Miracle-dz/UniFAR-FTUs huggingface dataset:
Some New Constructions of Centralized Coded Caching Scheme

: As an effective technique to reduce network congestion during peak traffic times, coded caching is being widely studied in wireless network. The coded caching scheme was first proposed by Maddah-Ali and Niesen in 2014. In the placement phase of off-peak times, each file is divided into F equal packets,and each user caches some packets of each file elaborately from the server. In the delivery phase of peak times, each user first requires a file from the server. And then according to each user’s cache, the server sends a coded signal with size at most R to users so that various user demands are satisfied.In order to study this problem better, the placement delivery array was defined by Yan Q et al. in 2016. And they found that the problem of designing a coded caching scheme can be converted into the problem of constructing an appropriate PDA.In this paper, we will construct three kinds of new PDAs based on t -design of k = t + 1 and the incidence matrix of a resolvable transversal design and the partitioning of the incidence matrix. Based on these PDAs, we present several new constructions of coded caching schemes, which have obvious advantages in packet number. The results of this paper enrich the constructions of coded caching schemes of low subpacketization level.
Tomato Defense to Oldium neolycopersici: Dominant OI Genes Confer Isolate-Dependent Resistance Via a Different Mechanism Than Recessive oI-2

Tomato powdery mildew caused by Oidium neolycopersici has become a globally important disease of tomato (Lycopersicon esculentum). To study the defense responses of tomato triggered by tomato powdery mildew, we first mapped a set of resistance genes to O. neolycopersici from related Lycopersicon species. An integrated genetic map was generated showing that all the dominant resistance genes (Ol-1, Ol-3, Ol-4, Ol-5, and Ol-6) are located on tomato chromosome 6 and are organized in three genetic loci. Then, near-isogenic lines (NIL) were produced that contain the different dominant Ol genes in a L. esculentum genetic background. These NIL were used in disease tests with local isolates of O. neolycopersici in different geographic locations, demonstrating that the resistance conferred by different Ol genes was isolate-dependent and, hence, may be race-specific. In addition, the resistance mechanism was analyzed histologically. The mechanism of resistance conferred by the dominant Ol genes was associated with hypersensitive response, which varies in details depending on the Ol-gene in the NIL, while the mechanism of resistance governed by the recessive gene ol-2 on tomato chromosome 4 was associated with papillae formation.
A Methodology for Unsupervised Feature Learning in Hyperspectral Imagery Using Deep Belief Network

Deep learning approaches have received major interest in the field of remote sensing. Hyperspectral imaging has rich data that are distributed in multi-dimensions. It is challenging to apply deep learning algorithms due to the limited amount of labelled data. So, unsupervised feature extraction approaches are used to overcome this limitation. In this study, we propose an unsupervised feature learning approach using deep belief network (DBN). In the proposed framework, the input hyperspectral image is segmented using entropy rate superpixel segmentation and filtered by domain transform recursive filter which extracts spatial and spectral information effectively. Then the features are learned by improved DBN. In the traditional methods, DBN is stacked with restricted Boltzmann machine (RBM) which is suitable for only binary value data. In the proposed framework, we used Gaussian–Bernoulli RBM which was constructed for real value data such as images. The experiments were carried out using Pavia University dataset. The results show that the proposed network has good performance in terms of classification accuracy and computation time.
A review on thermal analysis of hybrid greenhouse solar dryer (HGSD)

The objective of the paper is to present the modifications done in hybrid greenhouse solar dryers to make them more efficient. As hybrid dryers were developed to overcome the various limitations of other types of solar dryers. Due to modifications in dryers, the effect on various heat transfer parameters like heat transfer coefficient, drying time, drying efficiency, etc. is studied and also encapsulated in the paper. It is found that in most of the hybrid dryers, the maximum temperature of the drying chamber is around 65-80 ° C. This shows the suitability of a hybrid dryer for drying high moisture crops. Also, the drying time for most of the crops is around 2-3 days, which shows the faster moisture removal rate inside the hybrid dryers. According to the literature studied, the maximum drying efficiency of a hybrid dryer is reported by about 35%. The paper also encapsulates the various relations/equations used by different researchers to carry out the thermal modeling and heat transfer analysis of greenhouse dryers.
The relationship between self-forgiveness and human flourishing: Inferring the underlying psychological mechanisms

