Miracle-dz/UniFAR-FTUs huggingface dataset:
Deconstructing the Dimensions of Mycobiome Fingerprints in Luohandu Cave, Guilin, Southern China

Subterranean karst caves are windows into the terrestrial subsurface to deconstruct the dimensions of mycobiome fingerprints. However, impeded by the constraints of remote locations, the inaccessibility of specimens and technical limitations, the mycobiome of subterranean karst caves has remained largely unknown. Weathered rock and sediment samples were collected from Luohandu cave (Guilin, Southern China) and subjected to Illumina Hiseq sequencing of ITS1 genes. A total of 267 known genera and 90 known orders in 15 phyla were revealed in the mycobiomes. Ascomycota dominated all samples, followed by Basidiomycota and Mortierellomycota. The sediments possessed the relatively highest alpha diversity and were significantly different from weathered rocks according to the diversity indices and richness metrics. Fifteen families and eight genera with significant differences were detected in the sediment samples. The Ca/Mg ratio appeared to significantly affect the structure of the mycobiome communities. Ascomycota appeared to exert a controlling influence on the mycobiome co-occurrence network of the sediments, while Ascomycota and Basidiomycota were found to be the main phyla in the mycobiome co-occurrence network of weathered rocks. Our results provide a more comprehensive dimension to the mycobiome fingerprints of Luohandu cave and a new window into the mycobiome communities and the ecology of subterranean karst cave ecosystems.
Optimizing Path Tracing Probe Collection for Kafka-Based Storage: Identifying and Addressing Performance Bottlenecks

Efficient and accurate processing of Path Tracing probes is essential for using the Path Tracing technology and therefore analyzing and diagnosing service provider networks. This paper presents a modular and optimized probe collector that addresses several performance bottlenecks. The probe collector consists of three main components: collecting, processing, and publishing. The collector component captures the incoming probes from the physical interface, while the processing component inspects and formats the captured packets. Finally, the publisher component ensures efficient transmission of the processed packet to the designated Kafka topic. To evaluate the collector's performance under various traffic loads, a comprehensive testing methodology emphasizing packet loss rate, memory analysis, and CPU utilization is introduced. The results demonstrate the collector's ability to handle incoming Path Tracing probes with high rates and minimal packet loss. Different solutions for specific bottlenecks are described, which will help other developers to address their software problems. The resulting Path Tracing probe collector provides a reliable and scalable solution to collect, process, and publish the probes to Kafka, enabling further development in this area.
Watershed: A Comparative Study

Morphological gradient is often used to find the gradient of an image which is further used for the transformation. However, noise in the gradient image results in to over-segmentation which have an undesirable bad effect on resulting segmented image. For the fine segmentation results the quality of the gradient estimate has a major influence on the segmentation performance. In this paper it is shown that different types of gradient are computed with the help of different operators and further these various gradients are used to segment the image with the help of watershed transformation. The resulting image constitute of watershed divide lines and the catchment basins. The purpose of this work is to show that the gradient obtained through different operators like Sobel, Prewitt, kirsch etc. have varying effect on watershed in terms of peak signal to noise ratio. Keyword- Watershed, gradient, image segmentation.
Overview of Surface Modification Techniques for Titanium Alloys in Modern Material Science: A Comprehensive Analysis

Titanium alloys are acclaimed for their remarkable biocompatibility, high specific strength, excellent corrosion resistance, and stable performance in high and low temperatures. These characteristics render them invaluable in a multitude of sectors, including biomedicine, shipbuilding, aerospace, and daily life. According to the different phases, the alloys can be broadly categorized into α-titanium and β-titanium, and these alloys demonstrate unique properties shaped by their respective phases. The hexagonal close-packed structure of α-titanium alloys is notably associated with superior high-temperature creep resistance but limited plasticity. Conversely, the body-centered cubic structure of β-titanium alloys contributes to enhanced slip and greater plasticity. To optimize these alloys for specific industrial applications, alloy strengthening is often necessary to meet diverse environmental and operational demands. The impact of various processing techniques on the microstructure and metal characteristics of titanium alloys is reviewed and discussed in this research. This article systematically analyzes the effects of machining, shot peening, and surface heat treatment methods, including surface quenching, carburizing, and nitriding, on the structure and characteristics of titanium alloys. This research is arranged and categorized into three categories based on the methods of processing and treatment: general heat treatment, thermochemical treatment, and machining. The results of a large number of studies show that surface treatment can significantly improve the hardness and friction mechanical properties of titanium alloys. At present, a single treatment method is often insufficient. Therefore, composite treatment methods combining multiple treatment techniques are expected to be more widely used in the future. The authors provide an overview of titanium alloy modification methods in recent years with the aim of assisting and promoting further research in the very important and promising direction of multi-technology composite treatment.
Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.
Improved stacking ensemble learning based on feature selection to accurately predict warfarin dose

