Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Urban Ozone Levels and Trends with Semiparametric Modeling

Ozone in the planetary boundary layer of the troposphere is considered harmful to plants and human health. Surface ozone levels are determined by the strengths of sources and precursor emissions and by meteorological conditions. Therefore, assessing ozone trends is complicated by meteorological variability. Ozone data in the Chicago area over the period 1981 to 1991 were studied. In this article, ozone concentration was modeled as a year effect plus a nonparametric model in meteorological variables. In this way, long-term trend over the 11-year period was separated from the effects of meteorology. Estimation of long-term trend is important for assessing the effectiveness of environmental policy. The indications were that there was a decrease in ozone levels once the meteorological effects on ozone had been adjusted for. The models based on this technique were also shown to provide predictions that closely match the actual ozone levels.
YWHAH activates the HMGA1/PI3K/AKT/mTOR signaling pathway by positively regulating Fra-1 to affect the proliferation of gastric cancer cells

Fos-related antigen 1 (Fra-1) is a nuclear transcription factor that regulates cell growth, differentiation, and apoptosis. It is involved in the proliferation, invasion, apoptosis and epithelial mesenchymal transformation of malignant tumor cells. Fra-1 is highly expressed in gastric cancer (GC), affects the cycle distribution and apoptosis of GC cells, and participates in GC occurrence and development. However, the detailed mechanism of Fra-1 in GC is unclear, such as the identification of Fra-1-interacting proteins and their role in GC pathogenesis. In this study, we identified tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein eta (YWHAH) as a Fra-1-interacting protein in GC cells using co-immunoprecipitation combined with liquid chromatography-tandem mass spectrometry. Experiments showed that YWHAH positively regulated Fra-1 mRNA and protein expression, and affected GC cell proliferation. Whole proteome analysis showed that Fra-1 affected the activity of the high mobility group AT-hook 1 (HMGA1)/phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) signaling pathway in GC cells. Western blotting and flow cytometry confirmed that YWHAH activated HMGA1/PI3K/AKT/mTOR signaling pathway by positively regulating Fra-1 to affect GC cell proliferation. These results will help to discover new molecular targets for the early diagnosis, treatment, and prognosis prediction of GC.
Cancer patients with Angiotensin-converting enzyme (ACE) gene polymorphism and COVID-19 phenotypic expression predisposed to SARS-CoV-2 infection

Pathogenesis of COVID-19 has been linked to the Angiotensin system. Angiotensin-converting enzyme (ACE2) has been recognized as the specific receptor of the SARS-CoV-2 virus, serves as a cellular receptor for SARS-CoV-2, suggesting that a person's vulnerability to infection may be controlled by how much of the ACE2 gene is expressed. It is also possible that the severity of COVID-19 is related to the equilibrium between ACE1 and ACE2 activity, which has been linked to the etiology of respiratory disorders. This study aimed to investigate the association of ACE1 I/D polymorphism with the severity of Covid-19. The study looked at 113 people-(50 healthy controls, 63 people with Covid). Results for the ACE2 rs4240157 T > C polymorphism were obtained. Logistic regression was used to evaluate the distribution frequencies of variables across the study groups. The ACE1-CC*CT genotype (p = 0.049) and male gender (p0.001) were related to severe COVID-19. COVID-19 severity was found to be associated with the ACE2–CT genotype through multiple logistic regression under the co-dominant inheritance model: CC*CT Allele, 95% CI (0.0104 to 0.2954), Significance level, (0.0007) Odd Ratio (0.0556); CC*TT Allele, 95% CI (0.1854 to 6.1927), Significance level, (0.9386) Odd Ratio (1.0714); and CT*TT (19.2857). This was assuming the ACE2–CC*CT genotype was connected with covid-19 severity. However, the ACE2 polymorphism did not affect the development of illness. In conclusion, male gender, malignancy, and the ACE1 genotype were linked to a negative result of COVID-19. Our results indicated that ACE1-C/T might affect COVID-19 severity; however, this association was hypertensive status-specific. However, this finding needs to be confirmed in additional large samples.
Feedback Stabilization of Singularly Perturbed Systems Under Information Constraints

The quantized feedback control for a class of singularly perturbed systems is addressed, in which the controlled system and the controller are connected via a limited capacity communication channel. First, a proper coder–decoder pair is presented such that the transmission error decays to zero exponentially under information constraints. Then, a control law in terms of linear matrix inequalities is constructed to render the resulting closed‐loop system input‐to‐state stable with regard to the transmission error. Thus the asymptotic stability of the closed‐loop system is guaranteed. It is shown that the proposed method is simple and easy to operate. Moreover, an upper bound of the small perturbation parameter for the stability of systems can be explicitly estimated with a workable computational way. Finally, two examples are presented to show the effectiveness of the proposed method.
PRESERVING THE HISTORY OF AFRICAN AMERICAN EDUCATION: DIGITAL DOCUMENTATION OF ROSENWALD SCHOOLS – A CASE STUDY ON THE TANKERSLEY SCHOOL IN HOPE HULL, ALABAMA, USA

