Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Classical Methods and Variables Selection in Case of Multicollinearity: A Case Study with Real-Data

The addition of excessive variables to a model can lead to severe consequences. When a model contains numerous variables, it is likely that some of them will exhibit strong correlations. However, explanatory variables should ideally not possess strong relationships among themselves. This issue, known as multicollinearity, can significantly impact the interpretation of results by causing notable variations between models.
Exploitation of Probiotic a Lactobacillus rhamnosus Strain for Removal of Heavy Metal Residues from Milk

The ability of Lactobacillus rhamnosus to remove some heavy and trace metals from milk through fermentation was investigated. For this purpose, one hundred samples of raw milk, fermented milk, kareish cheese and ice cream were initially screened fordetection of cupper, zinc, lead and cadmium. Different concentrations were obtained and compared with some national and international findings. Upon milk fermentation with probiotic strain Lactobacillus rhamnosus, reduction was achived in the examined metals with the following percentages, Cupper 6.8%, Zinc 79.65%, Cadmium 21.62% and Lead 22.58%. The reduction in heavy metal residues declared the health benfits from the use of Lactobacillus rahmnosus and protection of Egyptian population from the risk of heavy metals exposure from milk and milk product consumption. Probiotic strain Lactobacillus rahmnosus could be a promising solution for lowering the milk content of toxic metals and decrease the exposure to heavy metals from milk consumption.
Role of FEF25–75 in characterizing outpatients with asthma in clinical practice

Background: Asthma is characterized by variable airflow limitation. FEF25–75 has been proposed as a reliable marker for bronchial obstruction, especially when FEV1 and FEV1/FVC are normal. Objectives: To investigate the role of FEF25–75 in patients with asthma seen in clinical settings. Materials and methods: The cross-sectional study included 439 (181 females and 255 males; mean age 39 years) outpatients with asthma who consecutively visited an allergy clinic for a routine assessment. History, physical examination, asthma control, and spirometry were evaluated. Results: FEF25–75 was impaired (< 65% of predicted) in 136 (31%) outpatients. Considering only subjects with normal FEV1 and FEV1/FVC, FEF25–75 was impaired in 71 (19.6%) subjects. In this subset, impaired FEF25–75 was associated with low FEV1 and FEV1/FVC values (OR 0.91 and 0.85, respectively), and presence of asthma symptoms (OR 2.19). Conclusion: FEF25–75 deserves adequate and careful consideration in patients with asthma and normal FEV1 and FEV1/FVC, as the presence of impaired FEF25–75 in this subset suggests a more specific approach.
Effect of nutrients and organic substances on growth and quality attributes of apple (Malus × domestica) cv. Anna insemi-arid region of Haryana

An experiment was conducted during 2020–21 and 2021–22 at Horticulture farm of Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana to study the effect of different plant nutrients and organic substances on growth, flowering, quality and yield attributes of the apple (Malus × domestica Borkh) cultivar Anna. The experiment consists of 19 treatments laid out in a randomized block design. Among the various treatments used, T12 (2.0% nitrogen plus 15% cow urine) was found superior for the growth characteristics of plants, viz. plant height (2.87 m) and plant spread (3.02 m). However, the number of days taken for flowering (70.83), fruit set (19.72 %), fruit weight (177.68 g), fruit diameter (6.40 cm), yield (11.25 kg/plant), TSS (11.45 °Brix), acidity (0.51%), total sugars (10.01%), total phenol (70.72 mg GAE/g), total anthocyanin (19.11 mg/100 g) and total carbohydrate (155.55 mg/g) content were significantly influenced by treatment T6 (400 ppm Boron + 4% Humic acid) during both the years.
A Middleware for Service Deployment in Contributory Computing Systems

