Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework of Metaverse Educational Learning of Virtual Reality Environment in Improving Current Learning Problem

The technology of virtual reality is a very new teaching technique. Virtual reality in the classroom can motivate students to learn. It follows the trend of faster, more engaging, and higher motivation. This research focuses on the current problem faced in the study, the proposed technology with a virtual reality environment, the learning element that should be in the classroom, and the suitable learning pedagogy that can be used. Based on the result, the proposed educational learning model framework is suitable to be used in learning in a virtual reality environment using suitable pedagogy techniques. The goal of this framework is to enhance current learning problems that higher students face.
Leukotriene D4-induced hypoxaemia in asthma is mediated by the cys-leukotriene1 receptor

Bronchoprovocation with cysteinyl-leukotrienes (LTs) induces airflow obstruction and gas exchange abnormalities, namely ventilation-perfusion ratio (V′A/Q′) imbalance. However, it is unknown which of the two different receptors for cysteinyl-LTs mediate these V′A/Q′ disturbances. In a double-blinded, crossover design, 10 patients with mild asthma were randomised to receive an oral single dose of the selective cysteinyl-LT1 receptor antagonist montelukast (40 mg) or placebo before leukotriene (LT)D4 inhalation challenge. Gas exchange, including V′A/Q′ descriptors were measured at baseline, 3 h after montelukast/placebo pretreatment and 5, 15 and 45 min after the LTD4 challenge. Compared with montelukast, inhalation of LTD4 induced a marked fall in forced expiratory volume in one second (mean±se 33±2%) and profound V′A/Q′ mismatching, reflected by a decreased arterial oxygen tension (from 100±4 to 75±3 mmHg) and an increased overall index of V′A/Q′ heterogeneity dispersion of retention minus excretion inert gases corrected for dead space (from 4.9±1.2 to 8.4±1.1; normal≤3.0; dimensionless), 5 min after placebo. Following montelukast, LTD4 produced no significant changes in any of the variables. In conclusion, these findings point to the view that leukotriene D4-induced gas exchange disturbances and bronchoconstriction are both mediated by the cysteinyl-leukotriene1 receptor.
Long-term (up to 4.5 years) treatment with fingolimod in multiple sclerosis: results from the extension of the randomised TRANSFORMS study

Objective The 12-month (M), phase 3, double-blind, randomised TRANSFORMS study demonstrated significant benefits of fingolimod 0.5 or 1.25 mg over interferon β-1a (IFNβ-1a) in patients with relapsing–remitting multiple sclerosis. We report the results of long-term (up to 4.5 years) extension of TRANSFORMS. Methods Patients randomised to fingolimod (0.5/1.25 mg) in the core phase continued the same dose (continuous-fingolimod) in the extension, whereas those on IFNβ-1a were re-randomised (1:1) to fingolimod (IFN-switch; IFN: 0.5/1.25 mg). Outcomes included annualised relapse rate (ARR), confirmed disability progression and MRI measures. Results are presented here for the continuous-fingolimod 0.5 mg and pooled IFN-switch groups. Results Of the 1027 patients who entered the extension, 772 (75.2%) completed the study. From baseline to the end of the study (EOS), ARR in patients on continuous-fingolimod 0.5 mg was significantly lower than in the IFN-switch group (M0–EOS: 0.17 vs 0.27). After switching to fingolimod (M0–12 vs M13–EOS), patients initially treated with IFN had a 50% reduction in ARR (0.40 vs 0.20), reduced MRI activity and a lower rate of brain volume loss. In a post hoc analysis, the proportion of IFN-switch patients with no evidence of disease activity increased by approximately 50% in the first year after switching to fingolimod treatment (44.3% to 66.0%). The safety profile was consistent with that observed in the core phase. Conclusions These results support a continued effect of long-term fingolimod therapy in maintaining a low rate of disease activity and sustained improved efficacy after switching from IFNβ-1a to fingolimod. Clinical trial registration No NCT00340834.
Impact of Bots and Suspicious Accounts on Online Brand Personality and Brand Sentiment

Social media increased the expressiveness of users and decreased the firms' control over their brand personalities. Brand managers need to process massive amounts of firm-generated and user-generated content to monitor the way a brand is being perceived on the social media networks. This study addresses two important questions: What is the alignment between the communicated and perceived brand personalities of Indian technology brands ? What is the impact of bots and suspicious accounts on the brand personality and brand sentiment ? Around 26,400 tweets of four technology brands in India were analyzed using a state-of-the-art deep learning model. This study provides the first glimpse of the congruence between communicated and perceived brand personalities of these brands. Also, contrary to the conventional thinking, this study established that the brand sentiment category - positive, neutral or negative - is unaffected by bots and suspicious accounts.
Automatic Quality Evaluation for User Generated Contents in Online Q&A Community Based on Word2Vec-CNN

