Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of cantilevered energy harvesters coupled with a topologically optimized piezoelectric layer oscillating in vortex

An efficient design analysis method for cantilevered beam-type piezoelectric energy harvesters was developed for the prediction of the electric power output, based on the finite element method and the design optimization of piezoelectric materials. The optimum topology of a piezoelectric material layer could be obtained by a newly developed topology optimization technique for piezoelectric materials which utilized the electromechanical coupling equations, MMA (method of moving asymptotes), and SIMP (solid isotropic material with penalization) interpolation. Using the design optimization tool, several cantilevered beam-type piezoelectric energy harvesters which fluctuated in the region of vortex shedding were developed, that consisted of two different material layers - piezoelectric and aluminum layers. In order to obtain maximum electric power, the exciting frequency of the cantilevered energy device must be tuned as close to the natural frequency of the beam as possible. Using the method, the effects of geometric parameters and several piezoelectric materials (PZT, PVDF, and PZT fiber composites) attached to the beam device on power generation were investigated and the electric characteristics were evaluated. The three kinds of material coefficients such as elasticity, capacitance, and piezoelectric coupling are interpolated by element density variables. Then, the shape and size design optimizations for the cantilevered beam geometries with an optimum piezoelectric topology have been performed for a base model.
Statistical wavelet and filter bank optimization

We deal with three problems of optimal multiresolution representation and approximation of random processes. The first problem is that of finding a scaling function which will give the best approximation of a random process at a given scale. Approximation of inner products of ensemble members of a random process and a scaling function is the second problem. The approach to the above problems is similar. We first find the autocorrelation functions of errors, then their power spectra and finally, the variances. The third problem is the coding gain optimization of a subband coding system, more specifically, the choice of filters in a filter bank which maximizes the coding gain. An optimization algorithm is derived and an efficient implementation scheme proposed. <<ETX>>
Evaluation of Messages on Changeable Message Signs as a Speed Control Measure in Highway Work Zones

The objective of this study is to measure the effectiveness of three commonly used changeable message sign (CMS) messages in reducing vehicular speeds and variances in highway work zones. This paper presents the results of a field study conducted on Interstate-90 in western New York State to evaluate the use of three different types of messages displayed on a CMS, as a speed control measure. The study included speed measurements of nearly 180,000 vehicles. The three types of CMS messages tested were: (1) RIGHT∣LANE∣CLOSED∼KEEP∣LEFT ; (2) WORK ZONE∣MAX SPEED∣45 MPH∼BE∣PREPARED∣TO STOP ; and (3) LEFT∣LANE∣CLOSED∼KEEP∣RIGHT . Of the three CMS messages tested, the CMS message stating “ WORK ZONE∣MAX SPEED∣45 MPH∼BE∣PREPARED∣TO STOP ” proved the most effective, significantly reducing vehicle speeds by 3.3–6.7 mi∕h (5.3–10.8 km∕h) . The other two CMS messages were not as effective. The standard deviation of speed for the CMS message: WORK ZONE∣ MAX SPEED∣45 MPH∼BE∣PREPARED∣TO STOP increased by 1.1–1.7 mi∕h (1.8.
Convolution neural network towards Monte Carlo photon dose calculation in radiation therapy.

