Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

In recent years, automatic plant disease recognition has gained huge interest in academia and industry. It is considered one of the promising technologies in precision agriculture. With the advancement of deep neural networks (DNNs), it is possible to develop various solutions for plant disease recognition. This paper analyzes the feasibility of using CPU-based desktop computers and GPU-based cloud-hosted services as back-end systems to develop tomato leaf disease classification models. The paper conducts a comprehensive analysis of state-of-the-art DNN architectures proposed for image classification. For each DNNs, various performance indices are measured. The attributes of these indices and their combinations are analyzed and discussed. The results show that EfficientNetBO and MobileNetV2 will provide the best results under most of the circumstances compared to other DNNs considered. In comparison with CPU-based systems, GPU-based systems perform better in almost every analysis performed in this study. The experiments conducted in this paper will help researchers and practitioners to select appropriate DNN architectures that better fit their resource constraints for practical deployment and applications.
Comparative Analysis between the performance of Cochlear Implant and Hearing Aid

Hearing plays a very important role in communication and development of an individual. Hearing impairment or hearing loss is the disability to listen the sounds clearly in normal conditions. Hearing impairment is the most common disorder prevailing in the world. Around 360 million of world's population is hard of listening under normal conditions. This can be caused due to many reasons which may include congenital factors, a prolonged disease, traumatic accident, or increasing noise pollution etc. Hearing loss can cause many learning problems, poor social interaction due to effect on communication. This can cause poor self concept in an individual. To cope up with this problem we have two solutions out there as cochlear implant and hearing aids. To provide the patients with the best solutions, in this study, the performance of cochlear implant and hearing aid are compared on the basis of a strategy called continuous interleaved sampling. For comparison between the performances of two solutions two main techniques are investigated in this study, MFCC and DTW. MFCC and DTW are used for feature detection and speech pattern matching between two signals respectively.
Perspectives of reactive ion etching of silicate glasses for optical microsystems

Abstract. We provide a review of the latest research findings as well as the future potential of plasma-based etching technology for the fabrication of micro-optical components and systems. Reactive ion etching (RIE) in combination with lithographic patterning is a well-established technology in the field of micro- and nanofabrication. Nevertheless, practical implementation, especially for plasma-based patterning of complex optical materials such as alumino-silicate glasses or glass-ceramics, is still largely based on technological experience rather than established models. Such models require an in-depth understanding of the underlying chemical and physical processes within the plasma and at the glass–plasma/mask–plasma interfaces. We therefore present results that should pave the way for a better understanding of processes and thus for the extension of RIE processes toward innovative three-dimensional (3D) patterning as well as for the processing of chemically and structurally inhomogeneous silicate-based substrates. To this end, we present and discuss the results of a variety of microstructuring strategies for different application areas with a focus on micro-optics. We consider the requirements for refractive and diffractive micro-optical systems and highlight potentials for 3D dry chemical etching by selective tailoring of the material structure. The results thus provide first steps toward a knowledge-based approach to RIE processing of universal dielectric glass materials for optical microsystems, which also has a significant impact on other microscale applications.
Linking Judging Moral to Acting Moral: A Relational Motivations Approach to Judging and Practicing Covid-19 Behaviors

Building on the Relational Motivation Theory, the present research argues that relational motivations (RM) underlie both the regulation and the moral judgment of socially (un)responsive Covid-19 behaviors (e.g., physical distancing) and links these two via moral identity. We hypothesize that different types of socially unresponsive behaviors are judged morally wrong through perceptions of RM violations and that a stronger concern for unity predicts the extent to which individuals self-report to perform socially responsive Covid-19 behaviors. Additionally, the role of moral identity as an individual-level moderator variable linking perceptions about RM violation to the practice of Covid-19 responsible behaviors is explored. The results support the predictions with data collected from participants living in Turkey and the USA. In both cultures, socially unresponsive Covid-19 behaviors of others were judged morally wrong through RMs, plus individuals’ general concern for unity predicted their self-reported socially responsive Covid-19 behaviors. Additionally, for the sample from Turkey, results revealed that the general concern for unity was positively associated with self-reported tendencies to perform Covid-19 socially responsive behaviors only among individuals with a low or moderate moral identity, but not when moral identity was high.
A new method for on wafer noise measurement

