Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental flows and water reserves: Principles, strategies, and contributions to water and conservation policies in Mexico

Since the Brisbane Declaration in 2007, implementation of environmental flows in public policies has witnessed a steady increase around the globe. Environmental water reserves are an annual volume that is designated to remain in the ecosystem for the sustainable management of river basins. In Mexico, these reserves are determined on the basis of the Mexican Environmental Flows Norm and must be established at a river basin scale through a presidential decree for 50 years. In this manuscript, we present and discuss the implementation strategy of the norm developed for the National Water Reserves for the Environment Program, and its results in 25 reference sites based on environmental flow assessments conducted from 2012 to 2015 using hydrological and holistic methodologies. An analytical evaluation revealed an overall consistency between the Norm's environmental objectives (baseline) and the current ecological conditions on‐site for the 80% of the cases (96% over high confidence rating). Furthermore, in 72% of the reference sites, the coefficient of variation among the reserve's was below the fourth quartile (<11%), whereas those remaining above that limit can be attributed to a difference in the methods' hydrologic scope. The recommended volumes for environmental allocation are feasible under the current water availability conditions in the 94% of the river basins. Although challenges have appeared in the process, to date, one reserve has been decreed on the basis of the strategic approach of setting sustainable limits of water allocation and being built an enriched flow‐ecology relationships' understanding system, urgently needed to prevent ecosystems degradation and secure ecological processes.
Real-time Measures for Brain Activity Detection to Live Indian Classical Music Stimulus

Numerous studies have explored the intriguing relationship between music and the brain, providing compelling evidence that listening to music has a profound impact on brain activity and stimulation. These investigations have uncovered intricate details about how the brain processes and responds to music, leading to a more nuanced understanding of the intricate relationship between music and the human brain. Despite extensive research on the subject, there remains a gap in our understanding of the specific effects of Indian classical music (ICM) as well as its therapeutic applications. This study proposes a real-time energy and distance measures for determining the brain areas that are activated by live ICM, while simultaneously accounting for musical impulse and EEG response uncertainty. For this, the authors used recursive Mahalanobis distance and energy measurements by finding recursive covariance matrix. Focusing on the Hindustani classical music (HCM) form of ICM, this study uses two chosen ragas with similar ascending-descending structure but differing in two normal and flat notes: Yaman and Puria Dhanashree (PD). Two of the volunteers involved in this study had prior musical knowledge on ICM and three had none. To precisely collect brain responses from different parts of brain regions, authors used a 24-channel electroencephalogram (EEG) cap to record brain responses. The results of this work show that different brain regions will get activated for different ragas. The listener’s knowledge on ICM also affects the regions of brain evoked. For the volunteer with knowledge in HCM, parieto-occipital region is evoked for both ragas. The one with carnatic music knowledge has more right temporal activation for both the ragas and the volunteers with no musical knowledge showed more activation in parieto-occipital region for PD. In summary, the relationship between music and the brain has the capacity to enhance mental productivity, induce relaxation, and contribute to both mental and physical well-being.
Design, Modelling, And Simulation of a Smart Grid System with Renewable Energy Penetration

In recent time, the power systems have experienced improvements in their operations in which one of product of these improvements is the concept of smart grid. The smart grid entails the use of modern electrical grid with the purpose of upgrading the conventional grid to accommodate and manage the new energy demand. The smart grid combines various aspects of new technologies that are key to a more stable and efficient power system. These technologies include the incorporation of smart meters, energy storage, high voltage power transmission and renewable energy. The use of renewable energy is driven by global warming concerns, and the need to reduce the dependency on fossil fuel. The smart grid enables different types of renewable energy sources to be integrated connect and supply power to the grid. To understand the relationship between smart grids and renewable energy systems, a numerical analysis of the IEEE 14 Bus system consisting of battery energy storage system, hydropower plant and wind power was designed and simulated on MATLAB/Simulink. The simulation was carried for two different cases, case one being a smart grid without renewable energy sources and case two with connected renewable energy sources. The results of the power flow solution were analyzed to understand the concept of power flow in aspect of integration of renewable energy on smart grids. The load flow results indicated that the addition of renewable energy increased the efficiency of the smart grid.
Real-Time Face Expression Using Deep Face Recoginition System

