Miracle-dz/UniFAR-FTUs huggingface dataset:
Tortricid moth fauna diversity (Lepidoptera: Tortricidae) in Kargil district, Ladakh, India

Tortricid moths were collected from different parts of Kargil during 2010-2012 to determine their diversity, evenness, dominance and species richness. The whole collection of 210 tortricid moth specimens comprised of 18 species were captured from diverse localities of Kargil by using light trap operated from dusk to dawn, out of which one specie (Acleris birdi sp. nov.) was reported as new to science and two species [(Crocidosema plebejana Zeller and Matsumuraeses phaseoli (Matsumura)] as new to Ladakh. When monthly data of individual count is taken into consideration, highest numbers of specimens (94) were captured in the month of August followed by July (76) while as minimum (8) were collected in the month of May and September each during entire study period. Further Diversity index (H), Evenness index (J), Index of dominance (D) and Species richness (M) were observed as 1.11627, 0.755707, 0.244293 and 7.320583 respectively.
Comparative study of the effects of two types of organic fertilizers (R1 and R4) and a chemical fertilizer (NPK) on some physico-chemical and biochemical parameters of lettuce (Latuca sativa L.)

The objective of this study was to evaluate the effect of two types of organic fertilizer (R1 and R4) and a commercial chemical fertilizer (NPK) on the nutritional composition of lettuce. For each type of organic fertilizer (R1 and R4), two concentrations were used (30% and 50%, v/v). The treatments [R1 30%, R1 50%, R4 30%, R4 50%, control and chemical (NPK)] were randomly assigned to units of a homogeneous experimental design. Three replicates were carried out per treatment. Twelve parameters were analysed: lipids, proteins, carbohydrates, total sugars, reducing sugars, fiber, moisture, ash, pH, vitamin C, polyphenols, flavonoids. Analyses show that the level of ash is high (1.01 ± 0.03%) in organic fertilizer R4 50% compared with chemical fertilizer (0.50 ± 0.02%) and the control (0.81 ± 0.08%). The results also showed a remarkable increase in antioxidant compounds (polyphenols and flavonoids), fiber and carbohydrates in lettuce fertilized with organic fertilizer R4 50% (polyphenols: 87.18 ± 0.74 mg/100 g, flavonoids: 1.44 ± 0.01 mg/100 g, fiber: 3.23 ± 0.14% and carbohydrates: 8.03 ± 1.64%) compared with the chemical fertilizer (polyphenols: 14.13 ± 0.33 mg/100 g, flavonoids: 0.82 ± 0.04 mg/100 g, fiber: 2.04 ± 0.10% and carbohydrates: 5.12 ± 0.05%) and the control (polyphenols: 55.80 ± 0.53 mg/100 g, flavonoids: 0.98 ± 0.01 mg/100 g, fiber: 2.39 ± 0.07% and carbohydrates: 5.27 ± 0.29%). The high amounts of phytochemicals, minerals and antioxidants recorded in this research give preference to the use of organic rather than inorganic fertilizers. In view of the results obtained, organic fertilizer R4 50% should be recommended to market gardeners.
An Adversarial Examples Identification Method for Time Series in Internet-of-Things System

Adversarial examples (AEs) make Internet-of-Things (IoT) systems to face a great challenge. The tiny fluctuations in AEs mislead the learning model, let the model accept, and make wrong decisions. So, accurately identifying AEs is significant for improving the robustness in IoT systems. In this article, a method to identify AEs for time-series data is proposed. We analyze the characteristics of adversarial time-series examples and normal time-series samples and propose a time-series representation method. The tiny fluctuations in AEs are easily discovered by this representation. A metric is also designed to support this representation method to identify AEs with supervised learning and unsupervised learning methods. Our representation method and the metric is applied with supervised learning and unsupervised learning methods to identify time series in real data set and data sets in the official UCR repository. The experimental results show that our method has a great ability to identify AEs, compared to previous approaches.
Leader–Follower Formation Control With Prescribed Performance Guarantees

