Miracle-dz/UniFAR-FTUs huggingface dataset:
A new modified logistic growth model for empirical use

Richards model, Gompertz model, and logistic model are widely used to describe growth model of a population. The Richards growth model is a modification of the logistic growth model. In this paper, we present a new modified logistic growth model. The proposed model was derived from a modification of the classical logistic differential equation. From the solution of the differential equation, we present a new mathematical growth model so called a WEP-modified logistic growth model for describing growth function of a living organism. We also extend the proposed model into couple WEP-modified logistic growth model. We further simulated and verified the proposed model by using chicken weight data cited from the literature. It was found that the proposed model gave more accurate predicted results compared to Richard, Gompertz, and logistic model. Therefore the proposed model could be used as an alternative model to describe individual growth.
House Price Prediction System using Machine Learning Algorithms and Visualization

This paper introduces a real estate price prediction system that uses machine learning algorithms. The system is intended to forecast the price of a home grounded on its various features such as location, square footage, number of bedrooms and bathrooms, and other related factors. The system uses a variety of machine learning algorithms, including linear regression, decision tree regression, random forest regression, and artificial neural networks to make accurate predictions. Algorithms are trained on a large dataset of house prices and their associated characteristics to learn the relationships between these factors and the corresponding prices. The system is measured against various performance metrics, such as: B. root-mean-square and r-squared score, to measure its accuracy and effectiveness. The results show that the system can predict house prices with great accuracy, making it a valuable tool for real estate agents, home buyers and sellers.The implementation of this system could lead to better informed and more efficient decisions in the housing market.
Fungal–Algal Association Drives Lichens’ Mutualistic Symbiosis: A Case Study with Trebouxia-Related Lichens

Biotic and abiotic factors influence the formation of fungal–algal pairings in lichen symbiosis. However, the specific determinants of these associations, particularly when distantly related fungi are involved, remain poorly understood. In this study, we investigated the impact of different drivers on the association patterns between taxonomically diverse lichenized fungi and their trebouxioid symbiotic partners. We collected 200 samples from four biomes and identified 41 species of lichenized fungi, associating them with 16 species of trebouxioid green algae, of which 62% were previously unreported. The species identity of both the fungal and algal partners had the most significant effect on the outcome of the symbiosis, compared to abiotic factors like climatic variables and geographic distance. Some obviously specific associations were observed in the temperate zone; however, the nestedness value was lower in arid regions than in cold, polar, and temperate regions according to interaction network analysis. Cophylogenetic analyses revealed congruent phylogenies between trebouxioid algae and associated fungi, indicating a tendency to reject random associations. The main evolutionary mechanisms contributing to the observed phylogenetic patterns were “loss” and “failure to diverge” of the algal partners. This study broadens our knowledge of fungal–algal symbiotic patterns in view of Trebouxia-associated fungi.
The Double-Leucine Motifs Affect Internalization, Stability, and Function of Organic Anion Transporting Polypeptide 1B1

Organic anion transporting polypeptide 1B1 (OATP1B1) is specifically expressed at the basolateral membrane of human hepatocytes and plays important roles in the uptake of various endogenous and exogenous compounds including many drugs. The proper functioning of OATP1B1, hence, is essential for the bioavailability of various therapeutic agents and needs to be tightly regulated. Dileucine-based signals are involved in lysosomal targeting, internalization, and trans-Golgi network to endosome transporting of membrane proteins. In the current study, we analyzed the 3 intracellular and 13 transmembrane dileucine motifs (DLMs) within the sequence of OATP1B1. It was found that the simultaneous replacement of I332 and L333 with alanine resulted in a significantly reduced level of the mature form of OATP1B1. The cell surface expression of I332A/L333A could be partially rescued by MG132, as well as agents that prevent clathrin-dependent protein internalization, suggesting that this dileucine motif may be involved in the endocytosis of OATP1B1. On the other hand, I376/L377 and I642/L643, which are localized at transmembrane helices (TM) 8 and 12, respectively, are involved in the interaction of the transporter with its substrates. I642A/L643A exhibited a significantly decreased protein level compared to that of the wild-type, implying that the motif is important for maintaining the stability of OATP1B1 as well.
Integer Linear Programming Roundoff Method for Arbitrary Planar Phased-Array Scanning