The study aims to examine the role of Self-Forgiveness in shaping the Human Flourishing of the adults. Two hundred fourteen participants (18 to 30 years) comprising 100 males (Mean Age = 22.15(1.61)) and 114 females (Mean Age = 22.00(1.95)) were chosen for the study. Self-forgiveness (Mudgal & Tiwari, 2017a) and Mental Health Continuum-Short Form (Keyes, 2005) were used as the tools. Self-forgiveness comprises Realization & Reparation, Guilt, Attribution and overall self-forgiveness (sum of the first three) while Human Flourishing consists of Hedonic and Eudaimonic (Social plus Psychological) Well-Being. The aggregate of Hedonic and Eudaimonic is overall Human Flourishing. The findings suggested no gender differences in Self-Forgiveness of the participants. Conversely, gender differences were observed in all the dimensions of Human Flourishing in favour of males. Irrespective of gender, Realization & Reparation was positively correlated with Hedonic, Social, Psychological and Eudaimonic Well-Being as well as Human Flourishing except for Guilt and Attribution that showed small positive or negative correlations. Irrespective of gender, Overall Self-Forgiveness correlated positively with all the dimensions of Flourishing. Gender and Realization & Reparation emerged as the significant predictors accounting for significant variance in all the dimensions of Flourishing while Guilt and Attribution did not. The findings suggested that remorse, easy acceptance of wrongdoing, repairing the relationship with self and others, ability to minimize negative emotions towards self, monitoring others’ positive behaviours and acknowledgements of valued and close relationships were the psychological mechanisms that may underlie the predictive strengths of self-forgiveness in regulating flourishing.
Long-term Assessment of Ozone Nonattainment Changes in South Korea Compared to US, and EU Ozone Guidelines

We conducted spatiotemporal assessments of ozone in South Korea from 1990–2020 to evaluate trends and compare changes in compliance based on South Korean, US, and EU standards. Observational data from nationwide air-quality monitoring stations were collected and converted to the maximum daily 8 hr ozone average (MDA8O3). Seasonal ozone variations displayed an overall increase across most of South Korea and a noticeably high rate of 0.86 ppbv/yr in Seoul, with an even higher rate 1.2 ppbv/yr for the fourth-highest MDA8O3. Recent air-quality regulations to reduce NOx emissions have been estimated to weaken NO titration effects, leading to higher ozone levels for VOC-limited urban areas in South Korea while decreasing ozone concentrations elsewhere. In recent years, nearly all monitoring stations have exceeded the South Korean MDA8O3 standard, leading to debate regarding the adequacy of current standards for monitoring changes in nonattainment. Comparison with EU and US standards showed that implementing these could significantly lower nonattainment events due to the easing of target threshold values by either percentile or concentration values. Relative distances in nonattainment percentages between South Korean and other standards indicated that the EU ozone guideline was most suitable for tracing recent ozone changes not apparent when using the South Korean or US standards.
Audio-Tactile Perception of Roughness

Auditory roughness is a perceptual attribute at the basis of phenomena such as consonance and dissonance in music. The psychophysical correlates of this attribute are often studied by combining two monochromatic tones slightly separated in frequency, leading to more or less rapid beatings. Interestingly, roughness is not limited to the auditory modality and it is possible to evoke the same kind of sensation through the tactile modality by using a vibrotactile actuator. Whether or not audio and tactile modalities share the same perceptual roughness properties is still an open question that may reveal common sensory processes between the two modalities. Here we investigate this question in 2 pairwise comparison experiments unveiling roughness curves in audio and tactile modalities. The results reveal similar roughness curves in both modalities, which suggests a common way of processing and perceiving beatings.
Cuckoo Optimization and Fuzzy Logic Classifier with an Enhanced Stacking Algorithm

— In the medical field accuracy plays an important role as it concerns with the life of an individual. Using machine learning techniques various research has been done to predict and classify diseases. Any have we are not able to detect which classifier produce best result. Also, there is a difference in the ratio of classes due to imbalanced data. We often face these types of data analysis in real life easily. In this research work the imbalanced data classification problem could be addressed in a systematic way by applying enhanced stacking with cuckoo optimization which helps to accelerates the performance of learning algorithms. Also, in this research work we propose a diagnosis system to classify heart disease using cuckoo search optimization to reduce feature with modified stacking also fuzzy logic system is used to predict the heart disease. We prove that ensemble classifier is more accurate than any single component classifiers. Initially the data collected are imbalanced. We balance the data and make it better prediction. To predict Ischemic Heart Disease in more accurate we propose a new ensemble heterogeneous classifier construction process with modified stacking to engender domain-specific configurations. We consider both the field ensemble learning and distributed data mining to create an efficient and enhanced version of the standard stacking ensemble learning techniques by using fuzzy logic and cuckoo optimization creating the meta classifier. The experimental result shows that enhanced stacking with cuckoo optimization outperforms the existing approaches.
Yield Gap, Risk Attitude, and Poverty Status of Aman Rice Producers in Climate-Vulnerable Coastal Areas of Bangladesh