A method for measuring the noise parameters of MESFETs and HEMTs is presented. It is based on the fact that three independent noise parameters are sufficient to fully describe the device noise performance. It is shown that two noise parameters, R/sub n/ and mod Y/sub OPT/ mod , can be directly obtained from the frequency variation of the noise figure F/sub 50/ corresponding to a 50 Omega generator impedance. By using a theoretical relation between the intrinsic noise sources as additional data, the F/sub 50/ measurement only can provide the four noise parameters. A good agreement with more conventional techniques is obtained. >
On-board battery charger for PHEV without high-voltage electrolytic capacitor

A new on-board charger for plug-in hybrid electric vehicles (PHEVs) is presented. It has a cascade structure of a high-frequency resonant converter with a variable structure according to the line voltage for electrical isolation and a boost converter for charge control and harmonic regulation of input current. This structure uses small link capacitors so that high-voltage electrolytic capacitors prohibited in automotive applications can be removed. The feasibility of the proposed charger has been verified with a 3 kW prototype.
A functional view reveals substantial predictability of pollinator-mediated selection

A predictive understanding of adaptation to changing environments hinges on a mechanistic understanding of the extent and causes of variation in natural selection. Estimating variation in selection is difficult due to the complex relationships between phenotypic traits and fitness, and the uncertainty associated with individual selection estimates. Plant-pollinator interactions provide ideal systems for understanding variation in selection and its predictability, because both the selective agents (pollinators) and the process linking phenotypes to fitness (pollination) are generally known. Through examples from the pollination literature, I discuss how explicit consideration of the functional mechanisms underlying trait-performance relationships can clarify the relationship between traits and fitness, and how variation in the ecological context that generates selection can help disentangle biologically important variation in selection from sampling variation. I then evaluate the predictability of variation in pollinator-mediated selection through a survey, reanalysis, and synthesis of results from the literature. The synthesis demonstrates that pollinator-mediated selection often varies substantially among trait functional groups, as well as in time and space. Covariance between patterns of selection and ecological variables provides additional support for the biological importance of observed selection, but the detection of such covariance depends on careful choice of relevant predictor variables as well as consideration of quantitative measurements and their meaning, an aspect often neglected in selection studies.
Artificial Neural Network–Based Heuristic Optimal Traffic Signal Timing

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level‐of‐service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level‐of‐service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of two‐phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth‐first search method (an enumeration method) and a direction‐search method that the authors developed. It was found that the OTSTM with the direction search resulted in “optimal” signal timings similar to the depth‐first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth‐first search cases—more than 10 times less. The study showed that once the artificial neural network is properly trained, heuristic optimal signal timing combined with artificial networks can be used as a decision‐support tool for dynamic signal control. This article demonstrates how OTSTM can quickly find an optimal signal‐timing solution for two‐phase traffic signals.
Formulation and Characterization of Nimodipine Nanoparticles for the Enhancement of solubility and dissolution rate

Nimodipine (NMD) is a dihydropyridine calcium channel blocker useful for the prevention and treatment of delayed ischemic effects. It belongs to class ? drugs, which is characterized by low solubility and high permeability. This research aimed to prepare Nimodipine nanoparticles (NMD NPs) for the enhancement of solubility and dissolution rate. The formulation of nanoparticles was done by the solvent anti-solvent technique using either magnetic stirrer or bath sonicator for maintaining the motion of the antisolvent phase. Five different stabilizers were used to prepare NMD NPs( TPGS, Soluplus®, HPMC E5, PVP K90, and poloxamer 407). The selected formula F2, in which Soluplus has been utilized as a stabilizer, has a particle size (77 nm) and polydispersity index (PDI) (0.016). The formulas with the smallest particle size were freeze dried with the addition of 1 % w/w mannitol as cryoprotectant. The saturation solubility of NMD in the prepared nanoparticles was increased twenty four-folds, and the complete dissolution was achieved at 90 minutes compared with pure NMD, which reaches only 6%. The formation of hydrogen bonding between NMD and the polymer or the cryoprotectant, as confirmed by the FTIR study. In conclusion, the preparation of NMD as polymeric nanoparticles is a useful technique for enhancing the solubility and dissolution rate.
The Perception of the Market and Operational Area of Business by Service Sector and Tourism Companies in terms of CSR implementation

