Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Opinion Toroidal Polarization: Insights from Bounding Confidence Beyond, Distance Matters

Deterministic dynamics is a mathematical model used to describe the temporal evolution of a system, generally expressed as dx/dt = F(x), where x represents the system's state, and F(x) determines its dynamics. It is employed to understand long-term system behavior, including opinion formation and polarization in online communities. Opinion dynamics models, like the Katz model and the logistic map, help analyze how individual opinions are influenced within social networks and exhibit chaotic behavior. These models are crucial for studying opinion formation and collective behavior on social media, especially in conjunction with branching theory. For instance, Galam's Ising model applies principles from physics to social sciences, representing individual opinions as"spins"and illustrating how local interactions influence consensus formation. The Bounding Confidence model considers opinions within a confidence interval, showing how opinions converge or polarize. These models effectively analyze opinion dynamics in online communities, aiding in understanding trends and viral phenomena on social media. This research aims to analyze discourse flow and opinion evolution, predicting future trends in online communities and decoding digital-age human interaction dynamics. Combining branching theory with opinion dynamics models enhances our understanding of digital communication. In the modified opinion dynamics model, the weight parameter h for distance introduces distance-based interaction terms. The update equation is adjusted to control interaction strength based on distance, with hij calculated from the distance dij. This customization allows for a more accurate representation of opinion dynamics in specific scenarios, forming the basis for discussions in this paper.
Concordance and Associated Factors in Diagnostic Criteria for Prediabetes and Diabetes: An Analysis of Fasting Glucose, Postprandial Glucose, and Glycated Hemoglobin

Introduction: Diabetes and prediabetes are rising chronic health conditions globally. Early and accurate identification of these disorders is crucial for effective prevention and management. Objective: To evaluate the concordance and associated factors of prediabetes and diabetes based on Fasting Glucose (FG), Postprandial Glucose (PPG), and Glycated Hemoglobin (HbA1c). Materials and Methods: Primary analysis was conducted on patients from a polyclinic located in Lima, Peru. Prevalences were assessed, concordance was evaluated through the Kappa index, and multivariable analyses were performed to identify associated factors for each. Results: A total of 624 participants were included. Isolated values of FG, PPG, and HbA1c for prediabetes accounted for 10%, 15%, and 7% of cases, respectively, while the intersection of all three accounted for 56% of the total. For Type 2 Diabetes (DM2), isolated values were represented in 10%, 16%, and 6% of cases, respectively, while the intersection of all three accounted for 31%. The concordance between FG and PPG was 0.6970 (p<0.001); between FG and HbA1c was 0.6163 (p<0.001); and between PPG and HbA1c was 0.6903 (p<0.001). Significant associations were found with factors such as gender, age, family history of DM2, alcohol consumption, and hypertension. Discussion: The results revealed that PPG detected more cases in isolation, followed by FG and HbA1c. Comparison with previous studies showed variations in prevalence, underscoring the importance of considering multiple criteria in diagnosis.
Anti-glycation properties of 05 spices and culinary herbs used in Benin

Diabetes mellitus is a major public health problem characterized by chronic hyperglycemia leading to several other pathologies. It causes oxidative stress, resulting in the overproduction of superoxide radicals and protein glycation, ultimately leading to the formation of Advanced Glycation Endproducts (AGEs). It has been shown elsewhere that spices and herbs commonly used in the culinary arts inhibit glycation and AGE formation. The aim of this study is to evaluate the inhibitory effect of five aromatic herbs and spices commonly used in Benin on protein glycation and AGE formation. Total flavonoids content and polyphenolic compounds were determined with respectively the aluminium chloride method and the Folin-Ciocalteu method. For glycation testing, serum pools from diabetics and non-diabetics were used. Serum fructosamines were measured using the nitro blue tetrazolium colorimetric test and AGEs were detected using a fluorescence method. All five herbs and spices had shown notable ability to inhibit fructosamine and AGE formation. L. nobilis was very rich in flavonoids and exhibited the strongest inhibitory activity against glycation and AGE formation. The results of this study suggested that the investigated five spices and culinary herbs have interesting properties to take in account in the therapy of diabetes and its related complications.
Adiponectin index to assess metabolic syndrome and insulin resistance in obese adolescents

