Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech To Image Translation Framework for Teacher-Student Learning

Most of the technical concepts written in textual way and these are communicated orally by teachers to students through limited images. Students not able to understand some of the complex topics in expected way. This will create a gap between subject and student's subject understandability. Representing and conveying through images is a convenient and effective way for Teaching Learning process. Filling this gap is very essential because understanding through visualization will enhance learning process. Though, the combo of speech encoding and computational translation in only one model causes a huge difficulty and many complexities. In order to reduce those efforts, speech to image translation framework plays significant role. With this motivation authors designed a framework for teachers-students learning process. In this framework, authors designed framework to abstract characteristics from audio files and trained model using unsupervised machine learning algorithm. In this firstly, a speech encoder encodes the audio and extract some significant and highlighted features from it and applying algorithm on this feature, Authors perform mapping process on these parameters and features of images which are present in dataset. After this process, GAN comes into play which produce corresponding image as output. In comparison to other methods like speech-text-image, text-image, speech-text models, our model approaches on synthesized dataset and reach to higher expectations of user.
Energy-carbon flow coupling analysis of solar photovoltaic(PV) system considering cost, policy and generation loss

Power generation accounts for the most of the carbon dioxide due to global warming intensifies. Compared with traditional power generation, solar PV system presents a greener and cleaner generation scene, and it is an important source of green energy in the future. However, the solar PV system also emits carbon dioxide from the whole life cycle. Hence, the coupling between energy flow and carbon flow does exist. The purpose of this work is to analyze the energy-carbon flow coupling in solar PV system. Firstly, life cycle assessment (LCA) method is adopted to analysis carbon emission of the solar PV system. Then, an analytical method considering power generation benefit, related cost, policy and generation loss of solar PV system is given. Finally, the industrial electricity consumption in Shanghai is analyzed.
The NorthGRIP deep drilling programme

Abstract The North Greenland Icecore Project (NorthGRIP) was initiated in 1995 as a joint international programme involving Denmark, Germany, Japan, Belgium, Sweden, Iceland, the U.S.A. , France and Switzerland. the main goal was to obtain undisturbed high-resolution information about the Eemian climatic period (115–130 kyr BP). the records from the Greenland Icecore Project (GRIP) and Greenland Ice Sheet Project 2 (GISP2) in central Greenland are different and disturbed down in the ice covering this period. Internal radio-echo sounding layers show that NorthGRIP, placed 325 km north-northwest of GRIP at the Summit of the Greenland ice sheet, is located on a gently sloping ice ridge with very flat bedrock and internal layers found so high that an undisturbed Eemian record is possible. Internal layers much farther above bedrock than their apparent counter parts at GRIP suggest that conditions are favourable for recovery of an undisturbed Eemian record. So far, a 1351 mdeep ice core (NorthGRIP1) and a 3001 mdeep ice core (NorthGRIP 2) have been recovered. the ice thickness is expected to be 3080 m, and the ice temperature at 3001 m is –5.6°C, so we expect basal melting at the bedrock. Most of the Eemian ice will be melted away, leaving only the last part and the transition between the Eem and the Last Glacial Period. At 3001 m the age of the ice is 110 kyr BP and the annual layers are of the order 1 cm.With modern methods the annual layers can be resolved, resulting in detailed information on the decline of the warm Eemian period into the Last Glacial Period.
Analysing Effectiveness of Different Physiological Biomarkers in Detecting Stress

Stress is a common part of our everyday life and helps us to handle certain situations in a better way. However, exposure to stress over a long period of time can cause mental and physical health complications. Many health complications associated with stress can be prevented with its early detection. Stress in humans is accompanied by certain physiological changes and monitoring those changes can help in its detection. In this, we study the impact of different physiological changes on stress detection. Different machine learning and deep learning techniques viz. Random Forest, AdaBoost, Decision Trees, Gaussian Naive Bayes and Artificial Neural Networks were used for stress detection using WESAD dataset, which is a dataset for stress and affect detection. Artificial Neural Network outperformed the machine learning techniques and achieved an accuracy of 93.6%. To analyse the effectiveness of each physiological biomarker in detecting stress, the artificial neural network was trained separately on every possible combination of biomarker set. The series of experiments showed that only three physiological biomarkers viz. three axes acceleration, electrodermal activity and body temperature, are sufficient to detect stress effectively.
SpriteHyper2vec: A SCSPRITE Data Completion Method Based on Hypergraph Random Walk

