Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Peripheral Eosinophilia at Diagnosis in Children With Inflammatory Bowel Disease

Background and Objective: Inflammatory bowel disease (IBD) encompasses 2 disorders of unknown etiology: Crohn disease (CD) and ulcerative colitis (UC). There has been a continuous search for markers for disease activity. Eosinophils are granulocytic leukocytes that are implicated in the pathogenesis of IBD. The aim of this study was to examine the prevalence and significance of peripheral eosinophilia (PE) at diagnosis in children with IBD. Methods: A comprehensive chart review of all children with diagnosed as having IBD between January 2006 and August 2014 was performed. Patients with PE at diagnosis were compared with those without in relation to disease clinical activity and disease course. Results: A total of 109 children (mean age 14.6 ± 2.77, range 4.5–17.9 years, 55 boys) with IBD (68 with CD and 41 with UC) who were studied for a mean duration of 2.82 ± 1.89 (range 0.1–9.2 years) were identified. At diagnosis, 44 (40.4%) children had PE, which was more prevalent in patients with UC compared with those with CD (61.3% vs 36.3%, P < 0.05). At diagnosis, PE was more common in patients with high eosinophilic count in colonic biopsy samples (P < 0.01) and was significantly associated with disease activity as indicated by Pediatric CD Activity Index for children with CD (P < 0.05), Pediatric UC Activity Index for children with UC (P < 0.01). Conclusions: PE is a common finding at diagnosis in children with IBD especially in those with UC. Patients with PE at diagnosis are more likely to present with higher clinical activity indices. PE is associated with more eosinophils in colonic biopsy samples.
Fake Profile Detection Using Machine Learning

Nowadays, Social Media Platforms on behalf of entities or individuals, can harm (SMPs) are being utilized by an enormous number their reputations and reduce the number of likes of users to get connected with their friends and and followers they receive. Additionally, they family. There are sites like Facebook, Instagram, experience unnecessary confusion with other Twitter where people spend a significant amount of time to get updated about the world. The data uploaded on social media contains their personal information, thoughts on certain topics, news, etc. The social media platforms verify the authenticity of the registered user. However, some of the users hide their identities and these people are threats to the security of other users’ data. These bot accounts are used to scam, or purposefully cause harm to people. There is a need for detection techniques to find and eradicate these bots as quickly as possible. In this work, we have proposed a Machine learning based model that can identify fake or bot created accounts accurately. This paper is divided into multiple parts: Introduction, Literature Review, Methodology, Results and Discussion, Conclusion. To validate the authenticity of our work, we have experimented over the publicly available dataset TwiBot-20 and achieved accuracy of 87%.
3D tracking swimming fish school with learned kinematic model using LSTM network

This paper proposes a reliable 3D fish tracking method using a novel master-slave camera setup. Instead of conventional dynamic models that rely on prior knowledge about target kinematics, the proposed method learns the kinematic model with a Long Short-Term Memory (LSTM) network. On this basis, the 3D state of fish at each moment is predicted by LSTM network. We propose to use an innovative master-view-tracking-first strategy. The fish are first tracked in the master view. Cross-view association is then established utilizing motion continuity and epipolar constraint cues. Experiments on data sets of different fish densities show that the proposed method is effective and outperforms the state-of-the-art methods.
Identification of areas with adequate temperature for the culture of cobia in cages along the Brazilian coast

: This study identified regions of the Brazilian coast that present more adequate temperature conditions for the cage culture of cobia ( Rachycentron canadum ). A survey of sea surface temperatures (SST) along the coast (up to the 200 m isobath) used data from the Aqua/NASA satellite. Water temperature classes that affect the development of cobia and may lead to changes in physiological activities, especially related to feed intake, growth, and mortality, were established, according to the literature. Cobia is able to grow at temperatures between 19 and 32 ºC, but the range considered optimum for growth was 27–29 ºC. The coastal area stretching from eastern Pará to the north of Bahia was considered to have the most suitable temperature conditions for commercial aquaculture of cobia. The coastline encompassing the state of Amapá to the west of Pará, and the whole coast of the southeast region presented SSTs above or below the range considered optimum for the growth of cobia, respectively. In the southern coast of Brazil, the commercial farming of cobia may not be feasible due to the low SST during most of the year .
Fake News Classification in Twitter Data Using Innovative K Nearest Neighbor Comparing Logistics Regression