Our research examined the attitudes of owners and managers of small and medium-sized enterprises in the V4 countries, who behave responsibly - they use corporate social responsibility in their managerial practice on issues focused on the market and operational area of business. The paper is based on a questionnaire survey with data collection from September 2019 to January 2020. Statistical methods of Pearson's chi-square and z-score were used to assess the hypotheses. The results proved that there are significant differences in companies in services and tourism in the context of implementing the CSR concept. Entrepreneurs who implement social responsibility with a significant positive difference agree that a company places great emphasis on the innovation of its products and services. Researchers found that the sector has an impact on the perception of decreasing customers' requests on specific products/services. We found that corporate social responsibility and its implementation in corporate practice positively affects the relationship of V4's small and medium enterprises in the services and tourism sector to service production innovation issues, which helps increase business performance and decline customer complaints.
A Review of Protein Structure Prediction using Deep Learning

Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction. We discuss various deep learning approaches used to predict protein structure and future achievements and challenges. This review is expected to help provide perspectives on problems in biochemistry that can take advantage of the deep learning approach. Some of the unanswered challenges with current computational approaches are predicting the location and precision orientation of protein side chains, predicting protein interactions with DNA, RNA and other small molecules and predicting the structure of protein complexes.
Perception of firefighters’ turnout ensemble and level of satisfaction by body movement

This study investigates firefighters’ perception of the protective turnout ensemble, more specifically, the level of satisfaction for a set of commonly performed body movements. Five hundred sixteen male and female firefighters completed our online survey. The current study identifies (1) the level of satisfaction with the fit of the turnout ensemble, (2) perceived reasons for discomfort and movement restriction in areas with low satisfaction, and (3) gender-specific fit issues. Female firefighters showed significantly lower satisfaction with the fit and functionality of the turnout ensemble in multiple areas than males. Common fit issues such as a stiff or oversized neckline and inappropriate location of knee pads, tightness in the lower pant leg, and inaccessibility of pockets that affect male and female firefighters are addressed. This study offers important implications to industry and academia involved in the study of protective clothing to increase the mobility, safety, and comfort of today's firefighters.
Lipid-anthropometric index optimization for insulin sensitivity estimation

Insulin sensitivity (IS) is the ability of cells to react due to insulin´s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. IR had been related to other metabolic disorders as metabolic syndrome (MS), obesity, dyslipidemia and diabetes. IS can be determined using direct or indirect methods. The indirect methods are less accurate and invasive than direct and they use glucose and insulin values from oral glucose tolerance test (OGTT). The accuracy is established by comparison using spearman rank correlation coefficient between direct and indirect method. This paper aims to propose a lipid-anthropometric index which offers acceptable correlation to insulin sensitivity index for different populations (DB1=MS subjects, DB2=sedentary without MS subjects and DB3=marathoners subjects) without to use OGTT glucose and insulin values. The proposed method is parametrically optimized through a random cross-validation, using the spearman rank correlation as comparator with CAUMO method. CAUMO is an indirect method designed from a simplification of the minimal model intravenous glucose tolerance test direct method (MINMOD-IGTT) and with acceptable correlation (0.89). The results show that the proposed optimized method got a better correlation with CAUMO in all populations compared to non-optimized. On the other hand, it was observed that the optimized method has better correlation with CAUMO in DB2 and DB3 groups than HOMA-IR method, which is the most widely used for diagnosing insulin resistance. The optimized propose method could detect incipient insulin resistance, when classify as insulin resistant subjects that present impaired postprandial insulin and glucose values.
The Effect of Age and Intrinsic Aerobic Exercise Capacity on the Expression of Inflammation and Remodeling Markers in Rat Achilles Tendons

