Miracle-dz/UniFAR-FTUs huggingface dataset:
Whale optimization approach for early heart disease prediction based on FCM using DCNN

Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.
The synergistic Effect of Both Mannan and Chitosan Oligosaccharides on Broilers Intoxicated with Aflatoxicosis

UMEROUS techniques were used for detoxification of aflatoxin. The present study was conducted to evaluate the addition of mannan oligosaccharides (MOS) and/or Chitosan (Chit) to lessen the toxicity of AFs in broiler chickens (cobb500), improving the performance, biochemical, hematological, antioxidant, and histopathological parameters and gene expression analysis of caspase3 and interleukin 1-β. Five equal groups of 50 broilers each were formed (n=10); negative control (G1) given the basal diet, positive control (G2) basal diet+ 100 ppb AFs kg-1diet; MOS received group (G3) given as basal diet+ 100 ppb AFs kg-1diet + MOS (1 g kg-1 diet); Chit received group (G4) given basal diet+ 100 ppb AFs kg-1diet +Chit (0.5 g kg -1 diet) ; and MOS/Chit received group (G5) given basal diet+ 100 ppb AFs kg-1diet + MOS (1 g kg -1 diet)+ Chit (0.5 g kg -1 diet). All treatments were given to broilers between 6 and 25 days of age. The combination of both MOS and Chit improved hematological parameters in aflatoxicosis-intoxicated groups. Both renal and hepatic markers were significantly improved in chicks intoxicated with aflatoxins and treated with MOS and/or Chit. A significant improvement of antioxidant markers in both MOS and Chit groups exposed to aflatoxicosis. Gene expression of Caspase-3 and IL-1β were significantly improved in chicks intoxicated with aflatoxins and treated with MOS and/or Chit. MOS and/or Chit reduced the levels of accumulation of aflatoxin in muscles, liver, and kidney tissues. The liver sections of the groups (G3, G4, and G5) exhibited significant enhancement in the hepatic parenchymal architecture.
Pelomedusa subrufa (Lacépède 1788) – Helmeted Turtle, Helmeted Terrapin

Figure 1. Adult male Pelomedusa subrufa, north of Cap Ste. Marie, southern Madagascar, January 2007. Photo by Anders G.J. Rhodin. Conservation Biology of Freshwater Turtles and Tortoises: A Compilation Project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group A.G.J. Rhodin, P.C. H. Pritchard, P.P. van Dijk, R.A. Saumure, K.A. Buhlmann, and J.B. Iverson, Eds. Chelonian Research Monographs (ISSN 1088-7105) No. 5, doi:10.3854/crm.5. 007.subrufa. v1.2008 © 2008 by Chelonian Research Foundation • Published 15 May 2008
Survey of Accuracy Prediction on the PlantVillage Dataset using different ML techniques

A plant is susceptible to numerous illnesses while it is growing. The early detection of plant illnesses is one of the most serious problems in agriculture. Plant disease outbreaks may have a remarkable impact on crop yield, slowing the rate of the nation's economic growth. Early plant disease detection and treatment are possible using deep learning, computer-vision, and ML techniques. The methods used for the categorization of plant diseases even outperformed human performance and conventional image-processing-based methods. In this context, we review 48 works over the last five years that address problems with disease detection, dataset properties, the crops under study, and pathogens in various ways. The research results discussed in this paper, with a focus on work published between 2015 and 2023, demonstrate that among numerous techniques (MobileNetV2, K-Means+GLCM+SVM, Residual Teacher-Student CNN, SVM+K-Means+ANN, AlexNet, AlexNet with Learning from Scratch, AlexNet with Transfer Learning, VGG16, GoogleNet with Training from Scratch, GoogleNet with Transfer Learning) applied on the PlantVillage Dataset, the architecture AlexNet with Transfer Learning identified diseases with the highest accuracy.
Validation of the Cognitive Reserve Index Questionnaire (CRIq) in Arabic

Cognitive reserve is the adaptability of cognitive processes in the face of brain aging and pathology. This study aimed to validate the Arabic version of the Cognitive Reserve Index Questionnaire (CRIq) in a healthy Lebanese sample. CRIq assesses cognitive reserve through three domains: education, working activity, and leisure time. Statistical measures, including descriptive and regression analysis along with structural equation modeling, were utilized to investigate the convergent and discriminant validity of the CRIq, incorporating fluid intelligence (Gf) and measures of cognitive function, long-term memory encoding and retrieval (Glr), and processing speed (Gs). Results from 174 participants revealed that the activities assessed by the CRIq-Arabic were comparable to the original CRIq study, with slight cultural differences. The internal consistency of the CRIq-Arabic was good (Cronbach’s α = 0.88), indicating reliability. Convergent validity was confirmed, with moderate to high loadings on the cognitive reserve latent construct. Discriminant validity was supported as correlations between cognitive reserve variables and non-target constructs (Gf, Glr, and Gs) were less than 1. The findings provide an initial psychometric validation of the CRIq-Arabic. Further research of clinical samples is needed to enhance its utility in neuropsychological practice.
Hybrid Fourier-Continuation method and WENO-Z finite difference scheme for multi-dimensional detonation structure simulations