This article addresses the problem of achieving relative position-based formation control for leader–follower multiagent systems in a distributed manner using a prescribed performance strategy. Both the first- and second-order cases are treated and a leader–follower framework is introduced in the sense that a group of agents with external inputs is selected as leaders in order to drive the group of followers in a way that the entire system achieves a target formation within certain prescribed performance transient bounds. Under the assumption of tree graphs, a distributed control law is proposed for the first-order case when the decay rate of the performance functions is within a sufficient bound. Then, two classes of tree graphs that can have additional followers are investigated. For the second-order case, we propose a distributed control law based on a backstepping approach for the group of leaders to steer the entire system achieving the target formation within the prescribed performance bounds. Finally, several simulation examples are given to illustrate the results.
Effects of occupational exposure to trace levels of halogenated anesthetics on the liver, kidney, and oxidative stress parameters in operating room personnel

Abstract This study was conducted to measure the concentration of halogenated anesthetics in breathing zone air of the personnel and evaluate their effects on hepatic and renal functions as well as oxidative stress parameters. Levels of these gases in air samples were lower than the national recommended exposure limits (50 ppm). Occupational exposure to anesthetic gases significantly induced oxidative stress in the operating room personnel, marked by increased plasma levels of transaminase enzymes accompanied by the marked decline of enzymatic and non-enzymatic antioxidant levels, and the rise of MDA levels (p < 0.05); these adverse effects would increase with increasing work experience.
Pandemics, Poverty, and Social Cohesion: Lessons from the Past and Possible Solutions for COVID-19

Since COVID-19 broke out, there has been renewed interest in understanding the economic and social dynamics of historical and more recent pandemics and epidemics, from the plagues of Antiquity to modern-day outbreaks like Ebola. These events can have significant impacts on the interplay between poverty and social cohesion, i.e. how different groups in society interact and cooperate to survive and prosper. To that effect, this survey paper provides an overview of how social responses to past pandemics and epidemics were determined by the epidemiological and non-epidemiological characteristics of these outbreaks, with a particular focus on the scapegoating and persecution of minority groups, including migrants. More precisely, we discuss existing theories as well as historical and quantitative studies, and highlight the cases and contexts where pandemics may lead to milder or more severe forms of scapegoating. Finally, we conclude with a summary of priorities for future research on pandemics and social cohesion and discuss the possible effects and policy implications of COVID-19.
Advanced Control Methods for Power Converters in DG Systems and Microgrids

In recent years, different advanced control methods have been successfully proposed as alternatives to conventional cascaded linear controllers for power converters in distributed generation systems and microgrids. The prime movers of this research are strong capabilities of advanced controllers to improve the dynamic performance and robustness of power electronic converters in these applications. This article first introduces the key roles and functionalities of voltage source converters in distributed generation systems and microgrids. Then, it describes how these functionalities are traditionally achieved by using linear controllers, and addresses their fundamental dynamic performance limitations. Afterward, the most prominent advanced control methods are overviewed. In this context, the implementation principles, advantages, and disadvantages of prominent model- and data-based advanced control methods are critically discussed and experimentally compared. This article ends with a discussion about promising research directions in the area of advanced control for power electronic converters.
The role of feeding strategy in the tolerance of a terrestrial salamander (Plethodon cinereus) to biogeochemical changes in northern hardwood forests

We investigated whether the trophic ecology of an apex predator is influenced by ecosystem-level nutrient depletion. The feeding behavior and nutrient assimilation of a terrestrial salamander, Eastern Red-backed Salamander (Plethodon cinereus (Green, 1818)), was surveyed along a gradient of forest biogeochemistry. Recent studies have documented populations of these salamanders in forests with low-pH soils that were long thought to be fatal. One mechanism that may enable P. cinereus to tolerate acid-impaired habitats is its generalist life history. We sampled diet, invertebrate prey abundance, and tissue composition of P. cinereus from sites that range in calcium availability and soil pH in northern forests of North America. We found that P. cinereus consistently exhibited a generalist feeding strategy, having diverse diets closely representing resource availability. Prey abundances were unrelated to the biogeochemical gradient (excluding gastropods), indicating relatively intact food webs. Although P. cinereus at the two most acid-impaired sites consumed more prey, overall trophic strategies were consistent across the gradient. Salamander tissue composition was unrelated to variation in forest biogeochemistry, although manganese levels were elevated in the most acid-impaired forests. We suggest that a generalist feeding strategy, combined with diverse and compositionally stable food webs, facilitates tolerance by this abundant predator of the challenges imposed by acid-impaired habitats.
THE EFFECTS OF CUSTOMERS’ CULTURAL VALUES ON THEIR PERCEPTIONS OF LODGING SERVICE QUALITY: A COMPARATIVE ANALYSIS OF CUSTOMERS AT TRADITIONAL JAPANESE INNS

