Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Quasi-Elliptic Filter in Groove Gap Waveguide Technology Using Overmoded Cavity for Low-loss High-power Applications at Q-band

This paper presents a new design of quasi-elliptic filters using groove gap waveguide technology at Q-band. The designed filter is an all-capacitive iris filter and the cross coupling is realized with the help of the phase reversed magnetic field in an overmoded waveguide cavity resonator using TE102 higher order mode. The concept is validated through a fourth order cascaded quadruplet filter designed with fractional bandwidth 1.97% at 38 GHz. The electrical performance of the filter is compared against a conventional rectangular waveguide based design for the same specifications. The high-power analysis of the filter is also carried out to demonstrate its practical applicability in high-power filter design.
Real-time detection and reading of LED/LCD displays for visually impaired persons

Modern household appliances, such as microwave ovens and DVD players, increasingly require users to read an LED or LCD display to operate them, posing a severe obstacle for persons with blindness or visual impairment. While OCR-enabled devices are emerging to address the related problem of reading text in printed documents, they are not designed to tackle the challenge of finding and reading characters in appliance displays. Any system for reading these characters must address the challenge of first locating the characters among substantial amounts of background clutter; moreover, poor contrast and the abundance of specular highlights on the display surface — which degrade the image in an unpredictable way as the camera is moved — motivate the need for a system that processes images at a few frames per second, rather than forcing the user to take several photos, each of which can take seconds to acquire and process, until one is readable. We describe a novel system that acquires video, detects and reads LED/LCD characters in real time, reading them aloud to the user with synthesized speech. The system has been implemented on both a desktop and a cell phone. Experimental results are reported on videos of display images, demonstrating the feasibility of the system.
Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used.
Measuring Quality at a System Level : An Impossible Task ? The Toronto Central LHIN Experience

Abstract Quality is being measured and reported across healthcare organizations and sectors, but efforts are rarely made to connect the activity in one organization to quality experienced by patients and clients in another part of the healthcare system. This article describes one regional health organization’s journey to measuring health quality at a system level. The authors describe a highly consultative and iterative process used to measure quality across the continuum of care, and the challenges experienced in approaching this type of measurement, and they highlight some of the early findings.
Performance analysis of machine learning classifier on different cow health datasets

Animal welfare and good health are crucial for dairy cow farms and long-term milk production. Unfortunately, daily animal condition monitoring is challenging, especially on big farms where staff members need more time to observe animals and spot early signs of sickness. The machine learning algorithm can be constructive in continuously monitoring animal conditions. The performance of different classifiers is studied here, based on the CDAC-developed innovative farm-based cow health monitoring system. The system comprises hardware components, a cloud infrastructure, a client application, and cutting-edge data measurement and analysis algorithms. The device can successfully monitor animal well-being and the estrus cycle after being tested in a real-world setting. In this study, data was collected from the IMU sensor attached to the neck of the cows. Along with this manual data annotation, CCTV footage of the livestock was also recorded as a means of data verification. The help of machine learning algorithms, Random Forest (RF), XGBoost (XGB), and Support Vector Machine (SVM) to classify the behavior of the cattle were taken. The algorithms were used on datasets with different features, showing additional accuracy with the other featured datasets. A brief study was presented about the improvement and change in accuracy after using the machine learning algorithm on different datasets with different features.
Occipital sinus pericranii superseding both jugular veins: description of two rare pediatric cases.

BACKGROUND AND IMPORTANCE The extracranial venous drainage of the brain is provided mainly by the jugular veins with the variable participation of extrajugular pathways. CLINICAL PRESENTATION Two cases of occipital sinus pericranii (SP) were observed. A 6-year-old girl presented with a progressively enlarging occipital lump; she was otherwise asymptomatic. An 8-year-old boy presented with mild nuchal headache. In the former case, neuroimaging examinations confirmed the diagnosis of SP; in the latter, SP was noticed on magnetic resonance imaging because it was neglected at physical examination. In both subjects, the radiological examination also showed bilateral occlusion of the jugular veins with ventricular dilation and herniation of cerebellar tonsils into the foramen magnum. CONCLUSION Exceptionally, SP was noticed to represent the main venous drainage of the underlying brain, thereby compensating for the intracranial hypertension due to venous engorgement. Preservation of SP was advised, and any surgical or endovascular treatment was contraindicated.
CallerId Application Design using Amazon Web Services

