Miracle-dz/UniFAR-FTUs huggingface dataset:
To study clinical profile and correlation of EEG and CT findings in diagnosis of focal seizures in children at medical institution in mid – Karnataka region

Focal-onset seizures account for approximately 40% of seizures in children. Focal seizures are most commonly associated with underlying brain pathology in extreme age groups. Clinical correlation and localizing the site of the lesion play an important role in the early diagnosis and proper management of focal seizures in children. Objectives: To study the clinical profile and correlation of EEG and CT findings in children admitted to the paediatric ward of Medical College.A prospective study on the correlation of focal seizures, CT brain, and EEG studies was carried out in children in the age group of 1 month–14 years admitted to the Department of Pediatrics at Medical College & Hospital, Karnataka. Among the (n=50) study subjects, the majority of the children with focal seizures were in the age group of 6–10 years, with a slight male preponderance. (n=22) (44% of the children) presented with focal-aware seizures. The most common brain lesions detected were calcified disc lesions and cysticercosis ring lesions (52%). 45% of children with focal seizures had epileptiform discharges. We also observed that complementary to CT brain scans in correlating with the clinical history. It was noted that44% of children with focal seizures had normal CT brain scans and normal EEG studies. A good clinical history is crucial in the diagnosis of focal seizures. Most focal seizures are associated with underlying organic brain lesions. A CT brain scan plays an important role in the detection of brain lesions in children with focal seizures. The EEG study complements history and CT scan of the brain in defining and correlating the lesions precisely.
Deep Neural Networks for Cooperative Lidar Localization in Vehicular Networks

The exchange of sensing information through Vehicle-to-Everything (V2X) communications enables the development of cooperative systems for localization augmentation in connected automated vehicles. In V2X scenarios, the integration of measurements from multiple vehicles enhances the environmental perception which is of the utmost importance for enhanced safety services. In this paper, we propose a Deep Neural Network (DNN)-assisted cooperative localization method that relies on a centralized road infrastructure and a network of lidar sensors at vehicles. The proposed algorithm is referred to as DNN Implicit Cooperative Positioning (DNN-ICP) and performs two tasks. At first, each vehicle processes its lidar point cloud by a 3D object detector to identify static objects in the surrounding. Then, the estimated objects are collected at the road infrastructure which uses the aggregated information to improve the localization. Numerical results in a realistic vehicular scenario are presented to quantify the improvement provided by DNN-ICP with respect to a non-cooperative vehicle positioning scheme, showing the reduction of uncertainty on vehicle positioning.
Divergent features of ERβ isoforms in triple negative breast cancer: progress and implications for further research

Estrogen receptor β (ERβ) was discovered more than 20 years ago. However, the extent and role of ERβ expression in breast cancer remain controversial, especially in the context of triple-negative breast cancer (TNBC). ERβ exists as multiple isoforms, and a series of studies has revealed an inconsistent role of ERβ isoforms in TNBC. Our recent results demonstrated contrasting functions of ERβ1 and ERβ2/β5 in TNBC. Additional research should be conducted to explore the functions of individual ERβ isoforms and develop targeted drugs according to the relevant mechanisms. Consequently, a systematic review of ERβ isoforms is necessary. In this review, we overview the structure of ERβ isoforms and detail what is known about the function of ERβ isoforms in normal mammary tissue and breast cancer. Moreover, this review highlights the divergent features of ERβ isoforms in TNBC. This review also provides insights into the implications of targeting ERβ isoforms for clinical treatment. In conclusion, this review provides a framework delineating the roles and mechanisms of different ERβ isoforms in TNBC and sheds light on future directions for basic and clinical research.
Numerical Study of the Energy Absorption Performance of 3D Printed Sandwich Structures

Nowadays, Fused Deposition Modeling (FDM) is a powerful tool for manufacturing complex components, due to its customizability, low cost, accessibility, and fast prototyping time. It is an alternative for creating thin-walled structures, as it allows for novel designs. This article focuses on the design and numerical evaluation of 3D printed sandwich structures for energy absorption applications. For this purpose, five structures of Acrylonitrile Butadiene Styrene (ABS) were designed. To ensure optimal performance, the 3D printing parameters were optimized based on the corresponding literature. The structures had cores based on polygonal and cell arrangements. The effects of cross-section and mass on energy absorption were analyzed, and parameters such as energy absorption, peak load, mean force, and crush force efficiency (CFE) were determined during the study. The structures were assessed by out-of-plane compression tests. The numerical analysis was executed using Abaqus finite element software. Re - sults showed that the energy absorption performance is primarily determined by the geometry and density of the structures. The best performance was found for a circular cellular structure, with a CFE of 0.884.
Practical Application and Methodological Considerations on the Basics of Sports Nutrition in Basketball: A Comprehensive Systematic Review of Observational and Interventional Studies