Growth in international travel has led to the need to be aware of cultural elements in designing services. This paper examines how consumers’ culture influences their perceptions of lodging service quality with strong cultural characteristics, by comparing the evaluation of Asian and Western guests at Japanese-style inns (ryokan). Results show differences in their perceptions, with Western guests evaluating the service more preferably than Asian customers. The study suggests lodging facilities to consider the culture of their guests when designing their service environment.
Response of physical quality and biochemical characteristics to different maturity stages of Mangifera indica (cv. Amrapali)

Mango fruit consumers often confuse to select exact fruits from markets. A study was conducted to analyze the physical quality and biochemical componentsat different stages ofmango cultivar “Amrapali”. The study was conducted at the postharvest laboratory, Department of Horticulture, PSTU during the period from January to December 2015. Stage-1 was marked when the peel color was dark olive green and turning toyellow at the seedand the subsequent maturity stages determined at 2-days intervals as stage-2 was olive, stage-3 was apple green, stage-4 was brown, stage-5 was saddle brown and stage-6 was dark golden rod in color. At each maturity stages, physical (peel color, firmness and weight loss) and biochemical (anthocyanin content, titratable acidity, pH, total soluble solids, ascorbic acid, reducing sugar, non-reducing sugar and total sugar) maturity indices were determined. The pH (4.54), anthocyanine content (430 mg/100 g), TSS (24.33%), reducing sugar (7.56%), non-reducing sugar (5.84%) and total sugar (13.40%) were significantly highest at stage-6 (dark golden rod colour). The titratable acidity (0.30%) and ascorbic acid (25.29 mg/100 g) were significantly highest at stage-1 (dark olive green). Firmness decreased gradually with the advancement of maturation and flesh color turned dark olive green to dark golden rod. Total soluble solids were increased while TA was gradually decreased with increase of maturity. Dark olive greencolour stage (stage-1) is suitable for harvesting and transportation due to highest physical qualityand dark golden rod colour stage(stage-6) is suitable for consumption due to highest biochemical composition.
Target Detection in Passive Radar Under Noisy Reference Channel: A New Threshold-Setting Strategy

In the detection theory framework, it is customary to assign a bound to the false alarm probability and to attempt to maximize the detection probability subject to this constraint. In the problem of moving target detection in passive radar with a noisy reference channel, we formulate a detection problem as a composite hypothesis-testing problem and solve it with the likelihood ratio test (LRT) principle, which is known as generalized LRT in the electrical engineering works of literature. In such a problem, we show that any uncertainty in the value of the direct signal-to-noise ratio of the reference channel, abbreviated as <inline-formula><tex-math notation="LaTeX">$\text{DNR}_r$</tex-math></inline-formula>, can result in excessive false alarm probability of the proposed noisy-reference-channel-based detector in the low-<inline-formula><tex-math notation="LaTeX">$\text{DNR}_r$</tex-math></inline-formula> regime. To facilitate efficient operation under uncertainty in <inline-formula><tex-math notation="LaTeX">$\text{DNR}_r$</tex-math></inline-formula>, we propose a new threshold-setting strategy to adjust the level of the proposed detector. Through extensive Monte–Carlo simulations, we examine the above problem and investigate the efficiency of the proposed threshold-setting strategy. Besides, we apply the framework of the kernel theory to the target detection problem of a noisy and ideal reference channel passive radar to propose two new detectors. As such, we replace the inner products of the proposed tests with appropriate polynomial kernel functions allowing for richer feature space to be deployed in the detection, achieving better detection performance. In this case, our detection performance results show that the kernelized detectors offer more that 1-dB signal-to-noise ratio gain as compared to their conventional counterparts.
Investigation of the antioxidative and radical scavenging impact of natural compounds in-vitro and on cell metabolism