In (Li et al. J. Sci. Comput. 2015, 64: 670–695), a Hybrid FC-WENO-Z scheme (Hybrid) conjugating the FourierContinuation (FC) method with the improved fifth order characteristic-wise weighted essentially non-oscillatory (WENO-Z) finite difference scheme for solving the system of hyperbolic conservation laws was developed. The Hybrid scheme is used to keep the solutions parts displaying high gradients and discontinuities always captured by the WENO-Z scheme in an essentially non-oscillatory manner while the smooth parts are highly resolved by a linear, essentially non-dissipative and non-dispersive FC method. A high order multi-resolution algorithm by Harten is used for measuring the smoothness of the solutions. In this study, the Hybrid scheme is employed in the long time simulations of multi-dimensional detonation structures which contain both discontinuous and complex smooth structures for the first time. The fine scale structures behind the detonation front and the quasi-steady state cellular structures of the peak pressure in the half reaction zone are well captured. A classical stable two-dimensional detonation waves shows that an improved resolution of the more fine scale structures of detonation waves as computed by the Hybrid scheme with less CPU times when compares with the pure WENO-Z scheme. The influence of initial and boundary conditions on the formation and evolution of the detonation structures are also illustrated with examples. Finally, the in-phase rectangular, out-of-phase rectangular and in-phase diagonal cellular structures in the three-dimensional detonation simulations are shown to demonstrate the ability of the Hybrid scheme in capturing the intrinsic evolution of the detonation fronts, which are in good agreement with the published results in the literature.
Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges

Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been used to identify different types of bridge damage. However, the value of deep-learning-based damage identification may be reduced by insufficient training data, class imbalance, and model-reliability issues. To overcome these limitations, this study utilized photographic data from real bridge-management systems for the inspection and assessment of bridges as the training dataset. Six types of damage were considered. Moreover, the performances of three representative deep learning models—Mask R-CNN, BlendMask, and SWIN—were compared in terms of loss–function values. SWIN showed the best performance, achieving a loss value of 0.000005 after 269,939 training iterations. This shows that bridge-damage-identification performance can be maximized by setting an appropriate learning rate and using a deep learning model with a minimal loss value.
Political Consumerism in the Built Environment

Architectural, engineering, and construction firms design the built environment in a continuing effort to appeal to the home-buying public. Firms’ decisions include the exterior finishes that the public see and judge. The main objective of this study was to investigate whether material design elements from the built environment would commonly be perceived to have a political affiliation. Additionally, the study investigated if political consumerism could be seen in respondents’ perceptions of the materials when aligned with (or in conflict with) their party. Architectural renderings of the same home with different exteriors (i.e., brick, stucco, or painted wood siding) were presented as experimental stimuli. The study collected data from 584 nationwide respondents. Interestingly, both major parties of Republicans and Democrats identified wood siding as representing their own political party while identifying an alternate exterior finish (usually brick) as representing their opposing party. The political consumerism behavior of reward was uncovered in respondents placing a higher perceived value on the exterior finish with which they identified.
Nutraceutical benefits of brown macroalgae Durvillaea antarctica: a bibliometric review

