Miracle-dz/UniFAR-FTUs huggingface dataset:
Parasitic Gate Capacitance Model for N-Stack Forksheet FETs

The gate-all-around nanosheet (NS) field effect transistors (FETs) are hailed as the most promising candidate for scaling the CMOS technology beyond the 5-nm technology node owing to their enhanced electrostatic integrity. However, the fabrication process dictates the minimum spacing between the PFET and NFET (PN margin) in the NSFET architecture and limits the scalability of the PN margin. Therefore, the forksheet (FS) FET architecture with PFET and NFET separated by a dielectric wall was proposed to minimize the standard cell height from six Track length of M1 (6T) to 5T. Furthermore, the FSFETs exhibit comparable static performance to the NSFETs while providing significant improvement even from a fabrication perspective. Although the FSFETs are expected to show a better dynamic performance owing to the reduction in the Miller capacitance, vertical stacking of FSs may lead to an increase in the parasitic capacitance and degradation in the overall system performance. Moreover, the dielectric wall may induce severe electrostatic coupling between the NFET and PFET. Therefore, it becomes imperative to analyze and formulate an analytical model for the parasitic capacitance components of the FSFETs. To this end, in this work, for the first time, we have developed analytical models for different parasitic gate capacitance components of ${N}$ -stack FSFETs and analyzed the influence of various structural parameters on the total parasitic gate capacitance of two architectures: split gate (SG) and merge gate (MG) FSFETs. The results of the developed analytical model overlap with the results extracted from TCAD simulations validating the accuracy of the proposed model.
Use of NIR hyperspectral imaging and chemometrics to quantify roots and crop residues in soil

Context: Monitoring of root development and crop residues decomposition in crop soils is important to understand the effects of agricultural practices and to improve them. In the fields studies on root system development, soil coring method allows multiple samplings but manual sorting of roots and crop residues extracted from soil samples before quantification is a tedious and time consuming step. Discrimination of roots and crop residues based on their Near Infrared (NIR) spectral signature and chemometrics was tested as a new rapid and reliable method.
A Classification Approach to Detect Social Support in Online Health Communities

Online Health Communities (OHCs) is a term that is commonly used to refer to online social networks related to health. There are two types of users in an OHC, namely authors and visitors. OHC authors are primarily patients and caregivers who use the platform to share their health journeys, learn about illnesses, seek social support, and connect with others in similar circumstances. OHC visitors are family members and friends who are following the authors’ journeys and offering social support. Different types of social support may be sought and provisioned in an OHC including prayer, esteem, emotional, network, instrumental, and informational. In this project, we employed classification algorithms to automatically identify the type of social support provisioned by an OHC visitor where this identification of social support can be used by a platform to enrich its services by, for example, identifying patterns that trigger more support provisioning, and by identifying which type of support keeps patients and caregivers more engaged on the platform. Identifying the social support provisioned in an OHC post is a multi-label classification problem since a single post may involve more than one social support type. Hence, we implemented a multi-label classifier as a set of independent binary classifiers, one for each of the following five social support types: esteem, emotional, network, instrumental, and informational. Each binary classifier is used to detect the existence or the absence of the corresponding support type in a given OHC post. Four of our classifiers are at least 85% accurate while the informational support classifier’s accuracy is 77%.
Porfirowa mineralizacja Mo–Cu–W w utworach prekambryjsko-paleozoicznych – analiza prognostyczna strefy kontaktu bloków górnośląskiego i małopolskiego

Porphyry Mo–Cu–W mineralization is associated with the boundary zone of the Upper Silesia and Ma3opolska blocks. It is encountered within Ediacarian and Silurian metasediments, as well as in Ordovician, Silurian and Devonian carbonates and siliciclastics, intruded by granitoids and porphyry dikes. Ore mineralization is represented by chalkopyrite, molibdenite and scheelite forming veinlets, impregnations and disseminations. Investigation of 66 km of cores from 284 prospective drill holes (done till 1992) provided new data representing the distribution of mineralization on a regional scale. Based on the contours of metal average contents, fve prospective areas (Nowa Wieœ ̄arecka–Myszków–Mrzyg3ód, excluding Myszków deposit, ̄arki–Kotowice, Zawiercie, Pilica and Dolina Bêdkowska) with porphyry Mo–Cu–W mineralization have been delineated on the Ma3opolska Block and Mys3ów area on the Upper Silesia Block. Formation of ore mineralization was preceded by contact-metasomatic metamorphism (predominantly by biotitisation of granitoids cover extending to 1500 m away from intrusion). Ores are spatially and genetically associated with zones of hydrothermal alteration (feldspathization, silification, epidotization, carbonatization, chloritization, sericitization) developing in and around granitoid and porphyry intrusions. Close spatial and genetic association between mineralization (supported by Re–Os 303–295 Ma ages of molibdenite), Variscan magmatism, alteration, and active Cracow–Lubliniec disslocation system clearly indicates postmagmatic, hydrothermal origin of mineralization. New exploration targets can be found at the border zone of Ma3opolska and Upper Silesia Block.
PEMBERIAN AIR GULA PADA BROILER PASCA MENETAS YANG DIPELIHARA SELAMA 4 MINGGU