The current systematic review (PROSPERO registration no. CRD42022334707) of observational (OS) and interventional studies (IS) aimed at evaluating the state of scientific knowledge on the basics of sports nutrition, framing discipline-specific dietary recommendations, and indicating potential directions for future studies in various age, experience level, and able-bodied abilities groups of basketball players (BP). A systematic search of PubMed, SPORTDiscus, and Web of Science ended on 20 December 2022. Records were excluded if reporting studies on animals, sport disciplines other than basketball, or supplementation protocols other than those related to macronutrients and hydration manipulations. Risk of bias (RoB) was evaluated using Cochrane RoB_2 tools, ‘JBI checklist for prevalence studies’, and ‘Quality assessment tool for before-after (pre-post) studies with no control group’. The relevant data was synthesized in tables and a narrative review was performed. Seventy-two records were included (2581 participants): 63 were on able-bodied BP (2433 participants) and 9 on para-athlete players (148 participants); 45 records were OS and 27 IS. The review disclosed widespread poor nutritional habits and knowledge and shortages in applying adequate nutritional and hydration practices in BP. Moreover, the systematic review revealed the lack of a sufficient number of investigations delivering reliable proof for framing discipline-specific and evidence-based recommendations on the basics of sports nutrition in basketball.
To be Conservative or to be Aggressive? A Risk-Adaptive Mixture of Experts for Mobile Traffic Forecasting

Mobile traffic forecasting plays a key role for optimizing the configuration of network cells. Network operators are very interested in predicting upcoming mobile traffic peaks in an accurate way, in order to improve quality of service via efficient resource allocation. However, forecasting potential peaks is very challenging considering that many peaks occur suddenly for no apparent reason; hence, it is very difficult to determine if a peak will occur in near future based on the temporal dynamics of mobile traffic, potentially leading to inaccurate predictions. To improve the performance of peak prediction, we propose a novel deep learning model called Mixture of Quantiles (MoQ). MoQ employs a mixture of experts model featuring a manager to fuse the predictions of multiple experts. In order to overcome the problem of overly smooth predictions on peaks, the experts are designed to have differentiated forecasting styles from conservative to aggressive. A cooperation mechanism is established through a carefully designed training process, whereby conservative experts are responsible for the forecasting of the off-peak region, and the employed experts are switched to the aggressive ones once the potential increasing trend is detected by manager, which leads to significantly improved peak predictions. Extensive experiments on real-world dataset showcase the effectiveness of the proposed MoQ model, which outperforms all the benchmarks and shows its superior performance in peak forecasting along with excellent interpretability.
Evaluating Account Representative Competence and its Impact on Tax Collection Performance: A Strength Typology-30 Study

This study examines how Account Representatives' tax revenue collection performance is determined by competence, leadership, and co-workers. Measurement is conducted using the Strength Typology-30 categorisation scale to find a personality profile that aligns with Account Representative tasks and functions based on the Regulation of Minister of Finance Number 45/PMK.01/2021. Data collection is done by sending the Strength Typology 30 test link and a Google Form questionnaire to 140 Account Representatives working in the Tax Service Offices within the Regional Office of the Directorate General of Taxes in Aceh as respondents. The data was analyzed using the multiple linear regression analysis method with the IBM SPSS 22 software program. The research findings reveal that competence, leadership, and co-workers play a crucial role in enhancing tax revenue performance by account representatives. There are 128 respondents categorised as “competent” and “sufficiently competent” on the Strength Typology-30 test, indicating that the majority of Account Representatives possess sufficient competence aligned with Account Representative tasks and functions. This study provides an overview of individual Account Representative competence based on the results of the Strength Typology-30 test, which can serve as a reference for the Directorate General of Taxes in devising human resource development policies, especially for the position of Account Representative.
Management of allergic rhinitis improves clinical outcomes of difficult-to-treat tic disorders or attention-deficit/hyperactivity disorders