Durvillaea antarctica , commonly known as Cochayuyo in Chile and Bull kelp in southern New Zealand, is a brown macroalgae recognized for its notable nutritional value. This includes high omega-3 essential fatty acids, vitamins, and particularly dietary fiber. Furthermore, it is acknowledged as a nutraceutical due to its antioxidative, anti-inflammatory, anticancer, antiviral, and anti-diabetic properties. An emerging industrial application involves using it as a reinforcing material in active food packaging with antioxidant capabilities. A comprehensive analysis was conducted wherein the Scopus database was used to conduct the initial bibliometric investigation encompassing the years since 1979. The search yielded 198 documents published in English, comprising 182 articles, 6 conference papers, 4 reviews, 3 book chapters, 1 note, 1 letter, and 1 erratum. Chile and New Zealand notably demonstrated high productivity, contributing more than 70 documents each. The most contributing authors, institutions, and journals in the field were systematically recorded. D. antarctica , due to its mounting scientific significance, is readily accessible in the temperate waters of the Southern hemisphere, offering substantial health and societal advantages. Bibliometrics was employed to generate graphical representations and thoroughly examine the most frequently cited documents, author networks, collaborative interactions, and topic categorization. The keywords were organized into clusters, one focused on phylogenetics and three centered on chemistry and metabolism, the physiology of brown algae phaeophyta, and the marine ecosystem, macroalgae, and biogeography. The analysis also highlighted the top three countries contributing to research in this domain: Chile, New Zealand, and Australia. This bibliometric and nutraceutical investigation serves as compelling evidence supporting the myriad applications of D. antarctica biocultural value in food science and technology, offering valuable insights to inform policy decisions. ABSTRACT
A corpus-based study of semantic neology of the covid-19 pandemic

Este artículo presenta una serie de estudios basados en corpus para la detección semiautomatizada de neología semántica en los sustantivos del español en 2022, año en el que todavía existía la pandemia del COVID-19, pero también otros eventos globales importantes, como la guerra de Ucrania o la inflación económica. Comparamos un corpus de textos de 2022 con un corpus previo a la pandemia, considerando cuatro tipos de cálculo de frecuencia y palabras clave y teniendo en cuenta la combinatoria léxica de los candidatos a neologismo. Los resultados indican que la combinación de palabras clave con textos agrupados según la temática da los mejores resultados para detectar neología semántica.
A Frequency Response Control Strategy for Medium Voltage Wind Turbines With Multiple Rotors

In a medium voltage wind turbine with multiple rotors, multiple rotors are used for the wind energy capture and directly drive permanent magnet synchronous generators (PMSGs). The medium voltage grid is coupled with a cascaded H-bridge (CHB) inverter. Advantages, such as more wind energy capture, higher reliability, and easy extension to higher power and grid connection voltage ratings, can be achieved. In this article, a novel frequency response control strategy for the wind turbine is proposed. Under the normal operation condition, all rotors are controlled individually to extract maximum wind energy. During the deloading operation, the coordinated control on all rotors is executed to obtain the required power reserve. In case the grid frequency sudden dip occurs, the power reserve is released quickly to provide the wind turbine frequency response. A novel control scheme to maintain the utmost transmitted power equilibrium between three phases, between modules in one phase, and three-phase grid connection current equilibrium at both deloading and frequency response modes is proposed, leading to the H-bridge inverter overmodulation risk reduction and grid connection current quality improvement. Simulation and experimental results have validated the proposed frequency response control strategy for the medium voltage wind turbine with multiple rotors.
Dense Dual-Branch Cross Attention Network for Semantic Segmentation of Large-Scale Point Clouds

Semantic segmentation of large-scale point clouds provides foundational knowledge for various geodetic and cartographic applications, including autonomous driving, smart cities, and indoor navigation. However, point cloud data’s unstructured and inherently disordered characteristics pose challenges in extracting accurate 3-D semantic information. In this study, we introduce a novel semantic segmentation network for large-scale point cloud scenes, referred to as dense dual-branch cross attention network (D2CAN). We propose a local multidimensional feature aggregation (LMFA) module to increase multidimensional feature representation types and preserve rich local details. Based on the augmented local features, an expanded dual-branch cross attention (EDCA) module establishes internal deep connections between multidimensional attributes and semantic features. This assists the network in reducing boundary ambiguities and expanding the receptive field, enabling the parallel capture of long-range contexts specifically adapted for large-scale scene point cloud segmentation. These two modules work collaboratively to constitute a local context deep perception (LCDP) block. To reduce information loss during feature sampling and propagation, we propose a global feature pyramid dense fusion (GFDF) block. This block adaptively integrates features across different scales and effectively captures global context with long-range dependencies. In conclusion, D2CAN combines LCDP and GFDF to aggregate both local and global contexts, resulting in robust feature discrimination for semantic segmentation of large-scale scenes. Our method’s effectiveness and superior generation ability have been validated across three challenging benchmarks and achieve state-of-the-art performance on Toronto-3D, SensatUrban, and Stanford large-scale 3-D indoor spaces (S3DIS) datasets, with mean intersection over union (IoU) values of 83.5%, 61.1%, and 72.3%, respectively.
New data on the distribution of six morphologically cryptic species of Niphargus stygius species complex (Amphipoda: Niphargidae)