Abstract. Established across the American South between 1917 and 1932, Rosenwald Schools were crucial for providing education to African American children during segregation. Now, many of these structures are abandoned and deteriorating, in urgent need of digital documentation and preservation. This paper introduces an innovative approach for using Matterport technology to capture immersive virtual tours and enhance Heritage Building Information Modeling (HBIM) with high-resolution imagery. The Tankersley Rosenwald School in Hope Hull, Alabama serves as the case study. Additionally, this paper features traditional Reality Capture techniques, such as Terrestrial Laser Scanning and aerial photogrammetry, in combination with Matterport technology to create a detailed HBIM model of the historic school. This model enables architectural and structural analysis, informing suitable stabilization, conservation, and restoration strategies. It also acts as a powerful visualization tool, allowing stakeholders to safely examine the school's architectural, structural, and historical aspects. The virtual tour produced through Matterport provides valuable data for developing an accurate HBIM model in Revit, leading to more effective conservation approaches. The integration of these technologies showcases the potential for digital preservation of built heritage and emphasizes the importance of innovative methods in cultural heritage documentation. By digitally preserving these historically significant schools, this research supports the conservation and restoration of a vital aspect of African American educational history and raises awareness of their cultural importance.
Wireless Middleware Solutions for Smart Water Metering

While smart metering applications have initially focused on energy and gas utility markets, water consumption has recently become the subject of increasing attention. Unfortunately, despite the large number of solutions available on the market, the lack of an open and widely accepted communication standard means that vendors typically propose proprietary data collection solutions whose adoption causes non-trivial problems to water utility companies in term of costs, vendor lock-in, and lack of control on the data collection infrastructure. There is the need for open and interoperable smart water metering solutions, capable of collecting data from the wide range of water meters on the market. This paper reports our experience in the development and field testing of a highly interoperable smart water metering solution, which we designed in collaboration with several water utility companies and which we deployed in Gorino Ferrarese, Italy, in collaboration with CADF (Consorzio Acque Delta Ferrarese), the water utility serving the city. At the core of our solution is SWaMM (Smart Water Metering Middleware), an interoperable wireless IoT middleware based on the Edge computing paradigm, which proved extremely effective in interfacing with several types of smart water meters operating with different protocols.
Improved Precipitation Forecasts Using Parameterized Precipitation Drag in a Hydrostatic Forecast Model

Abstract An empirical Rayleigh drag parameterization of the nonhydrostatic mechanisms of precipitation drag and small-scale diabatically induced mixing is introduced into a hydrostatic regional forecast model to curb excessive grid-scale precipitation production. To get the needed damping, the coefficient in the Rayleigh drag is set proportional to the predicted liquid water, similar in form to the precipitation drag exhibited in nonhydrostatic calculations. The Rayleigh drag parameterization is found to be greatly superior to using the full nonhydrostatic precipitation drag term that produces a damping that is much too large in magnitude for use in a hydrostatic model. Even a rescaling of this nonhydrostatic term is found to be unsatisfactory since it lacks the sensitivity needed for hydrostatic calculations, resulting in an inability to control excessive precipitation. To incorporate nonhydrostatic vertical acceleration effects into a hydrostatic model, a modified version of the quasi-hydrostatic approx.
Noise Pollution a Menace to Learning in Typical Tertiary Institutions in Nigeria

