Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape Preference for Trees Outside Forests along an Urban–Rural–Natural Gradient

In densely populated areas, essential sources of ecosystem services are represented by green infrastructure, which includes trees outside forests (TOF) that, regardless of their cover extension, are found on agricultural or urban land. This research aims to assess landscape preference for TOF along an urban-rural-natural gradient in relation to different levels of landscape heterogeneity. Analyses are based on the integration of a visual choice experiment (360 respondents) with a GIS-based landscape analysis at regional scale in a Mediterranean region in Central Italy. Main findings revealed that correlation between landscape preference and heterogeneity varies along the urban–rural–natural gradient and on the basis of the spatial configuration of the surrounding landscape. The additional value of TOF to landscape preference is closely and positively linked to the degree of landscape anthropization. Conversely, TOF contribution to landscape preference resulted negative in natural landscapes where they can be perceived as a disturbance of the wilderness. Considering the influence that landscape preference plays on cultural ecosystem services provisioning and, in turn, on decision making processes, our results can support landscape policy and planning in fostering or hampering TOF diffusion depending on the different territorial contexts. These findings endorse the importance of multi-functional approaches in future-oriented strategies, which should mediate between the human preference for TOF, their ecological role and the provision of other services.
Investigation of Biomechanical Characteristics of Orthopedic Implants for Tibial Plateau Fractures by Means of Deep Learning and Support Vector Machine Classification

An experimental comparative study of the biomechanical behavior of commonly used orthopedic implants for tibial plateau fractures was carried out. An artificial bone model Synbone1110 was used and a Schatzker V type tibial plateau fracture was created in vitro, then stabilized with three different implant types, classic L plate, Locking Plate System (PLS), and Hybrid External Fixator (HEF). The stiffness of the bone—implant assembly was assessed by means of mechanical testing using an automated testing machine. It was found that the classic L plate type internal implant has a significantly higher value of deformation then the other two implant types. In case of the other implant types, PLS had a better performance than HEF at low and medium values of the applied force. At high values of the applied forces, the difference between deformation values of the two types became gradually smaller. An Artificial Neural Network model was developed to predict the implant deformation as a function of the applied force and implant device type. To establish if a clear-cut distinction exists between mechanical performance of PLS and HEF, a Support Vector Machine classifier was employed. At high values of the applied force, the Support Vector Machine (SVM) classifier predicts that no statistically significant difference exists between the performance of PLS and HEF.
Combustion Performances of Spherical Al/AP Nanoenergetic Composites Produced by Solvent Evaporation and Crystallization

ABSTRACT In this study, aluminum/ammonium perchlorate (Al/AP) nanocomposites were prepared through a simple solvent evaporation and crystallization method to increase the interfacial contact area between the fuel and oxidizer, thus improving their energetic performances. This is an effective way of obtaining Al nanoparticles, which are desirable, while avoiding aggregation and the introduction of a non-component of solid propellants. The as-prepared nanocomposites were characterized and their exothermic behaviors and combustion performance were tested. Differential thermal analysis results show that compared to raw Al nanoparticles and the simply mixed samples, Al/AP nanocomposites release more heat. Compared to the combustion process of raw Al nanoparticles and the simply mixed samples, that of Al/AP nanocomposites need shorter combustion durations – the duration to maximum flame – and produce a bigger maximum flame. The better energetic performances are attributed to the self-supplying system and larger interfacial contact between the fuel and oxidizer, respectively. The energetic performances of Al/AP nanocomposites with different weight ratios of Al nanoparticles were also studied. Results showed that Al/AP nanocomposites with a 40% weight ratio of Al nanoparticles had the best combustion performance due to the interaction of the rapid accumulation of heat from the reaction between Al and oxygen and the increasing gas generation.
Correlations Between the Attitudes about Learning of After-School Club Students during School and the Teaching Quality of Elementary School Teachers

In this study, we investigated the correlations between the attitudes about learning of elementary students who attended after-school clubs and the teaching quality of their educators during school. Previously, scholars have focused on service quality and satisfaction from the point of view of the parents, while overlooking the attitudes of students and teachers. To investigate the correlation between student enthusiasm and teaching quality, we sent out a questionnaire survey, collected 343 valid responses, and tested the related data via regression analysis. From the teacher’s point of view, this study can be divided into five constructs: learning interest, self-regulated learning, completing homework, interaction with the environment, and preparation for examinations. We also examined the correlations between each construct and teaching quality. We found that learning interest, homework assignments, environment interaction, and preparation for examinations had a positive correlation with the teaching quality of the elementary level teachers. Lastly, based on our findings, we made recommendations on how to improve teaching quality and leaning in the classroom.
INTERMEDIATE UVEITIS AS THE FIRST PRESENTATION OF MULTIPLE SCLEROSIS