Penelitian ini bertujuan untuk mengetahui performan broiler umur 4 minggu yang diberi gula merah pada air minumnya pasca menetas dengan konsentrasi 0% sebagai kontrol, 5%, 10%, dan 15% berdasarkan rancangan acak lengkap. Penelitian dilaksanakan di Desa Moncongloe, Kecamatan Moncongloe, Kabupaten Maros, Sulawesi Selatan. Sejumlah180 ekor anak ayam broiler strain Cobb 500 dibagi berdasarkan 4 perlakuan dan tiga ulangan dengan jumlah ayam 15 ekor per unit kandang ukuran 25 cm 2 /ekor. Air gula merah diberikan selama 6 hari pasca DOC masuk kandang, secara adlibitum selama 12 jam. Pakan komplit starter diberikan dari pasca menetas hingga umur 4 minggu. Hasil penelitian menunjukkan pemberian air gula merah dengan konsentrasi berbeda tidak berpengaruh (p>0,05) terhadap konsumsi pakan, bobot badan, konversi pakan dan angka kematian
Open-Source Tool Based Framework for Automated Performance Evaluation of an AD Function

As automation in the field of automated driving (AD) progresses, ensuring the safety and functionality of AD functions (ADFs) becomes crucial. Virtual scenario-based testing has emerged as a prevalent method for evaluating these systems, allowing for a wider range of testing environments and reproducibility of results. This approach involves AD-equipped test vehicles operating within predefined scenarios to achieve specific driving objectives. To comprehensively assess the impact of road network properties on the performance of an ADF, varying parameters such as intersection angle, curvature and lane width is essential. However, covering all potential scenarios is impractical, necessitating the identification of feasible parameter ranges and automated generation of corresponding road networks for simulation. Automating the workflow of road network generation, parameter variation, simulation, and evaluation leads to a comprehensive understanding of an ADF's behavior in diverse road network conditions. This paper aims to investigate the influence of road network parameters on the performance of a prototypical ADF through virtual scenario-based testing, ultimately advocating the importance of road topology in assuring safety and reliability of ADFs.
Research on EEG technology based on convolutional neural network

Convolutional neural net is an advanced neural network whose structure consists of multiple convolutional layers and pooling layers, and its application in the field of image processing is outstanding. As an electrophysiological signal characterising brain activity, EEG signal is currently of great research significance in many fields such as artificial intelligence applications, intelligent medical care and human-computer interaction. In this paper, based on the deep learning method, based on the training of convolutional neural network CNN and LSTM hybrid model, the diagnosis of EEG signals of people with mild cognitive impairment is carried out, and the diagnostic accuracy obtained is as high as 98%, which confirms the feasibility of the application of convolutional neural network in EEG signals.
Identifying Emerging Competencies demanded by the Employers from MBA Finance Graduates: Using NLP and TF - IDF Algorithm

MBA graduates with the right combination of knowledge skills and abilities are assured of lucrative careers. India has the second-largest talent pool of MBAs in the world. But in the job market, there exists a gap between the competencies demanded by the employers and the ones exhibited by the graduates. On account of the above scenario, the researchers in this article have made an attempt to identify the emerging competencies demanded by the employers, from Finance graduates. The data available in the job portals are extracted to identify the key competencies expected from the employers. Method of text mining is executed to shortlist the key competencies by applying the TF -IDF weighting algorithm in Python language. The most important competencies that secured the highest score have been listed under three different domains including Knowledge, Skills, and Behaviour. Researchers have also recommended the best practices that higher education institutions can adopt in building industry-driven curricula to meet employer expectations.
A low-cost dual bandpass planar filter for WiMAX and mobile communications

This study proposes a new design of low-cost dual-bandpass filter for worldwide interoperability and microwave access (WiMAX) band at 3.50 GHz and mobile communications band at 1.19 GHz. A high pass filter and a stopband filter make up the new dual-bandpass filter structure. Different theoretical studies were carried out for the design of the proposed filter. This filter’s base is a RO5880 substrate with a dielectric permittivity constant of 2.2, loss tangent of 0.0009 and 1.6 mm thickness. High mashing density was used to validate the various simulated structures while accounting for two numerical methods: the moment technique and the finite integration method. The final circuit's overall dimensions are 60×178,3 mm2.
Application of Association Rule Algorithm in Virtual Business Risk Classification System and Identification Model

