Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring.
Characterization and Exploration of Latch Checkers for Efficient RAS Protection

Reliability has been, and continues to be a key consideration in the design of the IBM Z mainframe processors, and this has resulted in industry-leading performance with little-to-no downtime. In this paper, we analyze the various hardware reliability mechanisms that make the processor resilient to transient errors, and the checker architecture that enables their detection and correction. We characterize the error checking logic in the processor based on a detailed analysis of the actual design. Based on hardware measurements on a real Z processor, we then determine the error checkers that are critical from a timing standpoint. We propose algorithms that optimize checker selection without affecting either the RAS coverage or the detection of timing errors. Finally, we examine further potential runtime optimizations of checkers based on the logic utilization in representative benchmarks.
Bubble Formation from Non-Thermally Upgraded Kraft Paper in Alternative Insulating Liquids

Bubble formation in transformers could lead to their failure, resulting in severe outages in the electricity network. Hence, understanding the parameters that impact the bubble formation process is crucial to maintain the transformer’s dependable operation. Commonly, mineral oil and non-thermally upgraded Kraft paper have been used as the insulating materials for power transformers, and the risk of bubble formation has been studied for this material combination. However, new insulating liquids such as esters and gas-to-liquid technology-based oils are recently gaining more popularity and studies on bubble formation for these alternative liquids are limited. In this paper, the impact of alternative liquids on bubble formation has been investigated by using a small-scale test tube-based system. Studies have shown that when considering the water content in the paper at the time of bubble formation, the bubble formation temperature is comparable across different liquid types.
The coexistence of seagrass and coral at Bise, Okinawa, Japan

Interaction between the seagrass Thalassia hemprichii and the coral Montipora digitata in the combined community at Bise was investigated by using dome type of chamber incubations at in situ. Nutrient dynamic, dissolved organic carbon (DOC) and microbial community in four habitats as sand, seagrass, coral and seagrass with coral (sg+cr) were examined. Experiments were conducted for 24 hours under the natural light and temperature. Coral habitat showed higher dissolved oxygen (DO) values than other habitats. In this experiment, DO was significantly negatively correlated with alkalinity (r = 0.48, p ≤ 0.01, n = 28). Alkalinity values were higher in seagrass habitat due to the lack of capacity of the seagrass Thalassia hemprichii to use bicarbonate from the surrounding water while, in coral habitat low alkalinity values than other habitats were found due to coral calcification. Influence of ground water lead to high increased of nitrate in the seagrass habitat. During the light period, DOC concentrations were high in seagrass habitat whereas, the high value of DOC during the dark period in coral habitat was found due to mucus released from coral. Increased of organic matter in the surrounding water leads to increase of microbial abundance, therefore high abundance of bacteria in seagrass habitat was exhibited. In sg+cr habitat, bacterial abundance was lower than other habitats whereas, DO, Interaction between seagrass and coral |16 pH, nutrients and DOC values were exhibited intermediate values between seagrass habitat and coral habitat. In the combined community, DOC was supported from seagrass exudation, while coral supply dissolved oxygen and carbon dioxide during calcification process.
Development of Visual Smooth Pursuit Model Using Inverse Reinforcement Learning For Humanoid Robots

This Early in the $20^{\mathrm{t}\mathrm{h}}$ century, research on smooth pursuit began. Nowadays, it may be found in everything from little robots to sophisticated automation projects. There are now many study studies in this area, but they are all reward-based conventionally, which is not biologically feasible. In these techniques, the robot performs an action, and the agent determines the next course of action based on the performance and a certain kind of positive or negative reward. The reward in this thesis is derived from the sensory space rather than the action space, which enables the robot to predict the reward without any prior defined reward. PC/BC-DIM, a new Deep Inverse Reinforcement Learning (DIRL) technique, is presented. Rather than relying on previously specified rewards, PC/BC-DIM assesses the prediction error between certain inputs and determines whether or not to update the weight. It was controlled independently and successfully arrived at the target place, yielding satisfying results. The iCub humanoid robot simulator is used to evaluate the performance of the suggested system.
Smart Home Energy Scheduling Using Demand Side Management Programs

