Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating Tumor Cells in Oral Cancer

Till now, oral squamous cell carcinoma (OSCC) is graded as well-differentiated, moderately-differentiated, poorly-differentiated, and undifferentiated. However, this grading does not have a prediction of the prognosis of the patient. Also, prognosis impacts lymph node metastases, surgical margins, and vascular invasions (neural invasion, muscular invasion, salivary gland invasion). The prognosis of lymph node metastases is significant, which affects the survival of the patients which is 50%. So, a dependable blood marker is needed for prognosis in OSCC patients with loco-regional and distant recurrence. Some factors can be assisted only after surgery and invasive techniques to check the prognosis of the disease. Despite the ease of examining the oral cavity, there is no practical approach for non-invasive screening and detecting cancer. As it is abrupt to use such invasive procedures from time to time, there is a need for nonsurgical and reliable techniques to assess the progression of tumors. Also, frozen sections are helpful during the intraoperative procedure to evaluate the lymph node metastases. An increase in the number of tumor cells through blood is a significant event in disease metastases toward the peripheral blood. Oral health impact assessment instruments could aid in determining the quality of life, and their usage in the initial stages of oral carcinoma could help physicians choose the best treatment option for enhancing the quality of life.
Design and Performance Optimization of Novel Flux-Concentrating Spoke-Type IPM Machine

This paper introduces a novel spoke-type interior permanent magnet (IPM) machine featuring a flux-concentrating rotor. Its primary objectives are to increase the utilization of PM material and decrease leakage flux. The evolution of the proposed flux-concentrating PM from the spoke-type PM is presented, which demonstrates a higher PM utilization ratio on torque characteristics. To achieve a high-performance design, a multi-objective genetic algorithm (MOGA) is employed for design optimization. This optimization process focuses on maximizing PM material utilization while minimizing torque ripple. The optimized machine configuration is designed to reduce copper loss and enhance efficiency by reducing current density while maintaining a similar average torque to that of the spoke-type machine. The paper utilizes finite element analysis (FEA) to compare the electromagnetic characteristics of the two machines. The results show that the proposed structure improves efficiency by 1.2% and reduces PM volume by approximately 4% in comparison to the spoke-type machine.
Constraints in PK-8 STEM Classrooms: A Mixed Methods Study

This study explores the restrictions and barriers to STEM education, which has grown in popularity due to its focus on real-world problem-solving and educating students for 21st-century issues. Financial resources, curriculum standards, time, shortage of qualified teachers, lack of professional development opportunities, low student motivation, weak connections with individual learners, poor facilities, and lack of hands-on experiences are all constraints, according to studies. STEM classrooms face time constraints for instruction, high-stakes assessments, experiments, and projects. The study examines how these constraints affect instruction and if time is a grade-level constraint. This study also emphasizes the necessity for student-centered training that integrates grade-level knowledge from other disciplines, notably mathematics, into STEM activities. The study collected and analyzed qualitative and quantitative data from US STEM teachers (n=188) who taught students in grades PK-8. The survey included numerical, multiple-choice, and open-ended items. The quantitative investigation examined grade level and teaching time, while the qualitative analysis identified descriptive themes for perceived problems using a constant comparative method. To check for convergence, discrepancies, and data source links, the findings were pooled during interpretation. Three research question-based sections present the findings. A content analysis of teacher responses identifies the top STEM instruction hurdles in the first segment. Time was the biggest barrier, followed by budget and materials. Middle school teachers spend the most time teaching STEM in the second portion. The final component examines STEM teachers' time barriers and instructional time. Teachers who recognized time as a restriction had no statistically significant change in teaching time. These findings emphasize STEM teachers' challenges and potentially inform STEM classroom support. Convergent mixed-methods research identified PK-8 STEM instructors' issues and restrictions. Time, financing, and materials were the biggest STEM education impediments. Middle school teachers said financing was their biggest issue, while primary teachers said time was. The study also showed grade-level disparities in instructional time, suggesting varied STEM priorities. Self-reported data and sample size limit the study. The report advises that politicians and administrators address time as a major barrier to effective STEM instruction among K-12 teachers, particularly primary grade teachers. Research could examine teaching time by grade level and region.
Interpersonal and Intrapersonal Factors Associated with Academic Belonging of Engineering Graduate Students

