Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Optimal Configuration of Energy Storage and Converter in DC Distribution System

In the distribution system, energy storage system can be used to stabilize load fluctuations, while there is a key problem of how to optimize the allocation of energy storage capacity and energy storage converter power. In this paper, the time-series Gaussian distribution model is used to describe the load with power fluctuations. When the load exceeds the sum of the power of the power supply and the energy storage, this part of the load will be short of power. This paper analyzes the risk of this kind of power outage and analyzes the system’s demand for energy storage and the lack of power supply caused by insufficient energy storage in a continuous period with an analytical method. Then, the optimal configuration model of energy storage capacity and converter power is established with the goal of maximizing the benefit of the DC distribution system, and a solution to the model using the gradient descent method is designed. Finally, several examples are optimized using this method, which shows that the method proposed in this paper can realize the optimal configuration of energy storage capacity, energy storage power and converter power.
Synthetic Datasets for Numeric Uncertainty Quantification: Proposing Datasets for Future Researchers

In this article, we propose ten synthetic datasets for point prediction and numeric uncertainty quantification (UQ). These datasets are split into the train, validation, and test sets for model benchmarking. Equations and the description of each dataset are provided in detail. We also present representative shallow neural network (NN) training and random vector functional link (RVFL) training examples both of which are training models for the point prediction. We perform UQ with the consideration of a Gaussian and homoscedastic distribution. Distribution considerations and models are made quite simple for the following reasons: 1) much room exists for further explorations and improvements, 2) users of the dataset have simple training examples including the process of accessing data, and 3) users get an idea of probable result and the format of the result. The dataset and scripts are available at the following link: https://github.com/dipuk0506/UQ-Data.
NOMA Based Channel Access Scheme for IEEE802.11 WIFI

Over the past ten years, internet traffic has significantly increased as a result of the connecting of millions of intelligent communication and computer devices. To increase the spectrum efficiency of cellular networks, non-orthogonal multiple access (NOMA) solutions have recently been developed. This research initially creates an analytical model to calculate aggregate saturated throughput and also the energy usage of the WLAN. There have been various attempts to increase the energy economy of NOMA-enabled wireless connections while preserving its remarkable performance measures, such as high throughput & optimal optimised capacity, given that NOMA is the splitting multiple access approach for 5G. Research is being done on both the novel NOMA-based WLAN and also the traditional IEEE 802.11 WLAN. Its WLAN works well in terms of higher throughput and energy use. Different data transmission speeds are available under IEEE 802.11 standards, while dynamic rate adaption is used to maximise effectiveness. Station in IEEE 802.11 WLANs can operate at a variety of data speeds by implementing data rate adaptation.
Spoken Language Translation using Conformer model

One of the earliest significant projects for automatic speech translation is Spoken Language Translator (SLT). The SLT system can switch between English and other languages with an accuracy of roughly 75% and a vocabulary of about 1500 words. The SRI Core Language Engine serves as the foundation for the language processing components, which are specified and essentially built on top of it by employing a mixture of general grammars and techniques that allow for easy customization to specific domains. The process of translating spoken phrases into the target language is known as spoken language translation. This encourages communication between language speakers. The ASR and NMT systems are combined in the speech translation system. A speech recognizer collects the phrase after the speaker speaks. The input is then transformed into a sequence of words using a language's vocabulary and grammar using a sizable corpus of text. The text is translated into another language by the machine translation function. This description of SLT is a crucial source for academics curious about the current state of SLT.
Internet of Things Based Tired Detection using Deep Learning Techniques

Sleep is essential for human survival since it helps to restore and maintain our bodies' immune systems and other essential processes. One-third of a person's life is devoted to sleeping, although few are aware of the many positive aspects of this activity. Two distinct types of sleep, REM and NREM, have been identified. A good night's rest is achieved when REM and NREM sleep alternate in a regular pattern. Disruptions to this cycle, whether they originate physiologically or psychologically, have been linked to a variety of health problems. Polysomnography (PSG) equipment is often used in sleep labs inside hospitals to perform sleep studies. A polysomnogram is an in-depth medical technique that records a patient's vital signs while they sleep and necessitates a hospital stay. Clinically, sleep apnea is defined as a breathing disease in which there are periodic pauses in breathing lasting 10 seconds or more that occur more than five times during the night. Sleep apnea may be classified as either Obstructive, Central, or Mixed. The prevalent sleep problem known as obstructive sleep apnea (OSA) is caused by the relaxation of muscles in the upper airway during sleep. The purpose of this study is to provide a technique for screening for Obstructive Sleep Apnea by analysing Heart Rate Variability of Electrocardiogram (ECG) data while the subject is asleep. The goals of this study are to create computational approaches for identifying OSA based on characteristics extracted from Heart Rate Variability (HRV) signals derived from sleep electrocardiograms (ECGs). Physio Net's Apnea-ECG recordings serve as the source for the ECG data.
Secured machine learning using Approximate homomorphic scheme for healthcare

