Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Investigation of High-Efficiency L-Band MILO Using Reflector Stubs

A magnetically insulated line oscillator (MILO) is a renowned crossed-field high-power microwaves (HPMs) device with an intrinsic property of self-insulation against electrical breakdown, which corroborates its gigawatt range operation. This article presents an idea of a reflector in MILO for efficiency enhancement. Reflectors are placed symmetrically in the beam dump region. Effects of reflector radius and positioning on output power are also studied, and it is observed that a matched reflector causes a drastic improvement in efficiency. The presented reflectors in conventional MILO enhance the axial electric field (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {E_{z}}$ </tex-math></inline-formula>) in the slow-wave structure (SWS) region, which reinforces the beam–RF wave interaction. An eigenmode analysis is done to determine the dominant frequency of MILO. Simulation results show that the device is operating in the <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-band at 1.82 GHz. The dominant mode is <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {TM_{01}}$ </tex-math></inline-formula> with an average output power of 7.55 GW. The average power efficiency is 18.6%. The applied input voltage is 680 kV and current of 59.7 kA. While the conventional MILO only generates an average power of 5.2 GW at the same input power. PIC simulation results reveal that the efficiency of MILO with reflector configuration is 45% higher than that of conventional MILO with a commonly used beam stub position of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\lambda _{g}/4}$ </tex-math></inline-formula> from the extractor cavity.
Infrared time-sensitive target data augmentation algorithm based on generative model

Currently, infrared time-sensitive target detection technology is widely used in military and civil applications such as air defense and early warning, maritime surveillance, and precision guidance, but some high-value target images are difficult and expensive to acquire. To address the problems such as the lack of infrared time-sensitive target image data and the lack of multi-scene multi-target data for training, this paper proposes an infrared time-sensitive target data enhancement algorithm based on a generative model, which is a two-stage model. Firstly, in the first stage, the visible images containing time-sensitive targets are converted to infrared images by a modal conversion model based on CUT networks. Then in the second stage a large number of random targets are generated from the converted IR images using an adversarial random sample generation model to achieve the data enhancement effect. The coordinate attention mechanism is also introduced into the generator module in the second stage, which effectively enhances the feature extraction capability of the network. Finally, modal conversion experiments and sample random generation experiments are conducted, and the results show the feasibility of the data enhancement method of generative model proposed in this paper in IR time-sensitive target data enhancement, which provides a strong data support for improving IR time-sensitive target detection algorithm.
Interrelationship Between Intelligence Quotient and Space Maintainers Among Children: A Cross-Sectional Comparative Study

Introduction Intelligence quotient (IQ) is an indicator to measure a child's cognitive ability to learn or understand and to deal with new situations with their logical and analytical skills. Children with better IQ exhibit increased cooperation when undergoing dental treatments, leading to a positive attitude toward dental care. The primary aim of the study was to assess the interrelationship between the IQ of children, space maintainer therapy, and the behavior of children aged 6-10 years. Materials and methods A total of 104 children were divided into two groups: group 1 included children undergoing space maintainer therapy and group 2 included children who did not undergo space maintainer therapy. Their IQ scores were assessed using Raven’s Coloured Progressive Matrices and behavior and the Frankl behavior rating scale. The data were analyzed by SPSS Version 23 (IBM Corp., Armonk, NY). Independent t-tests were used to evaluate the differences between IQ and children with space maintainers, and Mann-Whitney U tests were used to assess the differences between behavior and space maintainers. Results The mean age of the participants was approximately 8.28 years. The mean IQ score of the group of children undergoing the space maintainer therapy was 90.69 ± 7.65 and that of the control group was 105.59±10.71. Based on the Frankl behavior rating scale, the mean score in the space maintainer group was 35.44 and that of the control group was 69.56. There was a significant association between IQ, behavior, and the presence of space maintainers. Conclusion The group of children undergoing space maintainer therapy demonstrated comparatively lesser IQ, and the majority of children exhibited negative behavior. Also, children wearing space maintainers had undergone one or multiple extractions, which is traumatic for children and may lead to them likely exhibiting a negative behavior than children in the control group. Hence, it may be concluded that intelligence, behavior, and space maintainers are all significantly associated with each other.
Spectral tilt underlies mathematical problem solving