PURPOSE The Monte Carlo (MC) algorithm has been widely accepted as the most accurate algorithm for dosimetric calculations under various conditions in radiotherapy. However, the calculation time remains an important obstacle hindering the routine use of MC in clinical settings. In this study, full MC three-dimensional dose distributions were obtained with the inputs of the total energy release per unit mass (TERMA) distributions and the electron density (ED) distributions using a convolutional neural network (CNN). A new Dose-mixup data augmentation routine and training strategy are proposed and applied in the training process. Attempts were made to reduce the calculation time, while ensuring that the calculation accuracy is comparable to that of the MC. METHODS Datasets were generated via the MC with random rectangular field sizes, random iso-centers, and random gantry angles for head and neck computed tomography (CT) images with Mohan 6-MV spectrum photon beams. 1500 samples were generated for the training set, and 150 samples were generated for the validation set. The T-MC Net model was obtained with the Dose-mixup data augmentation routine. The new CTs were used to test the performance of the model in the rectangular fields and the intensity-modulated radiation therapy (IMRT) fields. The mean ± 95% confidence interval of gamma pass rates were calculated. RESULTS For 150 rectangular field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 90.11±0.65%, 97.65±0.31%, and 99.16±0.19%, respectively. For the 100 IMRT field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 96.48±0.28%, 99.14±0.10%, and 99.63±0.06% respectively. For the 7-fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 97.06%, 99.10%, and 99.52%, respectively. For the 9-fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 98.16%, 99.61%, and 99.89%, respectively. CONCLUSIONS The feasibility of calculating dose distribution using a CNN with the TERMA three-dimensional distribution and ED distribution was established. The dosimetric results were comparable to those of the full MC. The accuracy and speed of the proposed approach make it a potential solution for full MC in radiotherapy. This method may be used as an acceleration engine for the dose algorithm and shows great potential for cases where fast dose calculations are needed. This article is protected by copyright. All rights reserved.
H pylori eradication and strict diet regimen`s synergetic effect on glycemic state in type two diabetics on insulin therapy

were randomized into 4 equal groups; Group 1 (M) received their routine medical treatment only, group 2 (MD) received medical treatment and individualized planned dietary regimen, group 3 (MH) received medical and anti-H. pylori treatment while group 4 (MHD) received medical, anti-H. pylori treatment and dietary regimen. Initial baseline evaluation included: diabetes panel, lipid profile , liver enzymes, renal functions and BMI which is repeated at the end of 6 months follow up. The results showed a significant decrease of mean FBG, PPBG and HbA1c levels at the end of 6 months intervention from baseline levels in the 4 groups, however the mean difference increased from group 1 to group 4. Mean BMI and Uric acid significantly decreased in all groups except group-M. Cholesterol and triglycerides significantly decreased in all groups. Non-statistically significant changes regarding ALT, AST and serum creatinine were observed in all groups. Based on these results it is recommended to use the combined regimen of medical treatment together with anti H. pylori and diet control for better glycemic and lipid profile control in patients with type II diabetes who depends on insulin therapy.
Performance analysis of wavelength conversion in SOI waveguides

In this paper the parametric amplification and wavelength conversion in a 1cm long silicon-on-insulator (SOI) waveguide has been investigated numerically using coupled wave equations. The wavelength conversion efficiency is analyzed and performance is presented with effect of pump/signal power and wavelength. The influence factors of noise figure are also considered.
A novel tri-state boost converter with fast dynamics

A challenging problem in the design of boost converters operating in continuous-conduction mode is posed by the dynamically shifting right-half-plane (RHP) zero in the converter's small-signal control-to-output transfer function. The paper proposes a novel tri-state boost converter without such a zero in the transfer function. The additional degree of freedom introduced in the converter in the form of a freewheeling interval has been exploited through an easy control technique to achieve this elimination. The absence of the RHP zero allows the control scheme to achieve larger bandwidth under closed-loop conditions, resulting in fast response. Analytical, simulation and experimental results of the tri-state boost converter have been presented and compared with those of the classical boost converter both under open-loop and under closed-loop operating conditions. The results clearly demonstrate the superior dynamic performance of the proposed converter. The proposed converter can be used in applications wherever fast-response boost action is needed.
ENDOPHYTIC BACTERIA FOR BIOLOGICAL CONTROL OF SUGARCANE SMUT DISEASE AND ITS EFFECT ON SOME PLANT GROWTH PARAMETERS