Medical disease prediction is a boon after the growth of the medical learning on the recent years The potential for Machine Learning (ML) to boost productivity is significant. To achieve good outcomes, the data quality for training ML models must be great. Any ML algorithm only performs well when it is provided with a tonne of high-quality training data. In order to obtain such high-quality data, several organizations collaborate. It is crucial to uphold data privacy and confidentiality while obtaining data from many patients of different regions. In specific the medical data should be dealt with importance and should be secured to prevent data breach. The proposed work uses CKKS approximate homomorphic encryption scheme to preserve privacy in data when it is fed into the machine learning models. Homomorphic features allow calculations on encrypted data to be done without any privacy leaks, and the user can safely decode the results. This paper proposes a secure mechanism to store and process medical records using homomorphic encryption scheme. The suggested method uses a logistic regression model that was trained using the pima indian diabetes dataset.
Firmware-based approach for efficient Power Management in Mobile Platforms

Increase in the computing power of mobile platform devices and the advancement in processor technology has led to the coexistence of thermal devices with multiple technologies to be part of the same platform. Though Advanced Configuration and Platform Interface (ACPI) provides thermal and power states information to perform power management, it isn’t sufficient to manage the interoperability of all the system components. As part of this paper, we will discuss the firmware-based approach of DPTF which resolves this issue. Also, we will discuss the extended power management methodology to prevent the voltage violation that might occur due to high power consumption by the turbo processor.
A Comparative Analysis on Predicting Brain Tumor from MRI FLAIR Images Using Deep Learning

It is still challenging to differentiate between normal cells and tumor demarcation in everyday clinical practice. With the use of the FLAIR modality known as Fluid Attenuated Inversion Recovery, a medical professional can learn more about tumor infiltration. Because the preponderance of the cerebrospinal fluid effect can be suppressed by the FLAIR modality. Moreover, one of the advantages of using FLAIR images is that they can be used for both 3D and 2D medical imagery. Therefore, this paper explores the idea of assessing and predicting brain tumors by implementing several types of deep learning CNN architectures, such as VGG16, ResNet50, DenseNet121 and others in a user-friendly functional U-Net architecture. The flexibility of using different pre-trained neural network models in a single architecture is the key advantage of our U-Net architecture. Hyperparameters of the architecture are adjusted and fine-tuned for the segmentation process in order to extract the core features of the tumor contour according to our problem. Having said that, this study's segmentation result on the dice similarity coefficient is 0.9165, 0.9175, 0.9137 and 0.9148 in the BraTS 2018, 2019, 2020 and 2021 datasets respectively.
An Enhanced Segmentation and Deep Learning Architecture for Early Diabetic Retinopathy Detection

Diabetic retinopathy is a serious complication needing prompt diagnosis and medication to avert vision loss. Lesions caused by the condition are difficult to track because they are hidden behind the eye's structure in small and subtle forms. To extract relevant features. , we created a robust pipeline using multiple preprocessing techniques. , image segmentation architecture (DR-UNet) with atrous spatial pyramid pooling. , and an attention-aware deep learning convolutional network with different modules based on ResidualNet. Empirical results show that our framework has segmentation accuracies of 87.10% (intersection over union) and 84.50% (dice similarity coefficient). Moreover. , classification performance of 99.20% provided better results than existing schemes. , as reinforced by the smooth convergence of training/validation loss and accuracy. This study has the potential to supplement traditional diagnosis to identify better the ailment in its early and advanced stages.
Explicit History Selection for Conversational Question Answering