Aquesta tesi adreca les mancances de la computacio en nuvol, els desktop grids i les xarxes d'igual a igual envers l'us de recursos no dedicats per a computacio de proposit general mitjancant la proposta del model de computacio contributiva, en que els usuaris contribueixen els seus recursos per tal de ser usats de manera col•lectiva. Una comunitat contributiva (agregacio de recursos contribuits) es pot utilitzar com una plataforma per a desplegar serveis, sent una alternativa a l'us de grans centres de processament de dades. Aquests serveis fan possible la utilitzacio de recursos sobrers de manera general, en comptes de limitar el seu us a l'execucio de tasques massives. Aquesta tesi desenvolupa el concepte de computacio contributiva presentant un middleware per a la construccio de comunitats contributives i el desplegament de serveis en els recursos contribuits. Aquest middleware, anomenat CoDeS, permet afegir recursos a la comunitat i desplegar serveis en ells, encarregant-se de mantenir els serveis disponibles utilitzant els recursos contribuits. Es auto-gestionat, descentralitzat, escalable, tolerant a fallades i pot tractar amb recursos heterogenis.
Information Exchange Track-Before-Detect Multi-Bernoulli Filter for Superpositional Sensors

In this paper we derive the Information Exchange track-before-detect Multi-Bernoulli (IEMB) filter for multi-target filtering with superpositional sensors. The IEMB filter propagates a multi-Bernoulli density through the filtering recursion and each Bernoulli is propagated with its own prediction and update step. At each update step, each Bernoulli filter exchanges the predicted mean and covariance matrix of its measurement contribution with the other Bernoulli filters. The exchanged information is then used by the filters to perform the update step. Additionally, we propose the Iterated Posterior Linearisation Filter (IPLF) implementation of the IEMB filter (IEMB-IPLF). We compare the IEMB-IPLF filter to a number of other non-linear filtering methods showing the benefits of the proposed filter.
Effects of feeding an inoculated corn silage with or without a direct-fed microbial on dry matter intake, milk production, and nutrient digestibility of high-producing lactating Holstein cows

Abstract This study evaluated the effects of inoculating corn silage and/or feeding a direct-fed microbial (PRO) on performance and nutrient digestibility of lactating dairy cows. At harvesting, corn silage was treated either with water (culated or not [CON]) or Lactococcus lactis and Lentilactobacillus buchneri (INC; SiloSolve FC) at 1.5 × 105 cfu/g of corn silage. Ten mini silos and one farm-scale silo bunker per treatment were prepared for the laboratory and the lactating dairy cow trial, respectively. Five mini silos per treatment were opened on days 2 or 90 post-ensiling for pH measurement, as well as chemical analysis and aerobic stability, respectively. The farm-scale silo bunkers were opened 77 d post-ensiling for the beginning of the lactating cow trial. Eighty lactating Holstein cows were assigned in a 2 × 2 factorial design to: (1) CON silage without PRO (CON-CON; n = 20), (2) CON silage with PRO at 14 g/head/d (CON-PRO; n = 20), (3) INC silage without PRO (INC-CON; n = 20), and (4) INC silage with PRO at 14 g/head/d (INC-PRO; n = 20). Concurrently with the feeding trial, eight cows per treatment were chosen for nutrient digestibility. The pH of the corn silage was not affected by the silage inoculant (P ≥ 0.29), but INC yielded greater concentration of acetic acid and longer aerobic stability (P < 0.01). Dairy cows fed INC had a lower mean total dry matter intake (DMI), milk protein content, and somatic cell counts vs. CON (P ≤ 0.02). On the other hand, milk and fat- and protein-corrected milk (FPCM) production efficiency, milk urea-N, DM, crude protein, and starch digestibility were greater for INC-fed cows (P ≤ 0.03). Feeding direct-fed microbials (DFM) improved mean body weight, milk yield, and FPCM, as well as milk protein and lactose yield (P ≤ 0.05), but reduced milk fat and protein content (P = 0.02). A silage inoculant × DFM interaction was observed for milk production efficiency, milk protein and lactose content, and somatic cell count (P ≤ 0.05). Dairy cows fed INC-CON had a greater milk production efficiency and milk lactose content (P ≤ 0.04), but INC-PRO had lower milk protein content and SCC (P ≤ 0.03). In summary, inoculating L. lactis and L. buchneri increased acetic acid content and aerobic stability of corn silage, reduced DMI, but improved milk production efficiency and nutrient digestibility of lactating Holstein dairy cows. On the other hand, feeding PRO improved milk, protein, and lactose yield. Additionally, combining the feeding of an inoculated corn silage with PRO reduced milk somatic cell count.
Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.
South Africa's Agrarian Question