Knowingly and unknowingly, noise pollution hurts the quality of our everyday life. The lecture hall is an auditory-verbal environment that requires pleasant acoustics; however, the main problem of it is noise pollution. Thus, this study aims at assessing students’ perceptions of the effect of noise pollution in lecture halls in a typical Nigeria institution, taking the lectures halls in the School of Agriculture and Agricultural Technology (SAAT), School of Environmental Sciences (SOES), the School of Health Teaching (SOHT) all in the Federal University of Technology Owerri FUTO. The study was pursued using a Quantitative and Qualitative approach that entailed the use of structured Questionnaires and observation. A total number of 150 questionnaires were distributed to the students because they are the major users of lecture halls, and a total of 134 questionnaires were filled and returned giving a percentage response of 89.3%. Data collected were analyzed using computer-based software statistical package for Social Science (SPSS) version 19.0 and descriptive tools such as frequencies, percentage values and Relative Important Index (RII). The result of the analysis revealed among others that the major causes of noise in lecture halls are generators, automobiles, and Echo of noise from adjacent buildings. Similarly, the study also revealed that noise exposure affects listening ability, concentration of students, and reading ability and its peak period is usually in the afternoon hours (1.00-3.00 pm). Also, one of the effects of noise is that it makes students lose concentration when receiving lectures since the bridge between students and lecturers is limited as a result of interference in communication between students and lecturers. Consequently, It is recommended that noise detectors should be located in lecture halls and school environments to know the noise level in lecture halls and perhaps, operate on the noise level recommended by the World Health Organization (WHO). Also, the use of noise-insulating materials during construction and maintenance, and the planting of natural shield trees within the study area to prevent noise from intruding into the lecture halls should be adopted. Educating students on the effect of noise pollution the and location of the generator set sets in lecture halls far away from the road will reduce noise pollution.
SEGMENTATION OF CYSTS IN KIDNEY AND 3-D VOLUME CALCULATION FROM CT IMAGES

This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image. Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation and 3-D volume calculation methods can provide practical supports to surgery options and medical practice to medical students.
Using long-range contact number information for protein secondary structure prediction

In this paper, we first combine tetra-peptide structural words with contact number for protein secondary structure prediction. We used the method of increment of diversity combined with quadratic discriminant analysis to predict the structure of central residue for a sequence fragment. The method is used tetra-peptide structural words and long-range contact number as information resources. The accuracy of Q3 is over 83% in 194 proteins. The accuracies of predicted secondary structures for 20 amino acid residues are ranged from 81% to 88%. Moreover, we have introduced the residue long-range contact, which directly indicates the separation of contacting residue in terms of the position in the sequence, and examined the negative influence of long-range residue interactions on predicting secondary structure in a protein. The method is also compared with existing prediction methods. The results show that our method is more effective in protein secondary structures prediction.
Effects of using P.juliflora leaves as additive in anaerobic digestion of poultry wastes

This study explores the influence of P.juliflora leaves as an additive in the anaerobic digestion of poultry droppings from layers and broilers. Six digesters (A, B, C, D, E, and F) were used with a retention time of 31 days. The dry weight content of the digesters include: 100% layer wastes (control), 100% broiler wastes (control), 95% layer wastes + 5% P. juliflora leaves, 90% layer wastes + 10% P. juliflora leaves, 95% broiler wastes + 5% P. juliflora leaves, and 90% broiler wastes + 10% P. juliflora leaves. Layer wastes plus 5% Prosopis Juliflora created 18% more biogas than layer wastes alone, and layer wastes plus 10% Prosopis Juliflora produced 22% more biogas than layer wastes alone, which was the control set-up. In comparison to digester broiler wastes alone, broiler wastes plus 5% and broiler wastes plus 10% both produced 20% and 24% more biogas, respectively. In conclusion, adding 5 and 10 percent of Prosopis Juliflora to poultry manure from layers or broilers has significantly increased the generation of biogas. Broiler wastes plus 10% Prosopis Juliflora yielded the most amount of methane and least amount of hydrogen sulfide, which makes it the most suitable substrate.
Prediction models of iron level in beef muscle tissue toward ecological well-being

BACKGROUND AND OBJECTIVES: Elemental status is associated with the biochemical processes occurring in the body. Beef, consumed worldwide, is an excellent source of iron in terms of quantity and bioavailability, providing up to 18 percent of the daily requirement. The level of iron in muscle tissue affects beef quality. Current methods used to assess iron content in cattle muscles are laborious and complex. Accordingly, the current study aimed to develop a fast and simple method to assess the elemental status of animals in vivo and in a minimally invasive way based on an effective model for iron-level prediction by using blood-analysis results toward ecological well-being. This method can overcome the shortcomings of currently used approaches. METHODS: Samples of diaphragmatic muscle weighing 100 grams, as well as blood samples, were obtained from Hereford cattle bred under typical conditions of an industrial complex in the south of Western Siberia, Russia. Elemental analysis was performed by atomic absorption method with electrothermal atomization. Regression analysis was conducted to estimate the relationships between iron level in the muscle tissue of Hereford cattle and independent values (blood parameters). An optimum model for predicting the iron level was established. The coefficients of regression models were calculated using the least squares method, and the values of the dependent variable corresponded with the Gaussian ones. A high correlation existed between independent variables. FINDINGS: An optimum model for predicting the iron level in the muscle tissue of Hereford cattle was established. It contained three predictors, namely, number of erythrocytes, color index, and globulin, as a result of selection based on internal and external-quality criteria. The model meets the necessary assumptions: the residuals are normally distributed, no autocorrelations exist, and the observations are influential. Furthermore, no signs of multicollinearity exist between the main effects of the model (variance-inflation factor = 1.2–1.7). CONCLUSION: The model can be used for the intravital analysis of iron level in the muscle tissue of cattle. In contrast to currently used methods, the approach proposed can be used for intravital analysis of the level of iron in muscle tissue, which is the most important advantage of the developed approach. The results can be used in ecology to assess ecological well-being and determine the allowable load of iron in animals. For veterinary medicine, the resulting model enables the evaluation of the iron level in the muscle tissue of Hereford cattle during their lifetime. Studying the effect of different factors on meat quality may allow to decrease or avoid useless measures used in farming, such as the excessive use of feed additives. In turn, these measures can decrease resource exploitation and increase farming productivity. Therefore, the results can guide the further development of sustainable farming. ARTICLE INFO
Peer Evaluation Tournaments