A phone number is a personal unique number which can identify its owner. Personal information can be exploited through a person’s phone number. Many methods such as phishing and spam attacks have been used to exploit a person’s personal information. Nowadays, a phone number has many linked important information such as bank accounts, and passwords which make it more vulnerable to attacks. The growing demand for accessibility and connectivity is creating demand for an efficient solution. Hence, a caller identification app which can identify caller ID and spam protection of the incoming call or SMS is required. It protects users from attending unwanted calls and messages. In this work, the design and working of a caller identification app using AWS services is explored. Many researchers have conducted studies and surveys to identify the process of caller identification and develop caller identification methods and have published their research about the same. Most of these methods are conventional. What makes the proposed model novel, is that it uses a cloud-based new age model with complete backend design to develop a strong caller identification application with its roots in the AWS cloud. Though other models have been successful, this model is better because of the benefits provided by the cloud, which include high availability, scalability, elasticity, security, and cost-friendliness.
Image-Based Virtual Fitting System with Garment Image Reshaping

We propose an image-based virtual fitting system to reproduce the appearance of fitting during online shopping for garments. Inputs are whole-body images of a fashion model and the customer. We create a garment image by cutting out the garment portion from the image of the fashion model. If the garment images were naïvely superimposed, the fitting result would look strange mainly because the shape of the garment will not match the body shape of the customer. In this paper, we therefore propose a method of reshaping the garment image based on human body shapes of the fashion model and the customer to make the fitting result more realistic. The body shape is automatically estimated from the contours of the human body, and can easily be retouched if necessary. The fitting result is refined further by automatic color correction with reference to the facial regions and a method of retouching parts that protrude from the rear of the garment image. We verified the effectiveness of our system through a user test.
Impact of ESG on Financial Distress in The Healthcare Sector Using Debt-Equity Ratio As A Moderator

This study analyzes how the Environment, Social, and Governance (ESG) factor, affects healthcare sector businesses' financial distress (FD) as indicated by Z-score. In this research, DER (Debt-Equity Ratio) moderates the relation between ESG and Z-score. The panel data (PD) regression analysis model (PDRAM) is used to examine the data. The data pertains to 33 companies providing healthcare services for twelve years (2011–2022). The debt-equity ratio moderates the relation between the explanatory factors and response factors. This work finds that there exists a non-positive relation established between ESG & Z-score. However, under the moderating impact of the D/E ratio, a positive relationship between Z-score and ESG is reported. This research finds practical implications in policy formulations at the governance level and project management at the corporate level. To the updated knowledge of authors, this research remains unique in analyzing a non-economic indicator's financial aspect in the healthcare sector.
Microcystins in potable surface waters: toxic effects and removal strategies

In freshwater, harmful cyanobacterial blooms threaten to increase with global climate change and eutrophication of surface waters. In addition to the burden and necessity of removal of algal material during water treatment processes, bloom‐forming cyanobacteria can produce a class of remarkably stable toxins, microcystins, difficult to remove from drinking water sources. A number of animal intoxications over the past 20 years have served as sentinels for widespread risk presented by microcystins. Cyanobacterial blooms have the potential to threaten severely both public health and the regional economy of affected communities, particularly those with limited infrastructure or resources. Our main objectives were to assess whether existing water treatment infrastructure provides sufficient protection against microcystin exposure, identify available options feasible to implement in resource‐limited communities in bloom scenarios and to identify strategies for improved solutions. Finally, interventions at the watershed level aimed at bloom prevention and risk reduction for entry into potable water sources were outlined. We evaluated primary studies, reviews and reports for treatment options for microcystins in surface waters, potable water sources and treatment plants. Because of the difficulty of removal of microcystins, prevention is ideal; once in the public water supply, the coarse removal of cyanobacterial cells combined with secondary carbon filtration of dissolved toxins currently provides the greatest potential for protection of public health. Options for point of use filtration must be optimized to provide affordable and adequate protection for affected communities. Copyright © 2013 John Wiley & Sons, Ltd.
Brain-Computer Interface Research Survey