Neural activity associated with successful cognition appears as a tilt in the power spectrum of the local field potential, wherein increases in high-frequency power accompany decreases in low frequency power. Whereas this pattern has been shown in a wide range of memory tasks, it is unknown whether this increased spectral tilt reflects underlying memory-specific processes or rather a domain-general index of task engagement. To address the question of whether increased spectral tilt reflects increased attention to a cognitive task, we collected intracranial recordings from three hundred thirty neurosurgical patients as they performed a mathematical problem solving task. We used a mathematical problem solving task, because it allowed us to decouple task-specific processes with domain-general attention in a novel way. Using a statistical model to control for inherent problem complexity, we classified individual math problems based on whether a subject performed faster than predicted (high-attention or fast) or slower than predicted (low-attention, or slow) based on residual response times. In contrast to the domain-general attentional account, problems that took longer than predicted produced stronger evidence for the spectral tilt: widespread increases in high frequency (31–180 Hz) power and decreases in low frequency (3–17 Hz) power across frontal, temporal, and parietal cortices. The pattern emerged early within each trial and was sustained throughout the response period but was not observed in the medial temporal lobe. The data show that engaging in mathematical problem solving leads to a distributed spectral tilt pattern, even when accounting for variability in performance driven by the arithmetic demands of the problems themselves, and suggest that broadband changes in the power spectrum reflect an index of information processing in the brain beyond simple attention to the cognitive task.
Attributes of innovation and adoption of mobile banking in Egypt

While studying the adoption of mobile banking, its antecedents must be considered to have a better understanding and complete picture of the factors affecting the adoption of mobile banking. There is a scarcity of studies about the effect of the (Rogers, 1995, 2003) five attributes of innovation on the adoption of mobile banking. So, this paper examined the effect of relative advantage, compatibility, complexity, trialability, and observability on the adoption of mobile banking in Egypt. The study hypotheses were tested using primary data collected online. The results show that relative advantage, compatibility, and observability have a significant positive impact on the adoption of mobile banking. On the other hand, the effect of complexity and trialability on the adoption of mobile banking was negative but nonsignificant.
Comparison of EEG- Based Deep Neural Network Classifiers for Emotion Recognition using Selected Electrodes

In this study, SVM and DNN models were employed to classify participants’ emotional states using the publicly available SEED dataset, achieving impressive accuracy rates of 79.8% for DNN and 79.4% for SVM, surpassing the accuracies of previous models. Furthermore, our electrode reduction study, which optimized electrode placement by focusing on emotionally relevant brain regions, yielded high accuracy. The use of EL further enhanced model accuracy to 81.3%. These classification results suggest that future research with more extensive datasets could lead to even more robust models. Consequently, this work has the potential to provide guidance for personalized recommendations in areas such as music, movies, books, and other choices tailored to individuals’ emotional states.
Toxicity Evaluation and Controlled-Release of Curcumin-Loaded Amphiphilic Poly-N-vinylpyrrolidone Nanoparticles: In Vitro and In Vivo Models

Curcumin attracts huge attention because of its biological properties: it is antiproliferative, antioxidant, anti-inflammatory, immunomodulatory and so on. However, its usage has been limited by poor water solubility and low bioavailability. Herein, to solve these problems, we developed curcumin-loaded nanoparticles based on end-capped amphiphilic poly(N-vinylpyrrolidone). Nanoparticles were obtained using the solvent evaporation method and were characterized by dynamic and electrophoretic light scattering, transmission electron (TEM) and atomic force (AFM) microscopy. The average particle size was 200 nm, and the ζ-potential was −4 mV. Curcumin-release studies showed that nanoparticles are stable in aqueous solutions. An in vitro release study showed prolonged action in gastric, intestinal and colonic fluids, consistently, and in PBS. In vitro studies on epidermoid carcinoma and human embryonic kidney cells showed that the cells absorbed more curcumin in nanoparticles compared to free curcumin. Nanoparticles are safe for healthy cells and show high cytotoxicity for glioblastoma cells in cytotoxicity studies in vitro. The median lethal dose was determined in an acute toxicity assay on zebrafish and was 23 μM. Overall, the curcumin-loaded nanoparticles seem promising for cancer treatment.
Mass Spectrometry Rearrangement Ions and Metabolic Pathway-Based Discovery of Indole Derivatives during the Aging Process in Citrus reticulata ‘Chachi’

