Miracle-dz/UniFAR-FTUs huggingface dataset:
Abnormal Object Detection of the Transmission Lines with YOLOv5 and Federated Learning

Detecting abnormal object intrusions of the transmission lines in a timely and effective manner is of great significance for the safe operation of the electrical power grid. However, due to the particularity and privacy policy of the power grid companies, there is a lack of available and effective dataset with open source. Besides, individuals tend to gather data in their commonly own regions, which would lead to data silos. In this paper, a dataset of the abnormal object intrusions of the transmission lines is first configured to meet the model training requirements. An improved abnormal object detection (AOD) method is proposed utilizing the federated learning. The FasterNet backbone and partial convolution are incorporated to improve the YOLOv5 based model detection speed. Further, the loss function Wise-IoU (WIoU) and Squeeze & Excitation Networks (SE) are applied to enhance the detection precision. Finally, the distributed training strategy of the federated learning is applied to overcome the data silos. Through the ablation experiments, practicality together with merits of the suggested approach are validated on the federated learning platform, achieving a delicate balance between the detection accuracy and speed with comparison analysis for the abnormal object detection in the transmission lines.
Designing a Smart Garden for Automated Plant Watering using Flutter and Internet of Things in the Context of Industry 4.0

Plant maintenance in educational institutions often needs consistent watering practices, leading to plant withering and decline. This research endeavors to develop an intelligent gardening system employing Flutter and the Internet of Things (IoT) to automate plant watering based on soil moisture levels. The research focuses on loose soil conditions, categorized through laboratory testing at a high school, with classifications ranging from 0.3V-1.27V for water-rich soil, 1.28V-1.79V for wet soil, 1.80V-2.60V for dry ground, and 2.61V-2.90V for parched earth. This study implements IoT principles into an Android application to enable automated plant watering when soil moisture levels indicate dryness
Numerical Analysis of Saturated Sand Under Dynamic Loads

In this study, the behavior of reclaimed soils including the effects of excess pore water pressure and the loss of strength under dynamic loads or earthquakes are investigated and simulated. The constitutive model based on the disturbed state concept (DSC) is introduced and DSC-DYN2D program is utilized in a numerical analysis. In the laboratory test, quasi-static and cyclic triaxial tests were carried out to determine parameters for the numerical analysis. Field tests were executed in Inchon International Airport with a 10 tons hydraulic hammer. In the field test, the data of dynamic loads and excess pore water pressure were measured using a 3-D geophone and a pressure transducer respectively, The data of field tests showed the accumulation of excess pore water pressure when rapid dynamic loads were applied. Especially, a back-prediction program based on DSC model is developed and verified its accuracy using various parameters from the cyclic triaxial test. As the results of numerical simulation, the predicted trends for excess pore water pressure compare well with the observed data. Based on the result of this research, it is found that the numerical analysis based on the DSC model is compatible to predict the softening behavior of saturated reclaimed soils under dynamic loads.
A Real-Time Load Forecasting Approach Based on Graph Convolution Neural Network in a Distributed IoT Energy System

With the development of edge devices, smart sockets are now capable of handling various power load data, which provide a new solution for edge computing and real-time load forecasting. In this paper, a distributed system that can centrally process load data is proposed, and a KGLSTM network based on graph neural networks and long short-term memory networks is introduced. In our proposed system, smart sockets are used for the collecting and processing of load data. The proposed KGLSTM network is based on a dual-branch structure. One branch extracts medium and short-term load data features by using LSTM, and the other branch extracts medium and long-term load data features by using graph neural networks. In terms of the training of model, a dynamic weighted loss function is proposed to guide the training, which improves the load prediction performance for peak load intervals. Finally, by comparing with advanced models, the effectiveness of KGLSTM and weighted KGLSTM for load prediction is validated, and the proposed two models also have a great performance in peak forecasting.
Research on Multi-Vehicle Power Distribution Method in Charging and Discharging Station Discharge Mode

With the popularization of electric vehicles and the vigorous development of V2G technology, the charging and discharging strategies of the current electric vehicle charging and discharging system for different charging and discharging requirements cannot meet the needs of both users and the power grid. Aiming at the discharge problem of electric vehicle charging and discharging station with bidirectional charging and discharging capability, a power allocation strategy is proposed. First, the user side is modeled in the electric vehicle discharge mode to determine the conditions under which participation in the discharge can be enabled. Secondly, combined with the constraints of the user side and the internal conditions of the charging and discharging station, the V2G capacity of the electric vehicle is predicted, and the discharge power allocation strategy of the charging and discharging station is formulated. Finally, according to the actual electric vehicle data simulation, the feasibility of the discharge power allocation strategy is verified, which provides a feasible reference for the management method of charging and discharging stations in the future.
Using stable isotopes of carbon to investigate the seasonal variation of carbon transfer in a northwestern Arkansas cave

Stable-isotope analyses are valuable in karst settings, where characterizing biogeochemical cycling of carbon along groundwater flow paths is critical for understanding and protecting sensitive cave and karst water resources. This study quantified the seasonal changes in concentration and isotopic composition (dC) of aqueous and gaseous carbon species—dissolved inorganic carbon (DIC) and gaseous carbon dioxide (CO2)—to characterize sources and transfer of these species along a karst flow path, with emphasis on a cave environment. Gas and water samples were collected from the soil and a cave in northwestern Arkansas approximately once a month for one year to characterize carbon cycling along a conceptual groundwater flow path. In the soil, as the DIC concentration increased, the isotopic composition of the DIC became relatively lighter, indicating an organic carbon source for a component of the DIC and corroborating soil DIC as a proxy for soil respiration. In the cave, a positive correlation between DIC and surface temperature was due to increased soil respiration as the organic carbon signal from the soil was transferred to the cave environment via the aqueous phase. CO2 concentration was lowest in the cave during colder months and increased exponentially with increasing surface temperature, presumably due to higher rates of soil respiration during warmer periods and changing ventilation patterns between the surface and cave atmosphere. Isotopic disequilibrium between CO2 and DIC in the cave was greatest when CO2 concentration was changing during November/ December and March/April, presumably due to the rapid addition or removal of gaseous CO2. The isotopic disequilibrium between DIC and CO2 provided evidence that cave CO2 was a mixture of carbon from several sources, which was mostly constrained by mixture between atmospheric CO2 and soil CO2. The concentration and isotopic composition of gaseous and aqueous carbon species were controlled by month-to-month variations in temperature and precipitation and provided insight into the sources of carbon in the cave. Stable carbon isotope ratios provided an effective tool to explore carbon transfer from the soil zone and into the cave, identify carbon sources in the cave, and investigate how seasonality affected the transfer of carbon in a shallow karst system.
Time Delay Stability Analysis of Pairwise Interactions Amongst Ensemble-Listener RR Intervals and Expressive Music Features

Time Delay Stability (TDS) can reveal physiological function and states in networked organs. Here, we introduce a novel application of TDS to a musical setting to study interactions between RR intervals of ensemble musicians and a listener, and music properties. Three musicians performed a movement from Schubert's Trio Op. 100 nine times in the company of one listener. Their RR intervals were collected during baseline (5 min, silence) and performances (∼10 min each). Loudness and tempo were extracted from recorded music audio. Regions of stable optimal time delay were identified during baseline and music, shuffled data, and data pairs from incongruent recordings. Bootstrapping was employed to obtain mean TDS probabilities (calculated based on all performances). A significant difference in mean TDS probability between music and baseline was observed for all musician pairs ($p <. 001$) and for cello-listener ($p=.025$) A significant decrease in mean TDS probability was observed for piano-violin ($p <. 001$), violin-tempo ($p=.045$), and cello-tempo ($p <. 001$) for mixed pairs. The highest intermusician TDS probabilities were observed in musically tense sections. This framework offers a promising way to track dynamic RR interval interactions between people engaged in a shared activity, and, here, between the people and music properties.
Model Based Change Detection Approach For Sensor Fault Identification in Battery Packs

In this paper, a model based sensor fault detection strategy is proposed for series connected Li-Ion cells. A state space model of the equivalent circuit of each cell is first derived and a Luenberger observer is designed for residual generation, which plays a crucial role in the fault detection process. Since the residual is influenced by the fault's magnitude, it may result in subtle changes that are challenging to identify. Therefore, a Quickest Change Detection (QCD) approach is presented to further process the residual and ensure sensor malfunctions are not missed. The Cumulative Sum algorithm is then used to solve the QCD problem. Simulation results are then presented to verify the feasibility of the proposed method.
An Euclidean Jordan Algebra of Symmetric Matrices Closed for the Schur Product

In this paper, we consider a particular real Euclidean Jordan algebras V of square real symmetric matrices closed for the Schur product of matrices such that its rank equals its dimension. Next, we present the definition of Krein parameters of the Euclidean Jordan algebra V and present some inequalities over these Krein parameters like those shown on Theorems 4.1, 4.2 and 4.3.
Synthetic Data Generation in Small Datasets to Improve Classification Performance for Chronic Heart Failure Prediction

In cardiology, we frequently develop machine learning models to predict events such as heart failure. Oftentimes, these events occur at low incidence in the available data, especially for under-represented subpopulations, which limits classifier performance due to class imbalance. To mitigate these issues, we investigate the use of synthetic data generation, or algorithms trained to mimic realistic patient data. In particular, we use synthetic data to augment training data for Catboost in classifying chronic heart failure using the University of California, Irvine myocardial infarction complications dataset (n = 1,700). Our primary metrics of interest are the mean and the variability of AUC and F1-Score across five-fold cross-validation. Overall, we find modest gains in performance over the baseline classifier with no augmented data. Nevertheless, the more sophisticated generators, both with and without hyperparameter tuning, did not confer better performance than simpler methods. Furthermore, all methods were subject to large variability in classification metrics across folds. While synthetic data generation is a promising tool for class imbalance, more investigation is needed to find optimal sample sizes and settings for the stability of results.
Investigation of Malware & Threat Analysis on APKs Using SVM & ANN Algorithm. -A New Approach

With 85% market share, Android is currently the one of the most popular smart-mobile device platform around the world. The popularity of Android encourages cyber criminals to produce harmful apps that might jeopardize the security and confidentiality of the mobile devices. Every year almost above 55% of apps are being rejected by google play store. Google has banned more than 22 harmful Adware applications from the google play store in the previous year, which added up to around at least 7.5 million downloads. In the year 2017, they had to remove around 700,000 applications that broke the standards sets by google play, and almost more than a million were identified as bad developers. There has always been struggle against malware it is never-ending process, and at the end it is always the user who has their significant number of personal and private data stolen by attackers, jeopardizing their privacy and leaving them vulnerable to additional threats. To tackle this increasing rate of malware production, a malware detection system that is both efficient and flexible is required. There are three primary ways in detection of android malware, they are static analysis, dynamic analysis, and hybrid. In this paper, we will look into malware detection techniques based on SVM and ANN for android applications. Here, we first compute the similarity scores between malware and benign applications in terms of suspicious API calls and use these similarity scores as features in the feature vector, then we train the SVM classifier with different risky permissions combinations as additional features for training. This model will demonstrate that the suggested technique can properly detect whether a particular program is malicious or not.
SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances

Protein functions are tightly related to the fine details of their 3D structures. To understand protein structures, computational prediction approaches are highly needed. Recently, protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques. Most of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function. Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues’ features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://github.com/gongtiansu/SASA-Net/
Voltage Control of Distribution Network Based on Parallel Control System

With the increasing penetration of renewable energy sources in distribution networks, the node voltage crossing limits caused by distributed generation (DG) has brought a very big challenge to the operation and scheduling of distribution networks. Therefore, this paper constructs a parallel control system for distribution network to guarantee the safe and reliable operation of the actual distribution network system. Firstly, we build an equivalent digital twin of the actual distribution network system based on NI PXI controller; then, proposes a centralized distribution network scheduling model based on second-order cone optimization, and coordinates the centralized optimization model based on the digital twin to determine the best voltage control strategy for the actual distribution network system through computational experiments and evaluation; secondly, improves the voltage control ability of the distribution network by parallel execution and coordinated optimization between the constructed artificial system and the actual system; finally verifies the feasibility and significant advantages of the parallel voltage control system for distribution networks by using the IEEE33 distribution network system as an example.
re-Inhibiting prolactin by cabergoline accelerates mammary gland remodeling during the early dry period in dairy cows

The inhibition of prolactin release using cabergoline, a dopamine agonist, is an effective strategy to accelerate the changes in mammary secretion composition after drying-off. The objective of this study was to determine how cabergoline may affect mammary tissue remodeling during early involution. Holstein dairy cows were treated with either a single i.m. administration of 5.6 mg of cabergoline (Velactis, Ceva Santé Animale, Libourne, France, n = 7) or placebo (n = 7) at the time of drying-off. Mammary biopsy samples were collected 1 wk before drying-off (d −6), after 30 h of milk accumulation (d 1), and again 8 d following drying-off (d 8) to determine changes in gene expression, lactoferrin content, and cell turnover. Blood and mammary secretion samples were collected at d −6 and again at d 1, 2, 3, 4, 8, and 14 following the abrupt cessation of lactation to evaluate indicators of blood-milk barrier integrity and other markers of mammary tissue remodeling. Cabergoline induced less SLC2A1 , BAX , CAPN2 , and IGFBP5 mRNA expression. In contrast, cabergoline did not modify changes in cell proliferation and apoptosis. Following the cessation of lactation, changes in mammary secretion composition (Na + and K + ) and blood lactose concentrations were indicative of a loss in the blood-milk barrier function in both treat-ment groups. Cabergoline treatment affected only Na + and K + concentrations at d 1, suggesting a moderate increase in tight junction permeability. The increase in the activity of MMP9 and in mammary epithelial cell concentration in mammary secretions was greater in cabergoline-treated cows than in control cows, suggesting more mammary tissue remodeling. The increase in lactoferrin immunostaining in the mammary tissue oc-curred earlier for cabergoline-treated cows than for con-trol cows, and was essentially localized in the stroma. Changes in some key markers of mammary involution suggest that cabergoline accelerates mammary gland remodeling. Thus, a single injection of cabergoline after the last milking would facilitate drying-off by enhancing mammary gland involution.
DIVERSIDAD GENÉTICA Y ESTABLECIMIENTO DE PRIORIDADES EN ESQUEMAS DE CONSERVACIÓN. EJEMPLO DE APLICACIÓN EN LA RAZA DE LIDIA UNIVERSIDAD COMPLUTENSE DE MADRID

The work in this thesis explores the mathematical aspects of priority settings in a set of conservation units (populations, lines, breeds, species.) in the framework of genetic diversity conservation programmes. Resources from governments and public organizations are always limited, and maximum profit must be obtained from them. In terms of diversity this means that they must be split proportionally, according to the share each unit has in the global diversity. However, the estimation of these contributions is not trivial nor has a unique solution. There is also a conceptual dichotomy regarding the importance of between units, as opposed to within units, variation. Initially, an outlook on the traditional between and within-population diversity measures is performed, to move then towards deepening into two recent criteria for genetic diversity measurement. Both of these new concepts were developed to be incorporated in the calculation of different types of unit contributions and the establishment of priorities among those units. The two methods, Weitzman’s and the Core Set method, are studied in depth, adding original contributions that help establishing their properties and improving their performance. Also reviewed is a third method of weighted contributions, whose
Numerical investigation of ﬂow structures development of the slat cove of the ﬂow past a three-element high-lift wing

— Three-dimensional simulations of the ﬂow past a 30P30N three-element high lift wing are performed at low Reynolds numbers (cid:2) O (cid:0) 10 4 (cid:1)(cid:3) using a high-order spectral element method. The study focuses on investigating ﬂow structures of the slat cove and G¨ortler vortices that appear on the top of the main airfoil. The spanwise velocity component is measured in the slat cove and shows a sinusoidal form with a ﬁxed wavelength corresponding to the later observed G¨ortler vortices. The amplitude of the spanwise velocity grows exponentially in time for approximately 6 orders of magnitude before a saturation is observed. Proper Orthogonal Decomposition (POD) modes are next used to analyze the ﬂow structures development of the slat cove for different time ranges. It is shown that the ﬂow in the slat cove is ﬁrst dominated by shear layers which evolve into spanwise perturbations. These perturbations later develop into bell-shaped structures and ultimately form G¨ortler vortices.
Efficacy and Practical Implementation of Fecal Microbiota Spores, Live-BRPK: A Novel Approach for Preventing Recurrent Clostridioides difficile Infection

bio-therapeutic products (LBP). During late 2022 and early 2023, 2 products received US FDA approval: fecal microbiota, live-jslm (Rebyota [RBL]; Ferring, Parsippany, NJ), a rectally administered therapeutic, and fecal microbiota spores, live-brpk (Vowst [VOS]; Seres Therapeutics, Cambridge, MA), an orally administered therapeutic.
Simulation and Experimental Study of a Novel Negative-Pressure Flapper–Nozzle Mechanism

As the manufacturing industry evolves, the significance of control valve positioners in chemical production escalates. The flapper–nozzle system, the heart of control valve positioners, directly influences the linearity of system control. Presently, studies on the flapper–nozzle system primarily focus on dynamic system modeling and computational fluid dynamics simulations. However, traditional flapper–nozzle mechanisms often fail to achieve linear control objectives. This paper proposes a novel negative-pressure nozzle structure to tackle this issue, combining computational fluid dynamics and experimental methods, and considering gas compressibility during high-speed flow. Both simulation and experimental results suggest that the new structure improves the supply air pressure and broadens the linear pressure output range of the system, showing significant potential for practical applications.