Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Insulation Challenges and Mitigation Strategies in (U)WBG Power Modules Packaging

In the ever-growing landscape of electrical power demand, the future of power electronics module packaging lies in the realm of wide-bandgap (WBG) materials, including silicon carbide (SiC), gallium nitride (GaN), and cutting-edge ultra WBG (UWBG) materials like diamond, aluminum nitride (AlN), and hexagonal-boron nitride (h-BN). These materials offer superior properties to traditional silicon-based devices, promising higher power density, reduced weight, and increased operating temperature, voltage, and frequency. However, pushing the boundaries for power electronics modules presents challenges in insulation systems as the encapsulation material and the ceramic substrate may not withstand the functional parameters, potentially leading to unfavorable conditions like high field stress and partial discharge (PD), ultimately resulting in insulation failure. This paper presents a thorough analysis of the characteristics of the electrical insulation materials used in power electronics devices based on the research in WBG packaging conducted in recent years. The significance of maximum electric field stress at triple points (TPs) is examined. Furthermore, the paper reviews the strategies and techniques employed to mitigate the challenges related to maximum field stress and PDs in both encapsulation and substrate materials. It is concluded that the mitigation strategies are promising in improving insulation systems for packaging, but the studies lack their implementation under actual operating conditions of WBG power modules.
Joint UAV Trajectory Planning and LEO-Sat Selection in SAGIN

In this paper, the problem of joint unmanned aerial vehicle (UAV) trajectory planning and low-orbit satellites (LEO-Sats) selection in space-air-ground integrated networks (SAGIN) will be investigated. This problem is of utmost importance when SAGIN is exploited for post-disaster relief services, where ground base stations (GBSs) within the post-disaster area are completely damaged or malfunctioned. In this scenario, UAV will provide wireless connectivity for the victims, while LEO-Sats will relay the UAV data to the nearest survival GBS. UAV trajectory should be optimized to maximize the collected data from the victims subject to its limited battery capacity, while it should jointly select the best LEO-Sat in each visited location within its optimized trajectory. The selected LEO-Sat should maximize UAV’s achievable data rate while maintaining a long remaining visible time to minimize frequent LEO-Sats’ handovers. In this paper, an online learning approach using multi-armed bandit (MAB) models will be proposed to address this highly dynamic problem. As LEO-Sat selection should be performed after UAV arrives at a dedicated location in its optimized trajectory, the problem is divided into two MAB stages. In the first stage, the battery constraint UAV trajectory optimization is modeled as budget constraint MAB (BC-MAB) game using BC-upper confidence bound (BC-UCB) algorithm. In the second stage, LEO-Sat selection in each visited location is modeled as contextual MAB with variable arms (CMAB-VA) game using LinUCB-VA algorithm. Numerical analysis confirms the superior performance of the proposed approach over candidate benchmarks.
Enhanced Document Classification Using Ensemble Techniques

Both natural language processing and information retrieval rely on document classification. With the exponential growth of digital documents, there is an increasing demand for accurate and efficient document classification techniques. However, document classification faces several challenges, including processing vast amounts of data and extracting features from large volumes of unstructured data, which is complex. Moreover, numerous input features can be extracted, but not all of them are relevant to the classification problem. Single-classifier models often have limitations in capturing the complex patterns and nuances present in diverse document collections. In this paper, we propose a new approach to enhance document classification through the utilization of ensemble techniques. We combine multiple classifiers, including SVM, Naïve Bayes, and AdaBoost as base models, and aggregate their predictions with a neural network (NN) as a meta-model to improve overall classification performance. The suggested method aims to enhance accuracy, robustness, and generalization in classification by harnessing the diversity and complementary strengths of different classifiers. This research contributes to the field of document classification by utilizing the AdaBoost algorithm as a base model in stacking techniques applicable to document classification. The experimental results demonstrate that the proposed method outperforms the most recent methods in classifying documents in terms of accuracy, precision, and recall. The accuracy of the proposed system reached 87%, and the F1 score reached 87.8%.
Semanformer: Semantics-aware Embedding Dimensionality Reduction Using Transformer-Based Models