Facial expressions are one of the ways people communicate. Deep learning techniques in relationships between people and computers are used in research on the progress of artificial intelligence as a powerful application method. Some people who see it occasionally fail to understand the expression or emotion it will generate. security of data, systems, and networks is critical to protecting against cyber threats and keep the confidentiality In psychology detecting reactions or gestures during communication requires study and appraisal of opinions in foreseeing an individual's or group's feelings. Innovation is crucial for companies to stay competitive and adapt to changing market demands. The paper presents the development of an algorithm that can anticipate and recognise the categorization of Applying feature extraction and the Convolution Neural Network (CNN) method in real-time with the OpenCV library, face emotions are identified. with reference to TensorFlow and Keras. The three key methods that make up the study design used in the Jetson Nano are face monitoring, emotional feature extraction, and facial emotion labelling. Facial Expression Recognition (FER-2013) and (CNN) techniques were used in a study to predict facial expressions, and the accuracy was 90.8% (ninety point eight).
Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models

Aviation safety is paramount in the modern world, with a continuous commitment to reducing accidents and improving safety standards. Central to this endeavor is the analysis of aviation accident reports, rich textual resources that hold insights into the causes and contributing factors behind aviation mishaps. This paper compares two prominent topic modeling techniques, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), in the context of aviation accident report analysis. The study leverages the National Transportation Safety Board (NTSB) Dataset with the primary objective of automating and streamlining the process of identifying latent themes and patterns within accident reports. The Coherence Value (C_v) metric was used to evaluate the quality of generated topics. LDA demonstrates higher topic coherence, indicating stronger semantic relevance among words within topics. At the same time, NMF excelled in producing distinct and granular topics, enabling a more focused analysis of specific aspects of aviation accidents.
LSTM-Based Hybrid Intrusion Detection System for Internet of Vehicles

The recent growth of the Internet of Things (IoT) has revolutionized vehicular networks into the Internet of Ve-hicles (IoV). Within the IoV infrastructure, modern vehicles are vulnerable to different and new types of cyber-attacks. Consequently, Intrusion Detection Systems (IDS) are extremely helpful in coping with such attacks, and there is a pressing need for the development of an advanced IDS that can efficiently detect attacks with a high detection rate and accuracy, and a low false alarm rate. Toward this end, we present a deep learning-based IDS for the identification and classification of potential cyber-attacks in the 10 V network. Specifically, a generative hybrid deep learning model that combines Long Short-Term Memory Variational AutoEncoder with Bidirectional Gated Recurrent Units (LSTMVAE-BiGRU) is proposed. Due to the integration of the LSTM network and the VAE in this architecture, the LSTM performs the encoding and decoding in the LSTMVAE paradigm. The LSTMVAE is forced to learn the most salient features of the training time series data by restricting the latent space to have a dimension that is less than the input. On the other hand, to learn latent representations, BiGRU collects local dependencies in a two-way time flow, or forward and backward. Lastly, the multi-class attack detection method is carried out using the softmax layer. Experimental findings demonstrate that the proposed IDS outperforms both baseline techniques that are widely used and cutting-edge approaches.
A Gym Monitoring System Using YOLOv5

The technique of predicting the location of human body parts and joints in a given video is referred to as articulated body pose estimation. We have proposed a new Gym monitoring system using deep learning concepts, in which working process has been divided into two steps. In first step, actions are tracked using one stage-based YOLOv5 algorithm which follows the rule of the social distance monitoring system. In the second step, recognized the threshold angle by pose estimation algorithms (media-pipe). Now we calculate the angle at joints of human pose, threshold angles may be different for different actions and based on these threshold angles count number of repeated actions. A display screen counts the number of repeated actions. If repeated actions are uncorrected then it will not count.
Spatially distributed denitrification in a karst springshed