Saudi Arabian decision-makers are seeking innovative and cost-effective solutions to meet the country’s power needs, understanding that it is impractical to continue in the existing scenario of high demand rise and low energy consciousness. One pretty powerful collection of technologies in the forthcoming inventory of electricity generation is demand side management (DSM), which comprises demand response (DR), energy efficiency (EE), and load management. Many electric companies have implemented DSM to flatten the load profile by altering the rate during the day to encourage citizens to minimize their electricity consumption during peak hours. In this project, DSM technologies are implemented. Based on pricing signals forecasted by a recurrent neural network model, the DR system adjusts loads from peak to off-peak hours. It was able to minimize the electricity bill for the householder. Furthermore, the EE system consists of an air conditioning (AC) system and a lighting system was implemented to reduce power consumption. The energy saved from the AC system is around 7.8% for a typical year for a typical villa in Al-Ahsa in the case of over insulation. Also, the lighting system saved around 25.1% of the energy consumption.
A Comparative Study of Different Transfer Learning Models for Classification of Urban Manhole States

With fast and rapid advancement of traffic in the urban areas of Bangladesh, manhole covers in these areas can be damaged for quite a few reasons. This phenomenon creates a need for human observation of manhole covers to prevent disastrous traffic accidents. However, manual observation of manhole covers is an inefficient and troublesome process in Bangladesh for a variety of reasons, including manpower shortages and lack of ethics. To prevent these problems arising from manual examination of manhole covers, this research study proposed a deep neural network (DNN) model-based automated system architecture that tends to replace the manual examination process. The system architecture will have the ability to classify the appropriate and damaged state of the manhole cover and will notify the authorities immediately if the manhole cover state is damaged. Several transfer learning models are tested to accomplish the highest accuracy while the loss is almost eradicated from the testing. The proposed model consists of an advantage over the previous used architectures as it performed well on both accuracy and have less loss in the training and validation process. The custom dataset utilized in this research study to train the DNN models was preprocessed and augmented by various effective and essential techniques and parameters, which subsequently scaled up the accuracy performance of the DNN models and drove the proposed DNN model’s accuracy of 98.68%.
Smart Pipeline Monitoring System: A Review

In Africa’s most populous nation, Nigeria, Pipelines are the primary way of transporting liquid and gas. Oil and fuel pipeline networks have proven to be the most secure and competitively priced way of transporting petroleum products while meeting increased demand for efficiency and durability. As a consequence of the negative impact of unreliable pipeline systems on society, the demand for a credible pipeline system is increasing by the day. Failure in pipeline systems can result in pollution and explosions, which pose a threat to people, property, and the ecology. Hence, this paper presents an overview of a smart pipeline monitoring system. The review encompasses the various methods of monitoring pipeline systems, ranging from traditional systems to IoT-based systems. From the papers reviewed, it was seen that there exist numerous methods to carry out pipeline monitoring. These approaches and methods produced different degree of accuracy and efficiency when applied in monitoring pipelines, and also depends on the content or material the pipeline carries. The review identifies key features of pipeline monitoring systems to include: real-time capability, predictive capability, scalability and accuracy. This paper reveals the possibility to adopt technology-based monitoring system as opposed to traditional methods.
The Effect of Spatial and Environmental Drivers on Patterns in Species Richness and Composition

Aim: The relationship between geographic distance and similarity in species composition is regularly used as a measure of species turnover and beta diversity. Distance decay analyses are applied, cited and compared despite variable extent, and different grain sizes of records (e.g. plots, islands, states) are regularly used within such analyses. Currently, differences among distance decay relationships that cover different grain sizes and extents are attributed to ecological processes that are suspected to operate differently over varying extent and grain size. We assess whether the implicit assumption that the distance decay relation is independent from grain size and study extent is valid, or whether sampling design could be the underlying cause for observed differences. Location: An artificial one-dimensional "landscape". Methods: The distance decay relationship was quantified in simulated communities. Grain and study extent were varied systematically. In each sampled data set the linear relation of Simpson and Sørensen similarity to geographic distance (on both a log-transformed and the original scale) between 100 even sized equidistant plots was assessed using linear regression and generalized linear regression with log-link function. Regressions were applied either including or removing zero similarities from the data. Results: Both, slope (measuring turnover) and goodness of fit measure r (quantifying the influence of space on species composition) of the distance decay relationship were strongly influenced by grain and study extent. Approaches that are able to cope with zero similarity values of large distance comparisons were less dependent on grain and extent. Main conclusions: Reported differences between landscapes detected by current distance decay measures cannot be explicitly traced back to ecological scale-specific processes. Instead, they can largely be attributed to sampling design and are highly sensitive to grain size and study extent. More appropriate approaches for the study of distance decay and the understanding of scale specific processes are required.
Survey and Analysis of NFT and Blockchain Technologies for Developing Agricultural Product Trading Systems