The rapid analysis and characterization of compounds using mass spectrometry (MS) may overlook trace compounds. Although targeted analysis methods can significantly improve detection sensitivity, it is hard to discover novel scaffold compounds in the trace. This study developed a strategy for discovering trace compounds in the aging process of traditional Chinese medicine based on MS fragmentation and known metabolic pathways. Specifically, we found that the characteristic component of C. reticulata ‘Chachi’, methyl N-methyl anthranilate (MMA), fragmented in electrospray ionization coupled with collision-induced dissociation (CID) to produce the rearrangement ion 3-hydroxyindole, which was proven to exist in trace amounts in C. reticulata ‘Chachi’ based on comparison with the reference substance using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Combining the known metabolic pathways of 3-hydroxyindole and the possible methylation reactions that may occur during aging, a total of 10 possible indole derivatives were untargeted predicted. These compounds were confirmed to originate from MMA using purchased or synthesized reference substances, all of which were detected in C. reticulata ‘Chachi’ through LC–MS/MS, achieving trace compound analysis from untargeted to targeted. These results may contribute to explaining the aging mechanism of C. reticulata ‘Chachi’, and the strategy of using the CID-induced special rearrangement ion-binding metabolic pathway has potential application value for discovering trace compounds.
An Integration and Mining of Multiple-Source Data for Real-Time Services

The technique that has been presented tackles and resolves problems that have previously impeded the retrieval of a variety of Internet of Things services. Users are taken through the planned architecture that will enable them to get the required services from the cloud. The form collects data on the user’s identity, location, service requirements, and the anticipated length of time spent waiting for service. The requests are categorized and placed in one of three different queues based on the length of time they have been waiting. We are able to respond more rapidly to requests that have reduced wait periods. At time T1, each and every request that was made is processed further in order to provide services. If the demands of two different users are the same, then the information about their location and the service they want will be integrated into a single request. This procedure is carried out in order to consolidate requests of a similar kind. In spite of the fact that the request is given more priority for speed, it nevertheless satisfies all requests coming from all users. To begin, let’s have a look at the many types of services that may be located in response to a request that has been made. In the research that we advise doing, we zero in on three distinct types of cloud storage services. The provision of services in the areas of medical, agriculture, and the environment are the three major categories. Each domain receives its own unique ontology, which is followed by the combining of disjoint nodes via the use of estimations of domain relevance and axiom similarity. After that, an ontology weights index is constructed with the help of the annotation. An index table is being constructed that will include user input about the quality of each service supplied by the top supplier. The cloud storage is responsible for maintaining the index of each and every domain. Indexes are compiled for every service in order to make it easier to conduct searches.
Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.
The Classification of Synoptic-Scale Eddies at 850 hPa over the North Pacific in Wintertime

Empirical orthogonal function (EOF) is applied to the study of the synoptic-scale eddies at 850 hPa over the North Pacific in winter from 1948 to 2010. The western developing pattern synoptic-scale eddies (WSE) and the eastern developing pattern synoptic-scale eddies (ESE) are extracted from the first four leading modes of EOF analysis of high-pass filtered geopotential height. The results show the following: (1) The WSE and the ESE both take the form of a wave train propagating eastward. The WSE reach their largest amplitude around the dateline in the North Pacific, while the largest amplitude of ESE occurs in the northeast Pacific. (2) The WSE and ESE are the most important modes of the synoptic-scale eddies at 850 hPa over the North Pacific, which correspond to the two max value centers of the storm track. (3) In addition to geopotential height, the WSE and the ESE also leave their wave-like footprints in the temperature, meridional wind, and vertical velocity fields, which assume typical baroclinic wave features. (4) The WSE and the ESE have an intrinsic time scale of four days and experience a “midwinter suppression” corresponding to the midwinter suppression of storm tracks.
Instrumentality and/or Identity? Explanation of Participation in Collective Action in the Gabonese Repressive Context Based on the Dual-Pathway Model of Social Movement Participation

