Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Surface Roughness Characterization in Turning of Monel 400 by Discrete Wavelet Transform in Comparison with Empirical Wavelet Transform to Calculate Prediction Error

To determine the Surface Roughness of the Monel 400 super alloy using a DWT Transform (Discrete Wavelet Transform) Algorithm and compare it with the EWT Transform (empirical Wavelet Transform) Algorithm to minimize the Prediction Error. Materials and Methods: Two groups, each consisting of 8 samples, were analyzed using a two-tailed T-Test, with a total of 16 samples being used. The calculations for this analysis were derived from clincalc.com and included an enrollment ratio of one, an alpha error of 0.05, a beta error of 0.2, a G-power of 80%, and a confidence interval of 95%. Results: When using the suggested approach, the resultant average error is proposed with a lower prediction error. The mean average error predicated on discrete wavelet transform is 1.33, Empirical wavelet transform is 2.76 and the significant factor is 0.002 (p <0.05). Hence, both the EWT algorithm and DWT algorithm are statistically significant. Conclusion: This study demonstrates that, in terms of prediction error, the Discrete Wavelet Transform algorithm is significantly better than the Empirical Wavelet Transform.
An Analysis of Deep Learning-Based Studies on Object Detection

Target or object identification has developed into a significant research focus in the last 25 more years and also is a practical problem in computer vision that is employed widely across the globe. It makes use of deep learning to quickly & accurately locate and identify the vast number of different object in the given image that fall into specified category. The algorithm falls into two groups under the model training approach: one is single-stage detection algorithm and other is double stage detection algorithms. The study paper presents in-depth introductions to the exemplary algorithms at each level. Then, after introducing both common public and unique datasets for target or object detection, numerous sample techniques are examined and contrasted in this area. Last but not least, various algorithms are used to forecast the probable difficulties in target detection.
Graph Computing Applications In Unit Commitment Historical Data Management

The development of new types of power systems poses several key challenges for super-large-scale online analysis and optimization. These challenges include data merging, computation performance, and operation optimization. The uncertainty arising from renewable energy sources and load fluctuations has resulted in highly volatile power flow patterns compared to previous systems. As a result, the operating states of the system are more vulnerable to potential operational risks during critical periods. From the perspective of unit commitment, optimizing system resources involves increasingly complex constraints and variables. Graph computing has shown exceptional performance in online power system analysis, particularly in topology-related analysis. This paper explores the application of graph computing techniques to enhance security-constrained unit commitment historical data management. It offers new insights into the utilization of graph computing to improve unit commitment.
Applying Structural Equation Modelling in Education Research

Structural equation modelling (SEM) is a set of statistical techniques used to simultaneously examine the structure of interrelationships among observed and latent variables represented by a series of equations, while accounting for measurement error. This paper presents a non-technical introduction to SEM and provides an illustration on how the fit between an SEM model (based on theory) and data – in the context of higher education – can be assessed through a measurement and structural model, as well as validated with a holdout sample to evaluate generalisability. SEM can serve as a very useful and powerful tool for the analysis of education data to support teaching and learning.
Fecal-based Health Status Prediction in Poultry Birds: A Blended Approach using Handcrafted and Deep Features

The well-being of animals and the profitability of poultry farming are both directly affected by the condition of the birds raised for food. Assessing the health of chickens has traditionally included a lot of physical effort and time-consuming procedures. In this study, a novel method is proposed for determining the health of chickens using fecal samples. Extraction of deep features from Convolutional Neural Networks (CNNs) is combined with the strengths of handcrafted features such as textual feature extraction and the Bag-of-Visual-Word (BoVW) proposed technique. The proposed hybrid model is meant to leverage the complementing traits of handmade and deep features to boost the accuracy of health status prediction. The proposed approach intends to provide a strong and efficient answer for poultry health evaluation by integrating information retrieved from the texture and visual content of fecal samples. Extensive tests were run, and hybrid model was compared to well-known deep architectures like Inception, ResNet50, and EfficientNet, to analyze the efficiency of the proposed model. The findings show that the proposed approach is superior to the others, with a remarkable 96 percent accuracy. This outstanding performance demonstrates the promise of proposed blended approach to greatly enhance the precision and efficiency of health status prediction in poultry birds, making it a useful resource for the poultry industry and animal health specialists.
Multi-Objective Optimization of Controller Placement in Distributed ONOS Networks

