Miracle-dz/UniFAR-FTUs huggingface dataset:
SOLID DISPERSION AS A POTENTIAL APPROACH TO IMPROVE DISSOLUTION AND BIOAVAILABILITY OF CURCUMIN FROM TURMERIC (CURCUMA LONGA L.)

This review article attempts to outline techniques and solid dispersion carriers that have been applied to improve curcumin's solubility and bioavailability in turmeric extract. This paper also examines the variables that impact the efficacy of curcumin solid dispersion. Turmeric (Curcuma longa L.) contains curcuminoids as bioactive compounds consisting of curcumin, dimethoxy-curcumin, and bis-dimethoxy-curcumin. Curcumin, as the main component, is proven to have several pharmacological effects. However, it has limitations in modern drug development, such as poor stability, solubility, and bioavailability. Many studies have been conducted to overcome these limitations, including the application of solid dispersion. The preparation methods of curcumin solid dispersions are carried out by solvent evaporation, fusion/melting, and co-milling, using various types of carriers. However, the formation of a solid dispersion system only sometimes provides a considerable improvement in solubility, dissolution, and bioavailability. Differences in the selection of preparation methods, carriers, and solvents result in various arrangements of particles in the solid dispersion that may affect the performance of the system. In addition, the type of carrier also has a role in increasing curcumin permeability and bioavailability. Hydrophilic surfactant carriers have inhibitory activity against body transporters, such as P-gp and MRP, that can help to increase curcumin’s bioavailability. Natural Deep Eutectic Solvent (NADES) as a novel alternative solvent also has promising opportunities for the development of curcumin solid dispersion. Therefore, selecting appropriate preparation methods, carriers, and solvents should be considered to achieve optimum solubility, dissolution, and bioavailability of curcumin.
M ODELING OF A H OLONOMIC O MNI -D IRECTIONAL V EHICLE WITH C ONVENTIONAL T IRES

: In this paper, we explore the modeling and control of a holonomic omnidirectional vehicle using conventional wheels. We begin with a review of the basic approaches to omni-directional vehicle design based on holonomic wheel designs. While attractive from a controls perspective, these wheels are limited to low speed applications, can only operate on smooth surfaces, and require custom designed components. It is possible to design an omnidirectional vehicle using conventional wheel designs. This approach permits high payloads, high speeds, and operation on uneven ground. The penalty is added control complexity. This paper focuses on the modeling, control and simulation of an omnidirectional vehicle using conventional wheels. The primary contribution of this paper is the development of a basic methodology for computing the kinematics and dynamic equations of motion for a general holonomic omnidirectional vehicle using conventional wheels. This basic approach extends the work of Wada to include the ability to model nonlinear wheel friction, external body forces on the vehicle and internal body forces on the vehicle due to tracking errors on the wheels. We provide, as an example, a test case based on a 5000 lb vehicle with a 10,000 lb payload capacity that is presently being developed for the Navy.
Embedding Safety in Integrative STEM Teaching Methods Courses for Pre-Service
 Elementary Teachers

Academic standards in the United States advocate for the integration of science, and technology and engineering (T&E) content and practices within the elementary grades (ITEEA, 2020; NGSS Lead States, 2013). However, elementary educators often receive limited preparation for developing and facilitating safer hands-on science and T&E learning experiences (Love, 2017a), which can contribute to their reluctance to integrate science and T&E instruction. This study addresses the issue by examining changes in elementary pre-service teachers’ (PSTs) views toward safety and perceived preparation to safely infuse design-based science and T&E instruction following participation in an integrative science, technology, engineering, and mathematics (STEM) education (Wells & Ernst, 2015) methods course. A cohort of 27 elementary PSTs were split into two class sections. The control group participated in a safety jigsaw lesson the first day of classes, whereas the experiment group participated in a safety warm-up activity at the beginning of every class throughout the 15-week semester. Findings indicate all participants reported significant gains in self-efficacy and expected outcomes toward safety, views about the percentage of time elementary integrative STEM lessons should include hands-on learning experiences, perceived knowledge of integrative STEM safety concepts, and perceived ability to safely teach integrative STEM lessons. Further analyses revealed no significant differences between the two class sections. Results suggest that, in addition to emphasizing and demonstrating required safety protocols before any activity, varying strategies used to embed safety instruction in methods courses can significantly increase elementary PSTs’ views toward safety in integrative STEM education.
Optimizing Parameters for Accurate Position Data Mining in Diverse Classrooms Layouts

Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain duration to establish a stop, this study presents a reproducible procedure to optimize a range parameter for K-12 classrooms where students sitting within a certain vicinity of an inferred stop are tagged as being visited. This extension is motivated by adapting parameters to infer teacher visits (i.e., on-task and off-task conversations between the teacher and one or more students) in an intelligent tutoring system classroom with a dense layout. We evaluate the accuracy of our algorithm and highlight a tradeoff between precision and recall in teacher visit detection, which favors recall. We recommend that future research adjust their parameter search based on stop detection precision thresholds. This adjustment led to better cross-validation accuracy than maximizing parameters for an average of precision and recall ( F 1 = 0.18 compared to 0.09). As stop sample size shrinks with higher precision cutoffs, thresholds can be informed by ensuring sufficient statistical power in offline analyses. We share avenues for future research to refine our procedure further. Detecting teacher visits may benefit from additional spatial features (e.g., teacher movement trajectory) and can facilitate studying the interplay of teacher behavior and student learning.
A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images

ABSTRACT Road segmentation from high-resolution visible remote sensing images provides an effective way for automatic road network forming. Recently, deep learning methods based on convolutional neural networks (CNNs) are widely applied in road segmentation. However, it is a challenge for most CNN-based methods to achieve high segmentation accuracy when processing high-resolution visible remote sensing images with rich details. To handle this problem, we propose a road segmentation method based on a Y-shaped convolutional network (indicated as Y-Net). Y-Net contains a two-arm feature extraction module and a fusion module. The feature extraction module includes a deep downsampling-to-upsampling sub-network for semantic features and a convolutional sub-network without downsampling for detail features. The fusion module combines all features for road segmentation. Benefiting from this scheme, the Y-Net can well segment multi-scale roads (both wide and narrow roads) from high-resolution images. The testing and comparative experiments on a public dataset and a private dataset show that Y-Net has higher segmentation accuracy than four other state-of-art methods, FCN (Fully Convolutional Network), U-Net, SegNet, and FC-DenseNet (Fully Convolutional DenseNet). Especially, Y-Net accurately segments contours of narrow roads, which are missed by the comparative methods.
Mobile application about syphilis for adolescents: appearance and content validation

: Objective: To validate the appearance and content of an application for the prevention and control of syphilis in adolescents. Method: A methodological study was carried out from January to June 2020. Twenty-two judges with experience in syphilis control participated, with an emphasis on adolescent health promotion, educational technologies, and instrument validation. An instrument with variables related to content, structure, functionality, and relevance was used. The Content Validity Index, Cronbach's Alpha Test, and Flesch's Readability Index were used. Results: In the three dimensions, the average Content Validity Index was greater than 0.80, which validates the contents of the application. The overall Content Validity Index of the application was 0.86, which is satisfactory and makes it possible to consider the application valid in terms of content and appearance. Concerning the total Cronbach's Alpha of the application, a value of 0.94 was obtained, demonstrating excellent homogeneity between the participants' responses. The readability test revealed that the technology is easy for the user to understand. Conclusion: The mobile application proved valid in terms of content and appearance, demonstrating that this is a reliable technological tool that adolescents can use as a health education strategy for syphilis prevention and control
FACTORS AFFECTING JOB SATISFACTION OF PRIVATE SCHOOL TEACHERS – AN EMPIRICAL STUDY ON PRIVATE SCHOOLS IN DEMATAGODA AND KOLONNAWA

Many studies of job satisfaction have been conducted in developed countries, but few in developing ones, including Sri Lanka, particularly in the field of education. Over the past years, it is evident that teachers working in private schools are enjoying inferior benefits and working conditions compared to government teachers, resulting in lower job satisfaction levels. As teachers play a crucial role in the learning process, the present study was designed to comprehend the factors affecting job satisfaction of private school teachers using four key aspects of job satisfaction advanced through literature. Using simple random sampling method, 196 respondents were selected from a population of 402 teachers in Dematagoda and Kolonnawa areas of Colombo District for the questionnaire survey. The results of descriptive analysis denoted a moderate level of job satisfaction of teachers while mean values of independent variables remained low. Correlation results indicated a significant and strong positive association between the independent variables and job satisfaction of private school teachers. Regression results revealed that the model is good fit and that independent variables together statistically significantly explain the variance in teachers’ job satisfaction. However, results show that salary and co-worker relationship have positive impact on job satisfaction of teachers while school resources with working condition and advancement and promotions have no significant impact job satisfaction. This study will shed light on the current knowledge on factors significant to the satisfaction of private school teachers and provide a road map for private school administrators on what aspects to focus more when designing policies in relation to their teaching staff.
A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography

The spread of the Internet of Things (IoT) has caused a variety of devices to communicate with one another, producing enormous volumes of data and nurturing a linked ecosystem. However, the privacy of users, the integrity of the data, and secure authentication are seriously jeopardised by this link. In this context, we suggest a unique technique that makes use of identity-based anonymity and cryptographic authentication in IoT infrastructure in order to solve these problems.In order to facilitate safe communications between IoT devices, users, and service providers, our proposed mechanism creates a third-party authenticated protocol. This anonymity protection reduces the danger of unauthorised tracking and data exploitation by securing user names and usage patterns from possible attackers.The proposed system ensures the integrity and confidentiality of data transmitted between IoT devices and service providers by utilising sophisticated encryption algorithms. Additionally, it simplifies the authentication procedure, improving system efficiency overall without sacrificing security. The proposed technique into practise and assessed it using real-world IoT infrastructure scenarios, showcasing how well it protects user privacy and ensures safe communication. The findings show that the mechanism has a low computing cost and can withstand a range of security threats, making it a viable option for identity-based anonymity and authentication in IoT networks. Our approach makes a significant contribution to protecting user data and encouraging trustworthy interactions in IoT infrastructure as the IoT environment continues to grow.
Authentication analysis of milkfish oil treated with activated charcoal and
bentonite using Fourier Transform Infrared Spectroscopy and Multivariate
Data Analysis

Adulteration issues are important not only for fats and oils consumers but also for producers and regulatory bodies, therefore authentication analytical methods are continuously developed. The objective of this study was to highlight the employment of attenuated total Fourier transform infrared (FTIR) spectroscopy and chemometrics for the authentication of milkfish oil (MFO) from pumpkin seed oil (PSO). In this study, ATRFTIR spectra of MFO, PSO and its binary mixtures were scanned, and the information obtained was used for quantitative analysis of adulterants and classification between authentic MFO and MFO adulterated with PSO. Two multivariate calibrations of partial least square calibration (PLSR) and principal component regression (PCR) were compared for their performance during quantitative analysis and linear discriminant analysis was applied for discrimination between authentic MFO and MFO adulterated with PSO. The results revealed that PLSR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was preferred for simultaneous quantitative analysis of MFO-treated with activated charcoal, while PCR using 2nd derivative spectra at wavenumbers of 1500-1000 cm-1 was suitable for analysis of MFO-treated with bentonite providing the acceptable values of R2 , root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Linear discriminant Analysis using normal FTIR spectra at the whole wavenumber region was successful for the discrimination of MFO treated with activated charcoal and bentonite with accuracy levels of 100%. It can be concluded that the chemometrics approach for ATR-FTIR spectra could be a simple and effective tool for the authentication of MFO.
MODULATING THE DRUG SOLUBILITY OF ACECLOFENAC BY DESIGN AS SOLID LIPID PARTICLES: IN VITRO/IN VIVO CORRELATION

Objective: The main objective of the study was to enhance the dissolution and hence the oral bioavailability of Aceclofenac (ACF). Methods: ACF was formulated as solid lipid particles (SLPs), which compressed into a tablet form for immediate release purpose and certain formulations were then coated by Eudragit RS100 polymer for sustained release action. SLPs of ACF were prepared by melt fusion method under the optimum conditions, using Compritol ATO 888 (Cr), Precirol ATO 5 (Pr), glyceryl monstearate, polyethylene glycols 4000, and Poloxamer 188 at different ratios SLP formulations were characterized for particle size, flow characteristics. The compressed tablets were identified in term of hardness, friability, content, moisture uptake, and in vitro release. Oral pharmacokinetics of the optimum tablet formulation and marketed tablet as reference were studied in rabbits. Results: SLP of acecloenac (ACF) showed accepted flowing properties, and the dissolution rate of the ACF from tablets was significantly enhanced compared to unprocessed drug. The results showed that about 45.5±2.5% of AC was released within 30 min from F1 while 12.7±4.5% was released from commercial AC tablets. The in vivo studies verified that the Cmax was 1.98±0.29, 2.10±0.33, and 4.83±86 µg/µl for the optimized immediate, sustained formula and commercial tablet, respectively. While the area under the curve from zero time to 24 h for the immediate and sustained release formula was 1.79, and 2.41 fold greater than the marketed formulation. Conclusion: The results showed that solid lipid particles under optimized conditions might be an efficient method for improving the solubility and hence the bioavailability of poorly soluble drugs likes ACF. The proper coating of the formula helps to achieve a convenient release of the drug.
Factors que afecten l’èxit d’expansió de poblacions d’aus en medis transformats per l’home: l’arpella "Circus aeruginosus" a la Vall de l’Ebre