In reality, business risks are ubiquitous. For example, fluctuations in market prices, price fluctuations, changes in social, political, and economic environments, natural disasters, etc., can all lead to commercial risks, and different types of commercial risks have different ways of handling them. Association rule algorithms can reflect the correlation between one thing and other things, and the factors that cause business risk are multiple and interrelated. This article aimed to apply association rule algorithms to identify business risk models and virtual business risk classification systems, so that the virtual business classification system can accurately identify business risks and classify them according to prescribed standards. The experiment proved that the virtual business risk identification and classification system designed using association rule algorithm in this article was indeed effective, and its evaluation and classification results for business risks were only about 2% different from those of professionals. It can accurately identify business risks and evaluate and classify them.
Broadband flexible metamaterial absorber using Carbon fiber loaded silicone foam

In this paper, we propose a new and original absorbing material based on silicone foam loaded with long carbon fibers (12 mm). This flexible composite is structured in a periodic way, like "metamaterials". The originality of the proposed absorbing material resides, on the one hand, in the association of two simple fabrication concepts (a simple production of the silicone foam and a simple periodic structuring of this foam) and, on the other hand, in the use of dielectric lossy material to make periodic broadband absorbing structures. The idea here is to replace standard metamaterial absorbers, based on structured metallic film (often copper) deposited on a dielectric substrate, by organic metamaterial. Thus, a flexible absorber of 16 mm thickness, with the shape of a "chessboard" and having a broadband absorption bandwidth with the reflectivity below −10 dB in the frequency range of 4-18 GHz is proposed and implemented. Moreover, we also obtain a good absorption response by varying the incident angle up to 30°.
Exploring the Therapeutic Significance of microRNAs and lncRNAs in Kidney Diseases

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two crucial classes of transcripts that belong to the major group of non-coding RNAs (ncRNAs). These RNA molecules have significant influence over diverse molecular processes due to their crucial role as regulators of gene expression. However, the dysregulated expression of these ncRNAs constitutes a fundamental factor in the etiology and progression of a wide variety of multifaceted human diseases, including kidney diseases. In this context, over the past years, compelling evidence has shown that miRNAs and lncRNAs could be prospective targets for the development of next-generation drugs against kidney diseases as they participate in a number of disease-associated processes, such as podocyte and nephron death, renal fibrosis, inflammation, transition from acute kidney injury to chronic kidney disease, renal vascular changes, sepsis, pyroptosis, and apoptosis. Hence, in this current review, we critically analyze the recent findings concerning the therapeutic inferences of miRNAs and lncRNAs in the pathophysiological context of kidney diseases. Additionally, with the aim of driving advances in the formulation of ncRNA-based drugs tailored for the management of kidney diseases, we discuss some of the key challenges and future prospects that should be addressed in forthcoming investigations.
Effect of Vagal Nerve Stimulation on Anxiety and Sleep Disturbances among Geriatric Population: A Pilot Study

Background: Anxiety during the COVID-19 pandemic has been a significant concern for people of all age groups,including geriatric individuals. The elderly population has faced unique challenges during this time, which cancontribute to increased anxiety levels. The uncertainty and fear surrounding the pandemic can lead to increasedanxiety and stress, which can negatively impact sleep quality and duration.Purpose: The aim of this study is to find out the effect of vagal nerve stimulation on anxiety and sleep disturbancesamong geriatric population.Methodology: Using the Purposive Sampling method, a total of 20 individuals were chosen based on the inclusionand exclusion criteria. The participants were divided into two groups using the closed envelope method, and theytook part in therapy sessions lasting 30 minutes, three times per week for a period of four weeks. The control groupreceived Jacobson’s progressive relaxation technique, whereas the experimental group received non-invasiveTranscutaneous auricular vagal nerve stimulation.Result: With a p value of 0.001, transcutaneous auricular vagal nerve activation significantly improved sleepquality and reduced anxiety after 4 weeks.Conclusion: Study showed that transcutaneous vagal nerve stimulation was effective in reducing Anxiety andimproving sleep quality in geriatric population.
Guia de Linguagem Simples para Visualização de Dados: Construção, Design, Avaliação e Contribuições

