Miracle-dz/UniFAR-FTUs huggingface dataset:
FAST AND EFFICIENT VISIBILITY RESTORATION TECHNIQUE FOR SINGLE IMAGE DEHAZING AND DEFOGGING

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to find the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene.
On the Security of Lightweight Authentication and Batch Verification in VANETs

Recently, researchers have focused on the authentication and key agreement (AKA) approach to enable a secure vehicle-to-everything (V2X) interface. Often, these security measures are effective against passive snoopers who tap the links to decipher the conversation. Nevertheless, it has been noted that if the protocol were severely constructed, an active saboteur may mimic legal vehicles or change the broadcast message. In this paper, we cryptanalyze a recently proposed AKA protocol shown as lightweight compared to the state-of-the-art. We employ conventional and cutting-edge techniques to decipher algebraic systems while releasing many forgeries. Using cryptanalysis, we demonstrate that the algorithms and characterizations of this protocol are susceptible to a range of security concerns, such as insufficient anonymity, session-independent key agreement, exposing multiple keys, replay, and vehicle impersonation. In addition, this article provides remarks for developing a secure lightweight V2X authentication system.
Analysis of MIMO NOMA using Iterative ML and LMMSE Detection

The non-orthogonal multiple access (NOMA) plays major role in radio access to improve the performance of individual for cellular communication technology as compared to state of art technology which is well-known as Orthogonal Frequency Division Multiple Access (OFDMA). This research work focus on the power domain NOMA, by employing superposition coding (SC) at sender and for cancellation of successive interference at receiver, together to estimate the channel performance. The present work examines and also suggest hybrid techniques which are combinations of NOMA and OFDM for better power allocation, bandwidth efficiency, fairness of users and modern analysis using NOMA which is part of 5G communications. One of the specific results of NOMA, MIMO and OFDM combination system are specifically focused in this paper. The LMMSE a linear detector is proposed to help in detecting multiple users and also provides low latency, in turn effects on the performance of BER and PAPR. A synchronized LMMSE is being demonstrated when the MIMO-NOMA combinational method is used to achieve better channel estimation and capacity allocation. To obtain the iterative identification rate that is required, the proposed research work is presenting matching constraints with the help of decoders and with the help of identifiers of MIMO-NOMA. The complete possibility of highest significant levels that are in the area of a symmetric MIMO-NOMA are presented for iterative LMMSE detection that achieves more performance, better than the modern approaches and it is evidently found that it has 1.4dB better than the previous throughput. Using Symmetric MIMO-NOMA, various efficiency of modulation strategies BPSK and QAM are used to encode the sender data. The proposed method has achieved significant amount of enhancement in various parameters, including BER LMMSE, PAPR and the capacity of the channel and it is tested and validated under $2 \times 2$ and $4 \times 4$ arrays of antenna sizes.
Bus Headcount Analysis App Using Deep Learning

Public transport is crowded, and with increasing demand, we must find a balance between space and allocated resources to meet the needs of residents. The BEST is the second largest mass transit system in Mumbai along the network of suburban trains and ferries, However, problems such as an irregular bus schedule and overcrowding are still faced.With the rapid development of technology such as informat ion, AI/ ML, and communication in recent years, it is becoming increasingly possible to improve the efficiency of conventional transportation systems through the application of advanced technologies. In general, most of the available passenger counting systems consist of sensors and counters to count the number of passengers on public transport but this system might give inaccurate counting. To overcome the highlighted issues, this paper proposes an automatic passenger counting system by using a deep-learning algorithm to count the number of passengers in the bus. The proposed system provides accurate and up-to-date headcount estimates. This headcount is further reflected in the android application along with several other features like Bus number, the route, and location of the bus. The passenger gets information whether the bus is crowded or not, making the journey comfortable. This approach has the potential to revolutionize the way that bus headcount data is analyzed, providing insights into passenger behavior, and enabling optimized resource allocation for bus operators.
A Multilevel Three-Phase Integrated AC–DC Bidirectional Resonant Converter for BESS