Academic belonging is an important construct essential for student success in engineering. It involves a sense of connectedness, acceptance, and identification within the learners' academic environment. Students may disengage and ultimately drop out of their program of study where the sense of academic belonging is lacking. This work in progress bridges the gap by examining the correlates of academic belonging in order to understand the processes by which graduate engineering students develop a sense of belonging within their chosen programs. 227 graduate students from six universities in the United States completed the Postsecondary Student Engagement (PosSE) survey. Using Pearson Correlation, we evaluated the relationships between major valuing, achievement striving, major satisfaction, peer interaction, positive faculty relations, passion, perseverance, and academic belonging. The results show that major satisfaction has a positive strong relationship with academic belonging, while major valuing and positive faculty relations have a significant positive medium relationship with academic belonging. It was noted that achievement striving, and peer interaction has small positive but significant relationships with academic belonging. The theoretical and practical implications of this study will be discussed.
Analysing the environment as a variable that affects prices: Willingness-to-pay and consumer behaviour in the hotel sector

Objectives | Respect for the environment could be a differentiating attribute over competitors and will lead to competitive advantages (Ladhari, 2009). Some authors highlight the fact that, providing the environmental setting is valued by clients, it becomes a differentiating output. Other authors consider environmental compliance as an element for improving the quality of the product and an attribute of the service provided by firms that has economic value for the consumer. The environmental sustainability of the activities carried out by firms is rarely used for explaining factors that have an impact on room pricing. In fact, only a few papers include an environmental attribute for justifying room pricing by using a hedonic function (Kuminoff, Parmeter & Pope, 2010; Kuminoff, Zhang & Rudi, 2010; García-Pozo, Sánchez-Ollero & Marchante-Mera, 2013; Sánchez-Ollero, García-Pozo & Marchante-Mera, 2014). Currently, environmental responsibility is having an effect on the behaviour of consumers when making purchasing decisions, influencing their reasons for travelling and the final choice of destination and service provider. In this work we analyze whether environmental variables have an influence in the willingness-to-pay (WTP) of the tourist.
Load-aware nonlinearity estimation for elastic optical network resource optimization and management

Elastic optical networks (EONs) have emerged as a promising technology to accommodate the high-capacity and dynamic bandwidth demands of next-generation wireless networks. However, similar to the traditional wavelength division multiplexing optical networks, there exist significant challenges to manage nonlinearity effects in EONs. In this paper, we first analyze state-of-the-art nonlinearity estimation solutions and propose a novel load-aware nonlinearity estimation method. We further present a resource allocation algorithm using the proposed nonlinearity estimation scheme. In case the new embedded lightpath brings additional nonlinearity blocking the existing requests, we propose a mixed integer linear programming model and two heuristic algorithms using the proposed nonlinearity model as the service reconfiguration scheme for efficient resource allocation in EONs. The objective of the solution is to minimize the spectrum resource usage while satisfying the bandwidth demands of the connection and ensuring the quality of transmission. The proposed solutions are evaluated using an extensive simulation with off-line traffic requests and incrementally loaded requests against the benchmark solutions for two types of traffic profiles in a small network with six nodes and nine links and the National Science Foundation network. The results presented in this paper validate the benefits of the proposed nonlinearity estimation model and the corresponding algorithms to minimize the number of allocated frequency slots and service request blocking ratio while improving the overall network capacity.
Quantifying Salient Concepts Discussed in Social Media Content: An Analysis of Tweets Posted by ISIS Fangirls

In this paper, we measure the extent to which we can accurately measure the salience of topics/concepts that might be of interest to an analyst tasked with analyzing the content posted on social media platforms. We also evaluate whether concepts like positive and negative sentiment can be meaningfully extracted from Social Media content. As a test case, we examined Twitter content generated by female users who are sympathetic to the Islamic State in Iraq and Syria (ISIS). Although the results were based on a small sample of users, we demonstrate that ISIS fangirls differ in the content of their tweets from other, non-radicalized, teenage girls, and that automated text analysis techniques can detect the differences. The basic technique proposed here is a promising step in devising techniques for quantifying the salient topics being discussed on social media platforms, and should be developed further to create more fine-grained exanimations of such content.
Nimodipine systemic exposure and outcomes following aneurysmal subarachnoid hemorrhage: a pilot prospective observational study (ASH-1 study)