scSPRITE is an emerging method to capture multi-way chromatin interaction and generate high-resolution, genome-wide three dimensional (3D) genome organization in single cells. However, the data obtained from scSPRITE often contain missing values and exhibit sparsity, making it difficult to directly observe local structures of the 3D genome at the level of single cells or even small cell populations. Additionally, comprehending the chromatin conformation composition entails a large number of cell populations by scSPRITE with huge cost. Therefore, it is valuable to impute these missing values and extract higher-order structures using computational methods. Here, we propose SpriteHyper2vec, a single-cell imputation algorithm based on hypergraph, specifically designed for scSPRITE datasets, which effectively capture local and global topological structures by generalizing Node2vec method. When compared with original method, SpriteHyper2vec significantly improves the similarity between small cell populations and the benchmark data. Furthermore, the completion capability of SpriteHyper2vec enables the identification of A/B compartment-like structures even in small cell populations. In summary, SpriteHyper2vec effectively enhances the visualization and comparison of 3D organization in small cell populations based on scSPRITE data.
AI and Public Archives Collaborative Leadership for Responsible Adoption

This panel aims to address the measures that cultural heritage institutions must undertake to ensure that their approaches to AI adoption are inclusive and equitable. Panelists from Virginia Tech, the National Archives and Records Administration, the Smithsonian Institution, and the Library of Congress will provide an overview of the current state of AI in large public-serving libraries, archives, and museums and will lead a discussion on how to incorporate AI in a responsible manner. The discussion builds on the panelists' ongoing work to critically examine the potential impacts and implications of the use of AI technology by cultural heritage institutions and strategies for addressing and mitigating its negative effects. The panel will engage the audience in a group discussion that aims to consider possible biases and harms that may arise from the use of automated technologies, build inclusive practices in AI development and implementation, and form new partnerships to promote the use of ethical AI in libraries, archives, and museums.
Picture Fuzzy Soft Matrices and Application of Their Distance Measures to Supervised Learning: Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN)

This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsistencies resulting from Cuong’s definition of picture fuzzy sets. Then, it introduces several distance measures of pfs-matrices. Afterward, this paper proposes a new kNN-based classifier, namely the Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN) classifier. The proposed classifier utilizes the Minkowski’s metric of pfs-matrices to find the k-nearest neighbor. Thereafter, it performs an experimental study utilizing four UCI medical datasets and compares to the suggested approach using the state-of-the-art kNN-based classifiers. To evaluate the performance of the classification, it conducts ten iterations of five-fold cross-validation on all the classifiers. The findings indicate that PFS-kNN surpasses the state-of-the-art kNN-based algorithms in 72 out of 128 performance results based on accuracy, precision, recall, and F1-score. More specifically, the proposed method achieves higher accuracy and F1-score results compared to the other classifiers. Simulation results show that pfs-matrices and PFS-kNN are capable of modeling uncertainty and real-world problems. Finally, the applications of pfs-matrices to supervised learning are discussed for further research.
The Eurasian ice sheet reinforces the East Asian summer monsoon during the interglacial 500 000 years ago

Deep-sea and ice-core records show that interglacial periods were overall less "warm" before about 420 000 years ago than after, with relatively higher ice volume and lower greenhouse gases concentration. This is particularly the case for the interglacial Marine Isotope Stage 13 which occurred about 500 000 years ago. However, by contrast, the loess and other proxy records from China suggest an exceptionally active East Asian summer monsoon during this interglacial. A three-dimension Earth system Model of Intermediate complexity was used to understand this seeming paradox. The astronomical forcing and the remnant ice sheets present in Eurasia and North America were taken into account in a series of sensitivity experiments. Expectedly, the seasonal contrast is larger and the East Asian summer monsoon is reinforced compared to Pre-Industrial time when Northern Hemisphere summer is at perihelion. Surprisingly, the presence of the Eurasian ice sheet was found to reinforce monsoon, too, through a south-eastwards perturbation planetary wave. The trajectory of this wave is influenced by the Tibetan plateau.
EnveRob: Integration of an Intelligent Robotic Arm System for Mail Delivery Applications

This research aims to develop an automated mail delivery system to explore the application of intelligent automation technology in different scenarios. The camera module and the gripper module are integrated to apply automation intelligent technology to robotic arms. By connecting sensors to the virtual and real world, the concept of a digital twin is also introduced into the task of automated mail delivery. This paper first introduces the reference technologies or architecture, followed by each module. It then elucidates the design concepts and processes of both the hardware and software components, as well as the implementation of digital twin technology. Finally, a fully automated mail delivery system is presented with the validation results and discussions of the subsystems.
Water Quality Sensor based on IoT

