Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Plant Health Monitoring: A CNN Model Based Rice and Cotton Disease Prediction

Rice and cotton are crucial cash crops in India that contribute significantly to the country’s economy and provide employment to millions of people. To ensure high crop yields, it is essential to effectively manage crop diseases. However, traditional monitoring methods relying on human observation can be time consuming and inefficient. Hence, a system that can accurately and effectively detect plant diseases at the beginning is needed. In this paper, we present our study into the training and evaluation of different CNN models using a shared dataset that focuses on five diseases, including blast, bacterial leaf blight, brown spot, and diseased or healthy cotton leaf in cotton plants. The ResNet, VGG19, and Inception models obtained accuracy percentages of 63%, 66%, and 89%, respectively. On the same dataset, the general CNN architecture model had a 95% accuracy rate. Our findings suggest that the basic CNN architecture can be used effectively to predict diseases in rice and cotton plants. The general CNN model outperformed the ResNet and Inception models, indicating that a simpler architecture can yield better results for this specific task. In the context of Indian agriculture, this work offers important insights for the creation of a precise and effective system for the early diagnosis of plant illnesses. Finally, based on the input given to the model the disease is predicted and a cure for that predicted disease is given both in organic and chemical methods.
Subalpine forest dynamics reconstructed throughout the last 700 years in the Central Pyrenees by means of tree rings and pollen

Understanding how climate has modulated forest growth and composition in the past is necessary to predict the influence of the ongoing climate warming on the dynamics of mountain forests. We studied the past dynamics of subalpine Pyrenean forests during the last 700 years by assessing the relationships between sedimentary pollen and tree-ring records, and their link with climatic drivers. We compared the pollen record and the montane pollen ratio, an integrative index obtained from sedimentary pollen that allows inferring past altitudinal variations in the montane–subalpine ecotone, with tree-ring width from mountain pine (Pinus uncinata) subalpine forests located in Central Pyrenees. To assess climate–growth associations, we related the dendrochronological data with instrumental meteorological records (1901–2010) and temperature reconstructions from the Pyrenees and Northern Hemisphere. Few robust associations were found between arboreal pollen taxa and tree-ring width series of the surrounding forests. However, significant correlations were found between the montane pollen ratio and tree-ring width series from nearby forests (located less than 10 km apart). This relationship could be potentially useful to infer long-term forest growth changes at decadal to centennial scales using the montane pollen ratio. On the contrary, our results show that tree radial growth has mainly been constrained by low temperatures although the growth sensitivity to climate has considerably varied over the last 700 years. Similar results were obtained for the last century as growth variability of these high-elevation forests is still driven by low temperatures, but a relaxation of this constrain in recent decades has been detected.
FedCAM - Identifying Malicious Models in Federated Learning Environments Conditionally to Their Activation Maps

Federated Learning (FL) is a machine learning paradigm enabling collaborative model training across multiple participants and a server with decentralized data while maintaining privacy. However, FL is susceptible to adversarial poisoning attacks where some malicious participants may fail the convergence or change the behavior of the model under some conditions. This paper introduces FedCAM, a robust framework designed to detect and exclude malicious model updates in FL. FedCAM uniquely utilizes Activation Maps (AMs) from representative data samples (a trigger set), unlike other methods relying on surrogate vectors of model parameters. It leverages a conditional variational autoencoder (CVAE), conditioned on the trigger set's label class, to evaluate the reconstruction error of geometric median normalized AMs. FedCAM dynamically determines the decision threshold for each round based on CVAE's error, proving more effective in detecting poisoning compared to existing methods, as demonstrated by our experiments on the IID MNIST dataset. This approach ensures efficient detection of malicious undates in every FL round.
The Sensitivity of Sprite Streamer Inception on the Initial Electron‐Ion Patch

Sprite streamers are bright atmospheric phenomena above thunderstorms powered by sufficiently high electric fields and free charges from inhomogeneities in the mesosphere or ionosphere. A common feature of recent simulations is that they model the streamer inception from spherical Gaussian electron‐ion patches. We here tackle the following question: How do the streamer inception time and streamer properties depend on the initial geometry? Therefore, we consider prolate (“cigar”) and oblate (“pancake”) electron‐ion patches aiming to understand the geometric influence on streamer inception speed, electric field evolution, branching time, and ohmic heating of streamers. We initiate patches of different geometry with fixed peak densities of 5·1011 m−3 or with a fixed total electron number of 9.40·1012 in ambient fields of 0.5 and 1.5 times the breakdown field and study the streamer evolution between 60 and 80 km altitude with a 2.5D cylindrical Monte Carlo particle code. We present the evolution of the electron density and of the electric field. In our simulations, the time for the electric field tips to develop into the regime where they can self sustain the discharge is shortest for streamers from prolate patches and longest for oblate patches. The branching time of negative fronts depends on the eccentricity and increases for oblate patches ranging from 5 to 8 μs. We observe ohmic heating with maximum temperature differences up to tens of kelvins depending on the eccentricity and density of the initial patch influencing the efficiency of plasma reactions in streamer channels.
Explainable Tracking of Political Violence Using the Tsetlin Machine