In this paper, an approximate linear formula of the relationship between element errors and beam pointing errors in an arbitrary planar array is derived. Based on the formula, a phase feeding method is proposed. The proposed method, referred to as integer linear programming (ILP) roundoff, significantly reduces beam pointing errors by controlling the state of the digital phase shifters through ILP. Examples are given showing that a <inline-formula> <tex-math notation="LaTeX">$10 \times 10$ </tex-math></inline-formula> element planar array with 5 bit digital phase shifters using this method can reduce beam pointing errors to less than 1% compared with other methods.
Superabsorbent biodegradable CMC membranes loaded with propolis: Peppas-Sahlin kinetics release

Propolis is a resinous product collected by honeybees with a complex chemical composition. Sodium carboxymethylcellulose is a polymer commonly used in wound care. The goal of the present work was to produce and characterize NaCMC membranes loaded with extract of Brazilian brown propolis (CMC-P). Flavonoids and phenolic acids were identified in the propolis extracts, where the main identified substance was kaempferide. The brown propolis extracted was active against S. aureus . The low swelling capacity and high gel fraction of CMC-P would be the consequence of propolis (responsible for a hydrophobic barrier) filling the pores of the membrane. Propolis could be anchoring the NaCMC chains (as observed by FTIR) due to interaction between components, which is corroborated by the CMC-P sample degrading less than the CMC sample (>400ºC). There was non-linear diffusion release kinetics for most phenolic substances of the propolis extract. The CMC-P sample presents potential as a dressing material.
Sub-Harmonic Synchronous Machine Using a Dual Inverter and a Unique Three-Layer Stator Winding

A dual inverter sub-harmonic synchronous machine having a distinct three-layer stator winding topology is presented. The two inverters provide two sets of balanced three-phase currents to the three-layer stator winding. These three-layer windings are arranged in such a way that they can generate both fundamental and sub-harmonic magnetomotive force. Furthermore, the fundamental MMF component of this machine is four times the sub-harmonic MMF component used for the brushless operation. In contrast, the subharmonic magnetomotive force is used for field excitation, which leads to brushless operation. An 8-pole 48-slot 2-inverter 3-layer subharmonic machine is designed to validate the concept through a 2-D finite element analysis of this new topology, showing a better overall torque profile and performance.
Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation

Areal density increase of heat-assisted magnetic recording (HAMR) media to 4 Tbit/in2 requires further optimization of microstructure and magnetic properties of FePt-X granular media. This has been hindered due to the large space of processing parameters and selection of optimum segregant. In this study, we demonstrate how data-driven approach can accelerate development of media with desired microstructure and magnetic properties. This approach consists of automatic harvesting of microstructural data from transmission electron microscopy images, and training regressor models capable of predicting optimum processing conditions leading to the desired combination of microstructure and magnetic properties in the FePt granular media. The feasibility of this routine is demonstrated on a series of FePt-C media samples and optimized processing conditions proposed by machine learning were verified experimentally. Such a data-driven workflow offers a promising way to realize 4 Tbit/in2 areal density in hard disk drives using HAMR.
AeVIO: Asynchronous Event based Visual Inertial Odometry