Background: Adiponectin level is decreased in obesity, and is suspected to be the cause of metabolic syndrome (MetS) and even linked with the onset of insulin resistance (IR). Adiponectin index (AI) has been used to determine IR and MetS in polycystic ovary syndrome (POCS). Objectives: To assess the usefulness of AI to determine IR and MetS in obese adolescents.Method: A cross sectional study was performed in obese adolescents from January to May 2020 in Sidoarjo and Surabaya Junior/High School, Indonesia.Results: AI had a weak negative correlation with body weight, waist circumference, hip circumference and body mass index. AI also correlated with triglyceride, systolic blood pressure, fasting insulin, homeostatic model assessment for IR (HOMA-IR) and high-density lipoprotein cholesterol HDL-c. Obese adolescent with MetS had bigger AI than non-MetS (0.21+ 0.18 vs. 0.13+0.09, p=0.0000). A similar phenomenon was seen in obese adolescents with IR (0.37+0.51 vs. 0.11+0.07, p=0.000). AI had a better prognostic value to determine IR than MetS, with larger area under curve (AUC), 0.890 vs. 0.606. Cut-off value to determine IR was <0.17, with sensitivity 81.3% and specificity 88.5%. Conclusions: AI is better to determine IR than MetS with a cut-off of <0.17 in obese adolescents.
Biodegradation of winery biomass wastes by developing a symbiotic multi-fungal consortium

iii Dedication . vi Acknowledgements . vii Declaration . viii Publications arising from the thesis . ix Table of contents . xi List of tables . xvii List of figures . xviii Annotation . xxv 1. Chapter 1 . 1 1.1. Overview . 2 1.2. Problems associated with biomass degradation . 3 1.3. Various biomass degradation approaches . 4 1.4. Aims and strategies of the project . 5 2. Chapter 2 . 7 2.1. Biomass composition . 8 2.1.1. Cellulose . 8 2.1.2. Hemicelluloses . 10 2.1.2.1. Xylans . 10 2.1.2.2. Xyloglucans . 11 2.1.2.3. Mannans . 11 2.1.2.4. β-glucans . 12 2.1.2.5. Pectins . 14 2.1.3. Lignins . 15 2.2. Biomass biodegradation . 18 2.2.1. Biodegradation: Issues and problems . 19 2.2.2. Structure of lignocellulose complex . 19 2.2.3. Microbial and chemical limitations . 21 2.2.4. Enzymatic limitations . 21 xiii 2.2.5. Product inhibition . 21 2.3. Biomass pre-treatment . 22 2.3.1. Physical pre-treatment methods . 22 2.3.1.1. Milling . 23 2.3.1.2. Irradiation and sonication . 23 2.3.2. Physico-chemical treatment . 24 2.3.3. Chemical treatments . 26 2.3.3.1. Acid pre-treatment . 26 2.3.3.2. Alkali pre-treatment . 27 2.3.3. 3. Organosolv treatments . 27 2.3. 4. Biological pre-treatment . 28 2.4. Fungal degradation of biomass . 28 2.4. 1. Cellulases . 29 2.4. 2. Hemicellulases . 32 2.4. 3. Lignases . 33 2.4. 3.1. Laccases . 35 2.4. 3.2. Heme peroxidases . 38 2.4. 3.2.1. Lignin peroxidases . 38 2.4. 3.2.2. Manganese peroxidases . 39 2.4. 3.2.3. Versatile peroxidases . 42 2.5. Biodegradation methods . 45 2.5. 1. First generation: Submerged fermentation . 45 2.5. 2. Second Generation: Solid State Fermentation . 47 2.5. 3. Third generation: Consolidated Bioprocessing . 50 2.5. 4. Symbiotic fermentation: SSF and thermophilic degradation . 53 2.6. Application of metabolomics in fungal biomass degradation . 56 2.7. Overview . 59 3. Chapter 3 . 60 3.1. Chemicals . 61 3.1.1. Chemicals and media . 61 3.1.2. Growth media . 61 3.1.3. Enzymes . 63
Transient Pressure Well Test Analysis of Fractured Geothermal Wells