In the is study, the endophytic bacteria were isolated from sugarcane stalks and tested for its in vitro antagonistic activity against Sporisorium scitamineum, the causal pathogen of sugarcane smut disease. Also some physiological characteristics such as its ability to produce some secondary metabolites i.e. PRproteins and growth-promoters were studied in both in vitro and in vivo. Only, 62 isolates among the total 240 isolates were found to have a bioactivity against the pathogen in vitro. Six isolates from the bioactive isolates were selected for its high antagonistic activity to study its potential to produce the enzymes (chitinase, and β 1,3 glucanase,) and growth-promoters (indole acetic acid (IAA) as well as siderophores and salicylic acid (SA)) in vitro. The selected isolates showed different degrees of antagonistic activity. The selected isolates were identified using the sequencing of the 16S ribosomal RNA (rRna) gene and subjected to mega blast-n in NCBI and proved to be Enterobacter sp. ( LUX 27), Kosakonia radicictans (LUX41), Kosakonia radicictans(BAN 33), Klabsiella oxytoca (BAN 39), Bacillus subtilis subsp. Inaquosorum(Q17), Pantoea sp. (SOH 29). In vitro studies proved that, all selected endophytic bacteria produced Nitrogenase and indole acetic acid (IAA) while, Kosakonia radicictans, Klabsiella oxytoca and Bacillus subtilis subsp. Inaquosorum produced salicylic acid. Not one of the selected isolated prove to produce Siderophores. Pantoea sp. , Kosakonia radicictans and Klabsiella oxytoca produced chitinase. While, the isolates Kosakonia radicictans and Bacillus subtilis subsp. Inaquosorum were the most effective in producing β 1,3glucanase. The bio-control activity of selected isolates was also studied in vivo under greenhouse conditions on sugarcane infected plants. All the tested isolates were completely reduced the disease compared to the infected control. All the selected endophytic bacteria increased Peroxidase, Chitinase and β-1, 3 glucanaseactivities in the treated plants while the isolates Kosakonia radicictans and Enterobacter sp. didn’t produce Polyphenol-oxidase in treated infected plants. The treated sugarcane plants showed a range of positive increase in Leaf Area, Stalk length, Stalk Diameter, number of tillers and total chlorophyll compared to the untreated healthy and infected control. The present study show the efficiency of using endophytic bacteria as a bio-control system against sugarcane smut.
Conjugated polymer film fabricated by in situ electro-chemical polymerization as effective interface modification material in organic solar cells

Electro-chemical polymerization has identified to be a facile and useful method for the preparation of electroactive and conducting polymer films, and capability of precise control of the film properties. With this strategy, a large number of conjugated polymers were developed as specific interface modification layers to meet the requirements from various electronic equipment. Here we report the synthesis of conjugated polymer film prepared by in situ electro-chemical polymerization as effective interface modification layer between ITO and ZnO in organic solar cells. By optimizing the polymerization potential to control accurately the thickness of conjugated polymer layer, the resulting devices show significantly enhancement of short-circuit current, with an optimized power conversion efficiency (PCE) of 14.9%. As a result, the reasonable interface modification strategy via electro-chemical polymerization seems to be able to bring a new design perspective for the development of high-performance organic solar cells.
Approach to trace and locate long-term voltage instability risk in power system planning

With the integration of a quadratic optimisation method into one single Monte Carlo simulation, the system-wide and local risk index functions are developed to quantify the relations between the system-wide and local voltage stability risk and load levels. Each defined risk index function is a function of load level under various possible pre-contingency and contingency states and load uncertainty. Based on the proposed risk index functions, a method of tracing and locating long-term voltage instability risk in power system planning is presented. The methodology can trace the overall system-wide voltage instability risk in a planning timeframe and locate weak branches and buses. The effectiveness of the proposed method is demonstrated using the IEEE 14-bus system and an actual utility system with 171 buses.
Impacts of Electrification on CO2 Emission Reduction Potentials in the G7 Countries

