Miracle-dz/UniFAR-FTUs huggingface dataset:
Unraveling the gut microbiota of Tibetan chickens: insights into highland adaptation and ecological advantages

Tibetan animals have several unique advantages owing to the harsh ecological conditions under which they live. Tibetan mammals have been studied extensively. However, understanding of the advantages and underlying mechanisms of the representative high-latitude bird, the Tibetan chicken (Gallus gallus, TC), remains limited. The gut microbiota of animals has been conclusively shown to be closely related to both host health and host environmental adaptation. This study aimed to explore the relationships between the cecal microbiome and the advantages of TCs based on comparisons among three populations: native TCs residing on the plateau, domestic TCs living in the plain, and one native plain species. Metatranscriptomic sequencing revealed a significant enrichment of active Bacteroidetes but a loss of active Firmicutes in native TCs. Additionally, the upregulated expression of genes in the cecal microbiome of native TCs showed enriched pathways related to energy metabolism, glycan metabolism, and the immune response. Furthermore, the expression of genes involved in the biosynthesis of short-chain fatty acids (SCFAs) and secondary bile acids (SBAs) was upregulated in the cecal microbiome of native TCs. Data from targeted metabolomics further confirmed elevated levels of certain SCFAs and SBAs in the cecum of native TCs. Based on the multi-omics association analysis, we proposed that the higher ratio of active Bacteroidetes/Firmicutes may be attributed to the efficient energy metabolism and stronger immunological activity of native TCs. Our findings provide a better understanding of the interactions between gut microbiota and highland adaptation, and novel insights into the mechanisms by which Tibetan chickens adapt to the plateau hypoxic environment. IMPORTANCE The composition and function of the active cecal microbiome were significantly different between the plateau Tibetan chicken population and the plain chicken population. Higher expression genes related to energy metabolism and immune response were found in the cecal microbiome of the plateau Tibetan chicken population. The cecal microbiome in the plateau Tibetan chicken population exhibited higher biosynthesis of short-chain fatty and secondary bile acids, resulting in higher cecal content of these metabolites. The active Bacteroidetes/Firmicutes ratio in the cecal microbiome may contribute to the high-altitude adaptive advantage of the plateau Tibetan chicken population.
Batwing Compiler: An Artificial Intelligence based Compiler

The dynamic landscape of programming languages presents both opportunities and challenges for software developers. This research introduces an innovative approach aimed at streamlining the coding process and bolstering developer productivity. It entails the creation of an AI-based compiler meticulously tailored to the Batwing programming language, with a primary focus on providing an accessible platform for beginners to learn coding in a comprehensible manner. Notably, this compiler caters to non-English speakers, offering native coding as a fundamental feature to enhance proficiency in programming languages. Central to this paper is an in-depth exploration of the Code Standardizer module. Furthermore, it presents a suite of versatile features designed to meet the diverse needs of both professional software developers and novice learners. These attributes empower users to efficiently tackle coding tasks, fostering a seamless transition from initial learning stages to advanced programming proficiency. In essence, this research encapsulates a transformative initiative that leverages AI-driven technology to simplify and democratize coding education. By making coding more accessible and intuitive, this compiler contributes to bridging the digital divide, enabling a broader and more diverse audience to participate in the world of programming.
Energy Management in Decentralized Network Microgrid Using Smart Contract Fostering a Seamless and Secure Energy Trading Ecosystem

The need for effective and secure energy management systems has arisen as a result of increased demand for energy, the increasing use of renewable energy sources (RES), and the introduction of decentralized power generation. Here, we explore the urgent issues and exciting possibilities posed by traditional centralized energy networks, with a special focus on three crucial dimensions: achieving supply-demand equilibrium, seamlessly integrating renewable energy sources, and maintaining participant trust and transparency. This paper suggests leveraging blockchain technology (BT) to create a decentralized network grid energy management system to address these issues. Individual participants can directly exchange energy with one another within the Decentralized Network Grid through peer-to-peer (P2P) trading. The network microgrid model’s mathematical optimization is designed to balance supply and demand, improve network microgrid stability, and optimize energy distribution. The aforementioned optimization uses information from grid constraints, usage patterns, and energy pricing to make wise decisions in real time. The concept aims to achieve energy efficiency, cost savings, and seamless integration of renewable energy sources by utilizing the capabilities of blockchain smart contract and proposed optimization algorithm.
Physiological Signals based Student Grades Prediction using Machine Learning

