Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Recognition and Machine Learning-based Student Attendance Monitoring System

AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recognition, the system makes it easier for teachers to keep track of students' attendance while also giving administrators useful information. The technology automatically logs student attendance, doing away with the necessity for manual human attendance taking. Each student's attendance rate is determined, and the attendance analysis is made available. Administrators can more easily identify students with strong or poor attendance records thanks to the visualization module of the system, which presents attendance data as a graphical representation. Administrators can use this function to ensure that students attend classes on a regular basis. Additionally, the system notifies students and their parents through email when a particular level of attendance is not met, encouraging students to improve their attendance by taking appropriate action and fostering accountability. The AI-based student monitoring system is a useful tool for teachers and students. It helps to raise academic attainment by promoting an accountability and attendance culture in organizations and institutions. The system also provides administrators with useful data and minimizes the effort for educators.
Project Based Learning (PBL) in English Drama Course: The Process and Its Impact on Students’ Speaking Skill

Project Based Learning (PBL) is a type of instruction whereby students spend a significant amount of time investigating and responding to a complex issue, problem, or challenge in order to learn knowledge and skills in the form of project. The objectives of this research were revealed the running process of PBL in English Drama course and investigated its impact on students’ speaking skill. This research was mixed-methods research, a case study at English Education Department of Universitas Borneo Tarakan. One lecturer and one class of students who attended the English Drama course served as the research subjects. This research collected quantitative and qualitative data such as documents, observation, and test. This data was analyzed quantitatively and qualitatively through coding and statistical analysis paired t-test. The results showed that the running process including Investigation, Planning, Implementation, and Reflection. The paired t-test result showed that Sig. 0.000 < 0.05, PBL implementation significantly enhances students' speaking abilities in English drama performance.
PREVENTION OF LONGITUDINAL CRACKING IN SURFACES AND FILLS. FINAL REPORT

Longitudinal cracking was discovered in flexible pavement constructed over high plasticity index (high-PI) clay embankments retained by reinforced soil retaining walls (RSRWs). The cracks were noted to occur at approximately the location of the interface between the clay embankment and the cohesionless backfill of the RSRW. Coring of the crack revealed the crack to be wider at the bottom than at the top suggesting that the tensile force causing the cracking was being applied at the bottom of the pavement structure. All Texas State Department of Highways and Public Transportation (SDHPT) Districts were queried to learn the extent of the longitudinal cracking problem. Only District 16, headquartered at Corpus Christi, was found to be experiencing the problem. Using soil from the same source as was used to construct embankments and RSRW backfill, four large laboratory models were constructed of the field situation. Each of the tests demonstrated that a vertical separation would occur between the clay embankment and sand backfill as a result of lateral shrinking of the high-PI clay over time due to climate. The third test showed that the vertical crack would propagate upwards through the flexible base material. The fourth test produced a crack through the flexible base but failed to produce a crack through the overlying HMAC because the HMAC adhered to the walls of the test box and separated from the caliche base. Lateral stresses imposed on the flexible base by the laterally shrinking clay embankment were found to be approximately 366 psi in the laboratory tests, well exceeding the tensile strength of normal HMAC. Thus, it was concluded that the longitudinal cracks in the District 16 pavements were the result of the clay embankments laterally drying beneath the installed pavement. Field instrumentation installed during the study was inconclusive with respect to measuring soil moisture condition changes occurring beneath the pavement because the monitoring sites were not paved until nearly the end of the study. However, the instruments showed that a change in soil suction pressures of up to 25 atm of pressure had occurred during the 1-year monitoring period as a result of changes in climate only.
Numerical investigation of large strains of incompressible solids

The work is concerned with development of algorithm for numerical investigation of stress-strain state of elastic solids, taking into account the incompressibility of materials. The kinematics of continua is described in terms of the deformation gradient tensor and left Cauchy–Green tensor. The stress state is described by the Cauchy stress tensor. Constitutive equations are received using a potential energy of deformation. An incremental method is used to solve the nonlinear problems. The penalty method is applied for a modelling of the incompressibility of the material. Numerical realization is based on a finite element method.
Congestion-aware Optimal Data rate Selection for Efficient Routing in Wireless Rechargeable Sensor Network

