Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Dexamethasone Implant on Retinal Nerve Fiber Layer and Optic Nerve Cup-to-Disk Ratio in Patients with Diabetic Macular Edema

Aim: The aim of this study is to investigate the effect of single-dose intravitreal Dexamethasone (DEX) implant on peripapillary retinal nerve fiber layer (RNFL) thickness and cup-to-disc (C/D) ratio in patients with diabetic macular edema. Material and Methods: Peripapillary RNFL thickness measurements and colored fundus photographs were compared before and 6 months after intravitreal DEX implant injection. The cup-to-disc ratios of fundus photographs were calculated using the Image-J program. Results: Fifty-eight eyes of 43 patients were treated with intravitreal DEX implants. The mean global and sectoral peripapillary RNFL thickness value had a slight thinning over the basal value. The global and nasal thinning was statistically significant (respectively; p=0.021, p=0.036). A small increase in C/D value was observed 6 months after the DEX implant injection; however, these changes were not statistically significant (p=0.285). The intraocular pressure was over 25 mmHg in 17% of the patients and it was controlled by medical treatment. Conclusion: In this study, in patients with diabetic macular edema, a mild but significant thinning of the global and nasal peripapillary retinal nerve fiber layer was observed 6 months after single-dose intravitreal implant treatment. This reduction did not cause a significant morphological change in the C/D value.
Gas equilibrium membrane inlet mass spectrometry (GE-MIMS) for water at high pressure

Abstract. Gas species are widely used as natural or artificial tracers to study fluid dynamics in environmental and geological systems. The recently developed gas equilibrium membrane inlet mass spectrometry (GE-MIMS) method is most useful for accurate and autonomous on-site quantification of dissolved gases in aquatic systems. GE-MIMS works by pumping water through a gas equilibrator module containing a gas headspace, which is separated from the water by a gas-permeable membrane. The partial pressures of the gas species in the headspace equilibrate with the gas concentrations in the water according to Henry's Law and are quantified with a mass spectrometer optimized for low gas consumption (miniRUEDI or similar). However, the fragile membrane structures of the commonly used equilibrator modules break down at water pressures ≳3 bar. These modules are therefore not suitable for use in deep geological systems or other environments with high water pressures. To this end, the SysMoG® MD membrane module (Solexperts AG, Switzerland; “SOMM”) was developed to withstand water pressures of up to 100 bar. Compared to the conventionally used GE-MIMS equilibrator modules, the mechanically robust construction of the SOMM module entails slow and potentially incomplete gas–water equilibration. We tested the gas equilibration efficiency of the SOMM and developed an adapted protocol that allows correct operation of the SOMM for GE-MIMS analysis at high water pressures. This adapted SOMM GE-MIMS technique exhibits a very low gas consumption from the SOMM to maintain the gas–water equilibrium according to Henry's Law and provides the same analytical accuracy and precision as the conventional GE-MIMS technique. The analytical potential of the adapted SOMM GE-MIMS technique was demonstrated in a high-pressure fluid migration experiment in an underground rock laboratory. The new technique overcomes the pressure limitations of conventional gas equilibrators and thereby opens new opportunities for efficient and autonomous on-site quantification of dissolved gases in high-pressure environments, such as in research and monitoring of underground storage of CO2 and waste deposits or in the exploration of natural resources.
The Surface Ocean - Lower Atmosphere Study: Science Plan and Implementation Strategy

SOLAS (Surface Ocean - Lower Atmosphere Study) is a new international research initiative that has as its goal: To achieve quantitative understanding of the key biogeochemical-physical interactions and feedbacks between the ocean and the atmosphere, and of how this coupled system affects and is affected by climate and environmental change. Achievement of this goal is important in order to understand and quantify the role that ocean-atmosphere interactions play in the regulation of climate and global change. The domain of SOLAS is focussed on processes at the air-sea interface and includes a natural emphasis on the atmospheric and upper-ocean boundary layers, while recognising that some of the processes to be studied will, of necessity, be linked to significantly greater height and depth scales. SOLAS research will cover all ocean areas including coastal seas and ice covered areas. A fundamental characteristic of SOLAS is that the research is not only interdisciplinary (involving biogeochemistry, physics, mathematical modelling, etc.), but also involves closely coupled studies requiring marine and atmospheric scientists to work together. Such research will require a shift in attitude within the academic and funding communities, both of which are generally organised on a medium-by-medium basis in most countries.
Increasing access to agricultural extension and advisory services: How effective are new approaches in reaching women farmers in rural areas?

