysis, we found that ApoA-I played a suggestively positive role in protecting against both total sepsis (OR, 0.863 per SD increase in ApoA-I; 95% CI, 0.780–0.955; P = 0.004) and sepsis28 (OR, 0.759; 95% CI, 0.598–0.963; P = 0.023). HDL-C levels were also found to suggestively reduce the incidence of total sepsis (OR, 0.891 per SD increase in HDL-C; 95% CI, 0.802–0.990; P = 0.031). Reverse-MR showed that sepsis28 led to a decrease in HDL-C level and an increase in TG level. In drug-target MR, we found that HMGCR inhibitors positively protected against total sepsis (1OR, 0.719 per SD reduction in LDL-C; 95% CI, 0.540–0.958; P = 0.024). LDL-C and HDL-C proxied CETP inhibitors were found to have a protective effect on total sepsis, with only LDL-C proxied CETP inhibitors showing a suggestively protective effect on sepsis28. In Mediated-MR, BMI exhibited a negative indirect effect in HMGCR inhibitors curing sepsis. The indirect impact of ApoA-I explained over 50% of the curative effects of CETP inhibitors in sepsis. Conclusions Our MR study suggested that ApoA-I and HDL-C protected against sepsis, while HMGCR and CETP inhibitors showed therapeutic potential beyond lipid-lowering effects. ApoA-I explained the effects of CETP inhibitors. Our study illuminates how lipids affect sepsis patients and the effectiveness of new drugs, opening new avenues for sepsis treatment.
Solar Power Remote Monitoring and Controlling Using IoT

The consumption of renewable energy is being encouraged all over to protect the planet from contamination. In the majority of the world's inhabited areas, solar energy is one of the cleanest sources of power. However, due to the high installation/maintenance costs and low efficiency, usage is still not prevalent. The solar power generated by the system is highly dependent on the weather and not uniform all the time. In this paper, an automated IoT-based solar power monitoring and tracking system is proposed and implemented to track the parameters of an RP2040-based system with 10 watts capacity solar panel. The system automatically turns the solar panel position to achieve maximum power output by tracking the sun. MPPT (Maximum Power Point Tracking) algorithm is used to track the voltage levels and through the internet, the parameters are visualized using a user-friendly GUI. Provision for adjusting the solar panel position is also incorporated in the system developed. This system gives a low-cost solution with improved efficiency through condition monitoring and automatically tracking the solar source for maximum power output. The proposed solar tracking system is less expensive to operate, simpler to install, and requires less maintenance.
Multivariable DC Bias Control of a High-Power AC–DC Dual-Active-Bridge-Based Converter

This article addresses the dc magnetization issue in high-power single-stage dual active bridge (DAB)-based converters. A multivariable control strategy capable of simultaneously regulating the dc bias currents is applied to an integrated ac–dc topology. The control approach relies on two input variables, the magnetizing current, and the secondary current dc offset, and two outputs, that is, the duty cycles applied to the primary-side and secondary-side switches. The currents are measured by Hall sensors comprising several samples to average the offset values. The locations of samples and the measurement accuracy are discussed to validate the acquisition concept. The transformer is modeled using state-space equations and the controllers are tuned using state feedback associated with an augmented system to achieve robust reference tracking and disturbance rejection. The controller is designed based on the Lyapunov equation. Experimental results are presented and discussed to validate the control strategy applied to an integrated ac–dc converter rated at 8.33 kW. Steady-state and dynamic results show that the introduced solution can eliminate the dc bias of all currents even during load changes, without significantly affecting the harmonic content of the acinput current.
Event-Triggered Sliding Mode Control of Switched Neural Networks With Mode-Dependent Average Dwell Time

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.
Efficiency and Sustainability in Simulation: Enhancing Accuracy of Chaotic Maps Using Kahan Summation

Mathematical modelling is essential in scientific research for approximating systems through equations for simulation, prediction, and investigation. While system identification techniques have become popular for obtaining models directly from data, finding an appropriate model for nonlinear systems can be difficult. In particular, conventional model validation approaches are ineffective for chaotic systems. Although numerical simulations are widely used to identify and analyse these mathematical models, less attention has been given to improving simulation accuracy, leading to imprecise results. This work proposes an efficient and sustainable approach to improve the accuracy of chaotic maps by reducing numerical errors and increasing precision during simulations of chaotic polynomial models. The Kahan summation is used to minimise the propagation of computational error, and the results are validated using symbolic computation. The proposed technique increases simulation accuracy, allows more iterates with no loss of digit significance, and represents a way to save energy by requiring less powerful computing. The main contribution of this work is the presentation of an effective method for reducing the influence of numerical errors, a simple mathematical tool with low computational cost. Future investigations could focus on the reduction in LLE despite the Kahan summation not changing the mathematical map.
Emotion Recognition and Stress Reduction Based on Electroencephalograph (EEG) Signals validated by Machine Learning Algorithms