Wireless Rechargeable Sensor Nodes (WRSNs) are widely used to sense, collect, and process data or signals transmitted among people, the environment, and computer systems. However, it faces severe threats such as congestion, resource deprivation, security risk, limited communication speed, high cost, limited bandwidth, and so on. To address some of these issues and to improve the QoS, this paper proposes an Adaptive Rate Determined Congestion Management (ARDCM) approach. It adopts a feedback-based system in which the incoming data rate is managed to increase when congestion is low and decrease when congestion is high. It guarantees congestion free data flow between upstream and downstream in data communication by controlling the data rate and buffer occupancy. The proposed approach is simulated on NS2 with varying numbers of active nodes in a simulated environment. The proposed model’s performance is evaluated by comparing the Packet Delivery Ratio(PDR), Packet Loss Ratio(PLR), delay, and throughput to the existing algorithms.
Australia’s Ongoing Challenge of Legacy Asbestos in the Built Environment: A Review of Contemporary Asbestos Exposure Risks

Asbestos remains ubiquitous in the Australian built environment. Of the 13 million tonnes of asbestos products installed in earlier decades, an estimated 50% remain in situ today. Because of the extensive past use of asbestos, and the increasing age of these products, the potential for exposure to asbestos fibres in both indoor and outdoor environments remains high, even while the actual asbestos exposure levels are mostly very low. Sources of these exposures include disturbance of in situ asbestos-containing materials (ACMs), for example during renovations or following disaster events such as fires, cyclones and floods. Our understanding of the risk of asbestos-related disease arising from long-term low-level or background exposure, however, is poor. We provide the most up-to-date review of asbestos exposure risks currently affecting different groups of the Australian population and the settings in which this can manifest. From this, a need for low-level asbestos monitoring has emerged, and further research is required to address whether current exposure monitoring approaches are adequate. In addition, we make the case for proactive asbestos removal to reduce the risk of ongoing asbestos contamination and exposure due to deteriorating, disturbed or damaged ACMs, while improving long-term building sustainability, as well as the sustainability of limited resources.
Comments on the Article: "Food Environment Research in Low- and Middle-Income Countries: A Systematic Scoping Review".

past and engulfed both the scientific community and lay press in controversy. Within this context the pressing question becomes what is currently known and on this basis how best to formulate dietary guidance to reduce CVD risk given the contemporary food supply, physical activity patterns, and available pharmacotherapy. Inextricably linked to this question and others that will follow is an acknowledgment that the conclusions drawn should not be written in stone and must be reassessed on a regular basis. Concurrent with this tack is how best to minimize risks associated with adherence to contemporary dietary guidance. Heileson concludes that the emphasis should be on “. [increasing] ω-3 intake; reducing concentrated sources of ω-6 such as seed oils; and switching to unprocessed sources of lean meats. ” I would contend the first and third recommendations are consistent with the preponderance of available data. Reducing concentrated sources of ω-6 such as seed oils is not and risks prompting the re-emergence of the lowfat diet craze which led to a dramatic increase in refined carbohydrate intake in the form of such items as fat-free cookies, cakes, ice creams, and, worse, salad dressings, and the adverse metabolic consequences thereof (10). Hence, in contrast to Heileson’s assertion, as concluded in the AHA advisory, “Taking into consideration the totality of the scientific evidence, satisfying rigorous criteria for causality, we conclude strongly that lowering intake of saturated fat and replacing it with unsaturated fats, especially polyunsaturated fats, will lower the incidence of CVD” is the best advice we can give, at this time.
Relación entre las estrategias de afrontamiento, ansiedad, depresión y autoestima, en un grupo de adultos con diagnóstico de cáncer

To carry out this project a descriptive correlational design was used in order to identify the existent relationship between coping strategies, self-esteem, anxiety levels and depression in a group of 17 adult patients diagnosed with cancer. The Rosenberg Self-Esteem Scale, The Hospital Anxiety and Depression Scale (had) and the modified Coping Strategies Scale (eec-m) were applied for this purpose. This research showed the following results: A correlation between anxiety and self-esteem levels, anxiety levels and coping strategies for problem solving, positive reappraisal and aggressive reaction. Likewise, a meaningful correlation between depression levels and coping strategies for problem solving, negation, aggressive reaction and the expression of the coping difficulty were observed.
Agricultural Crop Recommendation System

The Agricultural Crop Recommendation System using machine learning is a software application that utilizes artificial intelligence algorithms to provide recommendations to farmers on the best crops to plant based on their specific farming conditions. The system collects and analyzes various data sources, such as soil quality, climate, and historical crop yields, to make personalized crop recommendations. The system is easy to use and can be accessed through a web-based platform or mobile app. The implementation of this system can help farmers to optimize their crop production, reduce costs, and increase efficiency in agricultural activities.The implementation of a machine learning-based Agricultural Crop Recommendation System holds immense potential to transform the agricultural sector. By leveraging data-driven insights, this system can offer farmers recommendations that are both sustainable and profitable, enabling them to make informed decisions and enhance their overall productivity.
An Edge-Guided Weighted Image Interpolation Algorithm