iv Introduction 1 1 Why are extension and advisory services important to rural women? 2 2 Trends and developments in EAS delivery approaches 3 2.1 Government-led approaches 3 2.2 Road to reformed EAS delivery systems 3 3 Gender gaps in EAS delivery orientations 5 4 Emerging good practices for improving EAS delivery systems 6 4.1 Promoting gender-sensitive agricultural EAS systems through farmer group approaches 6 4.2 Delivering agricultural EAS through volunteer advisory services programs 7 4.3 Delivering agricultural EAS through information and communication technologies (ICTs) 8 5 Implementation challenges and options for engendering the EAS delivery chain 9 Conclusions 10
Investigation on Impacts of Multiple Uncertainties on Waste Energy Utilization System

The potential and method for using waste energy, as well as the predicted contribution of waste energy to energy conservation and emission reduction for industrial parks, are not clearly defined because the emission status quo and features are unknown. The present study aims to determine the uncertainties involved in sizing a waste energy utilization system and investigate their potential impacts on the optimal capacities of the key devices in the system and the utilization of the waste energy resources through the Monte Carlo method for incorporating multiple uncertainties in the planning stage. The case study results show that the uncertainty in the waste energy resources was found to impact the capacity of the organic Rankine cycle units to the greatest degree.
Optimization Enhancement of Output Voltage for PV System with 9 Level Inverter

Nowadays the need for electricity is increasing. To full fill the need for electricity by using two ways either non-conventional energy sources or renewable energy sources. Based on the cost and availability renewable energy source is the most possible energy source. The most available as well as cheap renewable energy source is solar. So, recently more research going on solar energy sources to utilize their maximum energy for different applications. The latest research trends in the field of solar are “Solar energy Conversion system”. The LUO converter is employed to increase the performance of the PV system by increasing the voltage that is output. The LUO converter is used to increase the oscillating PV voltage. For an AC load application convert DC-DC Converter voltage to 9 level Inverter Circuit. Analyze the Single phase, three-phase, and induction-based conditions in the proposed model.
Risk - Return Analysis of Portfolio Management Based on Marchowitz Model

Portfolio management is the process of managing money. It is the process of selecting and managing various investments to achieve specific goals for a company or individual. There is a science to choosing the right asset mix for clients and perfectly balancing their risk appetite. Portfolio managers rely on portfolio analysis to varying degrees. Identify investment opportunities; customize portfolios investment objectives, portfolio risk, and performance monitoring. Good portfolio management can lead to higher returns as it considers both long-term and short-term funding opportunities over a period. This increases the potential for efficient resources allocation and capital appreciation. The analysis confirms the existence of a clear tradeoff between risk and return, as predicted by the Markowitz model. The efficient frontier is constructed, showcasing a range of optimal portfolios that maximize expected returns for a given level of risk or minimize risk for a desired level of return. Diversification significantly reduces portfolio risk. As assets with low or negative correlation are combined, the overall portfolio risk is lower than the weighted average of individual asset risks. The level of risk exposure is measure of with the help of the standard deviations of the returns. The expected return is the weighted sum of the expected returns of the individual securities portfolio the weights being the probabilities of occurrence.
Evaluation of Application-Independent Unbiased Approximate Multipliers on Quantized Convolutional Neural Networks

Unbiasedness is considered an important property when applying approximate multipliers in applications that involve consecutive multiplication operations, and mean error (ME) is an application-independent error metric that can demonstrate the unbiasedness of an approximate multiplier. A few prior works have claimed that application-independent error metrics cannot guarantee reliable performance for real deep learning applications, but the effectiveness of ME on such applications has not been fully explored. This paper aims to fill this gap by providing Zero-ME 8-bit approximate multipliers based on the proposed double-carry 4-2 approximate compressor and evaluating the application-independent error metrics, including ME, and the classification accuracy of quantized convolutional neural networks (CNNs). The experimental results suggest that the proposed Zero-ME 8-7 multiplier has the potential as a low-power circuit for CNNs based on approximate multiplication. However, the results also indicate that evaluating the effect of bias in approximate multipliers for CNN applications using ME is insufficient, and it is expected to develop a new application-specific method for designing unbiased multipliers and a corresponding error metric.
Decoupling Design Based on Metasurface Superstrate for Millimeter Wave Antennas