People have long desired for the ability to communicate with technology merely by thought or for the creation of gadgets that can see into the minds and thoughts of others. Ancient mythology and contemporary science fiction stories are two examples of how these concepts have gripped the imagination of humanity. However, it has only recently become feasible for humans to have direct contact with the human brain because to developments in cognitive neuroscience and brain imaging technologies. Researchers have developed brain computer interfaces (BCIs) using these technologies, which are communication devices that doesn't depends on the brain's typical output channels of muscles and peripheral nerves. The expanding societal understanding of the requirements of those with physical disabilities has served as the main driving force behind this endeavour. Users in these systems purposefully alter their brain activity rather of generating signals that are capable of being utilised to operate computers or communication devices through bodily actions.
How Online Customer Review and Online Customer Rating Influence Customer Purchase Decision on E- Marketplace with Customer Trust as A Mediator

This research aims to determine the influence of online customer reviews and ratings on customer purchase decisions in e-marketplaces through customer trust as a mediator, particularly in the snack industry. The method used in this study is path analysis. This research employs a quantitative method and utilizes purposive sampling technique. The data used is primary data obtained from a survey and gained 150 respondents who have purchased snack products on e-marketplace websites and reside in the Greater Jakarta area. The survey data processing is tested using IBM SPSS Statistic 23 software. The achieved results are used to determine the direct and indirect effects of independent variables on the dependent variable. The research findings concluded that there is a significant influence of online customer reviews and online customer ratings on customer purchase decisions on e-marketplaces through customer trust as a mediating variable.
Micro Monitor for Four Physiological Parameters from Fingertip

Real-time monitoring and timely warning of human physiological parameters is very important and necessary for mastering an individual's health status. This project aims to select fingers with abundant blood vessels as the detection site, gather information on blood and vascular changes through photoelectric and temperature sensors, and extract features to obtain important physiological parameter values such as pulse, heart rate, saturation of peripheral oxygen, and body temperature, achieving the goal of multi-parameter synchronous monitoring. The results showed that the micro monitoring system has good stability and data accuracy with many characteristics such as small size, light weight, low power consumption, and easy portability. It is not only suitable for personal health monitoring, but also suitable for assisting medical workers in diagnosis and decision-making in professional settings. At the same time, it also provides a new approach and technical support for the field of mobile healthcare.
Towards Deploying Highly Quantized Neural Networks on FPGA Using Chisel

We present chisel4ml, a Chisel-based tool that generates hardware for highly quantized neural networks described in QKeras. Such networks typically use parameters with bitwidths less than 8 bits and may have pruned connections. Chisel4ml can generate the highly quantized neural network as a single combinational circuit with pipeline registers in between the different layers. It supports heterogeneous quantization where each layer can have a different precision. The full parallelization enables very low-latency and high throughput inference, that are required for certain tasks. We illustrate this on the triggering system for the CERN Large Hadron Collider, which filters out events of interest and sends them on for further processing. We compare our tool against hls4ml, a high-level synthesis based approach for deploying similar neural networks. Chisel4ml is still under development. However, it already achieves comparable results to hls4ml for some neural network architectures. Chisel4ml is available on https://github.com/cs-jsi/chisel4ml.
Analysis of Methods for Calculating Leakage Inductance of a High-Voltage Test Transformer

The article provides a comparison of leakage inductances obtained by analytical and numerical methods for shell-type and core-type transformers. For numerical calculations, 2D and 3D models of transformers in the Elcut and Comsol packages are used. The errors of the analytical method and numerical calculation using 2D magnetic field calculations for the high-voltage transformers have been established. Numerical calculation of 3D-model was adopted as an exact method.
Analyzing User Behavior in Social Media through Big Data Analytics

Social media platforms have transformed into vast reservoirs of intricate data, accumulating unprecedented volumes of user interactions and behaviors. Advanced big data analytics techniques have enabled a deeper understanding of this data treasure trove, shedding light on the intricate landscape of social media user conduct. This comprehensive guide provides insights into the realm of big data analytics for examining user behavior on diverse social media platforms. In our digital age, users generate staggering data sharing thoughts, opinions, preferences, and emotions online, spanning text-based posts, comments, multimedia content, and network interactions. The report delves into the analytical techniques driving this field, including natural language processing, sentiment analysis, machine learning, and network analysis. It scrutinizes significant research findings from these methods, such as identifying influencer networks, sentiment trends, and predictors of user engagement. However, the field confronts challenges, emphasizing the necessity of ethical data usage and user privacy protection. It also acknowledges complexities linked to real-time data streams, unstructured content, and nuanced interpretation to mitigate errors and biases. The report explores future directions, like countering misinformation, predicting social trends, and delivering highly personalized user experiences. This overview is a valuable resource for researchers, businesses, and policymakers, providing insights into big data analytics for understanding user behavior on social media.
Scientific and First Nation perspectives of non-timber forest products: a case study from the Shoal Lake watershed, northwestern Ontario