Improve the accuracy of identifying fake news on Twitter by employing the Advanced K Nearest Neighbor Algorithm then evaluating the results against those obtained through Logistic Regression Algorithm. Materials and Methods: The research is divided into two groups, each with a sample size of 42 individuals. The first group, comprising 21 participants, will apply the Advanced K Nearest Neighbor Algorithm, while the second group, also consisting of 21 participants, will use the Logistic Regression technique. The study has been designed with G power 80% parameters of α=0.05 and beta=0.2. Result: Innovative K nearest neighbor Algorithm 81.55 % identifies objects and increases the measured accuracy over Logistic Regression 79.0 % with implication value of 0.001 (p < 0.05). Conclusion: In terms of accuracy, the Innovative K Nearest Neighbor Algorithm outperforms Logistic Regression Algorithm.
Cybersecurity Culture Assessment at a University in Chile: A Pilot Study

This study examines cybersecurity culture within a university community, investigating the interplay among knowledge, empowerment, habits, informal practices, leadership, and professional ethics. Surveys, reliability tests, and correlations establish positive associations between knowledge and critical factors influencing cybersecurity practices, underscoring a robust relationship with the Cybersecurity Culture Indicator (CCI). The results demonstrate high overall reliability (McDonald's ${\mathrm{\omega }} = 0.924$, Cronbach's ${\mathrm{\alpha }} = 0.926$), emphasizing the consistent nature of item responses. Correlation analysis reveals interconnectedness among constructs, emphasizing the strategic impact of a strong cybersecurity culture on organizational dimensions.
WPT: A Smart Magnetic Resonance Technology based Wireless Power Transfer System Design for Charging Mobile Phones

Nowadays, nearly everyone has at least one portable digital gadget. Almost all handheld electronics run on batteries, which necessitates recharging using the traditional wired chargers. There has been a rise in the desire for greater battery life in portable electronics due to the rise in their use. These power packs require regular maintenance, such as charging or replacement. After some time, charging or replacing the battery becomes inconvenient, especially if there is nowhere to plug it in. To charge a portable gadget’s battery, we designed this method to provide timed electrical power transfer among the power transmission board and a suitable receiver in the device. In addition, the smartphone would have android-based software loaded that monitors the battery and communicates with the wireless transmitter in order to provide only the charging power that is actually needed. The communication circuit would then cut power, ending the charging process and protecting the battery from being overheated. If you didn't have to repeatedly plug and unplug your gadget, it would last longer, have a longer battery life, and be easier to use. Power that can keep the system operating all the latest technological items including mobile phones, tablets, wireless headsets and other similar new age gadgets is necessary as the size of devices continues to decrease daily with increased processing capabilities. A different method of charging electronics is required to increase their efficiency. Thanks to advancements in wireless power transfer, new charging methods have emerged that are both efficient and quick. Electromagnetic power transfer is the most efficient way for wirelessly recharging portable electronics.
Warped Reheating in Multi-Throat Brane Inflation

We investigate in some quantitative details the viability of reheating in multi-throat brane inflationary scenarios by estimating and comparing the time scales for the various processes involved. We also calculate within perturbative string theory the decay rate of excited closed strings into KK modes and compare with that of their decay into gravitons; we find that in the inflationary throat the former is preferred. We also find that over a small but reasonable range of parameters of the background geometry, these KK modes will preferably tunnel to another throat (possibly containing the Standard Model) instead of decaying to gravitons due largely to their suppressed coupling to the bulk gravitons. Once tunneled, the same suppressed coupling to the gravitons again allows them to reheat the Standard Model efficiently. We also consider the effects of adding more throats to the system and find that for extra throats with small warping, reheating still seems viable.
Fast Load Frequency Control for Power System Based on Fixed-Time Dynamic Surface Method