Aims: This case series aimed to evaluate the effects of treatment for allergic rhinitis (AR) in AR-diagnosed children with previous diagnosis of tic disorders/attention-deficit/hyperactivity disorders (TD/ADHD) but unresponsive to behavioral or medical treatment. Materials and methods: Between July 2016 and June 2021, children diagnosed with AR in our hospital were enrolled. All were diagnosed with TD/ADHD refractory to behavioral or medical treatment. The demography and clinical information were collected from medical records. The outcomes were visual analogue scale (VAS) for AR severity, Yale Comprehensive Tic Severity Scale (YGTSS) for TD symptoms, and Attention-Deficit Hyperactivity Screening Scale (SNAP-IV) for ADHD symptoms. Results: A total of 27 children (18 boys, 9 girls) were included, with a mean age 7.4 ± 2.9 years (3 – 17 years). They had undergone behavioral or medical treatment of TD/ADHD for 3.6 ± 1.9 years but without significant improvement in TD/ADHD symptoms. After 2-6 months of systematic treatment for AR, VAS was decreased to 0.4 ± 0.1 from 0.8 ± 0.2, YGTSS to 3.5 ± 0.7 from 6.8 ± 1.4, and SNAP-IV to 0.4 ± 0.1 from 0.6 ± 0.2 (all p < 0.001). No recurrence of TD/ADHD symptoms was reported during a mean follow-up of 2.4 ± 1.1 years (0.5 – 5 years). Conclusion: AR treatment improves TD/ADHD outcomes in children with difficult-to-treat TD/ADHD. In TD/ADHD children who are unresponsive to behavioral or drug treatment and have AR-related symptoms, AR examination and treatment are recommended for better prognosis.
Evaluation of the Community’s Awareness of Developmental Dysplasia of the Hip: A Cross-Sectional Study in Jordan

Background: Developmental dysplasia of the hip (DDH) is a condition where there is an abnormal relationship between the femoral head and acetabulum. DDH is prevalent in Jordan, where late presentations of complicated cases are common due to the absence of a national screening program and cultural behaviors that can exacerbate DDH progression. The study aims to prove that the absence of a nationwide screening program and low awareness of the population about DDH are the primary reasons for the high incidence of late-presenting DDH in Jordan. The study highlights the need for addressing modifiable risk factors like swaddling and correcting common misconceptions such as using multiple diapers as a treatment option. Methods: The cross-sectional study evaluated the awareness level of DDH among Jordan residents above the age of 18. An online questionnaire was used, containing two sections. The Chi-square test was used to analyze the level of awareness scores according to demographic variables and cultural norms such as tight swaddling and using multiple diapers. The sample was categorized into three groups based on their scores: low awareness (0-4), moderate awareness (5-8), and high awareness (9-12). Results: The study included 1013 participants. The results indicated that 48.9% had moderate awareness, 37.8% had low awareness, and 13.3% had high awareness of DDH. Gender and place of residence had no significant relationship with the level of awareness. However, the majority of participants with high and moderate levels of awareness were in the 25-40 years age group, and healthcare workers had the highest levels of awareness. The study showed that 33.9% of participants gained their knowledge from either relatives or self-experience, while awareness campaigns were the least assessed source of information with only 2.9% of participants getting their information from them. The study also revealed misconceptions regarding DDH, such as the belief that tight swaddling and wearing many diapers reduced the risk of developing DDH. Additionally, about half of the participants were unaware of the presence of screening guidelines for DDH. Conclusion: The study underscores the urgent need to establish a national screening program and awareness campaigns to increase knowledge about DDH and prevent late presentations of complicated cases in Jordan. The study findings provide essential information for the development of future DDH campaigns and screening protocols.
MULS-Net: A Multilevel Supervised Network for Ship Tracking From Low-Resolution Remote-Sensing Image Sequences

Ship detection and tracking from remote-sensing image sequences has become an increasingly important research point. However, there are still many challenges for ship tracking from low-resolution remote-sensing image sequences: 1) the dim and small objects contain only a few shape and texture features, making it difficult to detect and track ships; 2) broken clouds often resemble ships, resulting in false tracking; and 3) the ship may be occluded by clouds leading to missed tracking. To address these challenges, we proposed a novel multilevel supervision network for ship tracking from low-resolution remote-sensing image sequences. First, we designed a gradient difference-guided object clarification network component to significantly improve the object saliency, which is also implemented based on the multiframe correlation enhancement images to improve the feature strength of small targets in the input data. Second, to reduce the difficulty of completing complex tasks, a multilevel supervised network (MULS-Net) framework with multiple components was presented to improve the target clarity, detecting targets, and tracking targets step by step. Finally, to improve the trajectory integrity and tracking accuracy, a joint tracking method based on a low-frame-rate tracking criterion was proposed to control the state of the target tracking module. The method was validated on a self-assembled dataset from the GaoFen-4 satellite. The experiment results show the stronger competition and accuracy of the proposed method than other state-of-the-art object trackers.
From Tragedy to Activism: Publics’ Emotions, Efficacy, and Communicative Action on Twitter in the Case of the 2017 Las Vegas Mass Shooting

ABSTRACT Guided by the Anger Activism Model and pain and loss activism literature, this study analyzes online public discussion in a particular case of activism on social media (i.e., the 2017 Las Vegas mass shooting). On the basis of the content analysis of 4,611 tweets, this study explored the role of emotions as well as efficacy beliefs in engaging people in online political conversations during pain and loss events. Key insights about the joint effects of emotion and efficacy in activism communication provide communication practitioners and advocacy groups with more informative approaches to strategically communicate with activist publics in the online environment.
Data-Driven Tracking Control for Uncertain Linear Systems Using a Dual-System Approach