Karst spring measurements assess biogeochemical processes occurring within groundwater contributing areas to springs (springsheds) but can only provide aggregated information. To better understand spatially distributed processes that comprise these aggregated measures, we investigated aquifer denitrification evidence in groundwater wells (n = 16) distributed throughout a springshed in the Upper Floridan aquifer in northern Florida. Aquifer geochemistry, nitrate isotopes, and dissolved gases were compared against similar measurements at the spring outlet to evaluate spatial heterogeneity of denitrification evidence in relation to land surface–aquifer connectivity. Sample locations spanned spatial variation in recharge processes (i.e., diffuse vs. focused recharge) and proximity to sources of denitrification reactants (e.g., wetlands). Although no distinct spatial pattern in denitrification was uncovered, excess dissolved N2 gas measurements were only above detection in the unconfined springshed, with some evidence of a wetland proximity effect. Measured oxidation–reduction potential and dissolved oxygen poorly predicted denitrification, indicating that measured denitrification may be occurring upgradient from sampled wells. Despite dramatic spatial chemical heterogeneity across wells, mean values for recharge nitrate concentrations (0.02 to 5.56 mg N L−1) and excess N2 from aquifer denitrification (below detection to 1.37 mg N L−1) corresponded reasonably with mean spring outlet measurements for initial nitrate (0.78 to 1.36 mg N L−1) and excess N2 (0.15 to 1.04 mg N L−1). Congruence between groundwater and spring measurements indicates that combining sampling at the spring outlet and across the springshed is useful for understanding spatial aquifer denitrification. However, this approach would be improved with a high‐density sampling network with transects of wells along distinct groundwater flow paths.
DRL-Aided Joint Resource Block and Beamforming Management for Cellular-Connected UAVs

In this paper, we investigate a cellular-connected unmanned aerial vehicle (UAV) network, where multiple UAVs receive messages from base stations (BSs) in the down-link, and in the meantime, BSs serve their paired ground user equipments (UEs). To effectively manage inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design is formulated to minimize UAV's ergodic outage duration (EOD). To cope with conventional optimization techniques' inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is proposed, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in the discrete action domain and beamforming design in the continuous action regime, respectively. Numerical results illustrate the effectiveness of the proposed hybrid D3QNTD3 algorithm, compared to representative baselines.
Research on the Application of Time Series ARIMA Model and Neural Network Model

This article uses ARIMA model and BP (Back Propagation) neural network model, which is a nonlinear system with the most significant feature of high adaptability. This algorithm takes n samples of the research object as the input layer nodes of the neural network, takes the expected results as the corresponding output nodes, calculates weights and thresholds, and calculates the error values of the actual and predicted results. K-Means is a commonly used key method. Firstly, we need to randomly determine K as the initial point centroid. Then, each point in the dataset is assigned to a cluster and will be assigned a cluster corresponding to its centroid. The main idea of SVM is to find a hyperplane in the feature space, thereby separating as many data points of different categories as possible and maximizing the distance from the hyperplane. For linearly non separable data, it is necessary to select an appropriate kernel function to map the data into a high-dimensional space, so that the data can be linearly separable within the high-dimensional space. We use numerical optimization methods such as sequence minimum optimization (SMO) to solve the objective function and find the hyperplane parameters and thresholds that maximize the margin. AdaBoost (Adaptive Boosting) is an ensemble learning algorithm. It iteratively trains a series of weak classifiers and adjusts sample weights based on their performance to obtain higher weights for misclassified samples, thereby making the next weak classifier more focused on sampling these misclassified samples. Regarding the selection of weak classifiers, a weak classifier with poor performance will be selected in each iteration, in order to pay more attention to these misclassified samples in the next iteration and improve overall performance.
Facial Recognition-Based Emotion Detection for Enhanced Voter Experience

Facial recognition-based emotion detection technology has the potential to enhance the voter experience by providing real-time feedback on the emotional responses of voters during the voting process. This technology can be integrated with voting machines or mobile apps to capture voters' facial expressions and analyze them using machine learning algorithms to determine their emotions. The resulting data can be used to tailor the voter experience, such as providing additional assistance to voters who appear confused or frustrated. However, there are also potential privacy concerns and ethical considerations that must be addressed in implementing this technology. Overall, facial recognition-based emotion detection has the potential to improve the voter experience, but careful consideration must be given to the potential benefits and risks.
Identification of Human Movement Through a Novel Machine Learning Approach

This study aims to identify human movement activities using machine learning, focusing on a novel ensemble approach. The objectives are twofold: to apply a new machine learning method for activity recognition and to outperform recent approaches in accuracy. A standard dataset from Kaggle contained six activities: standing, sitting, lying, walking, walking downstairs, and walking upstairs. The study used an SVM and Logistic Regression ensemble alongside other standard classifiers. The proposed ensemble achieved an accuracy of 95.45% on the test data, surpassing other models. This research shows the potential of the ensemble approach for accurate human movement identification with potential uses in various domains.
An Industrial and Public Power Grids Malfunction Detection Towards Data Driven