The online Q&A (question-and-answer) community has emerged as a prominent platform for Internet users to access valuable information, but a notable disparity exists in the quality of answers provided. Hence, understanding and enhancing answer quality evaluation in the context of Chinese Q&A communities assumes paramount significance. First, a system of evaluation metrics for answer quality is constructed. Then, based on this metric system, the collected Zhihu data is annotated and an answer quality automated evaluation model using Word2VecCNN (convolutional neural networks) is developed. Utilize this model to perform both coarse-grained and fine-grained automated evaluation of answer quality in online Q&A communities. Compared to other models, the proposed Word2Vec-CNN-based automatic answer quality evaluation model in this research yielded superior experimental results, particularly in the realms of syntax, semantics, and pragmatics. Furthermore, it closely approximated the manually curated content found in “Zhihu Daily. ” It excelled in filtering high-quality answers, significantly enhancing users’ efficiency in acquiring knowledge during their actual browsing experiences within the Q&A community. Consequently, it demonstrates a stronger potential to leverage the pivotal role of Q&A communities in disseminating network knowledge.
Multivariate Forecasting of Network Traffic in SDN-Based Ubiquitous Healthcare System

The emerging 5G network has been revolutionizing the data transfer across the networks. Recently, advanced technologies transform the healthcare industry. With the emergence of 5G technology, smart and ubiquitous healthcare systems are interconnected with high speed and low latency. As ubiquitous healthcare applications require stringent network requirements, managing network traffic and resources becomes difficult. Internet of Medical Things (IoMT) devices generates a large volume and variety of data, including physiological signals, images, and videos. As many medical applications require real-time communication and data transfer, this puts high demands on the network, which needs handle low latency, high availability, and high reliability. Achieving consistent QoS is challenging due to the inherent limitations of conventional methods which are susceptible to network congestion and latency issues. The next generation of Software Defined Networking (SDN) in IoMT can play a vital role in supporting traffic management. Reliable classification and forecasting will enable the SDN controller to make optimal routing decisions dynamically. Therefore in this paper, a novel Medical Traffic Forecasting framework based on Weighted Multivariate Singular Spectrum Analysis (MTF-WMSSA) is proposed for an SDN-enabled IoMT healthcare network to analyze and forecast network traffic and ensure accurate real-time medical data transfer. The evaluation was conducted on the EHMS dataset to study the performance of the proposed method. The comparison of the proposed MTF-WMSSA is made with classical methods such as SVR and LSTM. The result shows that the proposed MTF-WMSSA exhibits improved classification accuracy of 93%. The network parameters that are forecasted are average traffic load, average packet arrival rate and average jitter. The MAPE of the multi-step ahead forecast is 13.64 and 11.41 for short and long intervals respectively. The forecasting algorithm proposed in this paper can efficiently determine future flow parameters which help to implement adaptive traffic engineering. The evaluation was conducted on the dataset to study the performance of the proposed method. The result shows that the proposed MTF-WMSSA exhibits better accuracy for multi-step ahead forecast and outperforms the classical methods.
Artificial Intelligence Based Real Time Surveillance and Object localization System

Real-time (RT) surveillance and object localization are very important for the detection of objects and their activities. It helps society to monitor the anomalous behaviours of objects and enhance security systems. For accurate and precise detection, an effective, fast, and reliable algorithm is needed. This paper proposes a novel real-time technique of surveillance and object localization, i.e., object and location prediction. The first step is to propose a novel technique of surveillance, in which the encoder-based features are extracted and then used for the prediction of object locations and their classes. The proposed technique is trained with a standard dataset. After that, in the second step, the proposed technique is deployed on a Jetson-Nano ARM Cortex-A57 based developer board for RT implementation. The proposed methodology shows the effective results in RT with an approximate accuracy of 97% and 99% with standard datasets.
Enhancing Object Detection Accuracy Through RGB and Event Fusion in Motion Blurred Images

Deep learning-based object detection exhibits impressive levels of accuracy. However, its performance significantly degrades when motion blur occurs in images captured with an RGB camera. In contrast, an event camera is a sensor that detects brightness changes for each pixel, offering advantages such as high temporal resolution and low latency. It has been recently applied to various computer vision tasks. In this paper, we propose a fused image that combines RGB and event data, leveraging the advantages of the event camera to enhance detection accuracy in motion-blurred images. No additional operations are required for deblurring. Thus, it incurs minimal computational overhead and can be used without modifying the existing object detection model structure.
Geological modeling by an indicator kriging approach applied to a limestone deposit in Indiara city - Goiás