Intermediate uveitis is a chronic, relapsing disease of insidious onset in which according to the SUN Working Group, the vitreous is the primary site of inflammation as determined clinically. Intermediate uveitis associated with systemic disease has variable course. Multiple sclerosis is associated with intermediate uveitis. We report a case of multiple sclerosis were intermediate uveitis was the first presentation of the disease. A 33-year-old man with a 5 months history of decreased vision in his right eye. Best-corrected visual acuity was 0.6 on his right eye and 1.0 on his left eye. Intraocular pressure was 17 mmHg and 15 mmHg on the right and left respectively. He had no signs of ocular surface inflammation, cornea clear, AC without signs of inflammation, lens clear. Right eye 3+ vitreous cells and vitreous condensations left eye 1+vitreous cells. Right eye fundus showed hyperemia of optic nerve head, irregular reflexes in the macula, peripheral retina without signs of retinitis or vasculitis. Left eye fundus showed only optic nerve head slightly hyperaemic. OCT, Indocyanine Green Angiography and fluorescein angiography were performed. We performed the investigations for diagnosing uveitis (angiotensin converting enzyme, serology for Treponema pallidum, Borrelia burgdorferi, Mantoux, neurological investigation) and all turned out to be negative except of MRI Brain and Spine that revealed multiple T2 hyperintense lesions consistent with multiple sclerosis. Multiple sclerosis may present initially with an intermediate uveitis. Multiple sclerosis should be suspected in patients aged 20-50 even without any neurological symptoms, noting that intermediate uveitis may precede other symptoms of demyelination. Key words: intermediate uveitis, multiple sclerosis, vitritis
The uncertain case for human-driven extinctions prior to Homo sapiens

Abstract A growing body of literature proposes that our ancestors contributed to large mammal extinctions in Africa long before the appearance of Homo sapiens, with some arguing that premodern hominins (e.g., Homo erectus) triggered the demise of Africa's largest herbivores and the loss of carnivoran diversity. Though such arguments have been around for decades, they are now increasingly accepted by those concerned with biodiversity decline in the present-day, despite the near complete absence of critical discussion or debate. To facilitate that process, here we review ancient anthropogenic extinction hypotheses and critically examine the data underpinning them. Broadly speaking, we show that arguments made in favor of ancient anthropogenic extinctions are based on problematic data analysis and interpretation, and are substantially weakened when extinctions are considered in the context of long-term evolutionary, ecological, and environmental changes. Thus, at present, there is no compelling empirical evidence supporting a deep history of hominin impacts on Africa's faunal diversity.
SARS-CoV-2 Therapy: Old Drugs as New Interventions

An outburst of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become a grave threat to global health and the economy. As of May 13, 2020, a total of 42,81,838 cases have been confirmed, with over 2,92,376 deaths worldwide. In India, 75,048 cases have been reported to date with 2,440 deaths. Management of this new coronavirus (COVID19) has mainly focused on infection prevention, case detection, monitoring, and supportive care. As there is no vaccine or specific antiviral treatment for human SARS-CoV-2, therefore identifying the drug treatment options as soon as possible is critical for the response to the COVID19 outbreak. Pro-inflammatory cascade and cytokine storm play a key role in the pathogenesis of new coronavirus. A large number of therapeutic interventions such as antiviral, antimalarial, convalescent plasma therapy, BCG vaccine, mTOR inhibitors, Tissue Plasminogen Activator, Human monoclonal antibodies, Anti-parasitic agents, Immunoenhancers, Nutritional interventions, JAK-STAT signaling inhibitors, ACE2 receptor modulators, and Angiotensin II receptor blockers have been either tried or suggested for effective treatment of patients with SARS-CoV-2 disease. Hence, we recommend that all the above potential interventions must be implemented in terms of their safety and efficacy through proper clinical experiments to control the emerging SARS-CoV-2 disease.
Radiotherapy as a Cure for Covid-19 Pneumonia: Is this a Possibility?