Event cameras have several advantages such as motion blur-free data output, high dynamic range, and better low light sensitivity. Visual-Intertial Odometry (VIO) solutions can benefit with the use of these sensors instead of traditional frame based cameras. However, their sparse and asynchronous data pose a challenge for traditional computer vision algorithms. To address these shortcomings, asynchronous (data-driven) approaches are needed for event-camera-based VIO solutions. This paper introduces an end-to-end data-driven event camera based Visual-Inertial Odometry (AeVIO) algorithm that performs the state update based on camera velocity. The scheme performs event feature detection and tracking asynchronously on the event stream and fuses feature measurements with IMU data using a structureless version of the Extended Kalman Filter to update state estimates. The algorithm’s performance is evaluated on various datasets, including simulated and real environments, and demonstrates better performance in the tested scenarios when compared with image-based MSCKF-mono.
Stability Analysis for a Class of Discrete-Time Switched Systems With Partial Unstable Subsystems

This brief is concerned with the stability problem for a class of discrete-time switched systems with unstable subsystems. By constructing a quasi-time-dependent Lyapunov function, stability analysis criterion for nonlinear switched systems is developed under a designed switching rule in which the fast and slow switching techniques are adopted for unstable and stable subsystems, respectively. Then, stability criteria for linear switched systems are presented. It is shown that the obtained results are more general and less conservative than existing ones. Some comparative simulations are provided to demonstrate the effectiveness of the proposed method.
Fabrication of ciprofloxacin loaded alginate/cockle shell powder nanobiocomposite bone scaffold

Orthopedic implant infection is one of the most challenging issues in bone tissue engineering industry. Hence, local delivery of antibiotics incorporated into a fabricated bone scaffold possibly provides a more rapid bacteria inhibitory effect. In this study, pure ciprofloxacin loaded alginate/cockle shell powder nanobiocomposite bone scaffolds are fabricated with 5 wt% and 10 wt% ciprofloxacin respectively and tested for drug encapsulation, drug release and antibacterial properties towards common implant infecting bacterial strains (Staphylococcus aureus and Pseudomonas aeruginosa). Results from the studies showed a low drug encapsulation and drug release regardless of the concentration of drugs loaded with no significant differences noted (p<0.05). However, bacterial inhibition studies through direct contact and using eluted samples from drug release studies showed some inhibitory effects towards the growth of both bacterial strains tested. These findings were further justified with microscopy observations on biofilm and bacterial colony formation. Mineralization studies conducted additionally indicated that the scaffolds characteristics was not compromised due to drug loading. Although pure ciprofloxacin may not be the most suitable antibiotic to be incorporated into the nanobiocomposite bone scaffold, the study did provide some insight to the possible use of the scaffold for future drug delivery applications.
Machine Learning-Driven Optimization of Inverter Drive Parameters for Enhanced Electric Vehicle Efficiency: An In-Depth Analysis and Application

Inverter drives play a crucial role in various industrial applications, where optimizing their parameters is essential for enhancing efficiency and performance. This work presents an advanced technique to optimize the DC Link Voltage (u_dc_k) parameter in inverter drives using machine learning techniques. Traditional methods of parameter optimization have faced challenges in terms of complexity and adaptability. This work proposes a data-driven approach that leverages machine learning models, including linear regression, decision tree, and random forest, to predict the optimized u_dc_k values. The dataset, comprising multiple features related to inverter drives, undergoes pre-processing to handle missing data and scale features. The performance of the machine learning models is evaluated using the root mean squared error (RMSE) metric, and k-fold cross-validation is employed for model comparison. Our results demonstrate the effectiveness of machine learning techniques in predicting optimized u_dc_k values, with the best-performing model yielding improved performance compared to traditional methods.
Comparative Analysis of Relational and Graph Databases for Data Provenance: Performance, Queries, and Security Considerations