Farmers of the coastal region in Bangladesh are vulnerable to various climatic hazards that affect agricultural productivity and livelihoods. This study investigated the yield gap, risk attitude, and poverty status of Aman rice farmers and also assessed the relationship among them in the climate-vulnerable southern coastal areas of Bangladesh. This study selected 125 Aman rice farmers using a simple random sampling technique to estimate three types of yield gaps. Risk attitude was calculated using the safety-first model, and the Foster–Greer–Thorbecke model was employed to estimate poverty status. Results revealed a significant amount of yield gaps in Aman rice production, while farmers had opportunities to increase their production through the optimal use and scientific management of inputs. The yield gaps are reflected in the farmers’ risk aversion attitude, with the majority of farmers being highly risk-averse. The study also revealed that half of the sampled farmers were poor, with a poverty gap of 15%. However, an increased number of non-poor was revealed due to a reduction both in yield gaps and farmers’ riskaversion attitudes. Therefore, the study suggests limiting the yield gap to manage farmers’ risk-aversion attitudes, which would also facilitate improving their poverty
Nature Inspired Optimization Algorithm for Loading Shedding in Power System

ARTICLE DETAILS ABSTRACT Article History Received: 30 May 2016 Accepted: 26 June 2016 Published Online: 10 July 2016 Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of nature inspired optimization algorithm known as particle swarm optimization (PSO) algorithm. The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive). The proposed algorithm is tested on IEEE 14 bus test system. The ability of the proposed method in terms of solution quality and convergence properties is compared with the Projected Augmented Lagrangian Method (PALM).
Relationship between OSDI, Tear Break Up Time, Schirmer Test and Pterygium Grades in Pterygium Patients

Cite this article: Gokmen O, Genc I. Relationship between OSDI, Tear Break Up Time, Schirmer Test and Pterygium Grades in Pterygium Patients. Ann Ophthalmol Vis Sci. 2020; 3(1): 1008. Onur Gokmen1*; Ipek Genc2 1Yuzuncu Yil University Faculty of Medicine, Department of Ophthalmology Van, Turkey. 2Health Sciences University Samsun Training and Research Hospital, Department of Ophthalmology, Samsun, Turkey.
Impact Assessment and Project Appraisal

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TRANSFORMERLESS SWITCHED-CAPACITOR INVERTER WITH REDUCED NUMBER OF SWITCHING COMPONENTS FOR PV APPLICATION Kasinath Jena

A novel transformerless switched-capacitor (SC) inverter with minimum part count and reduced voltage stresses is proposed in this paper. The suggested inverter generates a five-level waveform using nine switches, two capacitors, and a diode. Design description, working principle, and control techniques are described in detail. Some of the most remarkable properties of the proposed architecture are its ability to increase the input voltage while keeping zero leakage current and its self-balancing capabilities of the capacitors. A comparison to prior-art topologies demonstrates the suggested topology benefits. Lastly, a laboratory prototype has been built to check its feasibility and effectiveness. Keywords-Transformerless inverter, leakage current, component count per level.
IoT based Smart Irrigation System by using ESP32 and Adafruit IO

In India, agriculture has a prominent role and most of the farmers are using the traditional methods in farming. Due to the latest advancements in the technology, the agriculture sector is progressing to witness a drastic change in the near future. The Internet of Things (IoT) is one particular technological area which can help the farming community in numerous ways. The IoT can be utilized at various stages in agriculture starting from ploughing the field to selling the final agricultural produce in the market. This paper proposes a low cost method of smart irrigation by using ESP 32 microcontroller and Adafruit IO.
Media Exposure and Third-Person Perception: The Mediating Role of Social Realism and Proxy Efficacy

This study examines the influences of media exposure, social realism, and proxy efficacy on the perceived effects of news about the COVID-19 pandemic. Using data collected from a sample of 1,190 college students in China, this study found that exposure to news about the pandemic is positively related to social realism and proxy efficacy. Furthermore, social realism and proxy efficacy were significantly correlated with third-person perception (TPP). Most importantly, the results of the study show that social realism and proxy efficacy also mediated the relationship between exposure to COVID-19 news and TPP.
Phytochemical Constituent, HPLC Profiling and Antioxidant Activity of Passiflora incarnata and Arctium lappa Leaves Extracts