In distributed Software Defined Networking (SDN), multiple controllers need to maintain a consistent view of the network state among the controllers using consensus algorithms, which introduces additional communication overhead and network latency, especially in large-scale networks. Therefore, it is difficult to optimize the placement of controllers considering not only the delay between switches and controllers, but also the delay of consensus algorithms. This research utilizes Open Network Operating System (ONOS) as a platform for building distributed SDN, which employs an Atomix cluster to store and share the network state views across multiple controllers using a consensus algorithm. Therefore, the optimal placement problem in ONOS involves both the placement of ONOS controllers and the placement of Atomix nodes, which makes the problem more complex. In this research, we develop a practical model to estimate the Flow Setup Time (FST) as a measure of ONOS controller response time, and propose a method to determine the optimal placements of controllers and Atomix nodes by a multi-objective optimization. Moreover, we apply our method to several real-world networks on the Internet and visualize the experimental results. Based on the observed variations in FST across different network topologies, we provide a suggestion to improve ONOS performance.
Short-Term Load Forecasting in Power System Using CNN-LSTM Neural Network

The accurate forecasting of short-term load plays a significant role in power systems operation and planning. This paper suggests a short-term load forecasting model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The developed CNN-LSTM aims to capture both spatial and temporal dependencies within the load data, leveraging the strengths of both architectures. Simulations are performed using real-world power system load data. Comparative analyses are carried out against standalone CNN and LSTM models. The CNN-LSTM has significantly better forecasting accuracy than other models, showcasing its effectiveness in short-term load forecasting.
Physics- Informed Neural Network Approach for Numeric Modeling of Rapid Thermal Processing System

In order to obtain a numeric model of the temperature distribution on the wafer in a rapid thermal processing system, a neural network method named Physics-Informed Neural Network (PINN) is proposed in this paper. The PINN architecture is constructed with a network structure where the loss function is restricted to satisfy the partial differential equation and the boundary condition of the physical laws of the system. The network is trained with a sampling which depends on the spatial topology of the system. The proposed PINN is compared to the finite element method with experimental results showing the effectiveness of the PINN approach.
Big Data and Reinforcement Learning, Emg Clustering

The need of creating improved that are better equipped to manage “big data” has significantly grown due to the growing volume of data in electromyographic signal study. As a result, increasingly sophisticated created. This essay starts with a succinct overview of the key driving forces behind the expansion of big data age, then moves on to discuss the recent development of current shared data sets. After that, we cover current research and advancements in pattern recognition techniques that can be used for big data analytics. These new signal analysis approaches may be classified into two primary categories engineering utilizing a potential large specific focus on “deep learning”. Finally, suggestions for further pattern research are presented and explored.
Design and Implementation of Energy Carbon Management System for High Voltage Substation

An energy and carbon management system is designed and developed based on IoT architecture to meet the energy and carbon management needs of high voltage substation. Through the energy carbon data collection terminal and edge computing gateway, multi-dimensional operational parameters are collected from high-voltage substation energy consumption equipment, and the data are sent to the energy carbon management system through local wired/wireless communication network. The energy and carbon management application software realizes real-time online monitoring, early warning and system energy efficiency evaluation of the energy used in high-voltage substations. The engineering practice proves that the system can effectively realize online monitoring and evaluation of the energy consumption carbon operation indexes of high-voltage substation, timely find out the problems in operation, and provide a decision-making basis for energy saving and consumption reduction in power operation and inspection departments.
Investigating the Feasibility of Using HEXITECMHZ and Fully-Spectral X-ray Imaging to Detect and Diagnose Breast Tumours: an In Silico Study

Photon counting (PC) detectors have drawn a great deal of attention due to their ability to leverage spectral information from a polychromatic x-ray beam to improve soft tissue contrast and material discrimination in medical applications. They are nonetheless limited to sorting the entire polychromatic beam into a relatively small number of energy bins, which can span many 10s of keV each. In contrast, fully-spectral x-ray (FX) detectors produce full energy spectra for each pixel in the image, allowing for far more complex analysis of the materials under investigation. This work sets out to determine if the increased spectral information provided by FX detectors can be used to detect differences between breast tumours and healthy breast tissue by analysing their trace element compositions. An existing PC simulation framework (called CoGI) was modified to model the HEXITECMHZ FX detector developed by Rutherford Appleton Laboratory. This detector was simulated, along with the x-ray fluorescence spectra associated with both healthy and cancerous breast tissue phantoms. Of the trace elements considered, only 4 were detectable (Fe, Zn, Br and Rb) and of these the ratio between Fe and Zn provided the strongest method for differentiating the phantoms.
FEN-DQN: An End-to-End Autonomous Driving Framework Based on Reinforcement Learning with Explicit Affordance