Electrification is increasingly being considered to be a competitive option for CO2 mitigation. Because international cooperation is imperative for an effective climate change policy, it is important to obtain technology perspectives for countries with large emissions. This study attempted to quantify the CO2 emission reduction potential of two types of electric technologies: heat pump technologies (HPs) and electric vehicles (EVs). The reduction potentials were analyzed by considering an ideal situation where all appliances are substituted, and the CO2 emission was determined on the basis of the carbon intensity of electricity in 2006. The results showed that HPs can potentially reduce approximately 52% of the emissions from residential space heating and water heating in the G7 countries and that simply improving the efficiency of combustion-type appliances would potentially reduce emissions by approximately 28%. Moreover, EVs can potentially reduce approximately 48% of the emissions from passenger cars, and simply improving the fuel efficiency of conventional vehicles would potentially reduce emissions by approximately 40%. The reduction potential of these two technologies is approximately 10% of the total G7 emissions. In the case of countries that already generate low-carbon electricity, electrification can contribute to a further decrease in CO2 emissions.
A NOVEL APPROACH TO DETECT IOT MALWARE BY SYSTEM CALLS AND LONG SHORT-TERM MEMORY MODEL

As the Internet of Things (IoT) devices become voguish, malware detection on IoT devices is crucial today. In this paper, a novel approach to detect IoT malware based on dynamic analysis and deep learning is proposed. Our method combines an IoT-sandbox to extract system call sequences that are considered as sentences in natural language, then two Long Short-Term Memory (LSTM) model are used to classify. In our approach, a program is determined whether malware or benign by two representative values which are the results of LSTM models. Experiment results show that our proposed method outperforms other based-line machine learning models using similar system call feature in terms of accuracy, F1-Weight and the length of system call sequence. Our method uses quite short system call sequence of 150, but the highest accuracy 98.37 per cent and F1-Weight achieves 98.38 per cent. Therefore, the method can be used in early IoT malware detection solutions.
AUTOMATA THEORY: A GAMIFICATION APPROACH

Automata Theory, a theoretical branch of computer science, helps “to understand how machines compute functions and solve problems. An automaton is any machine that uses a specific, repeatable process to convert information into different forms. Gamification is an example of an automaton. The goal of gamification is the implementation of game mechanics in real-world contexts for non-gaming purposes, to foster human motivation and performance in regard to a given activity. This can be used for the automation” of mundane tasks. Customization and tailoring of interests, “content curation, life management, using all of these options, information can be gathered quickly and delivered effectively to” users. Based on this, a theoretical understanding of automata theory in gamification application was presented. The game mechanics configuration, when deliberately varied, in regards to the effect of fulfilling their basic functionalities can potentially improve user engagement, productivity, flow, learning, crowdsourcing, recruitment and evaluation, exercise, traffic violation, and so on.
Learning Representations for Zero-Shot Image Generation without Text

DALL · E has shown an impressive ability to generate novel—signiﬁcantly and systematically different from the training distribution—yet realistic images. This is possible because it utilizes the dataset of text-image pairs where the text provides the source of compositionality. Following this result, an important extending question is whether this compositionality can still be achieved even without conditioning on text. In this paper, we propose a simple but novel slot-based autoencod-ing architecture, called SLATE 1 , that achieves this text-free DALL · E by learning compositional slot-based representations purely from images, an ability lacking in DALL · E. Unlike existing object-centric representation models that decode pixels independently for each slot and each pixel location and compose them via mixture-based alpha composition, we propose to use the Image GPT decoder conditioned on the slots for a more ﬂexible generation by capturing complex interaction among the pixels and the slots. In experiments, we show that this simple architecture achieves zero-shot generation of novel images without text and better quality in generation than the models based on mixture decoders.
Numerical-Experimental Study and Solutions to Reduce the Dwell-Time Threshold for Fusion-Free Consecutive Injections in a Multijet Solenoid-Type CR System