Based on an in-depth analysis of several contrasting agricultural regions, this book aims to assess South Africa's on-going agrarian reform and the country's agrarian dynamics. The conclusion is without doubt: Twenty years after the first democratic elections, the country's land pattern remains almost unchanged, and primary agriculture and its broader value-chains are more concentrated than ever. Without fundamentally questioning the highly specialised, fossil energy and synthetic input dependent, oligopolistic entrepreneurial agricultural production model, which is presently structuring the sector and is guiding the reforms, a more equitable redistribution of resources and value-addition will by no means be possible. This book examines and contributes to the structural questions that underpin the current stagnation of South Africa's agrarian reform. Presenting fresh approaches in analysing agrarian issues and tools to assess farming systems and agricultural development, this incisive study will be an important resource to policy makers, academics and those with an interest in agrarian reform. (Resume d'auteur)
Understanding Newcomers’ Onboarding Process in Deep Learning Projects

Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the onboarding process of DL newcomers is, and if there exist commonalities or differences in the onboarding process for DL and non-DL newcomers. Therefore, we reported a study to understand the growth trends of DL and non-DL newcomers, mine DL and non-DL newcomers’ activities before their successful onboarding (i.e., past activities), and explore the relationships between newcomers’ past activities and their first commit patterns and retention rates. By analyzing 20 DL projects with 9,191 contributors and 20 non-DL projects with 9,839 contributors, and conducting email surveys with contributors, we derived the following findings: 1) DL projects have attracted and retained more newcomers than non-DL projects. 2) Compared to non-DL newcomers, DL newcomers encounter more deployment, documentation, and version issues before their successful onboarding. 3) DL newcomers statistically require more time to successfully onboard compared to non-DL newcomers, and DL newcomers with more past activities (e.g., issues, issue comments, and watch) are prone to submit an intensive first commit (i.e., a commit with many source code and documentation files being modified). Based on the findings, we shed light on the onboarding process for DL and non-DL newcomers, highlight future research directions, and provide practical suggestions to newcomers, researchers, and projects.
Genetic Diversity on Acerola Quality: A Systematic Review

: Acerola ( Malpighia emarginata DC.) is a super-fruit with high ascorbic acid content and its quality can be highly affected by the genetic and environmental conditions. This systematic review aims to provide an overview of the influence of genetic variability on acerola quality traits. It was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) recommendations. PubMed, Scopus and SciELO electronic databases were searched using terms acerola , selection , and genotypes . 29 studies were selected after assessing articles for eligibility criteria, which reported data on 22 quality parameters. A high genetic diversity was observed for all quality traits. Soluble solids and ascorbic acid contents are the main parameters determined in studies with acerola. Titratable acidity, SS/TA ratio and pH are also evaluated in most studies. Different studies have already identified possible genotypes for use in acerola breeding programs based on fruit quality, both for launching new cultivars and for use as parents in crosses. Our review is a useful basis for acerola breeding programs and germplasm conservation. Future studies are required to further identify and quantify bioactive compounds in acerolas of different genotypes.
A prospective review of the health-promoting potential of Jing Si Herbal Tea

Traditional Chinese medicine (TCM) has gained considerable attention over the past few years for its multicomponent, multitarget, and multi-pathway approach to treating different diseases. Studies have shown that TCMs as adjuvant therapy along with conventional treatment may benefit in safely treating various disorders. However, investigations on finding effective herbal combinations are ongoing. A novel TCM formula, “Jing Si Herbal Tea (JSHT),” has been reported recently for their health-promoting effects in improving overall body and mental health. JSHT is a combination of eight herbs recognized in Chinese herbal pharmacopoeia for their anti-viral, anti-aging, and anti-cancer properties as well as protective effects against cardiovascular, metabolic, neural, digestive, and genitourinary diseases. Thus, to better understand the beneficial effects of the ingredients of JSHT on health, this review intends to summarize the preclinical and clinical studies of the ingredients of JSHT on human health and diseases, and possible therapeutic effects with the related mode of actions and future prospects for their application in complementary therapies.
Impact of Blood-Count-Derived Inflammatory Markers in Psoriatic Disease Progression