Fracturing during injectivity testing can take place in geothermal wells when the reservoir has a low permeability or when the well has skin damage. The transient behavior (pressure falloff) of these wells can’t be matched using existing well test analysis methods. At the same time, modelling fracturing in geothermal reservoirs using rock mechanics and commercial finite element software is complicated due to several field uncertainties (e.g. formation height, reservoir permeability and porosity). In addition, rock mechanics data (rock stress, strain and Young’s modulus), which are normally unknown in geothermal fields. This makes it difficult to develop an appropriate fracture model that matches the field test data. This study attempts to develop a fracture model without integrating the rock mechanics. The model is setup with a simple grid using the TOUGH2 geothermal reservoir simulator and validated using the advanced pressure derivative transient analysis. Multiple subsets of fracture geometries were developed to represent the different stages of fracture closure during pressure falloff. The PyTOUGH code was used to simplify the running of the different fracture stages. The results are very promising and provide a clear justification and explanation for the fractured well behavior. This model should be of use in matching data from geothermal wells with similar pressure response.
Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid

Simple Summary Ascites cytology serves as a cost-effective and noninvasive primary screening test for metastatic colorectal cancer (CRC). Diagnosing metastatic carcinoma of the peritoneum based on biopsy results is challenging, and analyzing ascitic aspiration cytology shows a limited sensitivity and specificity, along with a high variability between observers. Our study aimed to develop an artificial intelligence (AI) model that enhances pathologists’ ability to accurately diagnose metastatic CRC within ascitic fluid, improving the diagnostic accuracy, specificity, and sensitivity. This deep learning approach demonstrated a high accuracy, sensitivity, and specificity in distinguishing between malignant and benign ascites. The findings from this proposed deep learning method hold significant promise for integrating AI into clinical practice to enhance the precision of diagnosing metastatic CRC cells in ascitic fluid. Abstract Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.
Probing a magnetar origin for the population of extragalactic fast X-ray transients detected by Chandra

Twenty-two extragalactic fast X-ray transients (FXTs) have now been discovered from two decades of Chandra data (analyzing $ sim $259 Ms of data), with 17 associated with distant galaxies ($ gtrsim $100 Mpc). Different mechanisms and progenitors have been proposed to explain their properties; nevertheless, after analyzing their timing, spectral parameters, host-galaxy properties, luminosity function, and volumetric rates, their nature remains uncertain. We interpret a sub-sample of nine FXTs that show a plateau or a fast-rise light curve within the framework of a binary neutron star (BNS) merger magnetar model. We fit their light curves and derive magnetar (magnetic field and initial rotational period) and ejecta (ejecta mass and opacity) parameters. This model predicts two zones: an orientation-dependent free zone (where the magnetar spin-down X-ray photons escape freely to the observer) and a trapped zone (where the X-ray photons are initially obscured and only escape freely once the ejecta material becomes optically thin). We argue that six FXTs show properties consistent with the free zone and three FXTs with the trapped zone. This sub-sample of FXTs has a similar distribution of magnetic fields and initial rotation periods to those inferred for short gamma-ray bursts (SGRBs), suggesting a possible association. We compare the predicted ejecta emission fed by the magnetar emission (called merger-nova) to the optical and near-infrared upper limits of two FXTs, XRT 141001 and XRT 210423 where contemporaneous optical observations are available. The non-detections place lower limits on the redshifts of XRT 141001 and XRT 210423 of $z gtrsim 1.5$ and $ gtrsim 0.1$, respectively. If the magnetar remnants lose energy via gravitational waves (GWs), it should be possible to detect similar objects with the current advanced LIGO detectors out to a redshift $z lesssim 0.03$, while future GW detectors will be able to detect them out to $z approx
Insights from Real-World Practice: The Dynamics of SARS-CoV-2 Infections and Vaccinations in a Large German Multiple Sclerosis Cohort