In ‘Multijet’ Common Rail (C.R.) diesel injection systems, when two consecutive injection current-pulses are approached to each other, the fusion of the two injections can occur. This causes undesired excessive amount of injected fuel, which leads to worsening of particulate emissions and fuel consumption. In order to avoid such a phenomenon, lower limits to the values of dwell time are introduced in the control unit maps, by means of a conservatively overestimated threshold, limiting the flexible management of multiple injections and C.R. system capability to perform a larger number of injection shots. The reason of the injection fusion is mainly due to the time delay between the electrical signal to the solenoid and the needle lift at both valve opening and closure. In particular, the dwell-time range inside of which injection fusion occurs was shown to decrease by reducing the nozzle closure delay. Experimental tests were carried out on a high-performance Moehwald-Bosch MEP2000/CA4000 test bench for determining the functional dependence of nozzle closure and opening delays on solenoid energizing time and nominal rail pressure. Besides, a mathematical relation between the solenoid energizing time and the injection time interval was determined. A Multijet C.R. injection system mathematical model, that was previously developed, including thermodynamics of liquids, fluid dynamics, subsystem mechanics, and electromagnetism equations, was applied to better understand the cause and effect relationships for nozzle opening and closure delays. In particular, numerical results on the time histories of delivery- and control-chamber pressures, pilot- and needle-valve lifts, mass flow rates through Z and A holes, were obtained and analyzed in order to highlight the dependence of nozzle opening and closure delays on electro-injector internal geometric features and on the needle dynamics. For all the considered operating conditions, the model predictions were compared to the experimental injection flow-rate patterns and to the pressure data taken at the injector inlet, for assessment. The nozzle closure delay was shown to strongly depend on the needle dynamics. Parametric tests were carried out with the numerical code by changing needle and control plunger mass, needle spring preload and stiffness, maximum needle stroke, in order to identify configurations useful for minimizing the nozzle closure delay. On the basis of the indications derived from these numerical tests, a modified version of the commercial electro-injector was realized so as to achieve effectively reduced nozzle closure delays and very close sequential injections without any fusion between them.Copyright © 2006 by ASME
A Survey of Hedge and Safe Havens Assets against G-7 Stock Markets before and during the COVID-19 Pandemic

IZA DP No. 14888 NOVEMBER 2021 A Survey of Hedge and Safe Havens Assets against G-7 Stock Markets before and during the COVID-19 Pandemic We propose a new Sharpe ratio index obtained from return and volatility spillover indices to individual assets from the whole financial system. We use our new approach to shed light on a new perspective on a hot topic examining the safe-haven assets after Covid-19. To do that, we compare both hedge and safe-haven properties of gold, Bitcoin, and crude oil against G-7 stock markets by using daily return and volatility data from September 2013 to October 2021. Our empirical findings show that the hedging effectiveness of gold, Bitcoin, and crude oil varies overtime before the Covid-19 pandemic. Furthermore, according to our analysis results, only Bitcoin acts as a safe haven against G-7 stock markets during most of the Covid-19 pandemic time. JEL Classification: C58, G10
THE EFFECTIVENESS OF TRANSCRANIAL DIRECT- CURRENT STIMULATION (TDCS) ON TEMPTATION CONTROL AND THE CRAVING FOR FOOD IN WOMEN WITH OVERWEIGHT BETWEEN THE AGES OF 30 TO 50 YEARS OLD IN TEHRAN

The aim of this study was to investigate the effectiveness of transcranial direct-current stimulation (tDCS) on the temptation and craving for food in women with overweight aged 30 to 50 years in Tehran. The present study was a trial with a pre-test and post-test design with a control group. The statistical population of the study included all women aged 30 to 50 years who were overweight in Tehran. By targeted sampling, 34 women overweight volunteered to be referred to Mehregan Neurology and Rehabilitation Center for Psychology and Rehabilitation in the winter of 2020. From this sample, 17 people were randomly assigned to the experimental group and 17 to the control group and both experimental groups were first tested. Then, transcranial direct-current stimulation sessions of the brain were performed by tDCS method on the experimental group. After the intervention, both groups were tested. The tools used in this study were the Food Craving Questionnaire-Trait (FCQ-T). The data were analyzed using SPSS-24 software and statistical method of analysis of covariance. The results show that tDCS has reduced the temptation and craving for food in overweight women in their 30 th and 50 th . Due to the effectiveness of this method in reducing the temptation and craving for food, this method can be used to reduce the craving of obese people and drug usage.