The mineral exploration activity consists of a set of successive stages that are interdependent on each other, in which the main goal is to discover and subsequently evaluate a mineral deposit for the feasibility of its extraction. This process involves setting the shape, dimensions and grades for eventual production. Geological modeling determines the orebody’s possible format in subsoil, which can be done by two approaches: vertical sections (deterministic methods) or geostatistical methods. The latter approach is currently being preferred, as it is a more accurate alternative and therefore, more reliable for establishing the physical format of orebodies, especially in instances where geologic boundaries are soft and/or with widely spaced sample information. This study uses the concept of indicator kriging (IK) to model the geologic boundaries of a limestone deposit located at Indiara city, Goiás State, Brazil. In general, the results indicated a good adherence in relation to samples. However, there are reasonable differences, particularly in lithological domains with a small number of samples in relation to the total amount sampled. Therefore, the results showed that there is a need for additional sampling to better delineate the geological contacts, especially between carbonate and non-carbonate rocks. Uncertainty maps confirmed this necessity and also indicated potential sites for future sampling; information that would not be obtained by usage of deterministic methods.
Deterministic Search Strategy of Compression Codes

Testing large circuits requires compression of test patterns before transferring them to the tested circuit and decompression on board. The compression and decompression mechanism may be built according to the linear block code rules. Broadening the decompressor structure search space to those using nonlinear block code is possible. Nonlinear block codes outperform their linear counterparts' compression ability. Their usage is limited due to the intricate search process. The code words' interconnectedness is difficult to exploit in the code word creation process as too many mutual dependencies exist among the code word bits. For this reason, random and partially random code word search methods were investigated in the past. This paper proposes two fully deterministic nonlinear code construction approaches. The obtained codes are significantly more efficient and easily scalable than the linear ones. We demonstrate their efficiency on codes with the number of specified bits equal to two, three, and four. The proposed methods may be used to create extensive codes, which are unachievable for previously published methods due to the explosion of computation time.
Three-Phase High-Power and Zero-Current-Switching OBC for Plug-In Electric Vehicles

In this paper, an interleaved high-power zero-current-switching (ZCS) onboard charger (OBC) based on the three-phase single-switch buck rectifier is proposed for application to plug-in electric vehicles (EVs). The multi-resonant structure is used to achieve high efficiency and high power density, which are necessary to reduce the volume and weight of the OBC. This study focuses on the border conditions of ZCS converting with a battery load, which means the variation ranges of the output voltage and current are very large. Furthermore, a novel hybrid control method combining pulse frequency modulation (PFM) and pulse width modulation (PWM) together is presented to ensure a driving frequency higher than 10 kHz, and this will reduce the unexpected inner resonant power flow and decrease the total harmonic distortion (THD) of the input current under a light load at the end of the charging process. Finally, a prototype is established, and experiments are carried out. According to the experimental results, the conversion efficiency is higher than 93.5%, the THD about 4.3% and power factor (PF) 0.98 under the maximum power output condition. Besides, a three-stage charging process is also carried out the experimental platform.
Enhanced Control of Photovoltaic Voltage Regarding Mixed Conduction Modes of Power Interfaces

The photovoltaic power output is characterized by a nonlinear and time-variant behaviour that changes significantly with the operating condition and environmental variables, e.g., solar irradiance and temperature. The switching operation of power interfaces is also nonlinear, time-variant, and load-dependence, inevitably experiencing both the continuous and discontinuous conduction modes (CCM/DCM) to accommodate the power variation. These lead to a difficult control problem and compromise the performance of maximum power point tracking (MPPT). This paper provides a comprehensive analysis of the system dynamics and proposes a universal but simple control law to support consistently stable, fast, and robust control operations.
Knowledge-Assisted Intelligent Maritime Multi-Ship Tracking

Deep learning-based methods for multi-object tracking (MOT) in maritime scenarios often encounter challenges such as high false negative(FN) rates, frequent identity switches, and discontinuous trajectories due to lighting variations and occlusions among ships. These issues arise because traditional tracking methods treat tracking as a subsequent task to detection, resulting in direct failures when detection fails. To overcome these challenges, we propose a novel knowledge-assisted maritime multi-ship intelligent tracking algorithm. By integrating the knowledge that “objects do not suddenly disappear” into the MOT framework through a target search method, our approach ensures that once an object is tracked, it will not be lost, thus minimizing dependence on the detector, reducing FN rates, and maintaining trajectory continuity. Our experimental results demonstrate that compared to traditional MOT methods that use the same detector, our tracking approach achieves a 20% reduction in FN rates and a 15% increase in Multiple Object Tracking Accuracy (MOTA).
More "normal" than normal: scaling distributions and complex systems