The inherent fluctuations of high penetrated renewables energy sources in power system results in larger and more sharp frequency deviation, which call for faster frequency response and fast load frequency control (LFC). This paper innovatively envisions LFC in power system with fixed-time dynamic surface method. Firstly, the virtual control law is designed through backstepping design to ensure the asymptotic stability of each subsystem. Additionally, dynamic surface control (DSC) is adopted to eliminate the influence by adding a first-order filter in the backstepping design. By using the fixed-time theory, the frequency of the LFC for power system reaches stability in a predetermined time. Finally, the simulation of the power system proves the effectiveness of the proposed method.
A Bayes testing approach to metagenomic profiling in bacteria

Using next generation sequencing (NGS) data, we use a multinomial with a Dirichlet prior to detect the presence of bacteria in a metagenomic sample via marginal Bayes testing for each bacterial strain. The NGS reads per strain are counted fractionally with each read contributing an equal amount to each strain it might represent. The threshold for detection is strain-dependent and we apply a correction for the dependence amongst the (NGS) reads by finding the knee in a curve representing a tradeoff between detecting too many strains and not enough strains. As a check, we evaluate the joint posterior probabilities for the presence of two strains of bacteria and find relatively little dependence. We apply our techniques to two data sets and compare our results with the results found by the Human Microbiome Project. We conclude with a discussion of the issues surrounding multiple corrections in a Bayes context.
The Critical Factors Affecting E-Government Adoption in India: A Conceptual Framework

E-Governance has been influencing the lives of common people widely. In this paper, UMEGA and its improved version were empirically investigated. Additionally, the extended version of the model outperformed the original one, with a tad more variance overall being accounted for by behavioural intention. It has been discovered that attitudes were significantly influenced by performance expectations, social influence, perceived risk, and computer selfefficiency, whereas behavioural intention was significantly influenced directly by attitudes, facilitating conditions. These findings force the variables to be considered when regulating the plans to raise the bar for quality. To collect information, a questionnaire was handed to the respondents. The two behavioural intention dimensions, such as confidence in the government and technology, were tested using the acquired data. T-test and ANOVA were specifically conducted on a sample of 70 respondents to measure the significance of Citizen to the aforementioned aspects of e-governance.
Biochemical and radiological predictors of malignant biliary strictures.

AIM Differentiation of benign biliary strictures (BBS) from malignant biliary strictures (MBS) remains difficult despite improvement in imaging and endoscopic techniques. The aim of this study was to identify the clinical, biochemical and or radiological predictors of malignant biliary strictures. METHODS We retrospectively reviewed all charts of patients who had biliary strictures (BS) on endoscopic retrograde cholangiopancreatography (ERCP) or percutaneous cholangiography (PTC) in case of unsuccessful ERCP from March 1998 to August 2002. Patient characteristics, clinical features, biochemical, radiological and biopsy results were all recorded. Stricture etiology was determined based on cytology, biopsy or clinical follow-up. A receiver operator characteristic (ROC) curve was constructed to determine the optimal laboratory diagnostic criterion threshold in predicting MBS. RESULTS One hundred twenty six patients with biliary strictures were enrolled, of which 72 were malignant. The mean age for BBS was 53 years compared to 62.4 years for MBS (P=0.0006). Distal bile duct stricture was mainly due to a malignant process 48.6% vs 9% (P=0.001). Alkaline phosphates and AST levels were more significantly elevated in MBS (P=0.0002). ROC curve showed that a bilirubin level of 84 micromol/L or more was the most predictive of MBS with a sensitivity of 98.6%, specificity of 59.3% and a positive likelihood ratio of 2.42 (95% CI=0.649-0.810). Proximal biliary dilatation was more frequently encountered in MBS compared to BBS, 73.8% vs 39.5% (P=0.0001). Majority of BBS (87%) and MBS (78%) were managed endoscopically. CONCLUSION A serum bilirubin level of 84 micromol/L or greater is the best predictor of MBS. Older age, proximal biliary dilatation, higher levels of bilirubin, alkaline phosphatase, ALT and AST are all associated with MBS. ERCP is necessary to diagnose and treat benign and malignant biliary strictures.
Performance Analysis of SWIPT Cooperative Relay Network with Ambient Backscatter