A high port isolation millimeter wave antenna array with compact size is proposed in this paper. The center distance between the two E-shaped patches is only 3.2mm, about 0.28 wavelength at center frequency, which will generate a strong mutual coupling. In order to suppress the coupling, a metasurface superstrate consisting of the split-ring resonators is designed, which is placed on the top of the antenna element. The simulation results show that the port isolation of the decoupled antenna array can be improved to over 25dB in the operating frequency range of 24.25-27.5GHz, which demonstrates the effectiveness of the proposed decoupling method.
Human Action Recognition and Counting Based on Embedded System

Although action recognition and counting based on embedded systems have already emerged in daily life, such as using mobile phones for exercise training, it is still a challenging problem to balance the fluency of operation in hardware systems and the accuracy of counting. A hardware platform with built-in acceleration modules and a moderately sized deep neural network may be a feasible solution. In this paper, the NVIDIA Jetson TX2 and the CNN model are selected to build a realtime application. Then, the feature extraction network and branch network are compressed using a lightweight method for better adaptation. Thus, whether it is a still posture or active movement, the key coordinate of the human body joint can be obtained, and then the actions can be distinguished using corresponding discrimination methods. Especially in the case of sports, such as Push-ups and Sit-ups, the angles of joints and bodies can be calculated mathematically and the number of actions can be estimated according to the action standards. The experiments show that the proposed algorithm can achieve 13~16 frames per second(FPS) and the accuracy of movement recognition and counting is higher than 90% in the choosen embedded system.
Monitoring Perishable Foods using Machine Learning and Internet of Things

The perishable food items are needed to be monitored in order to ensure food safety and quality before it is to be consumed. The perishable food items were preserved and packed to maintain the quality as well as safety in an appropriate environment. The environment has to be monitored using feasible technologies. The Internet of Things (IoT) is a proven technology to monitor and control anything in the remote place. The Machine Learning (ML) approach, an emerging field can be combined with IoT to monitor the perishable food items effectively. The proposed study focuses on a framework with IoT and ML for the smart monitoring of food items to maintain its safety and quality to avoid wastage of food items.
Design Of Frequency-Pattern Reconfigurable Patch Array Antenna for Radar Application

In this paper, frequency and radiation pattern reconfigurabilty are recognized using a simple structure of patch array antenna integrated with PIN diodes. The structure prototype is developed based on an array of classic patch antenna implemented in Rogres RT5880 and integrated with PIN diodes and slots. A classic power divider is used to feed the structure. By switching the different PIN diodes implemented on the top side of each antenna between OFF and ON states, frequency and pattern performances of the array antenna structure are adjusted. Therefore, two different resonance frequencies are produced with different radiation angles beam. Therefore, the proposed reconfigurable array antenna can switch between two operating radar frequency bands. Simulation results have demonstrated excellent reflection coefficients by -25dB at 2.58 GHz and 2.71 GHz with a radiation angle beam of ±5° at 2.58 GHz and ±12° at 2.71 GHz. In that case, an extension array structure, which consists of four elements, is designed to produce different pattern angles beam in the two resonance frequencies.
Glaucoma Detection using Fundus Images using Deep Learning

Typically, glaucoma and an increase in intraocular pressure coexist. Most people don't develop glaucoma until later in life, and it frequently destroys families. The optic nerve, which transmits images to the brain, could be damaged by the increased intraocular pressure inside the eyes. Glaucoma can soon cause substantial blindness or irreversible vision loss if the damage worsens. Most people with glaucoma don't experience pain or other early symptoms. Regular eye exams are crucial to detect glaucoma early and begin treatment before irreversible vision loss occurs. Some algorithms, including CNN and ANN, have been proposed for identifying Glaucoma in its early stages. A CNN-based architecture called GlauNet has been proposed for the identification of glaucoma. Although it was trained using 258 eyes with glaucoma and 439 eyes without the condition, the model is still unable to correctly categorise huge datasets. Create an automated glaucoma diagnosis system using Deep Learning (DL) and a convolutional neural network to get over the slow classification speed and high computing time. The ROI of RIGA, DRISHTI-GS1, and RIM-ONE datasets were used to train the model's VGG 19, ResNet 50, and built CNN architecture. In terms of accuracy and sensitivity, the suggested strategy fared better than the current model
Design of a Broadband MIMO Antenna with Shared Radiator