When modelling spatial distribution of species, it is often assumed that species are in equilibrium with the environment. However, this assumption can be violated under limited dispersal conditions. We analyzed occurrence data of an expanding population of the marsh harrier in peninsular Spain, during the breeding and wintering seasons, to address whether its current distribution can simply be explained in terms of environmental variables (climate and habitat) or whether non-environmental spatial constraints need to be considered. We did so by implementation of species distribution models constructed with environmental variables alone and with the inclusion of eigenvectors (to capture non-environmental spatial constraints). Our results revealed strong responses to environmental variability during both the breeding and the wintering seasons. Both habitat and climatic factors appeared to be important range controls. However, by including eigenvectors in models, we found evidence that marsh harrier distribution was spatially constrained beyond environmental variables during the breeding season, but not during its wintering. 1 Dept. Biologia Animal. Facultat de Biologia Universitat de Barcelona. Barcelona, Spain 2 Centre Tecnologic Forestal de Catalunya. Solsona, Spain 3 Depto. Sistemas Fisicos, Quimicos y Naturales. Universidad Pablo de Olavide. Sevilla, Spain Chapter VI
Factor structure of the de Jong Gierveld Loneliness Scale: An ESEM approach

This research aims to examine the reliability, convergent validity, and measurement invariance of the de Jong Gierveld Loneliness Scale (DJGLS). The study focused especially on the examination of the model-data fit of various competitive factor structures in a young adult sample. The results demonstrate that the bifactor-ESEM model shows a high model-data fit according to CFI and RMSEA. In this case, it has been determined that the cross-loadings defined by the bifactor-ESEM model have an increasing effect on the model-data fit. Also with the bifactor-ESEM model, DJGLS has one highly reliable general factor and two irrelevant subfactors. Metric measurement invariance according to gender was provided. DJGLS scores were correlated moderately and highly, and were statistically significant with external variables. Finally, it can be said that DJGLS is a measurement tool with construct and convergent validity and reliability in the young adult sample. In addition, DJGLS is essentially a uni-dimensional scale and shows the best model-data fit in the bifactor-ESEM model.
Vulnerability Detection in Computer Networks using Virtual Reality Technology

This paper challenges the time-related challenges inherent in conventional network security detection methodologies. It is achieved by incorporating virtual reality technology into the domain of computer network security detection. The research methodology employs optimization calculations to extract attributes that characterize network security vulnerabilities. Concurrently, the weighting of diverse vulnerability attributes is adjusted using a web crawler, a comprehensive list of injection points, and meticulous analyses of the attacks’ genetic characteristics. This collective approach facilitates the exploration of automated network security vulnerability detection within a virtual reality framework. The study’s empirical results demonstrate that the detection method proposed within this investigation exhibits a notably reduced delay of 75.33 milliseconds. The respective delays observed in the two conventional methods stand at 290.11 milliseconds and 337.30 milliseconds. The substantial decrease in detection delay validates the effectiveness and efficiency of the devised automated network vulnerability detection approach grounded in virtual reality technology.
Analysis, Prediction and Classification of Skin Cancer using Artificial Intelligence - A Brief Study and Review

World Health Organization (WHO) records that skin cancer has vigorously affected people in recent decades. Worldwide, many people are affected by skin cancer, and its affected count will increase yearly. Hence, skin cancer has become a threatening disease. Skin cancer prediction at an earlier time is becoming the higher priority and most challenging task worldwide. A computer-based diagnosis is needed to perform the automatic prognosis of skin cancer. It assists dermatologists in many ways, including the prediction of skin cancer at the earlier stages, easy to diagnose and effective. Nowadays, artificial intelligence based machine learning approaches have been implemented for an early prediction of cancer in the skin through medical images. This paper is focused on a detailed, comprehensive review of skin cancer analysis, forecast, and algorithmic-based procedures for classifying skin diseases. Moreover, this review paper focused on various stages of algorithm approaches for skin tumor detection like pre-processing data, segmenting data, feature selection, and disease classifier. This detailed review of neoplasm diseases like cancer on the skin is done based on machine and deep learning algorithms to help further research.
Generating Natural Language isiZulu Text from Mathematical Expressions

There is a lack of mathematical verbalisation, specifically in isiZulu, for the visually impaired. The problem can be solved by generating natural language descriptions of math expressions, so they can be used as input for existing text-to-speech systems. Current realisation tools do not apply to isiZulu because of the language’s agglutinative morphology and lack of resources. We discuss the approaches to natural language generation and see that the most appropriate realisation method for isiZulu is grammar-infused templates. Previous work done in developing a controlled natural language of isiZulu shows it can be accomplished through verbalisation patterns and a context-free grammar for the verb. Previous work in verbalising mathematical have used template and grammar-based approaches and translated the ambiguous LaTeX expressions into a less ambiguous MathML format. However, there is still room for improvement for LaTeX to MathML translation.
Household Leverage and the Recession

We evaluate and partially challenge the ‘household leverage’ view of the Great Recession. In the data, employment and consumption declined more in states where household debt declined more. We study a model where liquidity constraints amplify the response of consumption and employment to changes in debt. We estimate the model with Bayesian methods combining state and aggregate data. Changes in household credit limits explain 40% of the differential rise and fall of employment across states, but a small fraction of the aggregate employment decline in 2008-2010. Nevertheless, since household deleveraging was gradual, credit shocks greatly slowed the recovery.