In today’s interconnected world, we perceive and understand everything as a vast network of relationships. Whether it’s people, places, or events, our minds naturally associate them as nodes and edges in a graph. This inherent cognitive tendency in human thinking contrasts with the formalisms commonly used by computer scientists and technologists, such as relational databases and JSON documents. While these abstractions serve as convenient tools, they only partially align with the intuitive way our brains process information. Relational databases excel in handling structured data and enforcing strict relationships, making them ideal for transaction processing and deterministic analytics. However, their rigid constraints often hinder the exploration of distant connections and answering complex questions about relationships that span multiple layers. This paper focuses on non-relational databases, particularly graph databases, and presents a comprehensive review of Neo4j compared to traditional relational databases. Graph databases offer a more realistic representation of corresponding data, allowing for flexible querying and traversing relationships with ease. By embracing the graph model, we can unlock new insights and uncover hidden patterns within our interconnected world.
Safety considerations for assessing the quality of apps used during pregnancy: A scoping review

Objective Pregnant women are increasingly turning to apps targeting knowledge and behaviour change for supporting healthy lifestyles and managing medical conditions. Yet, there is growing concern over the credibility and safety of content within mobile health (mHealth) apps. This scoping review aimed to systematically and thematically consolidate safety considerations described in reviews evaluating pregnancy-specific apps. Methods PubMed, Ovid MEDLINE® and EPub, CINAHL, Web of Science, Cochrane Libraries, and SCOPUS were systematically searched to identify reviews that assessed apps targeting pregnant women. Data related to safety were extracted and thematically analysed to establish a set of relevant safety considerations. Results Sixteen reviews met the inclusion criteria. The included reviews assessed an average of 27 apps each and targeted pregnancy topics, such as nutrition and physical activity. Five major and 20 minor themes were identified, including information, transparency, credibility, privacy and security, and app tailoring. Information, transparency, and credibility relate to the evidence base of information within the app, privacy and security of apps relate to the protection of personal information and data, and app tailoring relates to the consideration of contextual factors, such as local guidelines and digital health literacy. Conclusions Results present possible safety considerations when evaluating pregnancy-specific apps and emphasise a clear need for consumer guidance on how to make informed decisions around engagement and use of mHealth apps during pregnancy.
Seasonal changes in food habits of Gyrfalcons in NE-Iceland

Food habits of three subpopulations of the Gyrfalcon Falco rusticolus were studied in coastal, lakeland and heathland habitats of NE-Iceland by analysis of 2,316 pellets collected from February 1984-July 1985. No coastal and lakeland Gyrfalcons traveled more than 7.5 km to reach areas with plenty of waterfowl or seabirds (average 3.4 km in winter). Heathland falcons traveled farther to reach equally rich areas for these kinds of prey, e.g. in winter 13-33 km (average 12.9 km). Rock Ptarmigan Lagopus mutus was the main prey for all Gyrfalcons constituting 61% of total pellet volume. Rock Ptarmigan was most important during the early phase of the breeding season (April-June) and in autumn and early winter (August-December). Factors determining high vulnerability in spring seem to be behaviour and coloration of males and in late summer, the large number of naive young. Importance of Rock Ptarmigan on heathland territories as measured by pellet volume ranged from 61-100% for the different collection periods, and 8-85% and 13-91% on coastal and lakeland territories respectively. Other prey groups of importance on heathland territories were waders (max. 12%) in summer and wood mouse Apodemus sylvaticus (max. 20%) and waterfowl (max. 20%) in winter. Lakeland Gyrfalcons increased their take of waterfowl (max. 56%) and waders (max. 13%) in summer and passerines (max. 19%) and wood mice (max. 43%) in winter. Coastal Gyrfalcons had the most variable diet: they increased their take of waterfowl (max. 42%) and waders (max. 21%) in summer and waterfowl (max. 68%) and alcids (max. 38%) in winter. Switching from Rock Ptarmigan to alternate prey involved in most cases a change of hunting habitat, from upland areas to wetland or coastal areas.
Predicting Dropouts Before Enrollments in MOOCs: An Explainable and Self-Supervised Model