The slow convergence rate is a thorny problem of current end-to-end autonomous driving paradigm with various traffic elements and tasks. In this paper, we propose an end-to-end autonomous driving framework FEN-DQN to simplify problems involving feature extraction network (FEN) with explicit affordance, along with some associated driving measurements such as vehicle speed and position. The FEN-DQN can be divided into two part. First, FEN is applied to map the forward-looking camera images into explicit affordances, which represent traffic information in low-dimension. Secondly, a deep Q-network (DQN) is used to map explicit affordances to vehicle actions. Based on the CARLA simulator, we use the OpenAI-Gym to construct a simulation scenario at traffic intersections to evaluate our proposed framework. In addition, we also conduct comparative experiments on different inputs to show the excellent effect of our framework. The results showcase that FEN-DQN could converge faster and perform better compared with the other inputs at traffic intersections in the simulation scenario with the assistance of FEN.
Advanced Privacy Preserving Model for Smart Healthcare Using Deep Learning

The rise of intelligent healthcare, the rise of the internet of medical things, the corona virus pandemic, and the rise of big data-based medical applications. The generated biomedical data is extremely private and confidential. Unfortunately, traditional medicine systems are unable to handle the enormous volume of biomedical data. Consequently, data is usually the cloud is used to store and share data. Afterward, several uses for the shared data are made such as investigation and the finding of novel information. Biomedical information typically appears in written form (such as test results, medication recommendations, and diagnoses). Unfortunately, such data is exposed to a variety of security risks and attacks, including privacy and confidentiality breaches. Despite significant advancements in the security of biological data, most current methods produce significant delays and are unable to handle real-time responses. In order to enhance the healthcare system, this study presents an innovative, fog-enabled, privacy-preserving paradigm termed “r sanitizer. ” This work is based on a Convolutional Neural Network with Bidirectional Long Short Term Memory (LSTM) and recognizes medical entities efficiently. The test results show that with a recall of 91.15%, precision of 92.6%, and a F1 score of 92%, r sanitizer beats the most recent models. Comparing the sanitization model to the state-of-the-art reveals a 28.77% improvement in privacy preservation.
Work-in-Progress of Exploring High School Students’ Science Concept During an Engineering Design Challenge

Engineering-based learning could bring integrated STEM issues to everyday life through a design process based on a group problem-solving approach. In this study, high school students participated in a mouse-trap car project that focused on engineering-based STEM learning. A qualitative approach was used to examine how STEM-related subjects were applied by students while they created design sketches and prototypes. Data were gathered through the documenting of student workbooks, direct observations of 28 students in class, and audio and video recordings of those observations. During the prototype design and development phase, the students focused on scientific concepts related to wheel friction and aerodynamics. By modifying the car's structure, adding rubber bands to the wheels to increase friction, and strengthening the force on the levers and rope reels, the students showed their abilities to improve the design. This study demonstrates how practical STEM learning activities such as engineering challenges help students integrate science and math topics in a systematic problem-solving process.
Research on the Construction and Application of Intelligent IoT System under Source Network Load Storage Collaboration

In recent years, with the large-scale development of the integration of source, network, load, and storage, as well as the innovative application of new technologies such as cloud, big, and mobile intelligent chains in the field of power, the IoT in power has developed rapidly and achieved certain results. However, in the process of development, problems such as insufficient real-time perception of various specialties, difficulty in sharing and reusing resources, and insufficient level of business intelligence have been exposed. This paper summarizes the principles of the current intelligent IoT system construction in the power grid, constructs the overall framework of the intelligent IoT system under the coordination of source network load storage, and explores IoT technology from typical scenarios, providing reference for the construction and application of intelligent IoT systems in the power industry.
Investigating the Relationship of Seminal Oxidative Damage with Smoking and Occupations in Normozoospermic Infertile Men

Objective: The study aimed to assess the level of seminal oxidative damage in normozoospermic infertile patients by evaluating 8-OHdG and also to analyze the relationship between oxidative damage in infertility and factors such as smoking, occupation and age. Material and Methods: Seminal plasma samples were obtained from raw semen samples from 13 normozoospermic infertile men among the patients who applied to Ondokuz Mayis University Urology Clinics for fertility evaluation and 10 normozoospermic proven fertile volunteers. Oxidative damage in seminal plasma was assessed by measuring the amount of 8-OHdG by indirect enzyme-linked immunosorbent assay. The data were also evaluated in terms of the smoking status and occupation stated by the patients and controls. Results: The fertile group had more seminal oxidative damage than the infertile group. (487.33±209.42 vs 283.12±102.73, P=0.013). Comparing the non-smoker men, the smoking group's total sperm count was decreased (84.94 ± 24.09 vs 115.80 ± 25.46, P=0.036). Age, semen characteristics, daily cigarette consumption, and partner age did not differ between the infertile and control groups. Additionally, no difference was found between the smoker and non-smoker groups, and also among the occupation groups in terms of oxidative damage (P>0.05). No correlation was found between age, oxidative damage, smoking and semen parameters (P>0.05). Conclusion: The findings of this study could indicate that the level of seminal oxidative damage alone cannot be effective in infertile men. Also, as smoking or occupation, more factors should be considered for evaluating oxidative damage in infertile men.
Brain Tumor Detection and Classification Using a Hyperparameter Tuned Convolutional Neural Network