In this paper, a broadband 5G MIMO mobile antenna with a shared radiator is proposed. The working band range is 3.3-6.0GHz, covering n77, n78, n79 and WLAN 5G working bands, which can meet a large number of application scenarios of 5G mobile phones. In addition, the antenna is applied to self-decoupling, defect ground, direction diversity and other technologies and principles in the decoupling way, without the use of complex decoupling structure, to ensure that its structure is simple and easy to process, small size is suitable for today's 5G mobile phones.
Cryptocurrency Price Prediction using Long Short Term Memory Method in Machine Learning

People are now investing more on crypto currency and Bit coin. Hence, predicting the price of a bit coin can save people from financial loss. Moreover, the price of bit coin also fluctuates based on the stock market prices. In order to predict the price of the crypto currency, this research study has used LSTM (Long Short Term Memory) to generate assessments based on bitcoin stock queries. Based on stock demand, the proposed LSTM model integrates with the Yahoo Finance to predict the price of the Bitcoin.
Development of surplus power generation forecast for use by residential loads

Energy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy.
Rapid NVM Simulation and Analysis on Single Bit Granularity Featuring Gem5 and NVMain

In emerging computing systems, non-volatile memory (NVM) is becoming an alternative to conventional main memory technologies such as DRAM. With its many advantages it also yields disadvantages, such as shorter memory cell life times. Due to its shorter lived nature, wear-leveling on main memory becomes an important aspect of systems using NVM. Hardware methods are implemented such as iterative write schemes (repeatedly sense and write), which will check a memory cell before changing it. But hardware methods alone do not provide optimal wear-leveling, so they should be used together with software based methods, which try to evenly distribute wear over large memory regions. However, for software wear leveling on NVM main memory no standardized evaluation setup exists. Which leaves most of these approaches incomparable to each other. In this work we will leverage existing solutions for evaluation such as Gem5 [1] and NVMain [20] and extend them with our own methods, analysis setup and benchmark suite. We thoroughly explain our setup in this article and provide a tool for a comparable and easy to use evaluation setup for software based NVM main memory wear-leveling.
A novel Double Frequency Cascaded Boost Converter

This study focuses on enhancing the functionality, effectiveness, and voltage gain of a converter in a single circuit. A cascaded boost converter is used to increase a converter's voltage gain whereas a double frequency boost converter is used to increase a converter's performance and efficiency. No circuit has been created that will simultaneously improve all three of them. The concept of a double frequency cascaded boost converter has therefore been discussed. A double frequency boost converter is a type of converter that combines the functions of a cascaded boost converter with a double frequency boost converter. The simulation results of the double frequency cascaded boost converter implies that all the three that is efficiency, performance and voltage gain is highly improved. A double frequency cascaded boost converter consists of a high frequency switch and a low frequency switch. A high frequency switch is connected to a high frequency capacitor and the low frequency switch is connected to the low frequency capacitor. A filter capacitor in added in order to remove the harmonics.
Battery-friendly charging process scheduling of electric vehicle fleets at company sites

Companies adopt electric cars for their vehicle fleets to be more environmental friendly and sustainable. However, there is a battery degradation over the course of time. Wrong charging behavior can accelerate this process. To improve the battery-lifetime, an intelligent scheduling of the charging processes is necessary. The schedule also needs to take other aspects, like the power grid or planned tours of the cars in consideration, to guarantee their full operability. The paper formulates this scenario as an integer linear problem to compute optimal battery-friendly charging schedules that take into account the various requirements and restrictions of electric vehicle fleets in companies. Results for examples from an application project validate the research.
Credit Risk Prediction Network Based on Semantic Feature Transformer and CNN

Credit risk is one of the main risks faced by financial institutions. Establishing credit risk prediction model is an important means to help financial institutions avoid risks and reduce losses. To fully extract the feature information in the credit data and improve the parallel computing capability of the model while reducing the network size, a credit risk prediction network based on semantic feature Transformer and CNN is proposed in this paper. Firstly, a Semantic Feature Transformer (SFTransformer) module is constructed by introducing semantic features into Transformer to extract credit risk features. Secondly, because one-dimensional convolutional neural network (1DCNN) has good feature extraction ability and parallel computing ability, the 1DCNN module is combined with the SFTransformer module to build a parallel network CNN-SFTransformer. Finally, the feature data of the borrower is fed into the CNN-SFTransformer model to calculate whether the borrower has default risk. In this paper, two real credit datasets from Australia and German in the UCI database are used for experiments. AUC and KS, two commonly used evaluation indicators, are used to evaluate the predictive performance of the model. Experimental results show that the CNN-SFTransformer model proposed in this paper has good performance in the task of credit risk prediction, with high accuracy and reliability.