Two-stage isolated bidirectional ac–dc resonant converter consists of a front-end hard-switched ac–dc power factor correction (PFC) rectifier/inverter followed by a soft-switched bidirectional dc–dc resonant converter. The switching frequency of the ac–dc stage is limited due to hard switching. This article presents a novel low device count isolated bidirectional multilevel bridge tapped resonant (MBTR) ac–dc converter for battery-to-grid integration. It is a unique integration of an ac–dc three-level diode-clamped converter followed by an isolated dc–dc bidirectional converter in which the two stages operate in a decoupled fashion so that the ac–dc stage operates with continuous conduction mode without imposing any variation of the duty cycle of the dc–dc stage. Resonant currents realize natural soft switching. A novel space vector pulsewidth modulation (SVPWM) technique is proposed to control dc-link voltage and bidirectional power flow. The proposed converter, modulation scheme, and control are validated with a 12-kW prototype. The ac voltage varies from 400 to 480 V, and the output battery voltage variation is 550–800 V. A peak efficiency of 95.8% is achieved while operating at 85–95-kHz switching frequency.
Design and Control of Bidirectional Converter for EV Application

Bi-directional converters are a class of power electronics converter that allows power flow in both directions. Bidirectional DC-DC converters are useful for switching between energy storage and motor in electric vehicles. Bidirectional DC to DC converters are classified as non-isolated and isolated DC to DC converters. The non-isolated configuration is deployed in electric vehicle for battery – motor interface whereas the isolated configuration is preferred for EV charging systems. The latest electric vehicles come with feature of regenerative breaking which optimizes battery usage. To incorporate such features which require electric power flow in both directions i.e., from source to load and from load to source, a bidirectional converter with special features is essential. This paper discusses design, operation, and implementation of configurable bidirectional DC to DC Converter used in Electric Vehicles along with hardware and simulation results.
First record of a Didemnum carpet ascidian from the southwestern Atlantic Ocean

In the last decades, species of Didemnidae (Tunicata: Aplousobranchia) have attracted attention for overcoming marine barriers, invading coastal areas and causing negative impact to local biodiversity and human facilities. Six years ago, large colonies of a didemnid species were observed growing on artificial substrate in a port in the southeastern Brazilian coast. Samples were collected and morphological and genetic analysis were done. In morphology, the species is similar to D. vexillum in colour, tunic surface consistency and internal organisational system. The morphology of the zooids is similar to D. perlucidum but differs in the number of stigmata, coils of sperm duct, spicules size and larval aspects. Phylogenetic analysis based on the COI gene indicated the Brazilian samples form a monophyletic group distinct from other species, including D. perlucidum , D. vexillum and D. pseudovexillum . Didemnum sp. demonstrated high reproductive activity during the winter season and can grow over native and non-native species, showing high invasiveness potential. Didemnum sp. was not recorded anywhere else until now and based on our findings it should be categorised as a non-native species. We highlight the need of a monitoring program and the evaluation of plans for eradication of the species.
Correlation of CYP2R1 gene promoter methylation with circulating vitamin D levels among healthy adults

Background & objectives: Despite being a tropical country, vitamin D deficiency is highly prevalent in India with studies indicating 40-99 per cent prevalence. Apart from calcium and phosphate metabolism, vitamin D is involved in cell cycle regulation, cardiovascular, hepatoprotection. The metabolism of vitamin D is regulated by vitamin D tool genes (CYP2R1/CYP27B1/CYP24A1/VDR). The promoter regions of some of these genes have CpG islands, making them prone to methylation induced gene silencing, which may cause a reduction in circulating vitamin D levels. Epigenetic basis of vitamin D deficiency is yet to be studied in India, and hence, this pilot study was aimed to analyze whether methylation levels of CYP2R1 gene were correlated with the levels of 25(OH)D in healthy, adult individuals in Indian population. Methods: In this cross-sectional study, healthy adults of 18-45 yr of age with no history of malabsorption, thyroidectomy, chronic illness or therapeutic vitamin D supplementation were recruited. DNA methylation analysis was carried out by methylation specific quantitative PCR. Serum calcium, phosphate and vitamin D levels were also quantified. Statistical analysis was done by R 4.0. 5 software. Results: A total of 61 apparently healthy adults were analyzed. The serum vitamin D levels did not correlate with CYP2R1 methylation levels in our study population. Significant positive correlation was observed between age and serum vitamin D levels. Significant association of gender was found with CYP2R1 methylation levels. Interpretation & conclusions: This study found no significant correlation between levels of CYP2R1 methylation and circulating 25(OH)D deficiency. Further studies on the Indian population having a larger sample size including entire vitamin D tool genes, among different ethnic groups may be conducted to elucidate molecular etiology of circulating 25(OH)D deficiency. The high prevalence of normal serum calcium and phosphate levels among vitamin D deficient subjects in this study coupled with the strikingly high prevalence of the deficiency at the national level, may suggest the need to revise the cut-off criteria for vitamin D deficiency in the Indian population.
Discursos de deputadas federais sobre mulheres: espaço institucional e mídias sociais