Brain tumor detection using MRI scans when integrated with a deep learning approach can be immensely applied in identifying the tumor at early stages, with minimum medical professional aid. This research paper aims to develop an advanced predictive model that accurately classify brain tumors as benign or malignant using MRI scans. Here, a novel convolutional neural network (CNN) model is proposed to automate tumor detection and improve diagnosis accuracy. The model used a dataset of around 7000 brain cancer data classified into 4 labels which include glioma, meningioma, pituitary, and no tumor. Data wrangling and pre-processing are then applied to unify the images into a single format and remove any inconsistencies. Further the records are segregated into train and test samples with a 70–30 split. The proposed model recorded an optimum accuracy of 94.82%, precision of 94.2%, recall value of 93.7% and f-score metric of 93.9% respectively. In conclusion, the paper concluded that the proposed model can be applied to enhance the precision of both brain tumor diagnosis and prognosis.
Stone Tool Forms and Functions: A Morphometric Analysis of Modern Humans' Stone Tools From Song Terus Cave (Java, Indonesia)

Stone industries from the beginning of the Holocene of South-East Asia are difficult to characterize typo-technologically. We apply modern morphometrics to informal pieces to complement usewear analysis and gain a better understanding of the relation between forms and functions. Both the log shape ratio (LSR) based on linear measurements and elliptical Fourier analysis (EFA) based on 2D outlines are used to analyse a sample of 2372 stone artefacts from Song Terus cave (Java, Indonesia). The results show that no specific form corresponds to a specific function or vice versa. Nevertheless, among used artefacts, EFA highlighted several shape tendencies.
Integration in Biomedical Science 2024: Emerging Trends in the Post-Pandemic Era

The global landscape has witnessed profound transformations during the COVID-19 pandemic, which has concurrently catalysed notable advancements in scientific research while inflicting detrimental effects. The integration of diverse disciplines has yielded new theories and tools, thus offering innovative solutions to complex challenges within the biomedical field. A review of 2023 revealed a discern-ible evolution in the bioscience and medical fields in the post-pandemic era
GETTING FROM A TO B: COMPLEXITIES OF TURN CHANGE AND RETENTION IN CONVERSATION

Spoken interaction proceeds as a series of contributions or utterances from participants. The question of who ‘has the turn’ and how turns are managed is fundamental to understanding of all genres of spoken interaction, and to conversation in particular, as turntaking is managed locally by participants. We use floor state, descriptions of who is speaking or not at any time, to study between and within floor state transitions (BST and WST), sequences of activity from one substantial stretch of single party speech to the next, analagous to turn change or retention. We explore the patterns of speech and silence in the Switchboard corpus of over 2000 casual two-party telephone conversations using an annotation and analysis paradigm that has previously exposed interesting features of multiparty talk. We find that over half of the transitions observed in Switchboard involve more than one interval of silence, a phenomenon first noted in multiparty talk. We also find that although the transition distribution in Switchboard’s dyadic conversations broadly follows patterns found in multiparty talk, there are fewer complex transitions observed. Index Terms : dialogue, corpus studies, turntaking, human-computer interaction
A COMPARISON OF PHYTOCHEMICALS PRESENT IN MIDWESTERN MEDICINAL PLANT EXTRACTS

Plants consumed as medicines are thought to exert their physiological effects in part through the activity of their secondary metabolites, which include molecules with antioxidant activities. In this study, the concentrations of total phenolic, flavonoid, and anthocyanin compounds of eight medicinal plant extracts collected from Earlham College in Richmond, Indiana were examined. The collected data showed a wide range of variation in the concentration of antioxidant compounds in the eight examined plant extracts; the locally-collected gingko samples had the highest total phenolic content and plantain had the highest flavonoid content, while the black raspberry and mulberry samples had by far the highest anthocyanin content. In addition to describing the chemical composition of medicinal plants valued in the Midwestern United States, we compared different sources (Earlham College vs. purchased) and preparations (acetone vs. hot water extraction) of gingko leaves and compared the chemical composition of extracts that underwent an unintended additional freeze-thaw cycle. Gingko leaf extracts have the highest phenolic content of all extracts examined, and the purchased gingko teas and powder had higher levels of phenolic contents than all the locally collected, acetone extracts. Additionally, among the Midwestern species tested, no significant changes were observed in the concentrations of compounds measured in the extracts that underwent an additional freeze-thaw cycle. This study not only compares the phenolic compound composition of medicinal plant extracts but also provides pertinent information on the collection, preparation and storage of plant extracts to conserve these phenolic compounds.