The dual-pathway model of social movement participation posits that engagement in social movements is done through an instrumental pathway (by evaluating the costs and benefits of participation) or an identification pathway (by becoming aware of the similarity and destiny shared with members of the same social category as oneself); these pathways are mutually exclusive. In the present research, this postulate, formalized and tested until now exclusively in a democratic context, is tested in a repressive context, which differs from the democratic context due to the quasi-systematic recourse of the institutional authorities to coercive tactics to prevent the expression of dissident discourse and conduct. The data were collected during a student social movement from 389 Gabonese students (232 men and 152 women), aged between 16 and 33 (M = 23.9, SD = 3.20). They were administered self-reported measures, focusing on instrumentality, identity, and intention to participate in protest action. The results report that the instrumental and identification pathways simultaneously predict the intention to participate in protest action. They are therefore not mutually exclusive, as the model suggests.
Systematic Assessment of the Catalytic Reactivity of Frustrated Lewis Pairs in C-H Bond Activation

Unreactive C-H bond activation is a new horizon for frustrated Lewis pair (FLP) chemistry. This study provides a systematic assessment of the catalytic reactivity of recently reported intra-molecular FLPs on the activation of typical inert C-H bonds, including 1-methylpyrrole, methane, benzyl, propylene, and benzene, in terms of density functional theory (DFT) calculations. The reactivity of FLPs is evaluated according to the calculated reaction thermodynamic and energy barriers of C-H bond activation processes in the framework of concerted C-H activation mechanisms. As for 1-methylpyrrole, 14 types of N-B-based and 15 types of P-B-based FLPs are proposed to be active. Although none of the evaluated FLPs are able to catalyze the C-H activation of methane, benzyl, or propylene, four types of N-B-based FLPs are suggested to be capable of catalyzing the activation of benzene. Moreover, the influence of the strength of Lewis acid (LA) and Lewis base (LB), and the differences between the influences of LA and LB on the catalytic reactivity of FLPs, are also discussed briefly. This systematic assessment of the catalytic activity of FLPs should provide valuable guidelines to aid the development of efficient FLP-based metal-free catalysts for C-H bond activation.
Exploiting Historical Diffusion Data to Maximize Information Spread in Social Networks

Information spread maximization is to find a small subset of nodes in social network such that they can maximize the expected spread of information. In this paper, we attempt harnessing historical information cascades data to learn how information propagates in social networks and how to maximize its spread. In particular, we proposed a voting algorithm to learn diffusion probabilities of edges from cascades data. Then a pruning method is developed to remove trivial edges whose weights are smaller than a threshold. Moreover, motivated by the social influence locality, we propose a Local Influence Model to evaluate node's influence within a local area instead of the whole network, which can effectively reduce the computational complexity. Based on Local Influence Model, we use greedy algorithm to find an approximate optimal solution. Experimental results show that our method significantly outperforms state-of-the-art models both in terms of information spread and algorithm runtime.
Kazakh Speech Recognition: Wav2vec2.0 vs. Whisper

—In recent years, the progress made in neural models trained on extensive multilingual text or speech data has shown great potential for improving the status of underresourced languages. This paper focuses on experimenting with three state-of-the-art speech recognition models, namely Facebook’s Wav2Vec2.0 and Wav2Vec2-XLS-R, OpenAI’s Whisper, on the Kazakh language. The objective of this research is to investigate the effectiveness of these models in transcribing Kazakh speech and to compare their performance with existing supervised Automatic Speech Recognition (ASR) systems. The study also aims to explore the possibility of using data from other languages for pre-training and to test whether fine-tuning the target language data can improve model performance. Thus, this work can provide insights into the effectiveness of using pretrained multilingual models in underresourced language settings. The wav2vec2.0 model achieved a Character Error Rate (CER) of 2.8 and a Word Error Rate (WER) of 8.7 on the test set, which closely matches the best result achieved by the end-to-end Transformer model. The large whisper model achieves a CER of approximately 4 on the test set. The results of this study can contribute to the development of robust and efficient ASR systems for the Kazakh language, benefiting various applications, including speech-to-text translation, voice assistants, and speech-based communication tools.