In this research, we have surveyed NFT technology and Blockchain technology. We have analyzed both technologies to apply them in the agricultural product trading to benefit farmers the most. We have examined the strengths of NFT and NFT Marketplace to find ways to utilize these strengths in agricultural product trading. Additionally, we have identified various obstacles and suggested solutions. From the research, we have found that agricultural product trading through NFT Marketplace can provide benefits such as transparency, allowing buyers and sellers to verify the relevant transactions, the number of trades, the initial purchase price, and the final selling price. Moreover, the NFT's ownership information cannot be forged as they are on the Blockchain. By integrating the system with the community enterprise, the government's support, and using crypto seed phrases and private keys tied to accounts, farmers can receive KYC (Know Your Customer) and prevent fraud. Furthermore, NFT Marketplace can show sales transactions of each product category, which can help farmers understand market demand and popularity. Additionally, buyers can choose popular and trustworthy sellers (farmers) based on transaction history.
UWB Cardioid-Shaped Vivaldi Antenna

This paper presents a cardioid-shaped Vivaldi antenna. The effect of antenna parameters (height of the tapered slot, width of the antenna, width of the feeding strip, and also the height of the strip of the antenna connected to the braid) on its radiation characteristics (VSWR and realized gain) is considered. The developed antenna has the overlap ratio is approximate 4.1 on the level of VSWR $\leq 3$ and operates in the frequency range from 3.658 to 15.000 GHz. The realized gain in the specified frequency band from 6.5 to 17.0 dB. The overall electrical dimension of this antenna is $5.75 \lambda\times 5.00\lambda\times 13.50\lambda$ at the frequency 15 GHz.
Development of Simplified Lumbar Spine Mechanism Implemented with Tendon-Driven Motion

The lumbar region of the spine has important characteristics for the investigation of pathological dysfunctions, where the major abnormalities present in the spine are caused. This paper presents the development and implementation of a controlled and simplified lumbar spine mechanism. The construction was done using 3D Slicer, SolidWorks and Ultimaker Cura software. The control action is based on a motion trajectory system realized with conventional, PID-type control. The physical implementation of the motion drive was performed and results show the correct execution of the planned movements, following the references specified for the movement of the structure.
Essential Evaluations on Developing Natual Ester Immersed 750kV Power Transformer

Natural ester insulating liquids have gained attention from the global power industry in recent years, and until today, the highest voltage level of in-service natural ester immersed transformer is 420kV. Many concerns are raised to explore higher voltage level and essential evaluations are needed. Such as a 750kV power transformer that was developed in China, with Um 800kV, BIL 900kV/1950kV/1550kV (AC/LI/SI). This single-phase transformer is rated at 120MVA, filled with FR3™ Natural Ester, passed all the type tests at once under witness from an authorized 3$^{rd}$ party laboratory in November 2022. Since there was no experience, either on design or on manufacturing of such high voltage ester filled transformer, evaluations comparing natural ester design with traditional mineral oil solution become essential. This paper covers most of the essential evaluations in the journey of aiming to make the world’s highest voltage level natural ester transformer, including design and manufacturing. Evaluation aspects need attention, such as liquid performance differences on breakdown voltage under different electrode and voltage configurations, the stopping length of streamers, the partial discharge initial voltage, and the impacts on the winding design, main insulation and leads of the transformer. Evaluation of the oil flow electrification performance, viscosity, water saturation, the impacts on cooling design, and evaluation on material compatibility between natural ester and construction materials of 750kV transformer demand attention. This paper can be valuable to the industry for exploring natural ester liquids application in UHVAC transformers.
Model-free Reinforcement Learning for Demand Response in PV-rich Distribution Systems

The recent government initiatives to decarbonize the power system and minimize the overreliance on fossil fuels will lead to massive and rapid adoption of solar photovoltaics (PVs) in distribution systems. Daytime solar power generation peaks necessitate demand-side flexibility to mitigate voltage dips and spikes that could disrupt the power grid. To this end, a model-free demand response framework is developed based on deep reinforcement learning (DRL) and using OpenAI Gym APIs. The DRL agent assumes the role of a load aggregator that directly controls a percentage of each load in the distribution system. The agent is then trained to optimize the control policy by taking nonuniform actions on each node. The system-wide objective is to minimize the voltage deviations from 3% of the nominal voltage in an effort to properly allocate the energy consumption throughout the day. The DRL-based demand response is trained and tested on the radial IEEE 33-bus distribution feeder, modified to have a high penetration of non-dispatchable PVs plants. Simulation results show that the proposed framework works as intended, shifting flexible demand to times of maximum solar power generation while maintaining an acceptable voltage deviation at each node.
Defect Diagnosis of Hot Rolling Steel Surfaces Based on Deep Learning