Amidst today’s fast-paced world, the escalating levels of stress among students have become a significant concern, and it appears to be inversely related to their academic performance. There have been several types of research on the link between stress and grade prediction. Our study aims to leverage the available dataset about the stress level in such a way that can predict students’ grades to comment on the student’s performance. The dataset is collected using the wearable device during three exams containing electrodermal activity, skin temperature, and heart rate from 7680 student subjects. Our approach involves employing a variety of machine learning and deep learning algorithms, including Decision Trees Regressor, CNN Regressor, KNN Regressor, Random Forest Regressor, and Support Vector Regressor models. Through our investigation, we aim to demonstrate the potential of wearable devices in extracting meaningful insights from physiological signals to predict students’ academic outcomes. The challenges associated with wearable observations present unique opportunities for addressing and mitigating stress-related issues among students, and this study harnesses the power of a publicly available dataset to contribute to this important area of research.
IWaOA: Resource Aware Scheduling Through Cloud Fog Computing Environment

In cloud and fog computing, task schedulingplays a role in maximizing the most of available resources and improving overall system efficiency. This work presents an improved form of the Walrus Optimization Algorithm (IWaOA) to handle task scheduling tasks in cloud fog computing (TS in CFC). By modifying the original algorithm in innovative ways, the suggested IWaOAis able to increase convergence and search efficiency by finding a happy medium between exploration and exploitation. Extensive tests using HPC2N real-world cloud fog computing datasets are used to compare IWaOA's performance to that of other popular task scheduling algorithms including WaOA,GAs, and PSOs. The outcomes prove that IWaOA is more effective than conventional methods in terms of task response time, makespan and throughput. The paper further evaluates IWaOA's scalability and robustness under different workloads and network conditions, demonstrating its flexibility and robustness in real-world cloud and fog computing settings. With the potential to increase throughput by 36%, reduce makespantime by 42%, and cut task response time by 26%, the improved Walrus Optimization Algorithm (IWaOA) Presents a favorable method for effectively tackling task scheduling challenges in cloud fog computing environments.
Detection System for Transmission Line Rail Transit Equipment Based on Improved Faster-RCNN

A safety detection system for railway transportation equipment based on image recognition is studied in this paper. The entire railway transportation equipment control system for mountain power transmission lines in forest areas is analyzed. Based on image recognition, improved Faster-RCNN algorithm combined with split-attention networks and self-calibrated convolutions to identify personnel around the railway transportation equipment during operation in the wild environment of mountainous forest areas is researched. And this improves the operational safety and reliability of transmission line rail transportation equipment.
Advanced control strategies for Servo motors: Analysis of PID, FUZZY-PID and MPC with cuk converter integration

This research paper presents a comparative study on speed control techniques for servo motors in industrial and automation applications. The three techniques under investigation are the conventional PID control, Fuzzy PID control, and Model Predictive Control (MPC). Each method's integration with a Cuk converter for non-isolated voltage conversion and high efficiency is explored. The paper begins with the development of a mathematical model of the servo motor system to facilitate fair comparison among the three control strategies. Design considerations for the Cuk converter and tuning parameters for each control technique (PID, Fuzzy PID, and MPC) are thoroughly discussed. MATLAB Simulink is utilized for the simulation and analysis of the proposed systems under various operating conditions. The simulation results showcase the performance of each speed control approach, including accuracy, stability, and dynamic response. The study reveals valuable insights into the strengths and weaknesses of each control technique when used in conjunction with the Cuk converter for servo motor speed control. By comparing PID, Fuzzy PID, and MPC approaches, this research contributes to a comprehensive understanding of advanced speed control methodologies, aiding in the selection of the most suitable strategy for specific servo motor applications. The findings are expected to pave the way for further advancements in industrial automation and contribute to the optimization of servo motor systems across various industries.
Decrypting Disruptive Technologies: Review and Research Agenda of Explainable AI as a Game Changer

Artificial intelligence (AI) has completely disrupted the entirety of the modern life. For researchers in computer science, AI experts, and professionals in a variety of fields, AI is nowadays an intriguing subject of study. However, leveraging AI techniques exposes users to potential risks as these techniques ordinarily function as black boxes, making users prey to inappropriate modeling methods as well as biased decision-making. To address this issue, the key objective of Explainable AI (XAI) frameworks is to render systems’ decisions comprehensible to the users so as to make them trustworthy and dependable. With the goal to present an extensive overview, this systematic review attempts to incorporate the majority of the XAI literature. A framework-based review of 122 articles was performed to accomplish this using the TCM-ADO framework (Theory, Context, Methods and Antecedents, Decision, Outcomes). The study further seeks to accentuate the significant gaps in the literature and offer specific recommendations for further research. These results and recommendations can be used by researchers in the fields of computer science, social sciences and others. By displacing the existing black-box AI with XAI, the integration of XAI into modern businesses has the ability to bring about social transformation by radically altering people’s perceptions of AI.
Dynamic Operating Envelope for Cost Optimization in Local Peer-to-Peer Energy Market