For the problem of poor performance of linear image interpolation and high complexity of nonlinear image interpolation, an edge-guided weighted image interpolation algorithm is proposed. Firstly, the edge feature of the low-resolution image is detected by the Sobel operator, and the edge strength and direction of the high-resolution image are calculated by the bilinear interpolation algorithm. Then, the warped distance of the reference pixels and weighted Lanczos kernel are calculated by the edge features of the high-resolution image. Finally, the reference pixels are weighted to obtain the resulting high-resolution image. The simulation results show that the proposed interpolation algorithm is more accurate than traditional interpolation algorithms, and the computing time of the upscaling by a factor of 2 for the $\mathbf{256}\times \mathbf{256}$ image with the proposed algorithm is less than 50ms, which is attractive for real-time communication and image applications.
Investigations on the Design and Simulation of Smart Irrigation Robot for Nurseries

The irrigation systems in nurseries can be the cause of various water supply errors- ranging from lack of sufficient water supply to over-watering and imbalance in the water pH levels. Based on the statistics recorded by the department of agriculture and farmers welfare, we have conducted we concluded that the water supply management and plant life maintenance are the major problems. Considering all these real-time errors, we have designed a smart irrigation robot functioning on the inputs from a group of soil moisture sensors which helps eliminate the monotonous task of monitoring and keeping records of the change in soil moisture.
Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis

The publication is an adaptation and translation of Qualitative Comparative Analysis (QCA) und fuzzy sets: ein Lehrbuch fur Anwender und jene, die es werden wollen, Leverkusen/Verlag Barbara Budrich, 2007. ISBN: 9783866490680. (http://hdl.handle.net/1814/24076)
Minimal Features based Non Invasive Cholesterol Computation using Machine Learning

Cholesterol level computation has always been an invasive and time-consuming process. This significantly hinders quick detection and delays treatment. Good and bad cholesterol exist in our body, but the latter leads to plaque formation and blocked arteries. These uncontrolled levels of bad cholesterol can cause various cardiovascular and other diseases. Conventional cholesterol calculation requires a clinical laboratory setup where specific analytical procedures need to be conducted on blood drawn invasively. As a result, a user-friendly, non-invasive method of monitoring cholesterol has become imperative. Here, the use of machine learning techniques is explored for the computation of cholesterol from the demographic and vital features of 573 subjects with ages ranging from 18 to 70. Five regressor models were implemented and compared to explore any correlation of the non-invasive features with each factor in the cholesterol lipid profile. Promising results are observed with support vector regressors and extreme gradient boosting regressors. Hence, this study can pave the way to designing and deploying a simple, reliable, non-invasive cholesterol monitoring device.
Comparison of various machine learning algorithms for estimating generalized propensity score

ABSTRACT In this paper, we conducted a simulation study to evaluate the performance of four algorithms: multinomial logistic regression (MLR), bagging (BAG), random forest (RF), and gradient boosting (GB), for estimating generalized propensity score (GPS). Similar to the propensity score (PS), the ultimate goal of using GPS is to estimate unbiased average treatment effects (ATEs) in observational studies. We used the GPS estimates computed from these four algorithms with the generalized doubly robust (GDR) estimator to estimate ATEs in observational studies. We evaluated these ATE estimates in terms of bias and mean squared error (MSE). Simulation results show that overall, the GB algorithm produced the best ATE estimates based on these evaluation criteria. Thus, we recommend using the GB algorithm for estimating GPS in practice.
Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Industrial IoT in MEC Federation System

The rapid growth of the Internet of Things (IoT) has resulted in the development of intelligent industrial systems known as Industrial IoT (IIoT). These systems integrate smart devices, sensors, cameras, and 5G technologies to enable automated data gathering and analysis boost production efficiency and overcome scalability issues. However, IoT devices have limited computer power, memory, and battery capacities. To address these challenges, mobile edge computing (MEC) has been introduced to IIoT systems to reduce the computational burden on the devices. While the dedicated MEC paradigm limits optimal resource utilization and load balancing, the MEC federation can potentially overcome these drawbacks. However, previous studies have relied on idealized assumptions when developing optimal models, raising concerns about their practical applicability. In this study, we investigated the joint decision offloading and resource allocation problem for MEC federation in the IIoT. Specifically, an optimization model was constructed based on all real-world factors influencing system performance. To minimize the total energy delay cost, the original problem was transformed into a Markov decision process. Considering task generation dynamics and continuity, we addressed the Markov decision process using a deep reinforcement learning method. We propose a deep deterministic policy gradient algorithm with prioritized experience replay (DDPG-PER)-based resource allocation that can handle high-dimensional continuity of action and state spaces. The simulation results indicate that the proposed approach effectively minimizes the energy-delay costs associated with tasks.
Two mathematical models for H1N1 influenza with antiviral treatment