Robust visual inspection systems have gotten more consideration in the area of product quality control in an effort to increase production effectiveness. The issue of hot-rolled steel strip surface examination utilising machine vision systems is the focus of the current study. A novel approach is suggested to diagnose (verify/classify) surface defects. The presented approach is a new simple construction supervised classifier “SC” based on Convolutional Neural Network (CNN) technique. The proposed approach has been applied and tested on a certified hot rolled steel surface defects dataset including healthy class and six common categories of steel flaws. An analysis of contrasts between SC and associated work approaches was performed. Results show that the proposed approach presents excellent performance in diagnosing flaws in the surface of hot rolled steel. The surface flaws dataset test results with 50% k fold for SC are 99.33% respectively.
Multi-Objective Optimization for an Online Re-Planning of Autonomous Vehicles

Autonomous vehicles are well-known for automated tasks that are difficult or dangerous to be performed by human. However, the environment in which those Autonomous Vehicle (AV) are evolving is generally hard to predict. Thus, the challenge is to achieve a predefined mission while adapting AVs to their shifting environment in real time as efficiently as possible. The mission often includes path planning problems, where self-adaptation to terrain modifications is required while maintaining contradictory objectives, such as safety, risk assessment, travelling time or distance or consumed energy. We choose to focus on supervision missions (covering area with a lidar, with pictures, searching, etc) with two objectives: travelled distance (that could later be modeled into time or energy consumption) and covered area. We propose a multiobjective optimization (MOO) framework for a self adaptation of autonomous vehicles, with an offline/online approach, in order to solve covering/monitoring missions. The offline process will predict a initial path for the AV and the online process will be useful for the dynamic path re-planning when obstacles are detected. Our results demonstrate the benefits of reusing the offline pre-computed solutions for the online phase and for dynamic path re-planning.
Design and Analysis of Wireless Power Transfer using Stacked Coil PCB for Inductive Power Charging

A wireless power system or wireless power transfer is an alternative for energy distribution using air or wireless media. This electrical energy transmission uses air as the medium to distribute energy where the energy source and load are located far apart. Therefore, many researchers are trying to increase the mutual coupling between coils to increase efficiency. One of the ways to improve mutual coupling is using a stacked coil PCB. Based on the proposed design, a rectangular design using FR4 PCB with dimensions of $5.4 \times 5.4 {\mathrm {cm}}$. Stacked coil fabrication with 5 turns as a transmitter and on the receiving side of a Stacked coil of 10 turns and a non-stacked coil of 1 turn as a comparison. Obtained from the S21 measurement received by the 10 turns coil, it has a value of 25dB with VNA and −29dB with SA, and −16dB with simulation. While the results from winding 1 with a distance of 5 cm obtained an S21 the values are −44dB, −52dB, and −37dB respectively.
A New Microstrip Directional Filter Configuration Composed by Hybrid-Mode Resonators

The design, fabrication and experimental test of a microstrip two-pole directional filter based on a new approach consisting of hybrid-mode resonators is presented in this paper. The use of hybrid even-mode and odd-mode resonators allows the structure to be composed by two stages having bandstop filter behavior, and following a symmetrical configuration, which in turn makes it easier to design and adjust while keeping low dimensions. The proposed filter is designed to operate over the uplink channel of the 1.8 GHz band used for mobile communications with a 4 % bandwidth.
Integrated Economic Dispatch and Automatic Generation Control for Two-Area

Control of power generation is significant to a power system. Reaching a steady-state response for frequency generation in a shorter time will make generation more stable in power supply than load distribution. The automatic generation control (AGC) controls the electrical generation frequency and transfers power to another area as required from economic dispatch (ED) with minimum disturbance. This work illustrates the classical and integrated economic dispatch with automatic generation control (ED-AGC) in one area. The classical ED frequently defines every generator’s base point power from optimization problems for AGC scheduling during generation. On the other hand, the integrated ED is the control feedback representing the base point power of every generator for AGC during load power variance. This work will also design the two areas of integrated ED-AGC and shows the different results for using classical and integrated ED-AGC in two areas system. The simulation program will generate a better frequency regulation response with better power generated from the integrated controller during load disturbance. It will provide more optimally result in the cost of generating power.