Massive Open Online Courses (MOOCs) belong to a new cloud-based service in education that suffers from low completion rates. Effective pre-learning intervention services, such as recommending courses with a high probability of completion or filtering courses with a very low probability of completion, will encourage students to spend more time and energy on proper courses, thus can reduce the dropout ratio. In practice, intervention services are introduced when students are predicted to drop out. However, existing methods concentrate on analyzing students’ learning actions and predicting final dropout after a period of enrollment, which are insufficient in preventing students from enrolling in unsuitable courses and withdrawing mid-way. This paper presents a neural network-based Explainable Self-supervised Model (ESM) to predict MOOC dropout before enrollment. Specifically, the student's learning actions on an unenrolled course are estimated using previous logs by the neural network. And then, the action's contribution to the completion of a course is calculated in a similar way. Therefore, the probability of completion for an unenrolled course is predicted by aggregating the learning actions and their contribution to the completion. To train the neural network, a self-supervised training strategy is proposed, where enrolled courses in the training data are randomly selected as validation in each epoch. The ESM outperforms existing methods in terms of prediction accuracy and efficiency. The average increment of Area Under the ROC Curve (AUC) and F-score (F1) in the two MOOCs datasets, XuetangX and KDDCUP, are 8.3% and 0.6%, respectively. Furthermore, the two pre-learning intervention services named courses recommendation and courses filtration are proposed. When courses are recommended, the completion rate increased from 22% to 60% in XuetangX, and from 27% to 45% in KDDCUP. By filtering courses predicted with low completion probability, 40% wasted time in uncompleted courses will be saved in XuetangX.
Validation of High Ocean Surface Winds from Satellites Using Oil Platform Anemometers

Reliable sources for validating wind observations made by spaceborne microwave radiometer and scatterometer sensors above 15 m s−1 are scarce. Anemometers mounted on oil platforms provide usable wind speed measurements that can help fill this gap. In our study we compare wind speed observations from six microwave satellites (WindSat, AMSR-E, AMSR2, SMAP, QuikSCAT, and ASCAT) with wind speed records from 10 oil platform anemometers in the North and Norwegian Seas that were provided by the Norwegian Meteorological Institute. We study various forms of the vertical wind profile, which is required to convert anemometer winds to a reference height of 10 m above sea level. We create and analyze matchups between satellite and anemometer winds and find good agreement up to wind speeds of 30 m s−1 within the margin of errors. We also evaluate wind speeds from several analyses [ECMWF, NCEP, and Cross-Calibrated Multi-Platform (CCMP)]. We find them to be significantly lower than the anemometer winds with their biases increasing systematically with increasing wind speed. Important components of our analysis include a detailed discussion on the quality control of the anemometer winds and a quantitative analysis of the uncertainties in creating the matchups.
Validation of item pool for early adolescents’ emotional skills assessment in music therapy

Introduction: Evaluating the validity of the content is an essential step in developing an assessment tool, including an analysis of the quality of the items within the tool. This study describes the content validation of items in the early adolescents’ emotional skills assessment tool in a music therapy context. Methods: Content validity was evaluated based on relevance scores provided by two expert panels. Psychometric scores were obtained by calculating item-specific content validity index (I-CVI), scale-specific content validity index (S-CVI) and modified kappa score. In addition, the coverage and understandability of the items were evaluated. Results: The validation process identified 60 valid items distributed across six components of emotional skills: expressing, monitoring, identifying, understanding, regulating and the ability to use emotional information. Item I-CVI scores ranged from 0.80 to 1.00, the scale content validity index (S-CVI) was 0.95, the modified kappa score ranged from 0.65 to 1.00, item coverage at scale level was 1.00, and item under-standability was 0.92. Discussion: The items developed in the study have high validity and are scientifically grounded. The items can be a first step towards a validated assessment tool to evaluate emotional skills in early adolescents. The added value of this study is that the set of items developed is the first to cover all the components of emotional skills identified in the literature. Therefore, music therapists can use the items to observe in more detail the different dimensions of emotional skills in early adolescents.