In this paper, we establish two mathematical models to study H1N1 influenza transmission dynamics. One model is for the case of concurrent treatment, in which we assume that untreated individuals are detected at random and moved to the treatment compartment at any time of their infected phase, and the other model deals with the case of early diagnosis, in which we assume that with some probability $\sigma\in[0,1]$, individuals are diagnosed at the moment of infection and immediately moved to the treatment compartment. Both models are analyzed including the derivation of the basic and control reproduction numbers, the proof of global stability of disease-free equilibrium points, and demonstrating how the acquired reproduction number can be used to explain the adverse effects associated with antiviral treatment. This effect is also explained using a quantity termed the total control reproduction number. We also compare the differences between the two models in evaluating outcomes of influenza. Numerical simulations are conducted to verify the theoretical analysis results.
An Analysis of Android Malware and IoT Attack Detection with Machine Learning

The term Internet of Things(IoT) describes a network of real-world items, gadgets, structures, and other things that are equipped with communication and sensors for gathering and exchanging data online. The likelihood of Android malware attacks on IoT devices has risen due to their widespread use. Regular security precautions might not be practical for these devices because they frequently have limited resources. The detection of malware attacks on IoT environments has found hope in ML approaches. In this paper, some machine learning(ML) approaches have been utilized to detect IoT Android malware threats. This method uses a collection of Android malware samples and good apps to build an ML model. Using the Android Malware dataset, many ML techniques, including Naive Bayes (NB), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF), are used to detect malware in IoT. The accuracy of the DT model is 95%, which is the highest accuracy rate, while that of the NB, KNN, and RF models have accuracy rates of 84%, 89%, and 92%, respectively.
Enhancing Anti-cancer Activity: Green Synthesis and Cytotoxicity Evaluation of Turmeric-Gold Nanocapsules on A549 Lung Cancer Cells

Background Lung cancer remains a major global health concern, with a notable increase in new cases in recent years. This study aims to investigate the cytotoxic effects of polymeric turmeric-gold nanocapsules on A549 human lung cancer cells, utilizing green-synthesized gold nanoparticles from Curcuma longa L. and ethyl cellulose-based nanocapsules. Methods Gold nanoparticles were synthesized using the aqueous root extract of Curcuma longa L. , and the resulting nanoparticles were characterized using UV-Vis, fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and energy dispersive x-ray (EDX) techniques. Subsequently, polymeric nanocapsules of turmeric with encapsulated gold nanoparticles were prepared. The cytotoxicity of these nanocapsules was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay on both A549 lung cancer cell lines and normal cell lines. Results The turmeric-gold nanocapsules exhibited a half maximal inhibitory concentration (IC50) value of 40 μg/ml, while the gold nanoparticles alone showed an IC50 value of 60 μg/ml when tested on A549 cells. Furthermore, apoptosis was observed in A549 cells treated with turmeric-gold nanocapsules. The combination of gold nanoparticles and turmeric polymer (gold turmeric nanocapsules) demonstrated a more potent anti-cancer effect on the lung cancer cell line, with an IC50 value of 40 μg/ml compared to green-synthesized gold nanoparticles (IC50 of 60 μg/ml). Conclusion The utilization of polymeric nanocapsules of turmeric, with green-synthesized gold nanoparticles, presents a promising solution to overcome the limited water solubility of turmeric. The results suggest that the combination of gold nanoparticles and turmeric enhances the cytotoxic effects on A549 human lung cancer cells. These findings contribute to the potential application of turmeric-gold nanocapsules as a novel therapeutic approach in lung cancer research.
Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 1: Epidemiology, Early Development, and Cohorts From Around the World

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation, in partnership with the Hospital for Special Surgery, convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations and clinical research gaps gleaned from 5 presentations given in the “how hip osteoarthritis begins” session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City.
Three-dimensional dynamic fracture in a transversely isotropic thermoelastic solid: Framework for study

Abstract A semi-infinite plane crack is at rest in an unbounded, transversely isotropic, thermoelastic solid. Point forces are applied to the crack faces, and translated with constant, subcritical speed at right angles to the initial crack edge. Fracture occurs, and a dynamic steady state is achieved: The crack edge is no longer rectilinear, but extension and translation speeds are identical. The crack plane contains the axis of material symmetry lies in that plane, and the initial crack edge is not aligned with that axis. An analytical three-dimensional solution is obtained, and a criterion for fracture based on dynamic energy release rate imposed, with kinetic energy included. A nonlinear differential equation for crack edge location and constraint equations result, and together form a framework for study of crack and contact zone geometry.