Resumo: Este artigo busca compreender os discursos de parlamentares brasileiras sobre mulheres e relações de gênero em pronunciamentos na Câmara dos Deputados e em suas mídias sociais pessoais. O objetivo é desvelar quais temas, reivindicações, símbolos e termos são enunciados por representantes mulheres localizadas em pontos diferentes do espectro político. Para tanto, a pesquisa se debruça sobre 344 posts de Instagram, 57.309 tuítes e 444 trechos de discursos das deputadas federais Gleisi Hoffmann (PT), Jandira Feghali (PCdoB), Bia Kicis (PL) e Carla Zambelli (PL) que datam de março e maio de 2019, 2020 e 2021. A partir da análise do discurso crítica e da análise léxica automatizada, os resultados apontam a baixa tematização de discussões sobre gênero no corpus e uma atuação discursiva próxima às pautas gerais de seus partidos, divididos entre oposição e base governista.
Alzheimer Detection Using CNN and GAN Augmentation

Alzheimer’s disease is a neurological condition which slowly weakens the memory, ability of thinking and reasoning along with the ability of performing day to day activities. The senior citizens are mostly affected with this disorder especially those in their sixties. The specific cause for this condition is still not clear but it can genetic, accidental or can be caused by some other circumstances considered as hypothesis at the moment. There are a few methods of detecting the disease but the MRI scans are the prominent ones among them. And for this research MRI scans are used in the form of scanned images. The disease has been classified into four categories for this research that are healthy, mild demented, very mild demented, and moderately demented. Deep learning algorithms are being used because of their efficient ways of working in the medical field. CNN the commonly used deep learning algorithm is kept as the base for the proposed model and the dataset is collected from Kaggle. The collected dataset is increased with the help of GAN augmentation to improve the accuracy of the model. The model gives accurate results up to 98.5% for detecting the disease and its categories. This model can help the medical workers in the form of a second opinion when combined with the present detecting techniques and can reduce their workloads.
Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems

Reinforcement learning (RL) algorithms require continuous interaction with the controlled plant to optimize the objective function and control policy. However, the security risk and poor real-time performance limit the application of RL algorithms. In this article, a data generation feedback relearning (DGFR) control algorithm is developed to avoid these limitations. The DGFR control algorithm improves the control performance by interacting with a proposed delayed neural network-based data generation model. Considering the time-delay characteristics of industrial production, the data generation model is different from the identifier of the system model, but it is used to generate the target data required by the DGFR control algorithm. The interaction between the algorithm and the data generation model avoids the risk of trial-and-error and improves real-time performance. Finally, the superiority of the proposed control algorithm is verified by giving comparative experiments in conjunction with a power grid system and a permanent magnet synchronous motor system.
Classification Using Machine Learning Techniques with Syntax Features

Sentiment classification has adopted machine learning techniques to improve its precision and efficiency. However, the features are always produced by basic words-bag methods without much consideration for words’ syntactic properties, which could play an important role in the judgment of sentiment meanings. To remedy this, we firstly generate syntax trees of the sentences, with the analysis of syntactic features of the sentences. Then we introduce multiple sentiment features into the basic words-bag features. Such features were trained on movie reviews as data, with machine learning methods (Naive Bayes and support vector machines). The features and factors introduced by syntax tree were examined to generate a more accurate solution for sentiment classification. Keywords—Sentiment classification; Syntax tree; POS features; Machine learning
Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations

Abstract. Statistical bias correction (BC) is a widely used tool to post-process climate model biases in heat-stress impact studies, which are often based on the indices calculated from multiple dependent variables. This study compares four BC methods (three univariate and one multivariate) with two correction strategies (direct and indirect) for adjusting two heat-stress indices with different dependencies on temperature and relative humidity using multiple regional climate model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling and end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence, which benefits the indirect correction of heat-stress indices depending on the adjustment of individual components, especially those indices relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the quantile delta mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.
Dynamics of microfluidic mixing using time pulsing

Many microfluidic applications require the mixing of reagents, but efficient mixing in these laminar systems remains a challenge. In this paper, we consider further the method of pulsed flow mixing which takes advantage of time dependency rather than spatial complexity. In particular, using computational fluid dynamics (CFD) we analyze the dynamics of the flow when the two inlets are pulsed at $90^\circ$ and $180^\circ$ out of phase. Both cases achieve enhanced mixing although better results occur in the first case. This is apparent in the concentration level plots as well as in the shape of material lines which show strong repeated stretching and folding at the confluence region.
MEMBRANE PERMEABILTY AND RELATIVE WATER CONTENT IN PHYSIC NUTS PLANTS SUBMITTED TO FAST WATER DEFICIT

The purpose of this study was to evaluate the membr ane permeability and relative water content in leaves of physic nuts submitted to water deficit. Experiment was conducted in randomized blocks design with four replicates, f ive plants for vase. Greenhouse grown physic nuts plants were submitted to different peri ods of hydric deficit: 0, 48, 120 and 192 hours. After period of hydric deficit, one plant wa s harvested to physiological evaluations and another kept for further evaluations after rewateri ng and recovering period of the plants. At the end of hydric deficit period and by the end of recovery period, the relative water content (RWC) and rate of electrolytes leakage (REL) in the leaves were evaluated. No significant differences were verified for RWC and REL between p eriods of hydric deficit, these parameters were maintained at constant levels durin g evaluations. After 120 hours of hydric stress were verified visual symptoms like wilting, yellowing and leaf abscission. These results demonstrate that physic nuts has defense me chanisms to lack of water. Evaluations made after recovery, demonstrated that physic nuts has a high capacity of recovering after periods of hydric stress by producing new growth le aves and buds.
β‐Catenin in the kidney stroma modulates pathways and genes to regulate kidney development

Kidney development is regulated by cellular interactions between the ureteric epithelium, mesenchyme, and stroma. Previous studies demonstrate essential roles for stromal β‐catenin in kidney development. However, how stromal β‐catenin regulates kidney development is not known. We hypothesize that stromal β‐catenin modulates pathways and genes that facilitate communications with neighboring cell populations to regulate kidney development.
Inverse Scattering Related to Cylindrical Bodies Buried in a Lossy Circular Cylinder with Resistive Boundary

Abstract An inverse scattering problem of cylindrical bodies of arbitrary cross section buried in a circular cylinder with resistive boundary is presented. The reconstruction is obtained from the scattered field measurements for a plane wave illumination under the Born approximation. Illustrative examples are presented in order to see the applicability of the method as well as to see the effects of some parameters on the solution.
The association between transient childhood psychotic experiences and psychosocial outcomes in young adulthood: Examining the role of mental disorders and adult attachment

Evidence suggest individuals with mental disorders and psychotic experiences (PE), even transient PE, show poorer psychosocial outcomes relative to those with mental disorders. The concept of “attachment” is hypothesized as the mechanism by which people seek support in times of need. This can be measured as discrete styles or as positive (low avoidance/anxiety)/negative (high avoidance/anxiety) dimensions. Adult attachment has previously been examined on PE risk factors, but not outcomes. This study aimed to examine the relationship between transient childhood PE and adult psychosocial outcomes, comparing those with and without mental disorders. Second, to examine the role of adult attachment.