Background With the rapid development of artificial intelligence, prediction of warfarin dose via machine learning has received more and more attention. Since the dose prediction involve both linear and nonlinear problems, traditional machine learning algorithms are ineffective to solve such problems at one time. Objective Based on the characteristics of clinical data of Chinese warfarin patients, an improved stacking ensemble learning can achieve higher prediction accuracy. Methods Information of 641 patients from southern China who had reached a steady state on warfarin was collected, including demographic information, medical history, genotype, and co-medication status. The dataset was randomly divided into a training set (90%) and a test set (10%). The predictive capability is evaluated on a new test set generated by stacking ensemble learning. Additional factors associated with warfarin dose were discovered by feature selection methods. Results A newly proposed heuristic-stacking ensemble learning performs better than traditional-stacking ensemble learning in key metrics such as accuracy of ideal dose (73.44%, 71.88%), mean absolute errors (0.11 mg/day, 0.13 mg/day), root mean square errors (0.18 mg/day, 0.20 mg/day) and R2 (0.87, 0.82). Conclusions The developed heuristic-stacking ensemble learning can satisfactorily predict warfarin dose with high accuracy. A relationship between hypertension, a history of severe preoperative embolism, and warfarin dose is found, which provides a useful reference for the warfarin dose administration in the future.
Generative Adversarial Network based Brain MRI Data Augmentation

Autism spectrum disorder (ASD) is becoming an increasingly important problem for individuals between the ages of 6 and 17. This disorder leads to a neurological condition impacting a person's ability to communicate and connect socially. The symptoms of ASD include despair, anxiety, hyperacidity, and so forth. Individuals can develop severe problems as a result of this. Therefore, a diagnosis in the early stages is reasonably necessary. The most common diagnostic method for autism spectrum disorder (ASD) is magnetic resonance imaging (MRI) of the brain. Deep Learning (DL) enables enhanced computational complexity, the capability to handle bigger data sets and high algorithmic efficiency. However, the performance of DL techniques is hugely affected by the data scarcity problem that occurs due to limited or uneven dataset size. Thus, there is a need to increase or balance the dataset size to equalize the qualitative and quantitative results of ASD detection. Therefore, this paper presents the Generative Adversarial Network (GAN) for brain MRI data augmentation. The performance of the suggested approach is evaluated on Autism Brain Imaging Exchange (ABIDE-I) dataset.
Structural Causes of Vulnerability to Flood Hazard in

This paper uses recent theoretical advances in the field of hazards research to inform the analysis of an empirical study on flood hazard conducted in central Pakistan. The investigation seeks to understand the causes of vulnerability and their development that culminates in disaster, with the basic presumption that empirical events have causal links going back to societal structures which are not measurable but contain the mechanisms that lead to the events and their percep- tions. A case study in five villages of central Pakistan was conducted to understand the elements of communities' and social groups' differential vulnerability to flood hazard. The elements of vulnerability are situated within a tripartite conceptual space of vulnerability, composed of entitlement relations, empowerment relations, and political economy. A modified "pressure and release" model was applied to the field survey results to understand the progression of vulnerability from the struc- tural abstract level to the concrete level of physical disasters. I concluded that the study communities' vulnerability was largely a function of their disempowerment.
Support Vector Machine for Person Classification Using the EEG Signals