Local electricity markets have emerged as a possible route for effective energy exchange among prosumers as the global energy landscape undergoes a paradigm shift towards decentralization and sustainability. This conference paper offers a thorough analysis aimed at reducing system costs in local power markets through peer-to-peer energy exchanges. The main goal of this research is to examine how the adoption of dynamic operating envelopes affects system costs while illuminating the possible economic advantages of these technologies in various circumstances. In order to give a comprehensive knowledge of the dynamics involved in local energy markets with dynamic export and import constraints for prosumers, the paper includes a careful examination of several examples and situations. The study is carried out for 27 household customers. The results show that, in the best-case scenario, the incorporation of dynamic operating envelope into the local power market can result in significant cost reductions of 12.14%.
Research on Segmentation Method of Brain Tumor Image Based on Deep Learning

In this paper, the U-Net model was constructed to segment the multi-modal MRI images by introducing the Attention mechanism, and the image characterization characteristics of each mode in the MRI images were used to train and learn, and the brain tumor core and substructure were segmented from normal brain tissue. Based on the selection of the BraTS2021 data set for training, the sample data set is used as the training set, verification set, and test set according to the ratio of 8:1:1. The SimpleITK library in Python is used to read the NIFTI file image and preprocess it with random clipping, center clipping, random flipping, Gaussian noise, contrast transformation, brightness transformation, data type conversion, etc., and extract the image features of the model in the training process. The experimental results show that the model established based on U-Net has good segmentation performance and high convergence speed, the highest accuracy rate of tumor enhancement area, core area, and overall area is 78.94%, 78.81%, and 88.75%, respectively, and the lowest loss function is 0.1653. The method has a high segmentation accuracy rate. It is less time-consuming and simple to operate and can provide a reference for MRI brain tumor image segmentation.
An Outlier Detection Algorithm based on Local Density and Natural Neighbors

Outlier detection is a hot research topic in data science, with the goal of identifying individuals in a dataset that are significantly different from other samples. This technique is widely used in fields such as image anomaly analysis and credit card fraud detection. Currently, there are many outlier detection algorithms available, but many of them struggle with the problem of difficult parameter selection and cannot simultaneously detect global and local outliers. To address this, we propose an outlier detection algorithm based on local density and natural neighbors (ODLDNN), which has the following advantages: (1) it inherits the non-parametric nature of natural neighbors and does not require the manual setting of parameter K; (2) it proposes a new method for calculating local density, which not only does not require parameters, but can also be used to detect both global and local outliers. Numerous experiments have shown that our algorithm can effectively detect both local and global outliers without the need for setting the neighborhood parameter K.
Evaluation of Stabilization of Sulfate Soils in Texas

15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Implementation of Stabilization of High Sulfate Soils in the Field URL:http://tti.tamu. edu/documents/5-4240-01-1.pdf 16. Abstract This implementation project was developed to provide technical support to the Texas Department of Transportation (TxDOT) in developing subgrade soil mixture designs in high sulfate soils and to monitor the performance of projects constructed following the guidelines established in Project 4240. Secondly, the researchers were to assess equipment needs of the TxDOT districts, train laboratory personnel in mix design procedures in high sulfate soils, and provide educational materials for TxDOT to train additional personnel.
Flexible Capacitive Pressure Sensors and Their Applications in Electronic Skin

Pressure sensor is an important component of wearable devices, and flexible capacitive pressure sensors are the first choice because of the advantages of low impedance, low power, and high stability. The sensor that is in direct contact with the skin is called electronics skin, and its main purpose is to detect various indicators of the target group and provide feedback. To meet people's pursuit of comfortable and efficient electronic skin, new materials and technologies are adopted for flexible capacitive pressure sensors. In this paper, the function and improvement process of flexible capacitive pressure sensors and their specific application on electronic skin were reviewed. Firstly, this paper introduces the principle of flexible capacitance pressure sensor and the structure of a simple capacitive pressure sensor. Then, the preparation methods and technologies of flexible capacitive pressure sensors in recent years are summarized, mainly involving the popular three-dimensional (3D) printing technology and organic polymer materials. Finally, the application of the flexible capacitive pressure sensor in the skin is summarized, such as health inspection, environment inspection and human-computer interaction.
A Single-Source-Based 13-Level Switched Capacitor Multilevel Inverter with Reduced Components