Topic shift is very common in multi-turn dialogues, making it a great challenge in the filed of conversational question answering. Existing methods usually select the most adjacent turns as history information, however, it is useless or even harmful in case of topic shift. This paper proposes two explicit history selection models: SHSM and DHSM, to address this issue. The former is a simple history selection model, which only selects $\boldsymbol{k}$ previous history turns; and the latter is a dependent history selection model, which selects the most relevant $\boldsymbol{k}$ history turns through a turn-dependent graph. The two models are then trained in a consistency framework. Experimental results on QuAC show that our model can cope with topic shift problem, and it outperforms existing state-of-the-art methods by 0.8 on $\boldsymbol{F}_{\mathbf{1}}$ score, 0.7 on HEQ-Q score, and 1.4 on HEQ-D score.
Research on Train Positioning Technology of Urban Rail Transit Based on Lidar

As the backbone of urban public transport, urban rail transit has gradually become the core of urban comprehensive transportation network, which is an important means to alleviate traffic congestion and save land resources and energy. Therefore, the safety of urban rail operation is very important, and the wrong positioning of the track will cause serious personal casualties and property losses. In order to realize the accurate positioning of urban rail transit, this thesis adopts the research of laser radar on train positioning. Combined with the laser point cloud matching algorithm, this thesis puts forward the train positioning technology scheme of Urban Rail Transit Based on Lidar. This thesis proposes the point cloud registration algorithm based on feature matching. After preprocessing the collected point cloud data, the point cloud registration algorithm estimate the motion state of Lidar and obtain the global map and the location of the train.
Snow Cover and Temperature Relationships in North America and Eurasia

Abstract In this study the snow cover extent during the autumn months in both North America and Eurasia has been related to the ensuing winter temperature as measured at several locations near the center of each continent. The relationship between autumn snow cover and the ensuing winter temperatures was found to be much better for Eurasia than for North America. For Eurasia the avenge snow cover extent during the autumn explained as much as 52% of the variance in the winter (December-February) temperatures compared to only 12% for North America. However, when the average winter snow cover was correlated with the average winter temperature it was found that the relationship was better for North America than for Eurasia. AS much as 46% of the variance in the winter temperature was explained by the winter snow cover in North America compared to only 12% in Eurasia.
A Nested-Grid-Based Energy-efficient Deployment Algorithm for Wireless Sensor Networks

Taking into consideration that the abnormal areas aren't distributed uniformly and nodes in wireless sensor networks(WSNs) are dense and redundant,an energy-efficient deployment strategy named Nested Grid based Energy-efficient Deployment Algorithm(NGEDA) is presented for WSNs.A non-uniform deployment algorithm is designed based on physical parameter gradient distribution.The performance of NGEDA employed in environment monitoring(e.g.forest fire monitoring) is analyzed and validated by Matlab simulation.Results show that NGEDA is energy-efficient,can quickly find the abnormal area,and can describe its parameter distribution in detail.
Total Surface Area of a Through-and-Through Oromandibular Defect Is Associated With Thromboembolic Events for Reconstruction With a Single Fibula Free Flap

Background Reconstruction of through-and-through composite oromandibular defects (COMDs) has been a challenge to plastic surgeons for decades. When using a free osteoseptocutaneous fibular flap, the skin paddle is restricted by the orientation of the peroneal vessels and the inset of bone segment(s). Although the combination of double flaps for extensive COMDs is viable and reliable, the decision of single- or double-flap reconstruction is still debated, and the risk factors leading to complications and flap failure of single-flap reconstruction are less discussed. Aim and Objectives The aim of this study was to determine objectively predictive factors for postoperative vascular complications in through-and-through COMDs reconstructed with a single fibula flap. Methods This was a retrospective cohort study in patients who underwent single free fibular flap reconstruction for through-and-through COMDs in a tertiary medical center from 2011 to 2020. The enrolled patients' characteristics, surgical methods, thromboembolic event, flap outcomes, intensive care unit care, and total hospital length of stay were analyzed. Results A total of 43 consecutive patients were included in this study. Patients were categorized into a group without thromboembolic events (n = 35) and a group with thromboembolic events (n = 8). The 8 subjects with thromboembolic events were failed to be salvaged. There was no significant difference in age, body mass index, smoking, hypertension, diabetes mellitus, and history of radiotherapy. The length of bony defect (6.70 ± 1.95 vs 9.04 ± 2.96, P = 0.004) and the total surface area (105.99 ± 60.33 vs 169.38 ± 41.21, P = 0.004) were the 2 factors that showed a significant difference between the groups. Total surface area was the only significant factor in univariate logistic regression for thromboembolic event (P = 0.020; odds ratio, 1.02; 95% confidence interval [CI], 1.003–1.033) and also in multivariate logistic regression analysis after adjusting confounding factors (P = 0.033; odds ratio, 1.026; 95% CI, 1.002–1.051). The cutoff level of total surface area in determining thromboembolic event development was 159 cm2 (P = 0.005; sensitivity of 75% and specificity of 82.9%; 95% CI, 0.684–0.952). Conclusions Free fibula flap has its advantages and drawbacks on mandible restoration. Because there is a lack of indicators before, a large total surface area may be an objective reference for single-flap reconstruction of through-and-through COMDs due to an elevated risk of thromboembolic event.
Evaluating Environmental, Social, and Governance Criteria and Green Finance Investment Strategies Using Fuzzy AHP and Fuzzy WASPAS