One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems.
Analyzing Customer Sentiments in Genshin Impact: A Business Intelligence Perspective on Player Perception

Customer perception plays a pivotal role in determining the success of a game in the competitive market landscape. It encompasses how players perceive various aspects of the game, including its mechanics, visuals, story, character design, and overall experiential quality. In data analysis, sentiment analysis has emerged as a powerful tool for gaining valuable insights into customer perception. This research aims to employ sophisticated sentiment analysis techniques to understand customer perception in the context of Genshin Impact comprehensively. Through sentiment analysis, this study explores the intricate relationship between customer perception and player sentiments expressed concerning Genshin Impact. An analysis of categorized tweets reveals significant sentiment patterns associated with using specific words. Negative sentiment is observed in tweets containing words such as “lazy,” “retired,” “uninstalled,” and “sad,” indicating the presence of unfavorable content. Conversely, positive sentiment is associated with words like “play,” which holds the highest frequency in positive keywords. Genshin Impact players use this term to express their enjoyment and satisfaction from engaging with the game. Additional positive keywords include “interesting,” “funny,” “gacha” (a term related to acquiring characters in the game), “lore,” and “characters. ” By leveraging sentiment analysis techniques, this research contributes to a comprehensive understanding of customer perception in Genshin Impact. The findings shed light on the prevailing sentiments of players and provide insights into the factors contributing to positive and negative perceptions of the game.
Instance keys: A technique for sharpening whole-program pointer analyses with intraprocedural information

Pointer analyses enable many subsequent program analyses and transformations, since they enable compilers to statically disambiguate references to the heap. Extra precision enables pointer analysis clients to draw stronger conclusions about programs. Flow-sensitive pointer analyses are typically quite precise. Unfortunately, flow-sensitive pointer analyses are also often too expensive to run on whole programs. This paper therefore describes a technique which sharpens results from a whole-program flow-insensitive points-to analysis using two flow-sensitive intraprocedural analyses: a must-not-alias analysis and a must-alias analysis. The main technical idea is the notion of instance keys, which enable client analyses to disambiguate object references. We distinguish two kinds of keys: weak and strong. Strong instance keys guarantee that different keys represent different runtime objects, allowing instance keys to almost transparently substitute for runtime objects in static analyses. Weak instance keys may represent multiple runtime objects, but still enable disambiguation of compile-time values. We assign instance keys based on the results of our analyses. We found that the use of instance keys greatly simplified the design and implementation of subsequent analysis phases.
Real-Time Independent Components Analysis for Dimensional Reduction of Hyperspectral Images Using Reconfigurable Hardware

Hyperspectral image analysis represents an extremely complex procedure from a computational point of view, mainly due to the high dimensionality of the data. This computational cost represents a significant disadvantage in applications that require a real-time response, such as fire monitoring, prevention and monitoring of natural disasters, chemical spills, and other environmental pollutants. Dimensional reduction allows us to decrease the size of the image while preserving the important discriminating features, by eliminating redundant data or noise. Due to their reduced size, weight, and power consumption when compared to other high-performance computing systems, reconfigurable hardware solutions, such as field-programmable gate arrays, have been consolidated in recent years as one of the standard options for the quick processing of hyperspectral remotely sensed images. In this paper, we have implemented an optimized hardware version of the Fast Independent Component Analysis (FastICA) version for dimensional reduction of hyperspectral images using an FPGA device. Our implementation achieves a 34 × speedup compared with its equivalent software version, achieving the objective of real-time processing considering the capture time of the AVIRIS spectral sensor.
Assessing the Viability of Generative AI-Created Construction Scaffolding for Deep Learning-Based Image Segmentation

Construction scaffolding serves as a pivotal temporary structure essential for construction activities, exerting a direct influence on site safety conditions. Unfortunately, the lack of documentation often leads to a shortage of training data necessary for employing image segmentation through deep learning for inspection purposes. In an effort to overcome this bottleneck, Generative AI, adept at creating images from pretrained data, emerges as a potential solution. However, the inherent black box nature of deep learning introduces the possibility of generating unrealistic images, thereb necessitating a rigorous evaluation, which constitutes the primary focus of our research. Our findings reveal that scaffolding images generated by Generative AI exhibit distinct features that our deep learning model successfully learned, resulting in an impressive mean average precision (mAP) of 82. Nonetheless, discernible patterns in image generation may be lacking, as evidenced by our deep learning system's ability to grasp scaffolding features proficiently, achieving a mAP of 69 even from the initial epoch. This observation suggests potential challenges in generating diverse scaffolding images through the Generative AI approach, emphasizing the need for further investigation before implementing it with real scenario images