User authentication is a pivotal element in security systems. Conventional methods including passwords, personal identification numbers, and identification tags are increasingly vulnerable to cyber-attacks. This paper suggests a paradigm shift towards biometric identification technology that leverages unique physiological or behavioral characteristics for user authenticity verification. Nevertheless, biometric solutions like fingerprints, iris patterns, facial and voice recognition are also susceptible to forgery and deception. We propose using Electroencephalogram (EEG) signals for individual identification to address this challenge. Derived from unique brain activities, these signals offer promising authentication potential and provide a novel means for liveness detection, thereby mitigating spoofing attacks. This study employs a public dataset initially compiled for fatigue analysis, featuring EEG data from 12 subjects recorded via an eight-channel OpenBCI helmet. This study extracts salient features from the EEG signals and trains a supervised multiclass Support Vector Machine classifier. Upon evaluation, the classifier model achieves a maximum accuracy of 92.9%, leveraging ten features from each channel. Collectively, these findings highlight the viability of machine learning in implementing real-world, EEG-based biometric identification systems, thereby advancing user authentication technology.
Intelligent Attack Detection in ROS-based Systems

Robotic systems, which were initially intended only for industrial work, have become a part of our daily lives in recent years. ROS is a popular middleware for developing applications in robotic systems. The security of ROS-based systems has gained importance with the expansion of robotic system usage. To enhance security, machine learning algorithms play an important role in robotic systems, especially in attack detection. This article provides an overview of the security of robotics by examining the machine learning based detection methods of cyber-attacks against ROS. We start by presenting security issues with a brief review of attack vectors in ROS. Next, we discuss the machine learning based attack detection approaches. Finally, we conclude by reviewing studies in the literature that use machine learning algorithms on attack detection in ROS. This survey aims to investigate the most popular and effective techniques applied to detect cyber-attacks, focusing on highlighting the latest advances in the field of machine learning in ROS attack detection. Our study will raise awareness among researchers in robotic system security and encourage researchers to upgrade attack detection in ROS-based systems.
Barriers and facilitators of breast self-examination among rural women of procreative age

Cil: Cilem teto studie bylo posoudit překažky a facilitatory v provaděni samovysetřeni prsů (BSE) u venkovských žen v prokreativnim věku, za ucelem zjistěni rakoviny prsu ve východni Indii. Metodika: Vzhledem k lekařskemu významu rakoviny prsu ve spolecnosti byla navržena kvaziexperimentalni studie, ve ktere byl proveden předběžný a nasledný test s vlastnim dotaznikem a kontrolnim seznamem, prozkoumani facilitatorů a barier a vlivu BSE u těchto žen. Vzorkem byly venkovske ženy ve věkove skupině 15 až 50 let, ktere byly vybrany podle výběrových kriterii vyskolených osetřovatelů. Zpocatku bylo ve studii zařazeno 553 žen, avsak pouze 502 z nich bylo použito k zavěrecne analýze a dalsich 51 bylo vylouceno z důvodu nedostatecne odpovědi. Výsledky: Po obdobi jednoho měsice byly výsledky posttestove studie statisticky nesmirně významne ve srovnani s výsledky předtestů ohledně znalosti a kroků BSE. Nebyla nalezena žadna významna asociace mezi barierami, facilitatorem BSE a dalsimi demografickými faktory. Zavěr: Tato studie dospěla k zavěru, že technika BSE může být využita vsemi venkovskými ženami v prokreativni věkove skupině - s pomoci vyskolených zdravotnických pracovniků a zdravotnich sester - kteři jim mohou pomoci porozumět významu BSE při detekci rakoviny prsu.
Self-Cleaning and Charge Transport Properties of Foils Coated with Acrylic Paint Containing TiO2 Nanoparticles

The study comprehensively investigates the design and performance of self-cleaning surfaces fabricated by coating aluminum foil with an acrylic paint matrix enriched with different content of titanium dioxide (TiO2) nanoparticles. The main goal was to assess the self-cleaning characteristics of the surfaces obtained. This study employs scanning electron microscopy (SEM) to analyze the morphology of TiO2-modified acrylic surfaces, revealing spherical particles. Raman spectroscopy elucidates signatures characterizing TiO2 incorporation within the acrylic matrix, providing comprehensive insights into structural and compositional changes for advanced surface engineering. Alternating current (AC) impedance spectroscopy was used to assess selected charge transport properties of produced self-cleaning surfaces, allowing us to gain valuable insights into the material’s conductivity and its potential impact on photocatalytic performance. The self-cleaning properties of these tiles were tested against three frequently used textile dyes, which are considered to pose a serious environmental threat. Subsequently, improving self-cleaning properties was achieved by plasma treatment, utilizing a continuous plasma arc. The plasma treatment led to enhanced charge separation and surface reactivity, crucial factors in the self-cleaning mechanism. To deepen our comprehension of the reactive properties of dye molecules and their degradation dynamics, we employed a combination of density functional tight binding (DFTB) and density functional theory (DFT) calculations. This investigation lays the foundation for advancing self-cleaning materials with extensive applications, from architectural coatings to environmental remediation technologies.
Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment.
Comparative Analysis of Maternal Colostrum and Colostrum Replacer Effects on Immunity, Growth, and Health of Japanese Black Calves