In this paper we introduce a framework that utilizes an architecture based on the Tsetlin Machine to output explainable rules for the prediction of political violence. The framework includes a data processing pipeline, modeling architecture, and visualization tools for early warning about notable events. We conducted an experimental study to explain and predict a one of the most notable events, - a civil war. We observed that the rules that we produced are consistent with theories that emphasize the continuing risks that accumulate from a history of conflict as well as the stickiness of civil war.
A Modified Artificial Potential Field using Perpendicular Force for Leader-Follower System

Artificial potential field (APF) is a path planning strategy that is widely used in robotics. Unfortunately, it is susceptible to local minima problems, especially on Symmetrically Aligned Robot-Obstacle-Goal (SAROG) configurations. In this paper, we propose a modified APF by introducing the perpendicular force for escaping the local minima. Furthermore, it is also being implemented for the leader-follower system. Here, the leader is assigned to reach the goal while avoiding obstacles. Simultaneously, the follower needs to track the leader while maintaining a safe distance. The effectiveness of our proposed method is verified by numerical simulations. The results show that the modified APF using perpendicular prevails in guiding the leader to reach the goal. At the same time, the follower can maintain a tolerated distance from the leader.
STSN: A Skim-Then-Scrutinize Network for Understanding Multimodal Educational Questions

Nowadays, online systems have collected millions of questions and produced many personalized applications, such as difficulty prediction. Therefore, obtaining a good question representation is significant. In intelligent education, different applications require different levels of information. For example, estimating the corresponding knowledge of a question requires only summary-level information, i.e., the gist of the question, while analyzing the similarity between questions requires understanding them at the instance level, i.e., all information of the question. However, existing solutions tend to represent the questions at a single level, resulting in poor generalization. To this end, we propose a novel Skim-Then-Scrutinize Network, STSN, which aims to improve generalization by reasonably representing a question at two levels. Specifically, we first design a multimodal embedding module for learning the embeddings of questions. Second, we introduce a skim-then-scrutinize module to systematically exploit the embeddings and generate the two-level representations. In the skimming submodule, we utilize a simple attention-based strategy to capture the key components and generate a summary representation. After that, in the scrutinizing submodule, we use the gist obtained in the skimming submodule to help model the correlations between different modalities, thereby generating an instance representation. Third, we propose an unsupervised optimization module with three objective functions. Extensive experiments on two real-world datasets demonstrate the generalization of STSN.
Comparative Analysis of Hybrid Long Short-Term Memory Models for Fire Danger Index Forecasting with Weather Data

In East Kalimantan, the absence of predictive tools for time series problems using data from Automatic Weather Stations further exacerbates this issue. Forest fires are a critical concern in this region, with their frequency and severity closely tied to weather conditions. In this study, we address this challenge by conducting an analysis employing various Long Short Term Memory (LSTM) models to predict time series data, specifically focusing on forest fire risk prediction through weather variables. The data used in this research is sourced from Station 1’s Automatic Weather Station system, encompassing 7778 rows of data from January to June 2023. Based on this data, we aim to develop an accurate predictive model for the Fire Hazard Index (FDI). The FDI is a crucial indicator of forest fire risk, and accurate predictions can help authorities take proactive measures to mitigate fire outbreaks. To achieve this goal, we compare several algorithms, including LSTM, GRU-LSTM, CNN-LSTM, BILSTM, and Stacked-LSTM, in forecasting the FDI for the Automatic Weather Station. Our performance evaluation involves essential metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Variation (CV) for Predicted values, and CV for Actual values. The findings of our study indicate that all tested algorithms exhibit promising performance in predicting the FDI variable, with negligible discernible differences among them. However, the GRU-LSTM algorithm demonstrates slightly superior performance, boasting an MAE of 20.762499, MSE of 1500.318800, and RMSE of 38.733949. This suggests that GRULSTM may be the preferred choice for FDI prediction in this context. Furthermore, it’s noteworthy that all the algorithms generate predictions that closely mirror the actual values of the FDI variable. This is evident from the congruent ranges of CV for Predicted and CV for Actual values.
Stock Price Prediction Using Time Embedding and Attention Mechanism in Transformers