This research study proposes a single-source-based 13-level switched capacitor multilevel inverter (SCMLI) with self-capacitor voltage balancing and self-output voltage boosting capabilities. The proposed SCMLI needs fewer switching devices, gate driver circuits, and power diodes than comparable single-source-based 13 level SCMLIs. The developed SCMLI’s cost-effectiveness compared to others is significantly increased by the reduced component required. Compared to other SCMLIs, the developed SCMLI provides the lowest cost function per level. The circuit setup, working modes, switch stress analysis, and optimum capacitor selection method for the developed SCMLI are all thoroughly described in the paper. A simulation analysis using MATLAB/Simulink and a practical study using a hardware prototype of the proposed SCMLI have verified its performances. It has been found that the developed SCMLI can realize 13 output voltage steps with 3 times higher voltage than input source and performs well under a variety of load circumstances, including resistive, inductive, and dynamic load change conditions.
Spectral Unmixing Using Shape-Driven Blind Deconvolution in Convolutional Autoencoder

This research paper involves the integration of spectral shape information into a convolutional autoencoder (CAE) for blind unmixing in hyperspectral image analysis. We propose a novel approach that incorporates shape-based similarity measures in the CAE to capture and represent underlying spectral characteristics. Extensive experiments on benchmark datasets demonstrate improved quality in blind unmixing. The impact of architectural choices is also investigated, providing insights for optimal configuration at the end. This research contributes to advancing blind unmixing techniques in the perspective of shape-driven information and offers a promising avenue for enhancing accuracy and efficiency in hyperspectral image analysis
mChemID: Android Application for Chemical Reactions using Speech Recognition

In the ever-evolving field of chemistry, the need for efficient and user-friendly tools to identify chemical reactions has become crucial. mChemID, a mobile application that leverages speech recognition technology to enable users to input chemical elements verbally and retrieve corresponding reactions from a cloud-based database, streamlining the learning process for students, educators, and researchers. It utilizes speech recognition technology to identify chemical reactions by the speech input as chemical elements. Objective: The primary objective is to develop a user-friendly mobile application, mChemID, that utilizes speech recognition technology to assist individuals in identifying chemical reactions by chemical elements through voice input. The application aims to provide a convenient and efficient tool for users to retrieve chemical symbols and corresponding reactions from a cloud-based database. Methodology: The Android platform’s built-in speech recognition functionality is used to verbally input chemical element names. Google’s speech-to-text service is used for transcription, ensuring accurate recognition. The application’s design and development are carried out using Android Studio, a popular development environment for Android applications. Cloud-based connectivity is integrated into the application to fetch and access the required chemical reaction data from a central database. Outcome: The mChemID offers a user-friendly and efficient means to facilitate and simplify the understanding and study of chemical reactions, contributing to the advancement of chemical knowledge and research.
Improving the Financial Health of DISCOM - A Case Study of Kerala

India’s electricity sector has witnessed a great revolution over the years. The access to power distribution has increased over the scale. The contributions from renewable energies are also manifold. However, the distribution wing still suffers from inefficiencies and losses. This paper focuses on analyzing the financial viability of the distribution company (DISCOM), Kerala State Electricity Board Limited. The main focus is to study and understand the performance of DISCOM in terms of its controllable and uncontrollable factors like operational and maintenance costs, fuel purchase costs and so forth. A detailed analysis is showcased using the pre, post, and during the Covid-19 pandemic data, to summarize the performance of the company and the reforms enacted by it. Moreover, initiatives like smart metering, internet of things (IoT) and data analytic tools are also being inferred to improve the financial health in the operation of the DISCOM under study.
Experimental Analysis on SVM-based Image Classifiers for Medical Applications

The Image processing is still being focused by the experts as one a thrust area. The different applications of the Image processing provide wide benefits to the society in their day to day life. The classification technique in the image processing is one of the robust approaches which are used, machine to understand and categorize the images automatically. This provides wide benefits to the industry such as satellite communication, telemedicine, cargo services, and entertainment. In this paper the contemporary techniques available in the image classification techniques are surveyed in detail and some of the classification techniques such as Support Vector Machine algorithm are verified using the Matlab tool to analyses its accuracy. The simulated outcomes are represented numerically as well as graphically for better understanding.