Research shows that stress is a typical reaction to external or internal irritants, but prolonged stress may impair health and brain function. Stress causes brain damage and sadness. Emotional awareness may reduce stress. Emotions complicate life. Assess emotions. Suicide note sentiment analysis has not used NLP and ML. Three parts follow. SVM algorithms categorise individuals as “under stress” or “not under stress” using EEG-based psychological states and signals. Merged LSTM reveals yoga and calming music reduce stress. Finally, emotions explain stress. DEAP synchronised brainwave dataset comprises patient data and EEG signal levels. Valence/arousal and EEG detect emotions. The discrete wavelet method (DWT) recovers alpha, gamma, theta, and beta EEG data spectral characteristics. Same-dimensional transforms isolate ICA features. ANN sorts emotions. SVM's 92.86 percent accuracy and LSTM's 75.23 percent show yoga and relaxing music soothe 75% of stressed people. A cross-validated RBF kernel-based SVM classifies beta emotions with 90.3% arousal and 91.11% valence. This research analyses mental stress EEG signals. It underlines research inconsistencies and suggests data analysis may cause them. Standardised approach, brain region of interest, stressor type, experiment duration, EEG processing, feature extraction, and classifier may alter results. Use appropriate traits to detect stress-related emotions. Yoga and music are also used to identify and treat stress in the study.
First impressions of a new face are shaped by infection concerns

Abstract Along with a classical immune system, we have evolved a behavioral one that directs us away from potentially contagious individuals. Here I show, using publicly available cross-cultural data, that this adaptation is so fundamental that our first impressions of a male stranger are largely driven by the perceived health of his face. Positive (likeable, capable, intelligent, trustworthy) and negative (unfriendly, ignorant, lazy) first impressions are affected by facial health in adaptively different ways, inconsistent with a mere halo effect; they are also modulated by one’s current state of health and inclination to feel disgusted by pathogens. These findings, which replicated across two countries as different as the USA and India, suggest that instinctive perceptions of badness and goodness from faces are not two sides of the same coin but reflect the (nonsymmetrical) expected costs and benefits of interaction. Apparently, pathogens run the show—and first impressions come second. Lay Summary: Our first impressions of strangers (whether they seem trustworthy, intelligent, unfriendly, or aggressive) are shaped by how healthy their faces look and by our unconscious motivation to avoid infections. Bad and good impressions turn out to reflect the concrete, potentially vital, expected costs and benefits of interacting with our fellow humans. Apparently, pathogens run the show—and first impressions come second.
Integrative Effect of UV-B and Some Organic Amendments on Growth, Phenolic and Flavonoid Compounds, and Antioxidant Activity of Basil (Ocimum basilicum L.) Plants

The application of organic amendments, biochar, and wood distillate (WD), as well as the exposure to UV-B radiation, are two sustainable ways to enhance soil fertility and increase plant nutraceutical quality, respectively. However, they have always been studied separately, without testing the eventual synergistic or antagonistic effect when applied together. The present study investigated the effects of biochar (2% w/w), WD (1:100), and their combination (BWD) on some biometric and biochemical parameters of basil plants (Ocimum basilicum L.) exposed to different doses of UV-B radiation (0, 1, 2 h d−1; UV-B irradiance of 1.36 W m−2) in controlled conditions. Root and stem length and weight were not affected by soil amendments, while 1 h d−1 UV-B increased the length (+28%) and weight (+62%) of the aerial part. When combining the above- and below-ground factors, a decrease in root length was observed in the 2 h d−1 UV-B-treated plants in both WD (−36%) and BWD (−39%) treatments. The co-application of below- and above-ground treatments generally decreased phenolic and flavonoid concentration in both fully expanded leaves and vegetative shoot apices. This preliminary study highlights an antagonistic action of the combination of the investigated factors, at these doses, on the plant growth and metabolism that should be considered.
Diseño, implementación y evaluación de un juego educativo basado en puzles con scaffolding orientado a la programación