The evaluation and prioritization of environmental, social, and governance (ESG) factors are critical for green finance investment strategies. However, ESG criteria are complex and varied concepts that call for a systematic and reliable ranking system to handle ambiguity and uncertainty in decision-makers’ preferences and assessments. The objective of this study was to examine and prioritize environmental, social, and governance (ESG) factors and investment strategies for the development of green finance. Although ESG criteria have gained importance recently, some research gaps still need to be filled. For this purpose, evaluating ESG criteria and integrating them with green finance investment strategies is imperative. This study employed the fuzzy analytical hierarchy process (AHP) method to assess and rank ESG criteria and sub-criteria and the fuzzy weighted aggregated sum product assessment (WASPAS) method to assess and prioritize the key investment strategies for the development of green finance. According to the fuzzy AHP findings, governance and social factors are secondary to environmental considerations in the creation of green finance. Green bonds, ESG integration, and renewable energy funds are essential to green finance methods, according to the fuzzy WASPAS data. This research provides information on creating sustainable and ethical investment strategies for green finance and successfully including ESG factors in investment decision-making processes.
Approximability of the Minimum Steiner Cycle Problem

In this paper, we consider variants of a new problem that we call minimum Steiner cycle problem (SCP). The problem is defined as follows. Given is a weighted complete graph and a set of terminal vertices. In the SCP problem, we are looking for a minimum-cost cycle that passes through every terminal exactly once and through every other vertex of the graph at most once. We show that, if P NP, there is no approximation algorithm for SCP on directed graphs with an approximation ratio polynomial in the input size. Moreover, this result holds even in the case when the number of terminals is 4. On the contrary, we show that SCP on undirected graphs with constant number of terminals and edge costs satisfying the beta-relaxed triangle inequality is approximable with the ratio beta^2+beta. When the number of terminals k is a part of the input, the problem is obviously a generalization of TSP. For the metric case, we present a 3/2- and a 2/3 log_2 k-approximation algorithm for undirected and directed graphs G=(V,E), respectively. For the case with the beta-relaxed triangle inequality, we present a (beta^2+beta)-approximation algorithm.
Multil-Kernels Integration for FCM Algorithm for Medical Image Segmentation using Histogram Analasis

This paper suggests a new process for medical image segmentation using the mixing of two different multi-kernels with spatial information in Fuzzy C-Means algorithm. In literature, it has proved that the multi-kernels outperform the single kernels. In this paper, the integration of two hyperbolic tangent kernels and two Gaussian kernels are used in the proposed algorithm for clustering of images. The presentation of the proposed algorithm is tested on Open Access Series of Imaging Studies (OASIS) MRI image data base. Also, the histogram psychiatry of MRI images are take place in this manuscript. The evaluation is tested in terms of Vpc, Vpe and Silhouette Value. The results after examination, the proposed method shows a significant enhancement as compared to other existing methods in terms of Vpc, Vpe and Silhouette Value under different Gaussian noises.
Remote detection of chloride ion content in concrete using SAR

Presence of chloride ions is the main cause responsible for the corrosion of steel rebars and other components in concrete structures. Detecting the presence and amount of chloride ions in concrete, however, is a challenging task. In this paper, we present our development on the use of synthetic aperture radar (SAR) imaging for the remote detection of chloride ion content in concrete. SAR imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete specimens (0.3x0.3x0.05 m3 ) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8%, and 10% of cement weight). Four SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated amplitude Iint, average maximum amplitude Imax, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude, critical contour area, and average maximum amplitude shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parametersprovides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.