Background Nimodipine improves outcomes following aneurysmal subarachnoid hemorrhage (aSAH). Guidelines recommend that all patients should receive a fixed-dose nimodipine for 21 days. However, studies reported variability of nimodipine concentrations in aSAH. It is not clear if reduced systemic exposure contributes to worsening outcomes. The aim of this study was to compare nimodipine systemic exposure in those who experienced poor outcomes to those who experienced favorable outcomes. Methods This was a pilot prospective observational study in 30 adult patients admitted to the University of Alberta Hospital with aSAH. Data were collected from the electronic health records following enrollment. Blood samples were collected around one nimodipine 60 mg dose at a steady state, and nimodipine [total, (+)-R and (−)-S enantiomers] plasma concentrations were determined. The poor outcome was defined as a modified Rankin Scale (mRS) score at 90 days of 3-6, while the favorable outcome was an mRS score of 0-2. The correlation between nimodipine concentrations and percent changes in mean arterial pressure (MAP) before and after nimodipine administration was also determined. Furthermore, covariates potentially associated with nimodipine exposure were explored. Results In total, 20 (69%) participants had favorable outcomes and 9 (31%) had poor outcomes. Following the exclusion of those with delayed presentation (>96 h from aSAH onset), among those presented with the World Federation of Neurological Surgeons (WFNS) grade 3–5, nimodipine median (interquartile range) area under the concentration time curve (AUC0-3h) in those with favorable outcomes were 4-fold higher than in those with poor outcomes [136 (52–192) vs. 33 (23–39) ng.h/mL, respectively, value of p = 0.2]. On the other hand, among those presented with WFNS grade 1–2, nimodipine AUC0-3h in those with favorable outcomes were significantly lower than in those with poor outcomes [30 (28–36) vs. 172 (117–308) ng.h/mL, respectively, value of p = 0.03)]. (+)-R-nimodipine AUC0-3h in those who did not develop vasospasm were 4-fold significantly higher than those who had vasospasm (value of p = 0.047). (−)-S-nimodipine was significantly correlated with percentage MAP reduction. Similar results were obtained when the whole cohort was analyzed. Conclusion The study was the first to investigate the potential association between nimodipine exposure following oral dosing and outcomes. In addition, it suggests differential effects of nimodipine enantiomers, shedding light on the potential utility of nimodipine enantiomers. Larger studies are needed.
Determination of the inhibition effect of hesperetin and its derivatives on Candida glabrata by molecular docking method

In the study, it was aimed to develop new candidate inhibitor molecules by targeting the AWP1 protein structure of Candida glabrata organism. Hesperetin molecule was taken as a reference and different substituted groups were attached to the determined ends of the molecule to increase the inhibition potential on the protein structure. A total of 100 molecules were designed and after conformer distribution using the Molecular Mechanics/MMFF method for each designed molecule, the area, volume, weight, energy, EHOMO, ELUMO, polarizability, dipole moment, log P values of these molecules were calculated using the Semi Empirical/PM6 method. Molecular docking studies of the optimized molecules were carried out through the Autodock Vina program. After the docking studies, the interactions of the designed molecules with the active site amino acids of the protein structure were analyzed by BIOVIA Discovery Studio Client software in case of possible mutation. As a result of the analysis, five molecules with higher binding energies than other designed molecules and currently used antifungal drugs were recommended.
The Effect of Cross-Sex Fecal Microbiota Transplantation on Metabolism and Hormonal Status in Adult Rats

Increasing evidence of sexual dimorphism in the pathophysiology of metabolic complications caused by sex steroids is under investigation. The gut microbiota represents a complex microbial ecosystem involved in energy metabolism, immune response, nutrition acquisition, and the health of host organisms. Gender-specific differences in composition are present between females and males. The purpose of this study was to use cross-sex fecal microbiota transplantation (FMT) for the detection of sex-dependent metabolic, hormonal, and gut microbiota changes in female and male recipients. Healthy non-obese female and male Wistar rats were divided into donor, same-sex, and cross-sex recipient groups. After a 30-day period of FMT administration, biochemical markers (glucose and lipid metabolism) and sex hormones were measured, and the gut microbiota was analyzed. The cross-sex male recipients displayed a significantly lower testosterone concentration compared to the males that received same-sex FMT. Sex-dependent changes caused by cross-sex FMT were detected, while several bacterial taxa correlated with plasma testosterone levels. This study represents the first to study the effect of cross-sex changes in the gut microbiome concerning metabolic and hormonal changes/status in adult non-obese Wistar rats. Herein, we present cross-sex FMT as a potential tool to modify sex-specific pathologies.
Learning Quadrupedal High-Speed Running on Uneven Terrain

Reinforcement learning (RL)-based controllers have been applied to the high-speed movement of quadruped robots on uneven terrains. The external disturbances increase as the robot moves faster on such terrains, affecting the stability of the robot. Many existing RL-based methods adopt higher control frequencies to respond quickly to the disturbance, which requires a significant computational cost. We propose a control framework that consists of an RL-based control policy updating at a low frequency and a model-based joint controller updating at a high frequency. Unlike previous methods, our policy outputs the control law for each joint, executed by the corresponding high-frequency joint controller to reduce the impact of external disturbances on the robot. We evaluated our method on various simulated terrains with height differences of up to 6 cm. We achieved a running motion of 1.8 m/s in the simulation using the Unitree A1 quadruped. The RL-based control policy updates at 50 Hz with a latency of 20 ms, while the model-based joint controller runs at 1000 Hz. The experimental results show that the proposed framework can overcome the latency caused by low-frequency updates, making it applicable for real-robot deployment.
A County Level Cross-Over Approach to Examining Weather Prior to Influenza Surges: 2003 Pennsylvania H3N2

This paper utilizes a county-level cross over approach to examine weather conditions during a hazard period just before an influenza spike and a control period just prior to the hazard period where most other conditions – population immunity, circulation of viral strains, latitude, travel were the same or similar. For the seven counties with a December spike, temperatures and dew points just above freezing were more likely to be seen in this hazard period (days 1 through 3) then in the control period (days 4 through 8). Two counties with a November surge were warmer in the control period with temperatures as high as an average of 12.6 degrees C, nevertheless the temperatures and dew points seen during the hazard period in the November spike counties are very similar to the December spike counties Other recent studies also support the plausibility of optimal influenza transmission at temperatures just above freezing.
New Modern Approach to Predict Users’ Sentiment Using CNN and BLSTM

In Today’s world social network play a vital role and provides relevant information on user opinion. This paper presents emotional health monitoring system to detect stress and the user mood. Depending on results the system will send happy, calm, relaxing or motivational messages to users with phycological disturbance. It also sends warning messages to authorized persons in case a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long-term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorithm
Leaning Assisted Thermal Dynamic Modeling of Air-Conditioned Building for Predictive Energy Management

Accurate thermal dynamic model is the cornerstone for advanced temperature regulations and energy saving of modern buildings. This article deals with the multi-step prediction of the building temperature that is influenced by the residential air-conditioning systems. A smart building energy management system is first developed in a networked environment, which integrates remote climate controllers and sensors to collect real-time data related to air conditioning (AC) performance. We propose a data-driven model based on the Long-short term memory (LSTM) network to accurately capture the intertemporal temperature changes of the room under varying external conditions. The LSTM models employ piecewise affine activation functions that are beneficial for predictive management. The algorithm and system implementation leverage the strengths of MATLAB and Python, where we provide a common approach for deploying deep learning models that can be transplanted between the coding languages. The verification is carried out based on data collected from simulation and real environment, which both demonstrates the desired performance of the proposed model.
Generating and Understanding Predictive Models for Student Attrition in Public Higher Education

Student attrition in higher education institutions is specially harmful in public funded institutions, and machine learning models have shown promising results predicting it and providing insightful information on its underlying causes. This work proposes using gradient boosting on decision trees to generate a high quality predictive model on data for almost 7,000 students of the University of Brasília, and applying SHAP to extract meaningful and comprehensive information on student attrition from it. Initial results indicate a strong predictive accuracy with over 90% recall and 85% f-score values and the SHAP analysis highlighted the importance of specific academic features, such as the student's performance on specific courses and all around performance during a period. More interestingly, the analysis provided insights on non academic features like age, commute time and whether the student enrolled through a social or racial quota or not. This may lead to better models and enhancing the discussions and changes on educational policies in the institution such as modifying a curricular structure or pursuing student housing closer to campuses.
Proposed hybrid Model in Online Education

The advancement of technology powering e-learning has brought numerous benefits, including consistency, scalability, cost reduction, and improved usability. However, there are also challenges that need to be addressed. Here are some key considerations for enhancing the technology powering e-learning. Artificial intelligence has revolutionized the field of e-learning and created tremendous opportunities for education Storage, servers, software systems, databases, online management systems, and apps are examples of such resources. This paper aims to forecast students' adaptability to online education using predictive machine learning (ML) models, including Logistic Regression, Decision tree, Random Forest, AdaBoost, ANN. The dataset utilized for this study was sourced from Kaggle and comprised 1205 high school to college students. The research encompasses several stages of data analysis, including data preprocessing, model training, testing, and validation. Multiple performance metrics such as accuracy, specificity, sensitivity, F1 score, and precision were employed to assess the effectiveness of each model. The findings demonstrate that all five models exhibit considerable predictive capabilities. Notably, decision tree and hybrid models outperformed the others, achieving an impressive accuracy rate of 92%. Consequently, it is recommended to utilize these two models, RF and XGB, for predicting students' adaptability levels in online education due to their superior predictive accuracy. Additionally, the Logistic regression, KNN, and AdaBoost, ANN models also yielded respectable performance levels, achieving accuracy rates of 77.48%, 83.77 ,74.17% and 91.06% respectively. In summary, this study underscores the superiority of RF and XGB models in delivering higher prediction accuracy, aligning with similar research endeavours employing ML techniques to forecast adaptability levels.