The ongoing pandemic of COVID-19 caused by the Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) has affected the healthcare system worldwide to a great extent. Millions of people have succumbed to the disease which might vary from mild febrile illness to severe Acute Lung Injury ultimately resulting in ARDS (Acute Respiratory Distress Syndrome). The mechanism of lung pathology due to SARS CoV-2 infection though unclear, is thought to be due to an imbalance in the inflammatory cascade; leading to a condition called Chemokine Release Syndrome. Low dose Radiotherapy which was historically used to treat Pneumonia, can serve as an approach for this subset of patients; by virtue of its anti-inflammatory response. Thus, in the present time, where a perfect cure is yet to derived all possible interventions should be looked upon with a ray of hope.
Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.
Performance analysis of gate engineered dielectrically modulated TFET biosensors

ABSTRACT This paper presents, a short gate dual metal source electrode dielectrically modulated tunnel field-effect transistor (SDM SE DM-TFET) has been compared with conventional dual gate-source electrode dielectrically modulated tunnel field-effect transistor (DG SE DM-TFET) for the biosensing application. Hence, gate work function engineering is applied in our proposed device in which gate metal is split into two different work function segments as and . Additionally, a potential of −1.2 V is applied to the source electrode having a work function of . is the gate electrode work function at the source side and is the gate electrode work function at the drain side. The investigation is performed by varying the dielectric constant and charge density for choosing an optimised value for . The performance comparison has been done in terms of current, sensitivity and sensing speed. It can be verified from the simulated results that our proposed device exhibits superior performance as compared to the existing structures.
Spatiotemporal coverage analysis of moving regions

ABSTRACT Moving objects databases are specialized for processing objects that have attributes which change over time. A moving region is a powerful data type in this context, describing a region which changes shape and position over a specified time interval. This data type is, for example, a suitable model for natural phenomena like hurricanes, clouds or forest fires. Several operations exist to process and analyze moving regions in moving objects databases. One useful operation is to determine which areas are covered for which time span during the movement of moving regions to calculate, for example, which areas are affected by a hurricane for a certain period of time. This paper presents a set of operations that can be used to analyze the spatiotemporal coverage behavior of a moving region to determine which regions are covered for shorter or longer than a given time, or which parts are covered for a specified time interval. Furthermore, the coverage duration of a single point and the average coverage duration for a given region can be determined.
Small signal model and RF performance analysis of InAs/GaSb hetero-junction tunneling field effect transistor

This work presents a comprehensive study of small-signal parameters based on radio frequency (RF) data of a heterojunction tunnel field effect transistor (HJ-TFET). The TFET device shows excellent small-signal analog and RF performance. Numerical device simulations of the optimized HJ-TFET shows ION/IOFF ratio of ~6 orders of magnitude. It is found that gate voltage dependency of HJ-TFET on the intrinsic parameters obtained from the non-quasi static (NQS) model differ from the conventional MOSFET due to band to band tunneling mechanism and inversion layer formation in the device. Based on this mechanism, the high frequency analog and RF performance of HJ-TFET were investigated using TCAD and modeled using NQS model, which was verified upto 100 GHz. The modeling results showed excellent agreement with the simulated small-signal parameters at multiple drain voltages (Vd). f
Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

— Despite sustainable development, increase in use smartphones, drones, satellite and other information communication technologies for data collection and analyzing for decision marking. Crop losses due to insect pests and diseases are a major threat to farming communities globally. In the case of rice, up to 37% of economic losses are caused by insect pests and disease infestation. Timely and accurate disease and insect pest diagnosis and management can not only reduce crop losses. In the last decade, Information and communication technologies (ICTs) have been increasingly used for information sharing. With mobile internet services becoming available in even the furthest locations, ICT-based agricultural solutions are finding a foothold on the farms of poor smallholders. ICT-based tool that supports diagnosis of insect pests and diseases and enables farmers to make timely decisions for better pest management. To identify the rice diseases at any untimely phases is not yet explored. Early classify and detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increa se country’s economic growth. The main challenges is to minimize the impacts of attacks. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper we review different disease classification techniques that can be used for plant leaf disease detection. Also we describe the data collection by information communications technology for rice leaf, different disease classification approaches that can be used for rice diseases detection. Thirdly we suggested the framework for convolutional neural network in Agricultural sector for detection and identification for innovation technology in agriculture.
Thermal analysis of a Gramme‐ring‐winding high‐speed permanent‐magnet motor for pulsed alternator using CFD