Chemical evaluated of some bio extracts as follows: Phytochemical screening, Total Polyphenols and Flavonoids content were determined in Passiflora incarnata and Arctium lappa leaves. Bio extracts were shown through color analysis containing phytochemical compounds such as (Terpens, Tannins, Flavonoids, Saponins, Alkaloids, Glycosides, Ph. Glycosides and Resins). Passiflora incarnata leaves have the highest of total polyphenols and flavonoids contents, which were 133.7mgGAE/g and 19.03mgQE/g, followed by Arctium lappa leaves, which were 128.5mgGAE/g and 16.05mgQE/g, respectively. Antioxidant activity of aqueous and methanolic extracts were determined using (FRAP and DPPH), The aqueous and methanolic extract of Passiflora incarnata leaves, have the highest of reducing power which were 198.79 and 210.51%, at the concentrations of 40mg/ml, respectively. Followed by aqueous and methanolic extract of and Arctium lappa leaves, which were 159.31 and 177.06%, at the same concentration. Also, when using (DPPH), of the same plant leaves, the IC50 value were 0.006 and 0.009 of aqueous extract, at the concentrations of 40mg/ml, respectively. While, the IC50 value were 0.004 and 0.007 of methanolic extract, at the same concentrations, respectively.
Determinants of Disagreement: Learning from Indian Inflation Expectations Survey of Households

This study explores the determinants of disagreement in households’ belief on future inflation. Households commonly show strong information rigidity as a consequence of stickiness in their information update (Mankiw and Reis, 2002, 2006). This paper contributes to the understanding of the formation of disagreement of the Indian households by investigating the effects of day to day purchasing experiences of the agents, the intensity of news about inflation in the media, and central bank transparency. We find the positive effects of their recent price experiences, media influence, and inflation targeting on lowering the disagreement. Female and Young people tend to exhibit stronger effects in comparison to their counterparts.
Efficacy of Some Probiotic Bacteria on Erwinia amylovora the Causal Agent of Fire Blight

Fire blight, caused by Erwinia amylovora bacteria, is one of the well-known plant diseases in the world including major diseases of the fruit trees, especially apples and pears. In recent years, due to chemical nature of the pesticides damaging human health and environment, the importance of biological control is considered as alternative measure to manage plant diseases. To investigate the possibility of biological control of the pathogens, healthy foliar samples of apple, pear, and quince trees were collected from different regions of Kerman Province, Iran, and then, biocontrol activity of antagonist agents was evaluated under laboratory conditions. On the basis of the results, some of the antagonists could decrease the symptoms of the disease by 14.28-79.59%. Laboratory evaluation included investigating the disease severity in immature pears, biocontrol activity of antagonist agents in the plate assay, inhibitory siderophore production, biofilm formation capacity, drought stress tolerance, and silver nanoparticle synthesis capability, which showed that these antagonist agents could potentially control the disease. Among the 9 well-performed antagonistic isolates from apples and pears, Vr87 isolate was selected. The studies were confirmed by amplifying part of 16S rDNA region of the isolate, using specific primers. By comparing the results on the NCBI website, the selected isolate was identified as Enterobacter sp. genus. Among all selected isolates as successful factors in controlling fire blight pathogen, including isolated isolates and isolates in the collection of Vali-e-Asr University, Bacillus subtilis strain BsVRU1 in the Vali-e-Asr University collection, with 73.5% inhibition, had higher inhibitory power than the other antagonist isolates against the pathogen of fire blight disease.
Real-time detection of Covid-19 positive persons using sniffer dog

Sniffer dogs are able to detect chemical particles and help for diagnosis of noninfectious and infectious diseases, such as cancer, malaria or viral infections. With the global COVID-19 pandemics, there is an urgent need to have a real-time and rapid screening of infected individuals. A dog from the Marseille Fire Brigade was trained to detect SARS-CoV-2 positive individuals. Then, this dog was conducted in a facility for dependent elderly people in May and June 2021 to detect positive patients, who were simultaneously tested by the gold standard RT-PCR method. The detection ability of sniffer dogs was associated with 91.6% sensitivity and 97.8% specificity. Trained dogs are capable to identify COVID-19 cases and could so been used as a preliminary reliable tool for diagnosis at keys entry points, such as for example airports, seaports or districts in a city and hospitals.
Augmenting Data to Improve Incremental Japanese-English Sentence and Sentence-ﬁnal Verb

Final verb prediction has been shown to help expedite simultaneous machine translation when translating between languages with different word orders. Prediction allows a system to begin translating earlier because it has access to information that it otherwise would need to wait for to begin translating. Speciﬁcally, if the system is able to predict the ﬁnal verb in a SOV sentence, it allows the system to start translating much earlier. This thesis examines current prediction mechanisms in neural machine translation models to determine what factors improve predictions between SOV and SVO language. We ﬁrst train a neural machine translation model to establish how well it can predict the English verb corresponding to the ﬁnal Japanese verb. We then train new models, with modiﬁed data to see it’s impact on the model’s ability to predict the English verb. We found that the model predicts the English verbs more accurately as more of the Japanese Sentence is revealed. Shufﬂing the preverb context as well adding subsentences to training data both improved the ability of the model to predict the English verb as well.