A vital resource in our daily life is water. The quality of the water can be detected by checking the total Dissolved Solids (TDS) in it. TDS represents the amount of organic or inorganic diffused contents in water. The contents can be either in the molecular form or iodized form. It gives the percentage of solids dissolved in a specific volume of water, expressed as ppm. The water can be from a variety of sources, including rivers and industrial waste water. Each water category has a different purity level. The chemicals added for water treatment, chemical fertilizers used in gardens, and plumbing contribute to TDS. Water is a universal solvent that readily attracts contaminants and can quickly absorb and dissolve them. Before using the water, it is mandatory to check the purity level of the water. A smart TDS measurement system is proposed here, which monitors the water quality and displays it.
Exposure to valproic acid (VPA) reproduces hdac1 loss of function phenotypes in zebrafish

Histone deacetylases are enzymes that remove acetyl groups from histone tails and are understood to act as repressors of transcriptional activity. Hdac1 has been previously shown to function in eye, pectoral fin, heart, liver, and pharyngeal skeletal development. We show that high doses of Valproic Acid (VPA) reproduce the hdac1 phenotype. We identify tbx5 genes as potential targets of Hdac1 in eye, pectoral fin, and heart development. Using timed exposures, we show that skeletal structures in the pharyngeal arches are impacted by VPA between 24-36 hours post-fertilization, indicating a role for Hdac1 during post-migration patterning, differentiation, or proliferation of cranial neural crest cells.
Planning of Community Microgrid for Improving the Resilience of System by Using HOMER Grid

Natural Disasters (NDs), such as tsunamis, floods, and earthquakes, may cause damage to the transmission and distribution infrastructures as well as communication systems. In these situations, generators may even be intentionally shut down for security reasons. The result is a major power outage or a blackout, which may disrupt industrial production, disturb commercial and household activities and imperil human lives. Contrary to large-scale power systems, Micro Grids (MGs) may offer a higher resilience to NDs because of their compact sizes, manageable loads, and constrained bounds. Such an MG may include Photo-Voltaic (PV) system, Battery Energy Storage (BES) system, winds turbine, diesel generators, and local electric loads. It operates in grid-connected in normal conditions, and in islanded mode during NDs, ensuring that no disruption in the supply of critical loads happens. The main objective of this article is to study the planning of Community MG (CMG) for hospital loads to ensure high resilience against NDs. Hospital loads in the Northwest Lombok region of Indonesia are considered as the case study. The goal is to achieve three days of uninterruptible power supply during NDs. HOMER Grid carries out the simulation research. According to the results, the planned CMG could provide the necessary hospital load during the intended time period without any interruptions.
An Effective Application of Deep Learning in the Early Diagnosis of Stroke

Stroke constitutes one of the primary reasons for death and severe weakness that affects millions of people worldwide and has a significant chance of developing into a pandemic in the coming years. Early diagnosis and fast treatment decisions are crucial in preventing paralysis, brain death, and other severe consequences of this condition. ML algorithms have been widely used to diagnose, analyze, and predict this disease, but there are data quality difficulties because the resources utilized to obtain the data are spread across multiple institutions. Hence, the deep learning framework with CNN is demonstrated in this paper. This technique makes use of a multimodal stroke dataset that is accessible to everyone in the Kaggle repository. 6100 MRI images are gathered for this process. The collected images contain noise and are of bad quality. So, the Weiner filter is utilized for pre-processing phase to remove the noise. Then, the watershed technique is employed for the segmentation process. Subsequently, Lion Optimization Algorithm is utilized for extracting the features. Lastly, CNN has been applied for the classification. By validating the performance measures, the LOA-CNN technique seems to be higher than the current techniques. The results demonstrated that the presented method achieves precision, accuracy, and recall rates of 99.32%, 99.52%, and 99.67%, respectively.
A Circular Conformal Vivaldi Antenna Array with Suppressed Sidelobes

This paper investigates self-adapting conformal antennas for dynamic circular surfaces. Specifically, the radius of the circular surface, antenna element spacing, and needed phase compensation are all developed. A Vivaldi antenna with a -10dB$S_{11}$bandwidth from 4.3-9.8 GHz spanning the full IEEE C-band and a moderate gain of 5-9dBi was built for theoretical validation. Vivaldi antennas as radiators a $1\times 7$ circular conformal antenna array with a scanning capability of ±45° was created. Later, a modified Vivaldi antenna was designed to address the issue of high sidelobe level without compromising the S11 bandwidth offered by the Vivaldi antenna. A $1\times 7$ circular conformal modified Vivaldi antenna array was developed with a scanning capability of ±45° was created. Both case phase compensation is applied to each antenna element according to the beam-pointing direction. Overall there is good agreement in the theory and simulations of the designs.
Latency Analysis and Reduction Methods for Edge Computing