Existe una falta de interes por parte de los estudiantes en el area de las Tecnologias de la Informacion y la Comunicacion (TIC) que se ve reflejada en el descenso de las matriculaciones en este ambito. El uso de metodologias de aprendizaje basadas en el Constructivismo combinadas con tecnologia software, se ha observado que es una buena solucion para afrontar dicha falta de interes. Sin embargo, actualmente no existen aplicaciones software que implementen estas metodologias pedagogicas y que proporcionen a los estudiantes los mecanismos de ayuda necesarios (Scaffolding) para darles soporte durante el aprendizaje de conceptos TIC. Una posible solucion a este problema es el uso de juegos educativos, los cuales implementaran tecnicas de Scaffolding que den el soporte necesario al estudiante para alcanzarlos objetivos de aprendizaje fijados. Por tanto, en este proyecto se disenara e implementara un juego educativo basado en puzles orientado a la Programacion que estara basado en un metodo aprendizaje basado en el Constructivismo en el que el estudiante construye su propio conocimiento. Una vez implementado, sera evaluado en un centro escolar por parte deestudiantes de ultimos cursos de ESO o Bachillerato.
A Multibreed Genome-Wide Association Study for Cattle Leukocyte Telomere Length

Telomeres are terminal DNA regions of chromosomes that prevent chromosomal fusion and degradation during cell division. In cattle, leukocyte telomere length (LTL) is associated with longevity, productive lifespan, and disease susceptibility. However, the genetic basis of LTL in this species is less studied than in humans. In this study, we utilized the whole-genome resequencing data of 239 animals from 17 cattle breeds for computational leukocyte telomere length estimation and subsequent genome-wide association study of LTL. As a result, we identified 42 significant SNPs, of which eight were found in seven genes (EXOC6B, PTPRD, RPS6KC1, NSL1, AGBL1, ENSBTAG00000052188, and GPC1) when using covariates for two major breed groups (Turano–Mongolian and European). Association analysis with covariates for breed effect detected 63 SNPs, including 13 in five genes (EXOC6B, PTPRD, RPS6KC1, ENSBTAG00000040318, and NELL1). The PTPRD gene, demonstrating the top signal in analysis with breed effect, was previously associated with leukocyte telomere length in cattle and likely is involved in the mechanism of alternative lengthening of telomeres. The single nucleotide variants found could be tested for marker-assisted selection to improve telomere-length-associated traits.
Factores que influyen en el rendimiento académico de los estudiantes de la Facultad de Ciencias Matemáticas de la Universidad Nacional Mayor de San Marcos

Factores que influyen en el rendimiento académico de los estudiantes de la Facultad de Ciencias Matemáticas de la Universidad Nacional Mayor de San Marcos Factors that influence in the academic performance of the students of the Faculty of Mathematical sciences of the Universidad Nacional Mayor de San Marcos Carlos César Trucíos Maza, María Estela Ponce Aruneri DOI: https://doi.org/10.33017/RevECIPeru2009.0023/ RESUMEN El rendimiento académico de los estudiantes de la Facultad de Ciencias Matemáticas de la Universidad Nacional Mayor de San Marcos, es una de las principales preocupaciones de los docentes y estudiantes de esta Facultad; nuestra investigación establecerá cuáles son los factores que influyen en su rendimiento. Seleccionamos una muestra aleatoria estratificada con selección sistemática de estudiantes, a los que se les administró un cuestionario que diseñamos para esta investigación, a los datos obtenidos se les aplicó Componentes Principales Categóricos (CATPCA) para reducir variables y Regresión Logística para encontrar los principales factores que influyen en el rendimiento académico de los estudiantes matriculados en el semestre académico 2008-I. Palabras Clave: Componentes Principales Categóricos (CATPCA), Factores, Regresión Logística, Rendimiento Académico. ABSTRACT The academic performance of students of the Faculty of Mathematical Sciences at the Universidad Nacional Mayor de San Marcos is one of the major concerns of teachers and students of the Faculty, our research will establish what factors influence their performance. We selected a stratified random sample with systematic selection of students, who were administered a questionnaire designed for this research, to data obtained were applied Categorical Principal Components (CATPCA) to reduce variables and logistic regression to find the main factors influencing in the academic performance of students enrolled in the semester, 2008-I. Keywords: Categorical Principal Components (CATPCA), Factors, Logistic Regression, Academic Performance.
The compressional wave and physical properties of inter-tidal marine sediments

New quadratic regression equations for inter-tidal sediments are presented, which relate compressional wave properties (velocity ratio and proportionality constant) to porosity, bulk density and mean grain size. The compressional wave properties were derived from compressional wave velocities and attenuation coefficients measured on inter-tidal sediments from 16 - 100 kHz using common experimental and processing techniques. The regression equations are more robust for velocity ratio than proportionality constant. Discrepancies between the new regression equations and those derived for submerged sediments support the conclusion that the sediment structure of inter-tidal sediments differs from that of submerged sediments. This is attributed to the different physical processes which govern the supply, deposition and dynamics of the sediment in each environment.