In recent years, transformer-based models, particularly BERT (Bidirectional encoder Representations from Transformers), have revolutionized natural language processing tasks, achieving state-of-the-art performance in various domains. In the context of natural language processing (NLP) and linguistics, understanding the semantic aspects of text is crucial for tasks like information retrieval, sentiment analysis, machine translation, and many others. However, the high dimensionality of BERT embeddings presents challenges in real-world applications due to increased memory and computational requirements. Reducing the dimensionality of BERT embeddings would benefit many application downstream tasks by reducing the computational requirements. Although there are prevalently used dimensionality reduction methods which focus on feature representation with lower dimensions, their application on NLP tasks may not yield semantically correct results. We propose a novel framework named as semanformer (semantics-aware encoder-decoder dimensionality reduction method) that leverages transformer-based encoder-decoder model architecture to perform dimensionality reduction on BERT embeddings for a corpus while preserving crucial semantic information. To evaluate the effectiveness of our approach, we conduct a comprehensive use case evaluation on diverse text datasets by sentence reconstruction. Our experiments show that our proposed method achieves high sentence reconstruction accuracy (SRA) more than 83% compared to the traditional dimensionality reduction methods such as PCA (SRA < 66%) and t-SNE (SRA < 9%).
Dugong (Dugong dugon) and seagrass in Thailand: present status and future challenges

Dugong and seagrass research in Thailand has been principally conducted by Phuket Marine Biological Center (PMBC), Department of Marine and Coastal Resources (DMCR). The first stranded dugong was reported in 1979. The interview surveys with villagers and aerial surveys for dugong population were started in 1993 and 1997, respectively. Several research topics were documented on dugong, both the biological and chemical aspects, and including the management plan. Seagrass surveys had been originally conducted in 1988 particularly in the Andaman sea coast and seagrass surveys in the Gulf of Thailand have been recently performed. Seagrass data-based information of PMBC was officially available in 2004. Although, the diversity of only 12 seagrass species have been reported in Thai waters, the seagrass beds have been shown to play an important role as the nursery ground of economically important species. We have reviewed the recent status and future research of dugong and seagrass in Thailand according to the following items: population, genetic divergence and threats; dugong behavior, feeding habit and acoustic survey; heavy metal and organotin compounds concentration; status of seagrass; marine organisms in seagrass beds; future challenges; and conservation and management.
Lossy Lempel-Ziv Coding for Federated Learning

Due to its privacy-preserving and promising learning performance, federated learning (FL) is widely used for distributed model training. However, the limited communication resources will severely slow down the training processing of FL. To address this issue, this paper, for the first time, applies Lempel-Ziv (LZ) coding into the FL system. It proposes a lossy LZ coding strategy that compresses the local model but allows a certain level of distortion. The convergence analysis shows that compared to the quantized stochastic gradient descent (QSGD), the lossy LZ coding further compresses the local models while maintaining the same sub-linear convergence performance when the distortion is relatively small. Another merit of the lossy LZ coding is that it can perform as a secondary compression after quantization or sparsification and thus is easy to implement. Experimental results demonstrate that it can significantly reduce the communication overhead than QSGD and achieve higher compression efficiency than Top-k sparsification under the same communication overhead.
Exploring Sentiment Analysis Algorithms in Power Generation for Automated Monitoring

this paper explores diverse sentiment evaluation algorithms used inside the automated monitoring of strength technology. Unique algorithms such as Naive Bayes, guide Vector Machines, Conditional Random Fields, and RNNs are mentioned and compared with recognition to their performance in this use case. The results imply the fine-acting fashions for every algorithm, with F1 rankings ranging from seventy-one. Three% (Naive Bayes) to 83.1 % (RNNs), permitting the discovery of diffused insight into the strength era area. Moreover, an evaluation of the fake positives generated via each algorithm will assist in refining the performance of the algorithms in the power technology domain.
A Wideband Antenna Calibration Network Using Multi-Layer Coupling Technology

In this brief, the design methodology of an antenna calibration network using multi-layer stripline-slotline coupling is proposed. A multi-layer coupler and a two-stage Wilkinson power divider are designed to ensure the basically stable amplitude-phase characteristics of coupling and achieve the wide frequency band. To minimize phase imbalance, the lengths of channels should be kept equal to the maximum extent possible. In addition, the transmission line conversion structure is applied at each port for integration with the antenna array. Aiming at the problems of signal crosstalk and mutual coupling, the isolated via holes are installed near the lines and metal spacers are placed over the ports. By using the semi-closed structure, part of substrates is dug out to expose the resistor welding position. A semi-closed calibration network in series-parallel-fed is fabricated to verify the design methodology. In 8–16 GHz, the amplitude-phase characteristics are considerable, in which the coupling is 37–44 dB and phase deviation is below 8°. The isolation between ports T1-T16 is greater than 33 dB and voltage standing wave ratio (VSWR) of them is lower than 1.8.
THE ZURICH ENVIRONMENTAL STUDY OF GALAXIES IN GROUPS ALONG THE COSMIC WEB. I. WHICH ENVIRONMENT AFFECTS GALAXY EVOLUTION?