This report provides a summary of initial findings from the project: Combining Scientific and First Nations’ Knowledge for the Management and Harvest of Traditional and Commercial Non-Timber Forest Products (NTFPs). This report is divided into five sections. The first section provides an overview of the research project. Section 2 presents an overview of traditional ecological knowledge research undertaken on NTFPs. Section 3 turns to an examination of a scientific approach to inventorying NTFPs. Management applications and conclusions are addressed in sections 4 and 5 respectively. Following Davidson-Hunt, Duchesne and Zasada (2001) the term non-timber forest products (NTFPs) is defined as those biological organisms, excluding timber, valued by humans for both consumptive and non-consumptive purposes found in various forms of forested landscapes. NTFPs moves the focus on forest management away from large industrial interests in forested landscapes and toward a consideration of the interests, values and activities of people who have largely been excluded from forestry research, planning and management. The purpose of the research project between the Natural Resources Institute, University of Manitoba and Iskatewizaagegan #39 Independent First Nation was to develop a model for cooperative research between First Nation harvesters and scientific researchers. The research project included an interest in theory building regarding social-ecological systems and resilience. A practical consideration of the project was to discover how research partnerships could be established between scientific institutions and First Nation communities to increase the wellbeing of rural communities. The objectives of the research project were: (1) To develop a model of cooperative research between rural/First Nation communities and scientific researchers which builds upon the contributions of both science and local/indigenous knowledge towards multifunctional and biologically diverse landscapes; (2) Document the traditional ecological knowledge of botanical NTFPs and the perception of the forest in terms of disturbance, succession and forest patches; and, (3) Develop a rapid inventory methodology based upon the Ontario Ecological Classification which assesses NTFP distribution, abundance and quality. The management implications of the research presented in this report suggest: (1) The importance of establishing protocols and agreements for the sharing of knowledge among First Nation peoples, managers and researchers; (2) the need to establish a common lexicon for the sharing of knowledge; (3) a requirement for more specification of First Nation values; and, (4) the development of community-based tools for assessing and communicating values such as rapid NTFP inventories. In conclusion, the research suggests a need to move away from incorporating or integrating knowledge systems toward working with First Nations to develop tools for communicating and implementing their visions for the land.
A Novel Power Reduction Technique in 6T SRAM using IGSVL and SGSVL FinFET

Power optimization is one of the most significant concerns in impending portable integrated circuits. The major problem in recent days is the increase in leakage current of the VLSI circuits due to the continuous flow of current. Leakage reduction is achieved by shrinking the threshold voltage, without heart rending the other design goals. Low power circuit operation is becoming an increasingly important metric for future integrated circuits. With existing technology scaling FinFET seem to resemble more likely an alternative for bulk CMOS. Compared to conventional CMOS, FinFET is said to be advantageous as the latter one is double gated. Simultaneous driven and Independent driven are the two different methodologies of working with the Double gates which assist in the power reduction. The approach in this work is the implementation of FinFET in various SRAM cells in 45nm technology using T-Spice for the reduction of leakage in the circuits.
An effective Convex-Hull Founded Borderline Recognition in Unattended Wireless Sensor Networks

An Unattended Wireless Sensor Network (UWSN) is made up of battery-operated sensor nodes that function under difficult conditions with little assistance from humans. Unattended WSNs are employed in a variety of applications, including monitoring volcanoes, military surveillance, and extreme environmental monitoring. Because event-based UWSN apps generate dynamic traffic, the network becomes congested, uses a lot of energy, and has malicious nodes present, which makes it perform poorly. Therefore, it is crucial to conduct research into developing an energy-efficient and dependable self-management framework that adapts dynamically to changes in network conditions. To assess the network's level of coverage, the borderline information is helpful. In the static sink scenario, the borderline nodes (or convex nodes) are employed as a backup path when there is a high load. The energy-aware convex hull method is used to identify the borderline nodes. Convex-hull procedures that are presented in three different forms: centralized method, distributed approach, and mobile sink approach. The application situation and deployment environment for the sensor network affect the algorithm's applicability. Borderline nodes have a variety of uses. The borderline nodes determine the coverage region of a network. The borderline nodes in a network with a static sink scenario are used to forward data when there is a high volume of traffic.