In this paper, we suggest exploring a concept for distant detection of faulty grid-supporting equipment utilizing little data. These devices and their auxiliary functions, such reactive power dispatch utilized for voltage management, will be crucial to the functioning of future electrical grids. Capability is being created to allow improved monitoring of grid-connected devices to ensure security of supplies and resilience by synthesizing and exploiting operational information from a distribution grid. Data-driven methods of anomaly detection are investigated as a first step. They are used on the device's operating data to identify anomalies. Early results indicate applicability and potential barriers.
Analyzing the Attack Pattern of Brute Force Attack on SSH Port

Cyberattacks are one of the threats in the world of technology. Various kinds of cyberattacks can be a threat to information security. One example of a cyberattack is a brute force attack. The attacker breaks into the system by using various login usernames and passwords. It is necessary to analyze cyberattacks using honeypot to prevent and improve information security from existing cyberattacks. This research aims to analyze the Brute Force Attack Pattern on SSH Port by utilizing a honeypot. This research collected attacks on the SSH port captured by the honeypot server. Visualization logs were created from the honeypot logs to observe the patterns of attacks that targeted the SSH port. We utilized visualization tools like Tableau and Metabase to analyze the attack patterns. The research uses action research methodology to analyze how to prevent attacks. The research results revealed the most exploited usernames and passwords by hackers, analyzed the IP and identified the command patterns that hackers most exploited. We provided recommendations to mitigate the attacks based on these attack patterns.
Commuting patterns of Czech households exposed to flood risk from the Bečva river

Using unique data collected in October–December 2012 we estimate the link between commuting for work and level of individual exposure to floods. We find that commuters on average have higher earnings than non-commuters. Individuals affected by one flood commute by 11.2% more than unaffected individuals. We conjecture that the increase is linked to intentions to cover flood-related losses, decrease households' vulnerability to flood risk or out-migrate from the risk areas. Individuals affected by at least two floods are by 20.2% less likely to commute relative to those unaffected. We explain this non-linear effect by the fact that many households out-migrate after the first flood. Stayers commute less, because they are different from non-stayers in some underlying characteristics related to education, employment and family circumstances, which strongly affect commuting behaviour. We further find that in a commuting family an individual is by 53.8% more likely to commence commuting relative to a non-commuting family. Choice of commuting destination is often similar to that of other family members.
Performance Analysis of Transfer Learning Models for Detecting Autism Spectrum Disorder Using Facial Images

Autism Spectrum Disorder (ASD) is a type of disorder which impacts how individuals communicate interact socially and behave. It is crucial to detect and diagnose ASD on for intervention and support. Over the years advanced deep learning models have shown great potential in various medical fields, including detecting autism. The face of a person can serve as a biomarker because it can reflect the state of the brain making it a convenient and straightforward tool, for diagnosis. In this study we employ learning methods based on convolutional neural networks (CNNs) to identify children with autism by analyzing their facial images. This research paper examines three learning models. Xception, VGG16 and VGG19. After conducting training and validation using the optimized configuration the Xception model showcases the level of performance, with an accuracy rate of 89% on test dataset. On the hand models, like VGG16 and VGG19 achieved accuracies of 76% and 80% respectively. To compare their effectiveness, in detecting autism using data. The study evaluates these models performance based on accuracy, sensitivity, specificity and computational efficiency.
Block Allocation of Systematic Coded Distributed Computing in Heterogeneous Straggling Networks

Recently, coding techniques have been introduced in distributed computing systems, i.e., coded distributed computing (CDC), to alleviate the heterogeneous straggler effect. However, these techniques bring about additional decoding latency impacting on the task completion time. In this paper, we study the issues of load allocation and latency analysis of systematic CDC in heterogeneous computation and communication straggling networks (HCCSNs). In order to exploit the partial works completed by straggling workers, we use the method of block division to accelerate the sub-tasks' results returning from all workers. Moreover, we attempt to leverage the systematic MDS code, which needs fewer decoding operations, to reduce the decoding latency, but it requires prior determining of the systematic blocks and the parity blocks on the master not on the workers. Therefore, in order to minimize both of the execution (communication and computing) latency and decoding latency, we propose two algorithms, i.e., greedy-based binary search algorithm (GBSA) and proportional systematic block allocation (PSBA), to obtain the optimal numbers of blocks and systematic blocks assigned to each worker, respectively. Simulation results are presented to show that GBSA and PSBA outperforms other conventional block allocation schemes in both execution latency and decoding latency with various straggling parameters.