The Zurich Environmental Study (ZENS) is based on a sample of ∼1500 galaxy members of 141 groups in the mass range ∼1012.5–14.5 M☉ within the narrow redshift range 0.05 < z < 0.0585. ZENS adopts novel approaches, described here, to quantify four different galactic environments, namely: (1) the mass of the host group halo; (2) the projected halo-centric distance; (3) the rank of galaxies as central or satellites within their group halos; and (4) the filamentary large-scale structure density. No self-consistent identification of a central galaxy is found in ∼40% of <1013.5 M☉ groups, from which we estimate that ∼15% of groups at these masses are dynamically unrelaxed systems. Central galaxies in relaxed and unrelaxed groups generally have similar properties, suggesting that centrals are regulated by their mass and not by their environment. Centrals in relaxed groups have, however, ∼30% larger sizes than in unrelaxed groups, possibly due to accretion of small satellites in virialized group halos. At M > 1010 M☉, satellite galaxies in relaxed and unrelaxed groups have similar size, color, and (specific) star formation rate distributions; at lower galaxy masses, satellites are marginally redder in relaxed relative to unrelaxed groups, suggesting quenching of star formation in low-mass satellites by physical processes active in relaxed halos. Overall, relaxed and unrelaxed groups show similar stellar mass populations, likely indicating similar stellar mass conversion efficiencies. In the enclosed ZENS catalog, we publish all environmental diagnostics as well as the galaxy structural and photometric measurements described in companion ZENS papers II and III.
Acoustic Signal Processing using Deep Learning for Intelligent Feed Monitoring of Dicentrarchus Labrax

Acoustic signal processing holds significant promise for real-time fish feeding intensity estimation in aquaculture. Unlike traditional methods reliant on visual cues or sensor data, acoustic analysis provides valuable insights into feeding behavior and demand. By capturing indicators such as water splashing, acoustic techniques can estimate the current feeding demand of fish. Acoustic techniques in aquaculture remain under explored, especially those delving into temporal information within acoustic spectrograms. This paper presents an intelligent monitoring approach using deep learning and acoustic signals. It investigates the perceptual domain of fish feeding acoustic spectrum recognition, extracting insights from Mel Spectrogram feature maps. Employing a supervised machine learning method with a multi-instance multi-label technique, the study classifies audio events during operational scenarios. Furthermore, the research assesses the effectiveness of the proposed neural network (NN) models for multi-label classification by comparing it with established NN architectures like AlexNet, ResNet18, and VGG11, showcasing its superior performance.
Optimization of Imprecise Multiplier Circuits by using Binary Decision Diagram

Imprecise multipliers are employed to enhance the efficiency and reduce the power consumption of arithmetic circuits while tolerating acceptable levels of inaccuracy in the outcomes. The multiplier circuit has a broader range of applications within embedded systems, particularly in the fields of digital signal processing, IoT and multimedia, etc. We proposed a systematic formal approach utilizing the Look-Up Table (LUT) employing the Burch method to evaluate the imprecise multiplier based on the Binary Decision Diagram (BDD) considering Worst Case Error constraint. Implemented 13 bit multiplier using normal and proposed imprecise multiplier shows 98% reduction in number of nodes. The proposed 25 bit imprecise multiplier based on Burch HL shows an 89% decrement in a number of nodes w.r. t. Burch LH method. BDD generation for imprecise multiplier is 75% faster than normal.
Broadband Microstrip Antenna Design for Wireless Communications

The Broadband Microstrip Antenna Design for Wireless Communications is an essential component of the modern telecommunications system. It is important to design an antenna that is efficient, cost-effective, and easy to install in order to successfully implement large-scale wireless networks that operate on a wide range of frequencies. These antennas must also have good radiation patterns to ensure that they can provide the required coverage for the users in the network. The Broadband Microstrip Antenna Design follows the principles of electromagnetic wave propagation and the design requirements of modern wireless communications systems. Design efforts for these antennas hinge on the principles of miniaturization, resistance to environmental factors (such as wind and rain), and wideband operation. Furthermore, modern antenna design includes testing radiation patterns and uploading the data to software-based simulation tools to ensure the design meets their requirements. The challenge of developing an efficient antenna design for wireless communication networks is faced by engineers and researchers alike. Microstrip antennas, also called patch antennas, are a popular choice for wireless communication applications, as they offer high efficiency, controllable power gain, and good cross-polarization.
Enhancing Restaurant Customer Review Analysis: Multi-Class Text Classification with BERT

Online customer reviews can be valuable information when processed with the right techniques. Businesses aim to enhance their strategies in managing operations by leveraging sentiment analysis from their clientele. In a business context, the primary focus of SA is to capture individual perspectives on products. Presently, sentiment analysis not only deals with emotional polarity but also classifies sentiments towards various product-related aspects. This research aims to classify restaurant customer review data on Google Maps into categories such as “menu,” “taste,” “indoor atmosphere,” and “outdoor atmosphere”. We utilize 450 entries for this dataset. The model used is Bidirectional Encoder Representation from Transformers (BERT), a technique based on transformer-based neural networks, capable of learning grammatical structures, interpreting semantics, and deriving relationships among contextual entities. The results show a less satisfying accuracy score of 41.110/0. This is due to several inaccuracies in the label annotations of the utilized data. Further analysis is discussed in the Discussion and Conclusion section.
Hidden regulators in attachment, separation, and loss.