Considerando a importância da Linguagem Simples na área da Interação Humano-Dados, sobretudo no desenvolvimento de visualização de dados mais compreensíveis. Este trabalho apresenta maiores detalhes sobre o processo de desenvolvimento do Guia de Linguagem Simples para criação de gráficos, com base em práticas consolidadas na literatura. O Guia abrange diversas práticas organizadas por categorias sobre os elementos comuns em gráficos (eixos, cores, legendas etc). Os resultados incluem avaliações do uso do Guia por não especialistas, demonstrando sua utilidade e identificando áreas de melhoria no guia.
DataCockpit: A Toolkit for Data Lake Navigation and Monitoring Utilizing Quality and Usage Information

Modern organizations amass their datasets into centralized repositories called data lakes, affording analytics as needed. The resultant scale and complexity of these data lakes, however, can make data navigation and monitoring challenging for users. We present DataCockpit, a Python toolkit that leverages datasets, usage logs, and associated meta-data to provision data usage and quality characteristics. DataCockpit computes these characteristics for each attribute (e.g., number of times it was queried for subsequent use in downstream applications) and record (e.g., number of non-missing, valid values) and aggregates them at the level of datasets. We develop a visual monitoring tool, powered by DataCockpit, and demonstrate how it can assist data / system administrators as well as end-users to effectively navigate and monitor a data lake. DataCockpit and the monitoring tool are available as open source software for developers to build custom monitoring applications on top of data lakes.
A Lightweight Speech Emotion Recognition Model with Bias-Focal Loss

A major challenge in speech emotion recognition (SER) is how to build a lightweight model with limited training data for applying on the devices with limited in resources. In this paper, we propose a lightweight speech emotion recognition (SER) model with Bias-Focal loss function, where a Dynamic Separable Convolution (DySC) block is designed to extract more fine-grained emotional features and makes the model smaller. We propose a Bias-Focal loss to address the issue of the inconsistent of training samples, while focusing on data points with high feature diversity during the training phase. Experimental results show that our proposed lightweight model is the smallest comparing with other methods, while the number of parameters in our proposed model is 0.85M. Meanwhile, our proposed model achieves the best performance that the score of Unweighted Accuracy (UA) is 75.01 %, and that of Weighted Accuracy (WA) and F1-score are 74.05% and 74.29 % on the IEMOCAP (scripted+improvised)dataset.
Ant-based friends recommendation in social tagging systems

Aggregated tagging behaviour in social tagging systems provides valuable information about the interests of the users which may help in broadening their perspective and expand their network of friends. This paper proposes an ant-based friends recommendation (AbFR) approach to recommend friends to the target user. In AbFR, prominent-N tags are recommended to the target user for a specific resource using tag co-occurrence graph where weights on the edges are computed based on pheromone updating strategy known from ant algorithms. Each target user's trust graph containing friends is maintained based on implicit feedback over a period of time. Optimal trust path is computed from the combined trust graph of all the users and friends are recommended to the target user based on foraging behaviour of biological ants. Experimental study conducted with delicious.com, a social bookmarking website shows significant improvement in discovering new friends.
Optical Coherence Tomography to Detect Macular Edema: A Comprehensive Approach

Macula of the retina is responsible for detailed central vision. Macular Edema is the inflammation or swelling of the macula. The severity of the disease ranges from blurred vision to blindness. Amongst various eye imaging modalities, Optical Coherence Tomography is capable of detecting macular edema both in its early and later stages. An algorithm to detect Macular Edema from OCT scan images has been presented. The images are de-noised by filtering followed by segmentation of the retinal layers that enclose the macular portion. A few textural and wavelet features of the images, thickness and area of the macular region in the OCT scans are evaluated as parameters to train the classifier and draw a classification between normal and diseased images. Support Vector Machine, a supervised machine learning model is employed for the binary classification. As the results are satisfactory, the proposed method is competent in efficient detection of Macular Edema.
Enhancing Vocal-Based Laryngeal Cancer Screening with Additional Patient Information and Voice Signal Embedding

Symptoms of laryngeal cancer manifest primarily through voice changes, and its diagnosis relies solely on laryngoscopy examinations, lacking objective indicators of voice alterations. Recent advances in deep learning have opened possibilities for vocal-based laryngeal cancer screening. However, the practical medical application remains constrained due to relatively low accuracy. In this paper, we propose a method that combines patient information and voice analysis with a CNN model to address this issue. Experiments demonstrate a 8% improvement in accuracy when additional information is embedded alongside voice signals, compared to using voice data alone in deep learning models. This approach holds promise for more effective laryngeal cancer screening and diagnosis.
Continuous Prompt for Chemical Language Model Aided Anticancer Synergistic Drug Combination Prediction

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical Language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies.