Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Identification and Phylogenetic Analysis of Salmonella‎ species ‎Isolated from Diarrheal Children and Dogs in Baghdad Governorate, ‎Iraq‎

This work aimed to use conventional PCR to identify Salmonella‎ spp. that ‎were isolated from diarrheal children and healthy and diarrheic dogs based on four ‎virulence genes, hilA, stn, spvR‎, and marT. Sixteen Salmonella‎ isolates including: 9 ‎isolated from children's diarrhea from three species (S. Typhimurium, S. Enteritidis, S. ‎Typhi) and seven isolated from dogs including (S. Typhimurium, S. Enteritidis, S. ‎Muenchen), were identified primarily by several methods. The PCR products of the 16S ‎rRNA gene were sequenced and examined using BLAST analysis to find differences and ‎similarities between these Iraqi isolates and already-known global strains in order to ‎construct the phylogenetic tree of S. Muenchen which was detected for the first time in ‎dogs in Iraq. The results of the study revealed that all isolates of Salmonella‎ obtained ‎from children possess the hilA and stn genes. The marT gene was detected in 88% of the ‎Salmonella‎ serovars, and the spvR‎ gene was carried in 55% of the isolates. Among dog ‎Salmonella‎ isolates, the hilA gene was detected at 100%, the stn gene was at 85.7%, the ‎marT gene was present at 71.4%, while the spvR‎ gene was found at 57.1%. The result of ‎DNA sequencing and phylogenetic tree indicated that the local Iraqi S. Muenchen was ‎extremely close to the national strain and share the same ‎16S rRNA gene sequence, the ‎isolate was registered at NCBI and became a global reference with the accession number OQ999043.1. In conclusion, the presence of these important virulence genes among ‎Salmonella‎ serovars isolated from children and dogs alerted on the potential risk of ‎contamination of the environment and may lead to a community health crisis‎.
Automatic Rough Georeferencing of Multiview Oblique and Vertical Aerial Image Datasets of Urban Scenes

Multi‐perspective airborne images that combine oblique and vertical views of the ground have proved to be a valuable source of information for numerous applications requiring a digital representation of the world. In this paper, an automatic methodology for rough georeferencing of large datasets of multiview oblique and vertical aerial images of urban regions without any metadata is proposed. Using feature‐based matching combined with robust model fitting and least‐squares techniques, the method requires the measurement of a minimum number of points with known coordinates in only one image. The results of this methodology are discussed through the presentation of a developed software suite which identifies the overlapping images, georeferences them, extracts their footprints, subdivides the images into groups based on these footprints and detects the images that cover a specific region.
Robust Eye Blink Detection Using Dual Embedding Video Vision Transformer

Eye blink detection serves as a crucial biomarker for evaluating both physical and mental states, garnering considerable attention in biometric and video-based studies. Among various methods, video-based eye blink detection has been particularly favored due to its non-invasive nature, enabling broader applications. However, capturing eye blinks from different camera angles poses significant challenges, primarily because the eye region is relatively small and eye blinks occur rapidly, necessitating a robust detection algorithm. To address these challenges, we introduce Dual Embedding Video Vision Transformer (DE-ViViT), a novel approach for eye blink detection that employs two different embedding strategies: (i) tubelet embedding and (ii) residual embedding. Each embedding can capture large and subtle changes within the eye movement sequence respectively. We rigorously evaluate our proposed method using HUST-LEBW, a publicly available dataset, as well as our newly collected multi-angle eye blink dataset (MAEB). The results indicate that the proposed model consistently outperforms existing methods across both datasets, with notably minor performance variations depending on the camera angles.
Survey of Multicast Routing Protocols in VANET

In this article multicast routing protocols in VANETs (Vehicular ad hoc NETworks) are classified and their comparative analysis is given. Multicast routing protocols enable the simultaneous delivery of packets from one source to multiple destinations. Based on the analysis of multicast protocols, it is concluded that when choosing the optimal one, careful consideration of the network’s conditions, the requirements it needs to fulfill, the number of nodes in the network, and the network’s environment is necessary.
Histopathological study of heart and cardiac vessels in autopsy cases from tertiary health-care center

Background: Atherosclerosis is a chronic inflammatory disease causing more morbidity and mortality in developed as well as in developing countries. Among Indians, atherosclerotic lesions occur 10–15 years earlier, and the lesions are formed in the advanced stage. It is difficult and expensive to evaluate the atherosclerotic lesions in living subjects. Atherosclerosis of cardiac vessels and myocardial infarction are the most common fatal cardiac diseases discovered in autopsies. The incidence of coronary artery diseases is increasing worldwide as well as in India, which carries a huge challenge in diagnosing the same. Aims and Objectives: The aims and objectives are to study the spectrum of histopathological findings in heart and atherosclerotic lesions in cardiac vessels in autopsy cases. Materials and Methods: This is a retrospective study done in the Department of Pathology at Government Kilpauk Medical College and Hospital over a period of 1 year (March 2022–December 2022). In this study, a total of 100 autopsy cases were taken to study the pathology of the heart and the grading of atherosclerotic lesions in cardiac vessels. Heart specimens were received from the Forensic Medicine Department. The atherosclerotic lesions of cardiac vessels were graded according to the American Heart Association (AHA) classification. Results: Among 100 cases studied during a period of 1 year, most of the cases were in the age group of 30–41 years. Male preponderance was noted when compared to females. Out of 100 cases, 79 cases had histopathological evidence of atherosclerosis, and other findings were as follows: one case of giant cell myocarditis, one case of pericarditis, and one case of adenocarcinoma of the lung extending into the coronary vessels. The most common type of atherosclerotic lesion seen was type 7 AHA, most frequently involving the aorta, followed by both coronary ostia and the left anterior descending artery. Conclusion: In this study, atherosclerotic lesions were observed very frequently in younger individuals. It is an indication for the implementation of antiatherogenic preventive measures in young populations. It highlights the importance of screening for cardiovascular risk factors at an early age.
Robust Adaptive System Identification and Noise Filtering Using Bregman Divergence Based Algorithms

This article proffers a new set of least mean square (LMS) algorithms based on the Bregman divergence class of information-theoretic divergence, which are named as Kullback-Leibler LMS (KLLMS), Itakura-Saito LMS (ISLMS), and ßLMS. To achieve a tractable analysis, a bound on the learning rate of proffered algorithms is derived. Steady-state mean-square deviation (MSD) and computational cost provide insight into the performance and efficiency of the proffered algorithms in the presence of Gaussian and impulsive noise. Simulation results also show the effectiveness of the proffered algorithms in adaptive noise filtering.
Forecasting Crime Rate Using Artificial Intelligence Applications

Crime is a ubiquitous and serious issue in modern society, with a plethora of crimes perpetrated on a daily basis, producing widespread unhappiness. Prevention of Crime is a major critical duty nowadays. Artificial intelligence has recently been an important role that is being noticed practically in every industry, Crime Prediction is also included in that. Comprehensive database has been kept for prior crimes, as this information can be used for future reference. Predicting the chances of future crimes can assist law enforcement organizations in preventing them before they cause the major happening by giving strategic and crucial information based on variables such as time, location and distance. However, the growth rate is considered on the basis of crimes are committed, precisely anticipating crime is a difficult undertaking. As a result, examining good crime rate prediction and analyzing or studying methodologies is critical in detecting and reducing the future crime rate. Multiple academics have introduced studies to predict crimes using various machine learning algorithms and input parameters such as random forest and decision trees. The ultimate goal is to demonstrate the value, need and efficacy of machine learning in predicting violent crimes in a particular region, so that police can use it to lower crime rates and increase societal well-being.
Citrus Leaf Disease Detection Using Various Deep Learning Architectures

The tangerines, oranges, limes, mandarins, grapefruits and lemons are the common citrus fruits grown all over the world. Every year, orange peel is damaged to the tune of nearly half due to various diseases of the plant. This study provides an overview of many Ways to identify and categorize diseases found in the leaves of citrus plants. This study presents a comprehensive classification of diseases affecting citrus leaves. The dataset had 609 images of five classes of diseases namely Black Spot, Canker, Greening, Melanose and Healthy. The pre-trained ResNet50 Convolutional Neural Network (CNN) architecture achieved an accuracy rate of 96.8 % an d exhibited minimal loss, registering at 0.1573. The findings showed that the given approach is successful in diagnosing different forms of illnesses in agricultural crops and can be used to protect crops in the agriculture industry.
Charlotte Area Traffic Light Dataset

We present a new traffic light object detection dataset of over 4,000 images labeled from dashcam video in Charlotte, NC. The dataset captures challenging weather and lighting, with daytime, nighttime, and rainy conditions. 7 annotation classes allow benchmarking of detection models like YOLOv5. Performance degrades at night and in rain versus daytime conditions. This geographically targeted dataset exposes region-specific factors impacting traffic light perception. It serves as a benchmark tailored to Charlotte to improve robustness across settings.
Enhancing Stability and Performance of Hybrid Offshore Wind Platforms: A Novel Fuzzy Logic Control Approach with Computational Machine Learning

Harnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has introduced a groundbreaking concept of hybrid offshore platforms, offering immense potential for energy absorption, reduced dynamic response, load mitigation, and improved cost efficiency. The primary goals of this study revolve around two key objectives: (i) the development of a regression-based modeling technique for the hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and (ii) the implementation of a customized fuzzy-based control mechanism to ensure platform stability. To achieve these objectives, computational Machine Learning (ML) tools, specifically Artificial Neural Networks (ANNs), are utilized to replicate the behavior of the detailed numerical model of the FOWTs integrated with OWCs. Subsequently, a Fuzzy Logic Control (FLC) scheme is employed to establish a structural controller that effectively mitigates unwanted vibrations. The experimental results confirm the potential of ANN-based modeling as a simpler yet effective alternative to complicated nonlinear NREL-5MW FOWT dynamical models. Furthermore, the use of the FLC system enhances platform stability in a variety of wind and wave conditions.
Beamforming and Direction of Arrival Estimation for Beam Management in 5G mmWave UDN

The distinctive feature of 5G ultra-dense networks (UDN), compared with mobile networks of previous generations, is more widespread utilization of beamforming (BF) and direction of arrival (DOA) estimation techniques, which require prompt and effective beam management (BM) procedures. High density of gNodeB (gNB) and User Equipment (UE) distribution even up to one device per square meter leads to prevalent line-of sight (LOS) conditions on the one hand and huge level of intra-system interference with difficultly predictable signal to interference plus noise ratio (SINR) on the other hand. To support acceptable SINR level in 5G UDN we propose to use BF and DOA estimation for beam management. Contribution of current research is simulation of a spatial separation of UE based on beam management and three-dimensional BF using adaptive antenna arrays with preliminary DOA estimation.
Along-Tract Parameterization of White Matter Microstructure using Medial Tractography Analysis (MeTA)

Diffusion MRI tractography is a noninvasive method to estimate the structural connectivity of white matter (WM) bundles (tracts) in the human brain, which can help us understand brain function and neurodegenerative diseases. Existing techniques for analyzing WM microstructure along the length of bundles often require registering all individuals into a common space that may ignore potentially key differences in the shape and alignment of the tracts. We propose the Medial Tractography Analysis (MeTA) method to reduce partial voluming and microstructural heterogeneity in dMRI metrics while retaining bundle shape and capturing the regional variation within bundles. We performed reliability, compatibility, and disease-based validations. MeTA showed moderate to good overall overlap for most bundles in a test-retest dataset and preserved regional compatibility when applied to a dataset of subjects scanned with both high and low angular resolution protocols. Diffusion tensor imaging (DTI) metrics along the length of MeTA bundles had strong associations with cognitive impairment in ADNI. MeTA may be a reliable approach to identify regional abnormalities in clinical populations across multiple diffusion acquisitions.
Oxidation of groundwater arsenic and iron

The behaviors of dissolved arsenic and iron are significantly decoupled in groundwaters beneath a closed landfill in southern Maine and in laboratory experiments using these groundwaters. At this site naturally-occurring arsenic is mobilized from the glacial sediments under reducing conditions induced and promoted by landfill leachate. Differences in arsenic and iron behavior were seen in laboratory titratiori experiments which progressively oxidize groundwater from this site and in field data for groundwater composition in the area surrounding this former landfill. The persistence of elevated levels of dissolved arsenic in groundwaters where dissolved iron is relatively low provides direct indication of the difficulty of preventing transport of arsenic from this type of site.
Legged Robot Running on Rough Terrains Based on Norm Regulation of Spring-Loaded Inverted Pendulum Model

Legged locomotion is a complex control problem involving multi-linkage, underactuation, and hybrid dynamics. To traverse through a compliant surface, the inclusion of uncertainties and unstructured disturbances aggravates the problem. A responsive and robust controller is essential, which may not be guaranteed by pure numerical methods such as learning-based and optimisation-based methods. In this paper, we investigate the running behaviour of a legged robot on a rough terrain with height and stiffness variation by proposing a control framework which comprises i) a limit-cycle-based state feedback controller to regulate the norm of the leg length, ii) a hip velocity controller, and a landing angle controller so that the robot can continuously control the energy and state to achieve the desired running height and speed even on a compliant surface. We demonstrate the effectiveness and robustness of the proposed method on a monoped and a biped in simulation and hardware, respectively.
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.
A Novel Population Optimizer for Unit Scheduling Problems in Power Systems

The unit commitment (UC) problem is the first step in power system optimal scheduling and system planning. However, the UC problem is a mixed integer optimization problem, which usually has the characteristics of high dimension, non-convex and nonlinear. Plug-in electric vehicles (PEVs) integration into the grid can help improve stability and flexibility of the grid. However, Large-scale PEVs charging demand may put pressure on the grid and may lead to grid overloads. Recently, a competitive swarm optimizer (CSO) is proposed to settle optimization problems, which is considerably challenging in evolutionary computation. In this paper, a binary competitive swarm optimizer (BCSO) is proposed to tackle UC problems integration with PEVs. Finally, comparison experiments on economic problems with dimensionality increasing from 10 to 100 units, which confirm the competitive performance of the proposed optimizer.
BAT SIGHTINGS ON KAPITI ISLAND, NEW ZEALAND,

Sightings by residents and visitors to Kapiti from 1906-1969 indicate one small colony of bats with a steady population of abc.at 12 located south of Rangatira. The flight pattern, times of emergence and the Jack of sightings during winter suggest that the long~tailcd bat (Chafinolobus tubercula/us Forster) may be the onJy species present. The 1907 report by Cockayne that the long~eared bat was abundant is taken as a misprint for long-tailed bat and not the rarer short-tailed bat (Mysiacina luberct/lata Gray). The continued survival of bats in the presence of high numbers of Rattus norvegicus, R. exulans and Tric/wsurus vulpecula and in a changing forest environment, is briefly discussed.
Algorithm for Assessing Cardiovascular Activity

This article describes an algorithmic complex for analyzing the parameters of the CVS and its activity. The undoubted advantage of this complex is the ability to analyze additional parameters of electrocardiograms and photoplethysmograms, such as blood pressure and cardiac output. The use of a large amount of additional diagnostic data, the use of modern algorithms, makes it possible to identify the risk of developing arterial hypertension (AH), as well as the possibility of developing heart failure. This article also includes a description of generally accepted signal processing algorithms. The effectiveness of application algorithms was evaluated using a verified database of signals and pathological forms. This article also presents an analysis of the main issues that appear when identifying the characteristic points of a pulse wave (PW) signal. To process synchronous recordings of pulse wave signals, blood pressure and electrocardiogram (ECG) in the Matlab environment, a software module has been developed that provides the possibility of synchronous signal construction, selection of characteristic points of these signals, calculation of parameters of cardiovascular activity, an indicator of the pulse wave propagation time and an assessment of its variability, calculation of stroke volume, as well as an assessment of the risk of developing heart failure.
Prediction of the vibroacoustic response of an electric window-lift gear motor

A numerical approach is proposed for analyzing the vibroacoustic behavior of an electric window-lift gear motor and differentiating the different excitation sources. These ones correspond to the normal electromagnetic forces applied to steel stator, the electromagnetic input torque fluctuation, the rotor mechanical imbalance, the worm gear static transmission error and mesh stiffness fluctuations and the gear wheel eccentricity. The computing of the vibroacoustic response is performed using an iterative spectral method. Spectrograms of the dynamic response show components corresponding to the harmonics of the excitation spectra as well as a frequency enrichment generated by the coupling between the mesh stiffness fluctuation and the other driveline excitation sources. Finally, the contributions of the different excitation sources to the the mean square velocity of the radiating surface and the equivalent global dynamic force transmitted to the supporting structure are compared.
Inter-Core Crosstalk Aware Deep Reinforcement Learning Based Resource Allocation in Multicore Elastic Optical Networks

We present a deep reinforcement learning (DRL) framework for the routing, modulation, core selection, and spectrum allocation (RMCSA) of the multicore elastic optical networks. Specifically, the DRL agent senses the impact of the inter-core crosstalk and makes the RMCSA decisions accordingly. Simulation results show that our method outperforms previous methods.
AHP Decision-Making Approach for Solar Photovoltaic (PV) Modules End-Of-Life (EOL) Disposition Strategies

Increasing energy demands globally have accelerated the development of renewable technologies including solar photovoltaics (PV). However, this development has unavoidably led to the emergence of significant volumes PV waste considering 20–30 years life span before End of Life (EOL). The standard decision makers practices for PV EOL are disposal to the landfill, recycling, or reuse. Due to complexity of the decision environment, this study aims to analyze and rank solar PV disposition alternative using Analytic Hierarchy Process (AHP) based on five main criteria including environmental, economic, social, policy and legislation and technical. AHP Aggregating Individual Priorities (AIP) aggregation method was utilized to evaluate the overall perspective of all the study participant. In addition, individual stakeholder group decision was assessed individually to understand potential different EOL disposition perspective from different stakeholder group. The findings of the analysis shows that the overall ranking of the alternatives based on the main criteria is leaning toward recycling compared to other alternatives. However, there is a variation in the decision between individual stakeholder group.
A Comprehensive Equivalent Circuit Model of Silicon-Based Segmented Microring Modulators for Electronic and Photonic Integrated Circuit Codesign

We present an equivalent circuit model for two-segment Si microring modulators (MRMs). The model consists of three blocks: electrical parasitics, electro-optic dynamics, and self-heating effects. Model parameters are derived through curve fitting based on Si MRM characterization. An excellent agreement between simulated and measured eye diagrams at a data rate of 106Gb/s was achieved. This equivalent circuit model can be effectively employed for driver design optimization and MRM wavelength stabilization circuitry improvement.