Ambient Backscatter (AB) is considered as one of the effective techniques to reduce the communication energy consumption. This work investigates the outage performance of simultaneous wireless information and power transfer (SWIPT) cooperative relay network based on ambient backscatter. The analytical expressions of the outage probability for the primary user and the secondary user are derived. The performance is compared with that of the traditional SWIPT cooperative relay network. The theoretical derivation is validated by simulations. And the simulation results demonstrate that the system throughput and energy efficiency of the SWIPT cooperative relay network using ambient backscatter are superior to that without ambient backscatter.
Securing data in Wireless Sensor Network using Hybrid ECC + AES Cryptographic Approach

This research work unveils a groundbreaking cryptographic strategy designed to bolster the security of wireless sensor networks (WSNs). By harnessing the combined strengths of Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard (AES), this study introduces a novel hybrid approach, denoted as ECC + AES cryptographic system. The primary objective of this system is to fortify the security of data transmission within WSNs. Crafted with a keen understanding of the challenges presented by resource-constrained environments such as WSNs, the ECC + AES solution demonstrates a remarkable capability to ensure data protection. The research's innovative outcomes address the inherent vulnerabilities that characterize WSNs, offering a robust and resilient approach to safeguarding sensitive data. The amalgamation of ECC and AES not only fortifies data security but also does so with minimal impact on resource allocation, effectively minimizing both memory usage and energy consumption. In doing so, the study bridges the gap between security and efficiency, presenting a cryptographic framework that is not only robust but also tailored to the practical demands of WSNs. In an era defined by the proliferation of data and the growing importance of secure communication, the ECC + AES cryptographic system emerges as a pioneering solution. By proposing a hybrid approach that effectively merges cryptographic prowess with resource efficiency, this research underscores its potential to reshape the landscape of data security within WSNs.
Design and Implementation of AI Machine Vision Inspection System with Multi-Optical Images for OLED Film Manufacturing

Machine vision technology is one of the main areas of rapid development in the modern world and is becoming increasingly important in various industries. This innovative technology uses the fusion of computer vision and artificial intelligence to process and analyze visual information, enabling the development of automated inspection systems and the automation of processes. In particular, machine vision technology is playing an increasingly important role in OLED film production and quality control. Currently, OLED film production is mainly based on visual inspection, but this method has various problems. It is labor-intensive and relies on humans, which limits the accuracy and consistency of the inspection. It is also difficult to identify defective elements with irregular shapes or sizes. To overcome these issues and revolutionize the field of OLED film production and quality control, we propose an OLED Film Inspection Architecture with Machine vision. This architecture utilizes advanced cameras and sensors to analyze the surface of OLED films in detail and automatically identify defective elements. In addition, by integrating multiple optical systems and AI deep learning algorithms, it can perform accurate inspections under various lighting conditions and detect defects in real time. By utilizing these advanced technologies, quality control and production efficiency can be greatly improved, which is expected to drive innovation in OLED film production. This research provides a high level of technical content and provides a bright outlook for the future of machine vision technology in the industrial field.
A Balance Control Strategy Based on the CP and DMP for Lower Limb Exoskeleton

While healthy users can walk in an exoskeleton without external assistive devices, balancing walking can be a difficult task for people with neurological conditions such as spinal cord injuries (SCI). The key issue, especially when subjected to external perturbations, is how the exoskeleton generates the appropriate gait to maintain the stability of the system after the human-exoskeleton system has been perturbed by external forces. To generate a human-like gait in order to make wearing more comfortable. In this paper, we propose an adaptive gait planning method that can maintain system stability by varying the step length depending on the magnitude of the perturbation to which the human-exoskeleton system is subjected. We model the human-exoskeleton system as a two-dimensional linear inversion pendulum model (2D-LIPM). The human-exoskeleton system is perturbed by external forces in the sagittal plane, and the location of the foothold is calculated in real-time using capture point (CP) theory, followed by foot-end trajectory planning using dynamic motion primitives (DMP). The proposed method can dynamically generate the trajectory of each step of the swinging foot, after learning the gait trajectory from a normal healthy human, and can adaptively plan the anthropomorphic gait online based on the magnitude of the perturbation. We validated the effectiveness of the proposed method by simulating it on the Simulink Simscape platform. The experimental results show that the proposed method can improve the self-balancing capability of the exoskeleton robot.
Regulation of exocytosis from rat peritoneal mast cells by G protein βγ‐subunits

We applied G protein‐derived βγ‐subunits to permeabilized mast cells to test their ability to regulate exocytotic secretion. Mast cells permeabilized with streptolysin‐O leak soluble (cytosol) proteins over a period of 5 min and become refractory to stimulation by Ca2+ and GTPγS over ∼20–30 min. βγ‐Subunits applied to the permeabilized cells retard this loss of sensitivity to stimulation (run‐down) and it can be inferred that they interact with the regulatory mechanism for secretion. While α‐subunits are without effect, βγ‐subunits at concentrations >10−8 M enhance the secretion due to Ca2+ and GTPγS. Unlike the small GTPases Rac and Cdc42, βγ‐subunits cannot induce secretion in the absence of an activating guanine nucleotide, and thus further GTP‐binding proteins (likely to be Rho‐related GTPases) must be involved. The enhancement due to βγ‐subunits is mediated largely through interaction with pleckstrin homology (PH) domains. It remains manifest in the face of maximum activation by PMA and inhibition of PKC with the pseudosubstrate inhibitory peptide. Soluble peptides mimicking PH domains inhibit the secretion due to GTPγS and block the enhancement due to βγ‐subunits. Our data suggest that βγ‐subunits are components of the pathway of activation of secretion due to receptor‐mimetic ligands such as mastoparan and compound 48/80.
Efficient Prequential AUC-PR Computation

When dealing with classification problems for data streams, we often need to compute the classification metrics in a prequential manner. The Area Under the Precision-Recall Curve (AUC-PR) metric is extensively used in imbalanced classification scenarios, where the negative class outnumbers the positive one. Despite its advantages, it may be computationally expensive to recompute that metric every time a new test instance becomes available. In this work, we present an efficient algorithm to compute the AUC-PR in a prequential way. Our proposed algorithm uses a self-balancing binary search tree to avoid the need to reorder the data when updating the AUC-PR value with the most recent data. Our experiments take into consideration six well-known, publicly available stream-based datasets. Our experiments show that our approach can be up to 13 times faster and use 12 times less energy than the traditional batch approach when considering a window of size 1,000.
Embodiment in conversational interfaces: Rea

In this paper, we argue for embodied corrversational charactersas the logical extension of the metaphor of human - computerinteraction as a conversation. We argue that the only way to fullymodel the richness of human I&+ to-face communication is torely on conversational analysis that describes sets ofconversational behaviors as fi~lfilling conversational functions,both interactional and propositional. We demonstrate how toimplement this approach in Rea, an embodied conversational agentthat is capable of both multimodal input understanding and outputgeneration in a limited application domain. Rea supports bothsocial and task-oriented dialogue. We discuss issues that need tobe addressed in creating embodied conversational agents, anddescribe the architecture of the Rea interface.