Love and grief, two of the most intense and complex human emotions, have their origins in the infant's attachment to her parents and in her responses to separation from them. In early development, when these responses are first evident, we have no descriptions of the inner experience of these emotions and no verbal assessment measures to guide us in our research. In this regard, studies of human infants are no different from research with young animals; we must use observations of behavior and of physiological responses to make inferences about the nature of the processes responsible for the elicitation, the control of intensity, and the termination of these early emotion states. Research on young mammals of widely divergent species has revealed a remarkable similarity between infant animal and human behavior, particularly with respect to behaviors used to maintain proximity and those elicited by separation. In addition, a number of physiological responses to separation have been described that generalize widely across species from laboratory rats to rhesus and pigtail macaque monkeys. These cross-species similarities encourage us to believe that we can learn something about the basic processes underlying attachment, separation, and loss in human infants from experimental studies on the young of other species. Contemporary concepts of attachment grew out of attempts to understand responses to separation and loss (Bowlby 1969/1982, 1980) but are currently much more focused on individual differences in the development of patterns of attachment behaviors and of the mental representations that
Study of Motion Planning of Quadcopter Under Uncertain Environment

The consequences of drone movement state (motion planning), such as position, orientation and speed, during the travelling mission to the destination are crucial factors in its safety, resource consumption and task accomplishment. The unmanned aerial vehicle can be obliged in an unknown environment to follow an unplanned trajectory when faced with obstacles. Hence, this paper suggested two motion planning approaches for quadcopter navigation implemented in two scenarios under uncertain environments with incomplete information about the investigated environment. In the first scenario, the quadcopter’s motion will be controlled using the PID control algorithm to accomplish multiple goals in an environment artificially created to be full of obstacles. The pure pursuit control algorithm, employed for the same purpose in the same uncertain environment, is included in the second scenario. In both cases, the quadcopter will navigate and avoid obstacles using a vector field histogram plus algorithm and a LiDAR sensor in an obstacle avoidance system. We will compare the two tests regarding the time and distance required to evaluate the suggested scenarios’ performance in different environmental conditions and show the advantages of utilising such strategies. MATLAB Simulink was used to conduct the simulation.
Water Event Categorization Using Sub-Metered Water and Coincident Electricity Data

This study evaluated the potential for data from dedicated water sub-meters and circuit-level electricity gauges to support accurate water end-use disaggregation tools. A supervised learning algorithm was trained to categorize end-use events from an existing database consisting of features related to whole-home and hot water use. Additional features were defined based on dedicated irrigation metering and circuit-level electricity gauges on major water appliances. Support vector machine classifiers were trained and tested on portions of the database using multiple feature combinations, and then externally validated on water event data collected under dissimilar conditions from a demonstration house in Austin, Texas, USA. On the testing data, a trained classifier achieved true positive rates for occurrences and volume exceeding 95% for most categories and 93% for toilet events. Performance for faucet events was less than 90%. Initial results suggest that dedicated sub-meters and circuit-level electricity gauges can facilitate highly accurate categorization with simple features that do not rely on flow rate gradients.
Dementia Disorders in a Birth Cohort Followed From Age 85 to 88: The Influence of Mortality, Refusal Rate, and Diagnostic Change on Prevalence

The prevalence of dementia increased in women (from 31% to 46%) but not in men (from 27% to 25%) in a representative birth cohort followed from age 85 to 88. The increase was mostly attributed to a higher rate of new cases among women than among men. The proportion of moderate to severe dementia increased, and mild dementia decreased, mainly because of progression of mild dementias to severer forms and because most new cases were of moderate to severe degree. The proportion of vascular dementia was 47% at age 85 and 54% at 88 despite a higher mortality in vascular than in other dementias. Diagnosis changed to vascular dementia in 9 out of 31 cases of Alzheimer's disease because of new cerebrovascular events. This study illustrates that prevalence is influenced by several factors, such as number of new cases, refusal rate, diagnostic change, and mortality. These factors act in different directions and may differ between populations.