Psoriasis is a chronic immune-mediated disease, linked to local and systemic inflammation and predisposing patients to a higher risk of associated comorbidities. Cytokine levels are not widely available for disease progression monitoring due to high costs. Validated low-cost and reliable markers are needed for assessing disease progression and outcome. This study aims to assess the reliability of blood-count-derived inflammatory markers as disease predictors and to identify prognostic factors for disease severity. Patients fulfilling the inclusion criteria were enrolled in this study. Patients were divided into three study groups according to disease severity measured by the Body Surface Area (BSA) score: mild, moderate, and severe psoriasis. White blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), derived neutrophil-to-lymphocyte ratio (d-NLR), systemic immune index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) positively were correlated with disease severity (p < 0.005). d-NLR, NLR, and SII are independent prognostic factors for mild and moderate psoriasis (p < 0.05). d-NLR is the only independent prognostic factor for all three study groups. Moderate psoriasis is defined by d-NLR values between 1.49 and 2.19. NLR, PLR, d-NLR, MLR, SII, SIRI, and AISI are useful indicators of systemic inflammation and disease severity in psoriasis.
The Apical Endocytic-Lysosomal Apparatus in CLCN5 Mutations with Phenotypic-Genotypic Correlations in Three Cases

Dent disease type 1 is characterized by pathogenic CLCN5 gene variants and impaired receptor-mediated endocytosis in proximal tubules. However, mutation-related abnormalities in proximal tubules have not yet been described. Here, we present three patients with CLCN5 alterations and distinct morphological changes of the apical endocytic-lysosomal apparatus. The proximal tubular ultrastructure was investigated in kidney biopsy samples of three boys genotyped for non-nephrotic proteinuria. Controls: seven patients with nephrotic-range glomerular proteinuria. The genotyping findings revealed an already-known missense mutation in one patient and hitherto undescribed frameshift variants in two patients. Low-molecular-weight proteinuria, focal global glomerulosclerosis, proximal tubular changes, and tubular calcium deposits characterized each case. Three subsets of proximal tubular cells were observed: those without any abnormality, those with aplasia of apical endocytic-lysosomal apparatus and shrinkage of cells, and those with hypoplasia of apical endocytic apparatus, accumulation of proteinaceous substance in dysmorphic lysosomes, and dysmorphic mitochondria. The distribution of subsets varied from patient to patient. In one patient with a frameshift variant, an oxidative stress-like injury of proximal tubular cells and podocytes accompanied the above-mentioned alterations. Focal aplasia/hypoplasia of apical endocytic apparatus and subsequent changes in cytoplasmic organelles characterized proximal tubules in the CLCN5 pathogenic variants.
More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From

We examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm-specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard-to-quantify aspects of firms' fundamentals, which investors quickly incorporate in stock prices. We also thank two anonymous referees. Finally, we are especially grateful to the editor, Cam Harvey, and an anonymous associate editor for their excellent suggestions. The authors are responsible for any errors.
Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.
Evaluation of Marginal Bone Height Changes in implant-supported mandibular overdenture retained with Two Different Materials of Bar Attachments.

Objective: To estimate crestal bone height changes and biting forces in three implant supported mandibular overdenture retained by Poly ether ketone (PEEK) bar and chromium cobalt (Co-Cr) bar attachment. Materials and Methods : Twenty completely edentulous patients with age range above 60 years were participated in this study. Three implants were placed in the inter foraminal area; two implants were placed bilaterally in the right and left canine areas and one near the midline. In all patients, conventional maxillary full dentures were constructed against implant-supported mandibular overdentures.A random dividing was made for all patients to be two equal groups according to the bar attachments material. Polyether ether ketone (PEEK) bar attachments were fabricated to hold the overdenture for patients in group A, while custom-designed chromium cobalt (Co-Cr) bar attachments were constructed for patients in group B. Crestal bone loss around implants was assessed by CBCT after denture pick up (T0) base line ,after 3 months(T3),