Old age, adiposity, and metabolic disorders are known as risk factors for chronic tendinopathy, which is a common problem in both athletes and the general population. However, the importance of these influencing factors has not yet been well understood. This study investigated alterations in gene expression and histology of Achilles tendons of young (10 weeks) and old (100 weeks) rats bred for low (low capacity runners, LCR) and high (high capacity runners, HCR) intrinsic aerobic exercise capacity. In this rat model, LCR displayed a phenotype of reduced exercise capacity, higher body weight, and metabolic dysfunctions compared to HCR. We hypothesized that the risk factors for tendinopathy in old LCR could lead to more pronounced impairments in Achilles tendon tissue. In quantitative real-time PCR (qPCR), age-related downregulation of tenocyte markers e.g., tenomodulin, genes related to matrix modeling and remodeling (e.g., collagens, elastin, biglycan, fibronectin, tenascin C) as well as transforming growth factor beta 3 (Tgfb3) have been detected. Inflammation marker cyclooxygenase 2 (Cox2) was downregulated in old rats, while microsomal prostaglandin E synthase 2 (Ptges2) was upregulated in old HCR and old LCR. In all groups, interleukin 6 (Il6), interleukin 1 beta (Il1b), and tumor necrosis factor alpha (Tnfa) showed no significant alteration. In histological evaluation, tendons of old rats had fewer and more elongated tenocyte nuclei than young rats. Even though a higher content of glycosaminoglycans, a sign of degeneration, was found in old HCR and LCR, no further signs of tendinopathy were detectable in tendons of old rats by histological evaluation. Low intrinsic aerobic exercise capacity and the associated phenotype did not show significant effects on gene expression and tendon histology. These findings indicate that aging seems to play a prominent role in molecular and structural alterations of Achilles tendon tissue and suggests that other risk factors associated with intrinsic aerobic exercise capacity are less influential in this rat model.
A model exploration of carrier and movement transmission as potential explanatory causes for the persistence of foot-and-mouth disease in endemic regions.

Foot-and-Mouth Disease (FMD) is a virulent and economically important disease of livestock, still endemic in many areas of Asia and Sub-Saharan Africa. Transmission from persistently infected livestock, also known as carriers, has been proposed as a mechanism to support the persistence of FMD in endemic regions. However whether carrier livestock can infect susceptible animals is controversial; recovered virus is infectious and there are claims of field transmission, but it remains undemonstrated experimentally. Alternate hypotheses for persistence include the movement of livestock within and between regions, and fomite contamination of the environment. Using a stochastic compartmental Ordinary Differential Equation (ODE) model, we investigate the minimum rates of carrier transmission necessary to contribute to the maintenance of FMD in a region, and compare this to the alternate mechanism of persistence through cattle shipments. We find that carrier transmission can theoretically support persistence even at transmission rates much lower than the highest realistic rates previously proposed, and that the parameters with the most effect on the feasibility of carrier-mediated persistence are the average duration of both the carrier phase and natural immunity. However, shipment-mediated persistence remains a viable alternate mechanism for persistence without carrier transmission. This article is protected by copyright. All rights reserved.
A low noise, low power DC/DC converter for cell phone power applications

The goal is to design a low noise, low power DC/DC converter and controller IC to power an Analog Devices' digital signal processor that will run at 1.2 V from a 3.6 V battery and has a rated maximum current draw of 300mA. A switch-mode DC/DC converter is proposed as a possible way of efficiently stepping down the battery voltage to 1.2 V. The control chip is designed using Taiwan Semiconductor Manufacturing Corporation's 0.25 pm CMOS process. Component selection issues for the power stage and input and output filters are discussed. Dynamic modeling of the DC/DC converter and feedback control design are also presented. A 1.5 MHz, synchronously rectified, current-mode controlled step-down converter was designed and simulated using Analog Devices' spice simulator ADICE. Thesis Supervisor: David J. Perreault Title: Assistant Professor Thesis Supervisor: Thomas J. Barber Jr. Title: Design Engineer, Analog Devices
Dynamics of Clustered Nuclear Engines

A short review is presented of investigations concerning clustering of multiple identical solid core nuclear engines in a polygon arrangment, for space propulsion. A unique aspect of nuclear engine clustering is the coupling between reactors due to the exchange of neutrons which propagate transients within the system. A method has been developed at Douglas for evaluating the reactivity coupling effect for various reactor types and cluster arrangements. The results are in good agreement with available experimental data. An analog model of a clustered nuclear propulsion system has been used for simulating the operating cycle, with emphasis on the inherent control offered by the presence of hydrogen in the reactor. Coupling effects during transients, including start-up, abnormal reactivity perturbations, and an engine-out event are the major points of interest.