Predicting stock prices accurately is a complex task that involves analyzing a wide range of factors such as company performance, industry trends, market conditions, and even global events. Recently, transformer-based models have shown promising results for sequential data analysis, including stock price prediction. However, predicting stock prices accurately requires considering the temporal patterns in the data, which can be achieved by incorporating time embedding techniques into the model. Here in this paper a transformer-based model was proposed for stock price prediction that incorporates time embedding and the moving average technique. The proposed model uses historical stock prices and financial indicators as input, and the output is the predicted stock prices. Time embedding is used to capture the temporal patterns in the stock prices, and the moving average technique is used to remove the noise in the input data and improve the models accuracy. The performance of proposed model which is based on transformer is evaluated on publicly available dataset of various companies i.e IBM, APPLE and TATAMOTORS. The results we achieved is really good and transformer-based models have really proven to be suited for Stock price prediction.
Preventing Ethereum Blockchain Re-Entrancy Attacks Using Smart Mutex Lock Sum

Since the smart contracts are being introduced, they are being subjected to various vulnerabilities. These vulnerabilities pose a major threat to participant's money and cause conflicts. Smart contracts are self executing contracts that use blockchain technology to transfer values among peers in which there is no control of a central authority to run and execute transactions. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. As technology advances, many reentrancy solutions have been implemented and various detection approaches have been proposed. In this paper, it proposed a “Smart MutexLock” mechanism to prevent Ethereum reentrancy. Since the smart contracts on blockchain platforms like Ethereum do not directly support mutex locks due to their deterministic and asynchronous nature, it have used mutex locks in an effective and secure manner. The mutex mapping is used to keep track of whether a particular address is currently in a transaction to prevent reentrancy. The mutex variable is set to true before any external call, preventing further calls until the transaction is completed. Once the transaction is complete, the mutex variable is set back to false and user is notified to ensure a safe transaction
Multi-Dimensional Electric Carbon Index Cluster Analysis Based on Electricity Consumption Data in Administrative Area

On the basis of the electricity consumption data of the district, this study uses the method of multi-dimensional electric carbon index cluster analysis to study electricity consumption. By analyzing multiple dimensions of electricity consumption, such as carbon intensity and energy efficiency, the characteristics and differences of electricity carbon emissions based on administrative regions are studied. The results show that the electric carbon index of different administrative regions is significantly different, and the electric carbon emission level and energy utilization efficiency of each region are different. Based on this, the administrative region is divided into several cluster groups in order to further study and manage its electricity consumption behavior. This method based on cluster analysis can provide guidance for each administrative region, and promote the optimization of electricity consumption and carbon emission reduction among regions.
Brazilian Journal of Chemical Engineering ON THE PREDICTION OF PSD IN ANTISOLVENT MEDIATED CRYSTALLIZATION PROCESSES BASED ON FOKKER-PLANCK EQUATIONS

A phenomenological model for the description of antisolvent mediated crystal growth processes is presented. The crystal size growth dynamics is supposed to be driven by a deterministic growth factor coupled to a stochastic component. Two different models for the stochastic component are investigated: a Linear and a Geometric Brownian motion terms. The evolution in time of the particle size distribution is then described in terms of the Fokker-Planck equation. Validations against experimental data are presented for the NaCl-waterethanol anti-solvent crystallization system. It was found that a proper modeling of the stochastic component does have an impact on the model capabilities to fit the experimental data. In particular, the GBM assumption is better suited to describe the antisolvent crystal growth process under examination.
One-Stage Deep Learning Approach for the Detection of Fractures in Cervical Spine

Fractures occurring in the cervical spine are a severe medical condition that can have fatal consequences or result in permanent paralysis that lasts a lifetime. Rapid identification and location of vertebral fractures are critical to prevent neurological deterioration and paralysis after trauma. Cervical spine fractures are injuries that must be handled with care. Some are stable, some are not, and mismanagement and misdiagnosis can lead to life-altering consequences. This paper proposes a YOLOv7(You Only Look Once v7) based method to locate the area that may contain fracture. Experimental results on RSNA 2022 [1] Cervical Spine Fracture Detection competition dataset have proven that the suggested model performs better than the most advanced methods in terms of higher detection efficiency, faster detection times, and high accuracy.
Dense Scale Network with Multi-task Enhancement for Crowd Counting

Crowd counting has been widely used in industrial monitoring systems, urban planning, and traffic management. Most existing crowd counting methods based on density map regression focus only on the extraction of multi-scale features, and use mean square error to calculate the error between the ground truth and the density map estimated by the model, without further studying the influence of the foreground and background on the crowd counting task. However, multiple foreground or background instances would negatively affect the accuracy of crowd counting, especially in some complex industrial application scenarios. To address this issue, we combine the crowd counting and foreground-background segmentation tasks with the collaborative training model, and combine the dilated convolution network and self-attention to propose a multi-task dense scale network (M-DSNet). With the proposed network, the error of desity map prediction is efficient reduced. In addition, we present an ablation study of each component and compare our approach with the literature with respect to four crowd counting datasets. Our approach achieved a remarkable improvement over baseline methods.
Improved Forecasting of Electric Vehicle Charging Load using Neural Architecture Search