With the proliferation of mobile networks and the Internet of Things in recent years, latency has become a major issue due to the massive amount of data. The data center location does not allow for the handling of large amounts of data. So it can only be handled if a server is placed near the end user and between the data center and the device. Edge computing is a distributed computing environment that moves computation and data storage closer to the point of use to improve response time and save bandwidth. This paper provides an overview of edge computing technology as well as preliminary observations for determining important characteristics for dealing with the latency analysis problem. This observation is based on a variety of parameters, and how well it works with existing edge computing infrastructure.
Decadal Changes in the Mode Waters in the Midlatitude North Pacific

Abstract Temporal changes in the properties of the North Pacific subtropical mode water (NPSTMW) and the North Pacific central mode water (NPCMW), which occurred around the mid-1970s, are investigated using temperature data composited for the two decades bounded by the mid-1970s: 1966–75 and 1976–85. Properties of these mode waters changed greatly after the mid-1970s. The colder NPCMW was formed and widely distributed during 1976–85. In the NPSTMW formation area, warmer water occupied the southwestern part, and colder water occupied the northeastern part during 1976–85. The cause of this change is discussed with regard to the heat flux and wind stress data. The cooling can be explained as a result of changes in surface heat flux and heat divergence in the Ekman layer, that is, a larger amount of heat released from the ocean surface and an increased southward Ekman transport of cold water due to intensification of the westerlies. In particular, the latter plays a dominant role in the observed cooling. On t.
A Half-turn Winding Layout in Planar Transformers

This paper presents an analysis of the current distribution on the parallel conductors of typical interleaved planar transformers. Through the investigation, it is discovered that the S-P-S-P-S winding layout with "n+1" parallel layers exhibits a half-turn winding current distribution, resulting in lower AC resistance. Simulation and experiment results are used to validate the current distribution in the proposed half-turn transformer, demonstrating a nearly 20% reduction in winding loss and leakage inductance compared to conventional layouts.
Dividing Rectangular Closed Conduit Flows

The characteristics of dividing flows in closed rectangular conduits are studied to determine experimentally the various energy loss coefficients for a range of discharge ratios Q3 /Q1 and three ratios of the width of the branch to the width of the main. The contraction coefficient and the loss coefficients related to branching flow are determined using the results of an existing lateral outlet model for two-dimensional flow. For the branch flow, two different procedures are used to obtain the variation of an energy loss coefficient and the contraction coefficient dependent on the discharge ratio. The two procedures yield results which are in reasonably close agreement. Detailed velocity and pressure distributions are presented for branching flows in closed rectangular conduits to understand some of the flow processes involved. The results presented will be useful to the hydraulic engineer in the design of flow systems encountered in water and wastewater purification plants.
Towards Optimal Microbiome to Inhibit Multidrug Resistance

In this research, we propose an explanatory model that learns combinations of microbiome signatures that help predict reduced multidrug resistance (MDR) of disease-causing pathogens. A twin regression network in our model provides a means for estimating the MDR presence using microbiome samples. Further, we extend our model by using a backtracking approach that determines the effect of specific microbiome signatures on the prevalence of MDR, to identify potential beneficial combinations. Using high-throughput Illumina sequencing of the 16SrRNA gene data from 41 pastured poultry flocks in southeastern U.S. farms, the regression model identifies the factors influencing MDR prevalence in common foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth.
Inference of threshold network models of the tryptophan operon in Escherichia coli

A recent Boolean model of the tryptophan operon in Escherichia coli has been introduced, which exhibits a desired set of fixed points modeling the operon state being on or off. Nevertheless, when updated synchronously, the model also reveals spurious limit cycles with a larger basin of attraction than the desired fixed points. This paper presents the search via evolutionary computation for threshold network models that exhibit the desired fixed points without any limit cycles. The proposed framework was applied using differential evolution and particle swarm optimization, the latter being the most efficient and effective method based on the results obtained by the simulations. Particle swarm optimization found two correct threshold networks, one of which has only weight values {-1,0,1}, making the model more interpretable.
Salient Region Detection in Natural Images using EM-Based Structure-Guided Statistical Textural Distinctiveness

The objective of salient region detection is to separate salient region from entire image. This salient region detection framework consists of a structure-guided statistical textural distinctiveness approach. This approach includes the five main stages: i) image decomposition ii) textural representation, iii) texture modeling, iv) matrix construction, and v) saliency map computation. In the image decomposition stage, decomposition of image into structural image elements to learn a structure-guided texture model. In second stage, define a rotational-invariant neighborhood based texture feature model that represents the underlying textural characteristics of regions in a local manner. In texture modeling stage, Sparse texture modeling is done using structure-guided texture learning. In matrix construction stage, characterize all pair-wise statistical textural distinctiveness within the sparse texture model of the image and construct a textural distinctiveness matrix. In the final stage, the saliency of a region can be computed as the expected statistical textural distinctiveness of the region in the given image . The proposed approach has been extensively evaluated on images from MSRA-1000 datasets.