: Experts (e.g., academics) or team members (e.g., in ﬁrms) are often best at evaluating each other. Peer evaluation tournaments could be useful for revealing performance. However, rampant opportunities for cheating may throw a wrench in the process, unless, somehow, players have a preference for honest reporting. In a recent paper, Dufwenberg and Dufwen-berg (2018) oﬀer a theory of perceived cheating aversion, which we argue can be naturally extended to our multi-player setting with subjective performance evaluations. Players trade oﬀ desire to win and dislike of being identiﬁed as a cheater. We derive a set of predictions, and test these in a controlled laboratory experiment.
SYSTEMATIC REVIEW

Background: Dental fibers were first introduced by Smith (1960) which are used in various fields of dentistry. Dental fibers are classified into two categories, natural and synthetic. Glass fiber that is often used in dentistry is E-glass fiber because of its resistance to chemical reactions, has good aesthetic properties, has almost the same mechanical properties as dentin and is biocompatible. In the oral cavity, acidic conditions occur due to consumption of food and beverages containing acid. Acidic conditions in the oral cavity can affect the mechanical properties of the composite resin. Factors that affect the mechanical properties of fiber reinforced composite are the properties of the composite material and fiber used, fiber quantity, fiber position, fiber direction, fiber adhesion to the composite matrix. To determine and study the effect of acidic pH on the mechanical strength of e-glass fibers. Method: This type of research is a literature review using a systematic review approach. Result: The decrease in compressive strength and resistance was influenced by structural disturbances and loss of most components due to acid exposure. The decrease in the tensile strength of the glass fiber increases the time associated with immersion of the fiber surface which can interfere with the continuity of the e-glass network. Acidic drinks, carbonated drinks, and alcohol can produce erosion on the surface of the composite resin. The longer the immersion, the more solution particles that enter the resin microporosity space and reduce the strength of the composite resin. Conclusion: The mechanical strength of e-glass fiber will decrease with the length of exposure to acid. Types of acids that can affect the decline in mechanical strength are strong acids such as hydrochloric acid (HCl), sulfuric acid (H2SO4), nitric acid (HNO3
Knowledge Augmentation for Early Depression Detection

Individuals continue to share their mental health concerns on social media, providing an avenue to rapidly detect those potentially in need of assistance. While users of immediate need can be recognized with relative ease, early-stage disorder users in the boundary region pose a greater challenge to detect. The minimal posting histories of such users further complicate proceedings. However, these same boundary region users would benefit greatly from timely treatment; hence, detecting their mental health status is of utmost need. Additionally, pointers to identify the type of depression could be of great help. Augmenting knowledge for low posting users can help to solve this problem. We propose an NLP based method ‘STBound’ that intelligently determines the optimal region for knowledge augmentation. It answers three crucial questions: when? , for whom? and how much? to augment - to resolve this imbroglio. Our proposed selective knowledge augmentation method contributes to early depression detection performance improvement by an average of 11.9% in F1 score. Further, this approach shows promising performance enhancement of 12.1% in F1 score for the critical task of separating these boundary region users with bipolar depression. STBound identifies those depressed users in the boundary region who would otherwise go unidentified.
Authoring for Story Sifters

We discuss the issues of authoring for story sifters : systems that search for compelling emergent narrative content within the vast chronicles of events generated by interactive emergent narrative simulations. We describe several different approaches to the authoring of sifting patterns that specify how to locate particular kinds of narratively potent situations; address the relationship between sifters and the simulations they operate over from an authoring perspective; and sketch several possible approaches to the authoring of sifting heuristics , or high-level encodings of what makes for a compelling story that could be used to guide a sifter’s behavior