The SARS-CoV-2 pandemic profoundly impacted people with multiple sclerosis (pwMS). Disease-related aspects and demographic factors may influence vaccination rates, infection susceptibility, and severity. Despite prior research, comprehensive real-world data obtained throughout the pandemic remain limited. We investigated SARS-CoV-2 vaccination and infection patterns in a large monocentric real-world cohort. We collected prospective data from medical visits at the MS Center Dresden, Germany, from the pandemic’s beginning until 31 May 2022. Logistic regression and rank correlation analyses were used to explore associations between SARS-CoV-2 outcomes and patient characteristics. Of 2115 pwMS assessed (mean age 46.5, SD ± 12.9; median expanded disability status scale 2.5), 77.9% were under disease-modifying treatment (DMT), primarily B-cell depletion (25.4%). A total of 35.5% reported SARS-CoV-2 infections, and 77.4% were fully vaccinated. PwMS with increased disability, older age, and comorbidities were associated with higher vaccination rates, possibly due to the awareness of these populations regarding complications of SARS-CoV-2 infections. Infections were more common in younger females, people with a lower degree of disability, those with relapsing MS, and those who were not vaccinated. PwMS on B-cell depletion reported more infections than untreated pwMS and those receiving other types of disease-modifying therapy, despite higher vaccination rates. Most infections were mild, with no severity differences according to demographic or disease-related factors, except for gender. Notably, all fatal cases occurred in unvaccinated pwMS. Our studies suggest that demographic and disease-related factors, especially age and the use of B-cell depletion, significantly influenced SARS-CoV-2 vaccination and infection rates in our cohort. These factors may be considered in future preventive campaigns in further pandemics.
IMEO: Anomaly Detection for IoT Devices using Semantic-based Correlations

—In the Internet of Things (IoT) security, anomalies due to attacks or device malfunctions can have serious consequences in our daily lives. Previous solutions have been struggling with high rates of false alarms and missing many actual anomalies. They also take a long time to detect anomalies even if they successfully detect anomalies. To overcome the limitations, this paper proposes a novel anomaly detection system, named IoT Malfunction Extraction Observer (IMEO), that utilizes semantics and correlation information for smart homes. Given IoT devices installed at home, IMEO creates virtual correlations based on semantic information such as applications, device types, relationships, and installation locations. The generated correlations are validated and improved using event logs extracted from smart home applications. The finally extracted correlations are then used to simulate the normal behaviors of the smart home. Any discrepancy between the actual state of a device and the simulated state is reported as abnormal while comparing correlations and event logs. IMEO also utilizes the observation that malfunctions of IoT devices occur repeatedly. An anomaly database is created and used so that repetitive malfunctions are quickly detected, which eventually reduces processing time. This paper builds a smart home testbed on a real-world residential house and deploys IoT devices. Six different types of anomalies are analyzed, synthesized, and injected to the testbed, with which IMEO’s detection performance is evaluated and compared with the state-of-the-art correlation-only detection method. Experimental results demonstrate that the proposed method achieves higher performance of detection accuracy with faster processing time.
Descripción del crecimiento en ovinos criollos utilizando el modelo Brody

This study was aimed at using the Brody model for determining the growth curve for a creole sheep population kept on extensive grazing. The data came from weighing 59 animals (448 readings) born between August and September 2012 which were kept on Altamar SA’s sheep-farm in Buenavista in the Cordoba Department in Colombia. Statistical Analysis System’s (SAS) univariate nonlinear (NLIN) regression mode was used for obtaining the estimates, considering the fixed effects of the mothers’ amount of births and the amount of lambs per birth. β0 was estimated as being 58.47 ± 7.8, β1 0.98 ± 0.03 and β2 0.0054 ± 0.003. No significant effect was found on β0, β1 and/or β2 (p>0.05) regarding the amount of births. A significant effect (p≤0.05) on β0 and β1 was found regarding the amount of lambs per birth. Percentage maturity estimated at four months was 46.6 ± 4.6 and 66.1 ± 4.5% at seven months. Estimated age at 75% maturity was 274 ± 84 days and 596 ± 202 days at 95% maturity. It can be concluded from this study’s results that the estimated values for percentage maturity and age at maturity in the target sheep population were low; this means that a selection programme should be introduced and the animals’ zootechnical management improved to ensure maturity being reached in the shortest time possible.
Analyzing Colorectal Cancer at the Molecular Level through Next-generation Sequencing in Erbil City

Colorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths globally. It is characterized as a genomic disorder marked by diverse genomic anomalies, including point mutations, genomic rearrangements, gene fusions, and alterations in chromosomal copy numbers. This research aims to identify previously undisclosed genetic variants associated with an increased risk of CRC by employing next-generation sequencing technology. Genomic DNA was extracted from blood specimens of five CRC patients. The sequencing data of the samples are utilized for variant identification. In addition, the Integrative Genomic Viewer software (IGV) is used to visualize the identified variants. Furthermore, various in silico tools, including Mutation Taster and Align GVGD, are used to predict the potential impact of mutations on structural features and protein function. Based on the findings of this research, 12 different genetic variations are detected among individuals with CRC. Inherited variations are located within the following genes: MSH6, MSH2, PTPRJ, PMS2, TP53, BRAF, APC, and PIK3CA.
The long arm of the EU? Evidence of Europeanization of spatial planning in Albania and Switzerland

The long arm of the EU? Evidence of Europeanization of Spatial Planning in Albania and Switzerland / Berisha, Erblin; Solly, Alys; Cotella, Giancarlo. ELETTRONICO. (2019), pp. 2563-2573. ((Intervento presentato al convegno XXXII Congresso Geografico Italiano L’apporto della geografia tra rivoluzioni e riforme tenutosi a Roma, Italia nel 7-10 Giugno 2017. Original The long arm of the EU? Evidence of Europeanization of Spatial Planning in Albania and Switzerland
Effect of Calving Season on Productive Performance of Dairy Cows

The aim of the present research was to study the influence of the calving season in conditions of the upcoming climate changes on the productive traits of dairy cows in Bulgaria. The study was conducted on a cattle farm with a capacity of 500 dairy cows, which were loose-housed in open free-stall barns (shed-type). In the research, 286 lactations of 199 Holstein cattle from the studied farm were included. The cows with the highest average milk yield for lactation—8522.2 kg—calved in the spring, while the cows with the lowest milk yield—8082.7 kg—calved in the summer. Cows that calved in the spring had the highest maximum daily milk yield (lactation peak)—38 kg—whereas cows that calved in the summer had the lowest—35.7 kg. Regarding the composition indicators of milk, fat, and protein content, no significant effect of the calving season was found, but there was a tendency for the lowest values for the percentage of fat in milk to be reported for cows that calved in the summer—3.68%—and the highest for those calved in the spring—3.71%. Regarding the percentage of protein in the milk, the lowest values were observed for cows that calved in autumn—3.19%—and the highest for cows that calved in summer—3.27%.
On the Performance of Interference-Based Energy-Harvesting-Enabled Wireless AF Relaying Communication Systems

This article considers an interference-based radio-frequency energy harvesting (RF-EH)-empowered wireless dual-hop amplify-and-forward relaying system in which an ambient interferer is beneficially utilized as the solely free power source for EH and detrimentally considered as the dominant factor that corrupts its receivers. Three EH modes are considered and analyzed separately. In mode I, energy is harvested only by the source; in mode II, energy is harvested only by the relay; and in mode III, energy is harvested concurrently by both the source and relay. Under these modes, exact and approximate analytical expressions are derived for the system's outage probability, which are directly used to determine the system's delay-limited throughput as a performance figure of merit. Thorough numerical and simulation results are presented to verify the analytical work and to demonstrate the system's throughput performance under different system and channel parameters. For example, results reveal that for given channel conditions, increasing the interferer's power reduces the throughput in case of modes I and II, and has no effect on it in case of mode III. Also, for given interferer's power, improving the channel conditions between the interferer and a harvesting node, improves the throughput, while improving them between the interferer and a receiving node, degrades the throughput.