This letter proposes a dual-system approach to designing tracking controllers for uncertain linear time-invariant systems by explicitly combining prior and data-based knowledge about the uncertainty. The tracking controller is transformed into a static state-feedback controller for an augmented system. We derive data-driven synthesis conditions that robustly guarantee stability and $\mathcal {H}_{2}$ performance for all systems consistent with prior knowledge and gathered data. Importantly, the synthesis conditions are jointly linear with respect to the prior and data-based multipliers, which significantly reduces conservatism and improves performance. Additionally, we further develop the data-driven regional pole placement approach to fine-tune the closed-loop dynamic response. A numerical example illustrates the effectiveness of our method.
Tri-Modal Medical Image Fusion and Denoising Based on BitonicX Filtering

Medical image fusion integrates beneficial information from images of different modes and has been studied as a crucial auxiliary medical technology; however, current research mainly focused on two-modal fusion problems [e.g., computed tomography (CT)-magnetic resonance imaging (MRI), MRI-positron emission tomography (PET)], and little attention was paid to tri-modal medical image fusion (e.g., CT-MR_T2-single photon emission CT (SPECT), MR_T2-MR_Gad-SPECT). In this article, we present a tri-modal medical image fusion and denoising method that contains fusion models (I) and (II). A three-layer decomposition based on a bitonicX filter was designed for model (I). Particularly, the adaptive morphological gradient pulse-coupled neural network (AM-PCNN) and the coupled neural P (CNP) system-based rules were proposed to fuse the detail and texture layers, respectively. The middle-fused result was obtained by synthesizing the prefused energy, texture, and detail layers in the model (I). In model (II), for the fusion of the functional image and middle-fused result, we performed a “detail-energy” decomposition with the bitonicX filter. We also propose a gradient energy clarity (GEC) operator and energy-based rules for fusing the detail and energy layers. The final fused result was obtained by adding the prefused detail layer and the energy layer in the model (II). Numerous experiments were conducted to assess the fused images objectively and subjectively, demonstrating that the proposed method developed in this study surpasses the state-of-the-art methods in noise-free and noise fusions. Meanwhile, the average levels of the proposed method are 38.39%, 2.46%, 1.01%, 36.28%, 11.23%, 20.82%, 8.46%, and 2.52% higher than that of the compared methods evaluated by the metrics, including interactive information metric (QMI), Tsallis entropy metric (QTE), noncorrelated information entropy metric (QNCIE), phase consistency metric (QP), Chen-Blum metric (QCB), structural similarity metric (QSSIM), contrast-to-noise (CNR), and correlation coefficient (CC), respectively. Our code is available at https://github.com/JEI981214/Tri-modal-bitonicX-based.
Aureobasidium pullulans Treatment Mitigates Drought Stress in Abies koreana via Rhizosphere Microbiome Modulation

The Korean fir tree Abies koreana, an endangered species in Korea, faces threats primarily from climate change-induced stress and drought. This study proposed a sustainable method to enhance A. koreana drought tolerance using a black yeast-like fungus identified as Aureobasidium pullulans (AK10). The 16S/ITS metabarcoding analysis assessed the impact of drought and AK10 treatment on the seedlings’ rhizosphere microbiome. Results revealed a profound drought influence on the microbiome, particularly affecting fungal mycobiota. Drought-stressed seedlings exhibited elevated Agaricaceae levels, opportunistic fungi generally associated with decomposition. AK10 treatment significantly mitigated this proliferation and increased the relative abundance of beneficial fungi like Cystofilobasidium and Mortierella, known biocontrol agents and phosphate solubilizers. A notable reduction in the phytopathogenic Fusarium levels was observed with AK10, alongside an increase in beneficial bacteria, including Azospirillum and Nitrospirillum. Furthermore, the conducted correlation analysis shed light on microbial interrelationships within the rhizosphere, elucidating potential co-associations and antagonisms. Taken together, the isolated A. pullulans AK10 identified in this study serves as a potential biostimulant, enhancing the drought tolerance in A. koreana through beneficial alterations in the rhizosphere microbiome. This approach presents a promising strategy for the conservation of this endangered species.
A Large-Scale Study of Cookie Banner Interaction Tools and their Impact on Users' Privacy

Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner's pervasiveness as they interrupt ''natural'' browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners. In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for ''cookie banner interaction. '' We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user's preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set---even for extensions that aim to implement the same cookie choice. Extensions for ''cookie banner interaction'' can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.