Simple Summary The immunity, growth, and health of Japanese black calves was evaluated via feeding maternal colostrum versus colostrum replacer. Calves that received maternal colostrum showed significant increases in T cells and B cells in the peripheral blood. In addition, the expression levels of interleukin-1β–, interleukin-2–, and interferon-γ–encoding mRNAs were significantly higher in the maternal colostrum group. A lower incidence of disease and higher carcass weight in calves fed maternal colostrum were observed. These results suggest that, compared to maternal colostrum, the use of colostrum replacers may result in delayed immune system activation and slower growth in calves. Abstract Maternal colostrum (MC) is an important source of nutrients and immune factors for newborn calves. However, when colostrum is unavailable or of poor quality, a colostrum replacer (CR) may be a suitable alternative to MC. As stock-raising farmers must make informed decisions about colostrum feeding management, this study was conducted to determine the effect of feeding MC versus CR on the promotion of immunological status, growth, and health in pre-weaned Japanese black (JB) calves. Sixteen newborn JB calves were fed MC after birth, and 16 JB calves were fed CR. For the MC group, the numbers of γδ T cells, CD4+ cells, CD8+ cells, CD4+CD8+ cells, B cells, and MHC class II+ cells were significantly higher compared with the CR group. Furthermore, the expression levels of interleukin (IL)-1β-, IL-2-, and interferon-γ (IFN-γ)-encoding mRNAs were significantly higher in the MC group compared with the CR group. A lower incidence of disease in 1-month-old calves and higher carcass weight in the MC group were observed compared with the CR group. These results suggest that CR activates the immune system delayed in calves compared with MC. MC increases populations of various immunocompetent cells, which can reduce infection rates and improve body weight gain.
Analysis of Machine Learning Classifiers for the Detection of Lung Cancer from Micro Array Gene Data

Microarray gene data is a good resource for studying gene expression patterns of various biological processes and diseases. The paper adopts a classification method of high-dimensional microarray gene data for lung cancer by utilizing dimensionality reduction and feature selection techniques. The dimensionality reduction is done by Short Time Fourier Transform (STFT) and utilizes Scalogram as the feature selection technique. The classifiers, namely Non-Linear Regression (NLR), Gaussian Mixture Model (GMM), Softmax Discriminant Classifier (SDC), SVM (polynomial) and SVM (RBF) are employed for classification. The classifiers’ performances are analyzed and compared. The SVM (RBF) classifier with the Short-Time Fourier Transform Dimensionality Reduction method and Scalogram 1024 feature selection achieved the highest accuracy of 96.13 % compared to other classifiers.
YOLO on GPU Implementation for a Vision-Based Fruit Select and Place System with a 5-DOF Robotic Arm

This article introduces a cost-effective approach for a vision-guided robotic pick-and-place arm and demonstrates how this produces a faster inference rate and greater efficiency than existing products, which are typically constructed with rather high-end equipment. A YOLOv4-tiny algorithm-based object detection model is trained to classify and localise the desired object using a custom-created dataset. A monocular camera was also calibrated to determine its intrinsic and extrinsic parameters, which facilitated the determination of coordinates. GPU-based NVIDIA Jetson NANO is utilised to process the live stream captured by a camera, not only due to its faster inference capability and computations but also because of its quicker inference capability. The fabrication of 5-degree-of-freedom robotic limb results in improved positioning, more precise target object placement, and a larger work envelope. This arrangement identifies and localises the target object within the image frame by recognising it in its surroundings. Later, the image coordinates are mapped to real-world coordinates and fed to the robotic arm, which applies the appropriate inverse kinematics to plan the trajectory and implement the pick-and-place task. This low-cost system resulted in 40 frames per second of real-time inference speed, which is nearly double that of existing models.