In the modern world, due to global climatic changes, the Automobile Sector is generally shifting towards Electric Vehicles due to its benefits over Internal Combustion Engines (ICE). This rapid increase in the Electric Vehicle Charging stations causes increase in overall Energy demand. Thus, forecasting of power demand becomes a key ingredient for Electric Vehicle Charging Infrastructure Management. In this paper, two of the most popular Machine Learning forecasting models namely Bidirectional Long Short-Term Memory Network (Bi-LSTM) and Long Short-Term Memory Network (LSTM) are used for prediction of Electric Vehicle Charging Load demand whereby, both the network hyper parameters are tuned using traditional Grid Search Method and Neural Architecture Search (NAS) Method. The models are tested on the dataset provided by Electric Vehicle Supply Equipment (EVSE) using Adaptive Charging Networks (ACN) by California Technology. The present work proposes a SARSA-based Neural Architecture Search (NAS) method to optimize the design of the forecasting models and results show satisfactory improvement in forecasting performance over models designed by conventional Grid Search-based approaches.
ℵ 0 -CATEGORICITY OF SEMIGROUPS II

A BSTRACT . A countable semigroup is ℵ 0 -categorical if it can be characterised, up to isomorphism, by its ﬁrst-order properties. In this paper we continue our investigation into the ℵ 0 -categoricity of semigroups. Our main results are a complete classiﬁcation of ℵ 0 -categorical or-thodox completely 0-simple semigroups, and descriptions of the ℵ 0 - categorical members of certain classes of strong semilattices of semi-groups. ℵ 0 -categorical and Semigroups and Rees matrix semigroups
Transport and Tourism

This book discusses the relationship between transport and tourism with a particular focus on how to improve the destination function of gateways. The case study chosen is the Cook Strait ferries and the ferry ports of Wellington and Picton, in New Zealand. The perspectives of ferry passengers and transport and tourism operators are presented in order to compare and contrast the realities of Picton, a small resort town, and Wellington, the capital city.
TESTING EVOLUTIONARY THEORIES OF DISCRIMINATIVE GRANDPARENTAL INVESTMENT

Summary This study tests two evolutionary hypotheses on grandparental investments differentiated by the child's sex: the paternity uncertainty hypothesis and the Trivers–Willard hypothesis. Data are from two culturally different countries: the Dutch Longitudinal Aging Study Amsterdam (n=2375) and the Chinese Anhui Survey (n=4026). In the Netherlands, grandparental investments are biased towards daughters' children, which is in accordance with the paternity uncertainty hypothesis. But in China, grandparental investments are biased towards sons' children, which is in conflict with the paternity uncertainty hypothesis. This study found no support for the Trivers–Willard hypothesis. These results raise doubts over the relevance of paternity uncertainty as an explanation of a grandparental investment bias towards daughters' children that is often found in Western populations. The results suggest that discriminative grandparental investments are better understood as the outcome of cultural prescriptions and economic motives.
Effects of substrate composition, stream-bed stability, and sediment supply on survival and trophic role of a dominant stream grazer

Understanding the effects of disturbance on river environments and rheir biotic communities has long been a goa! of stream ecologists and resource managers (e.g. REsH et al. 1988). Food web structure and energy flow patterns through river communities can be highly altered by changes in geomorphic processes such as the frequency and intensity of scouring floods (POWER 1992), sediment composition and supply, and channel shape and srabiliry (LIGON et al. 1995). Such changes often result from dams and diversions built to store and redistribute water within and berween warersheds (DYNESIUS & NILSSON 1994). Sediment movement during high flows is an important mechanism regulating benthic invertebrate populations (HOLOMUZKI & BIGGS 2000). Large, univoltine taxa wirh relatively blunt body forms are more susceptible to being dislodged or crushed by moving sediment parricles, and rheir populations are slower to recover from scouring floods than small, multivoltine taxa with higher immigration rates (e.g. TüWNSEND & HILDREW 1994). In norrhern California rivers with regulared flows, bed-scouring winter floods are often reduced or eliminated. As a consequence, functionally important food chains become shorrer as Iarge predatorresistant grazers, such as the stone-cased caddisfly Dicosmoecus gilvipes, come to dominate the primary consumer trophic level and reduce energy flow to higher rrophic levels (POWER er al. 1996, WoorroN et al. 1996).