Due to the merit of very-short end turns, Gramme-ring windings are widely used in high-speed permanent-magnet motors (HSPMMs). This study investigated the thermal issues of a 300 kW, 12,000 rpm Gramme-ring-winding HPSMM with housing water jacket and forced air cooling, based on the computational fluid dynamics (CFD) and numerical heat-transfer calculations. The reasons that cause the overheat of the prototype in the experimental test are thoroughly analysed. The high wind resistance of air gap as the ventilation duct and the large interface gap between the stator lamination and housing water jacket is thought to be the main reasons. Then improvement of the ventilation structures is studied to decrease the wind resistance. Besides, the modification of water channels is also investigated to avoid the influence of a large interface gap on the water cooling performance. Finally, a new cooling structure, combining the slot ventilation ducts and inside water channels, is proposed for the HSPMM with Gramme-ring windings. With the new cooling structure, the temperature rise of the motor is decreased, and the power density can be increased.
ACT-SVM: Prediction of Protein-Protein Interactions Based on Support Vector Basis Model

The interactions between proteins play important roles in several organisms, and such issue can be involved in almost all activities in the cell. The research of protein-protein interactions (PPIs) can make a huge contribution to the prevention and treatment of diseases. Currently, many prediction methods based on machine learning have been proposed to predict PPIs. In this article, we propose a novel method ACT-SVM that can effectively predict PPIs. The ACT-SVM model maps protein sequences to digital features, performs feature extraction twice on the protein sequence to obtain vector A and descriptor CT, and combines them into a vector. Then, the feature vectors of the protein pair are merged as the input of the support vector machine (SVM) classifier. We utilize nonredundant H. pylori and human dataset to verify the prediction performance of our method. Finally, the proposed method has a prediction accuracy of 0.727897 for H. pylori data and a prediction accuracy of 0.838799 for human dataset. The results demonstrate that this method can be called a stable and reliable prediction model of PPIs.
Two gastrointestinal parasites from freshwater sharptooth catfish, Clarias gariepinus (Burchell, 1822)

Clarias gariepinus is a common and important tropical catfish in Africa and the Middle East (Clay, 1979; Marcogliese and Cone, 2001, Hassan et al., 2010). It is widely distributed, occupying tropical swamps, lakes, and rivers in Africa (Olufemi et al., 1991). C. gariepinus is regard as one of the best models of omnivorous fishes (Holden and Reed, 1972; Clay, 1979). It is considered as a predator, feeding mainly on aquatic insects, molluscs, fishes; it also feeds on plant debris and fruits (Micha, 1973 and ARTICLE INFO ABSTRACT Article History: Received: June 15, 2020 Accepted: July 5, 2020 Online: July 8, 2020 _______________
Conceptualizing feedback in computer-based L2 language listening

Abstract This paper reports the results of three independent qualitative case studies that examined interactions with feedback as L2 listeners worked in self-regulated environments. Data on high-intermediate learners of English were collected from case studies conducted in Australia, Colombia and Chile. Results show that L2 listeners across case studies used feedback to assess performance, to ease task completion, and to regulate listening comprehension processes. We discuss reasons along with integrated data and propose a three-level preliminary framework for conceptualizing feedback in computer-based L2 listening.
The effects of environmental factors on acid-phase digestion of sewage sludge

The two-phase anaerobic digestion process consists of two fermenters operated in series, wherein the acidification and gasification reactions are optimized in separate reactors to improve the overall system conversion efficiency. This paper discusses the results of tests conducted with bench-scale complete-mix acid-phase digesters to determine the effects of culture pH, temperature, and hydraulic retention time (HRT) on the efficiency of sewage sludge digestion. Tests were conducted at culture pH's of 5, 5.5, 6, and 7 at mesophilic (35/degree/C) and thermophilic (55/degree/C) temperatures and at HRT's of 1.3 and 2 days. Digestion efficiencies were determined on the basis of volatile acid production, gas production, and the reduction of the major particulate components of the feed sludge (crude protein, carbohydrates, and lipids). Efficiencies were highly dependent on each of the three control factors and were generally optimized at the higher extremes of pH, temperature, and HRT tested. 9 refs., 2 figs., 10 tabs.