Subterranean amphipods, morphologically resembling Niphargus stygius species complex, were molecularly analysed. We isolated genomic DNA and amplified the subunit I of mitochondrial cytochrome oxidase gene (COI) for 94 specimens from 37 localities. We report on six new localities for N. chagankae, one for N. cvajcki, eight for N. gottscheeanensis, two for N. kenki, one for N. malagorae and two for N. zagrebensis. New data extend previously known distribution ranges of N. gottscheeanensis and fill the missing gap between the remote N. kenki occurrences. We report on new co-occurrence data for two species pairs, i) N. chagankae and N. likanus, and ii) N. gottscheeanensis and N. podpecanus.
Understanding alcohol use and changes in drinking habits among people with a severe mental illness: a qualitative framework analysis study

Introduction Individuals with a severe mental illness (SMI) are more likely to drink at harmful levels or abstain. While it is known that drinking patterns change over time, the reasons for this among those with a SMI are unclear. This study aimed to (i) explore the experiences with alcohol, particularly in relation to mental health symptoms, and (ii) how drinking patterns have changed over time, among individuals who have a SMI diagnosis, who either currently drink alcohol or no longer drink. Methods One-to-one semi-structured telephone interviews were conducted to address the study aims. Current drinkers’ alcohol use was assessed using the Alcohol Use Disorder Identification Test. A framework analysis was used to address the study aims with a specific focus on the differences in the experiences with alcohol use between current and former drinkers. Results 16 participants were interviewed, and five themes were developed. The analysis highlighted how alcohol was increasingly used to cope with (i) trauma, (ii) SMI-related symptoms, or (iii) stress. Among those with a SMI, non-drinking was facilitated through declines in SMI-related symptoms, previous negative consequences due to alcohol and changing the social environment. Current drinking habits were facilitated through changes in the reasons for drinking and adopting different alcohol moderation techniques. Discussion Among those with a SMI diagnosis and who either currently drink alcohol or no longer drink, our findings support the self-medication hypothesis and drinking motives model. However, our findings indicate the need for further development of drinking to cope with a focus on symptoms of a SMI and trauma. Our findings also have implications on specialist alcohol and mental health services, the need to improve individuals’ understanding of SMI, and the need to identify reasons for drinking among those with a recent diagnosis of a SMI.
NLRP6 controls pulmonary inflammation from cigarette smoke in a gut microbiota-dependent manner

Chronic obstructive pulmonary disease (COPD) is a major health issue primarily caused by cigarette smoke (CS) and characterized by breathlessness and repeated airway inflammation. NLRP6 is a cytosolic innate receptor controlling intestinal inflammation and orchestrating the colonic host–microbial interface. However, its roles in the lungs remain largely unexplored. Using CS exposure models, our data show that airway inflammation is strongly impaired in Nlrp6-deficient mice with drastically fewer recruited neutrophils, a key cell subset in inflammation and COPD. We found that NLRP6 expression in lung epithelial cells is important to control airway and lung tissue inflammation in an inflammasome-dependent manner. Since gut-derived metabolites regulate NLRP6 inflammasome activation in intestinal epithelial cells, we investigated the link between NLRP6, CS-driven lung inflammation, and gut microbiota composition. We report that acute CS exposure alters gut microbiota in both wild-type (WT) and Nlrp6-deficient mice and that antibiotic treatment decreases CS-induced lung inflammation. In addition, gut microbiota transfer from dysbiotic Nlrp6-deficient mice to WT mice decreased airway lung inflammation in WT mice, highlighting an NLRP6-dependent gut-to-lung axis controlling pulmonary inflammation.
Low-Level Westerly Winds, Topography, and Tropical Cyclogenesis of Arlene (2005): Observations and Model Simulations

The evolution process of the low-level westerly winds over the eastern North Pacific is investigated to understand the tropical cyclogenesis (TCG) of Tropical Storm Arlene (2005) over the Intra-Americas Sea (IAS). Also considered are the influences of the topography of the Central American mountain region interacting with the low-level westerly winds on Arlene's TCG by comparing results from a modified-topography simulation of Arlene's TCG with those from a simulation with the original topography in the Weather Research and Forecast (WRF) model. Interactions among large-scale circulations associated with subtropical highs in both hemispheres and an anticyclone over the warm eastern North Pacific produced low-level westerly winds into the IAS. WRF model experiments with a virtually elevated terrain filling in mountain passes in Central America resulted in the delayed and suppressed development of the incipient storm. The model experiments suggest that the low-level winds and moisture fluxes from the eastern North Pacific passing through the low-level mountain passes in Central America could play a critical role in the TCG process and perhaps also sustenance of storms over the western Caribbean.