In an aerobic environment the occurrence of reactive oxygen species (ROS) is a common phenomenon. The diverse roles of ROS in cellular function and in diseases make them a target of interest in many research areas. Substances capable of directly or indirectly reducing the (harmful) effects of ROS are referred to as “antioxidants”. However, the term is applied miscellaneously in the chemical and the biological context to describe different attributes of a substance. In this work the potential of an electrochemical assay to detect different ROS in-vitro was explored. The method was optimized to investigate the radical scavenging activities (antioxidant potential) of trolox and different plant compounds (ascorbic acid, caffeic acid, epigallocatechin gallate, ferulic acid, kaempferol, quercetin, rutin, and Gynostemma pentaphyllum extract) in-vitro. The obtained data was compared to established antioxidant in-vitro assays. Further, the impact of the plant substances on cellular parameters was evaluated with the electrochemical assay and established cell assays. The optimization of the electrochemical assay allowed the reproducible detection of ROS. The sensor electrode proved differently sensitive towards individual ROS species. The highest sensitivity was recorded for hydroxyl radicals while superoxide and hydrogen peroxide had little impact on the sensor. Extracellular ROS concentrations could be detected from cell lines releasing elevated ROS into the extracellular space. The antioxidant activity of the investigated plant substances could be demonstrated with all in-vitro assays applied. However, the absolute as well as the relative activity of the individual substances varied depending on the experimental parameters of the assays (pH, radical species, phase, detection method). The plant compounds modified redox related intracellular parameters in different cell lines. However, a direct correlation between intracellular and extracellular effects of the plant compounds could not be established. The work demonstrates the feasibility to use the electrochemical assay to sense ROS as well as to evaluate the radical scavenging activity of molecules. The in-vitro antioxidant activities demonstrated for the individual plant substances are not reliable to predict the cellular effects of the molecules.
Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials

In this paper, a vehicle Re-ID framework which integrates image processing and traffic flow model is developed. First, the CNN network is applied for vehicle detection and tracking and extracting attribute recognition. Particularly, attributes including vehicle color, type, make, and Re-ID feature are extracted to derive a similarity matrix between upstream and downstream vehicles. However, solely using these features could not achieve satisfaction matching accuracy. Our testing only shows a moderate accurate of around 72.3%. To further improve the Re-ID rate, this paper integrates visual information with the well-known IDM car-following mode. In our framework, IDM is first used to estimate the arrival time window for each upstream vehicle; and then with this time window derive a filter matrix which set the similarity as 0 for the matching vehicles outside the time window. Combining similarity matrix and filter matrix, the new developed Re-ID framework improves the matching rate to 95.7%. Furthermore, the proposed framework can even help identify vehicles that may have changed lanes, overtaken vehicles or driven on a sideroad. Such information is certainly valuable for future research on performance measure, traffic control, and congestion mitigation. Considering the significance of the trajectory data to nowadays traffic control and management and popularity of today’s surveillance cameras, this research certainly will contribute to the improvement of arterial traffic performance measure and efficient control.
Which Medium and Ingredients Provide Better Morphological Differentiation of SH-SY5Y Cells?

Human SH-SY5Y cell line has been used as an in vitro model in neuroscience research. However, many researchers emphasized that there are many differences between differentiated and undifferentiated characteristics of SH-SY5Y. Although Retinoic Acid (RA) generally have been used for differentiation of SH-SY5Y cells, which protocol provides better differentiation have not been cleared yet. Therefore, we compared RA and the other mediums in different treatment periods for obtaining better differentiated, carrying a neuron-like phenotype (N-type) characteristics, of SH-SY5Y human cells. The cells were pre-treated with different mediums for different treatment periods and compared with both for each other and the control group. When we pre-treated the cells with RA for 5-day and followed by the Mix medium (Neurobasal, B27, db-cAMP, KCI) and BDNF, neurite length and MAP2 expression of the cells were found significantly higher than the control group. In conclusion, we showed that more than one agent (RA) is clearly necessary to reach better differentiation of SH-SY5Y cells.
Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction

Video venue category prediction has been drawing more attention in the multimedia community for various applications such as personalized location recommendation and video verification. Most of existing works resort to the information from either multiple modalities or other platforms for strengthening video representations. However, noisy acoustic information, sparse textual descriptions and incompatible cross-platform data could limit the performance gain and reduce the universality of the model. Therefore, we focus on discriminative visual feature extraction from videos by introducing a hybrid-attention structure. Particularly, we propose a novel Global-Local Attention Module (GLAM), which can be inserted to neural networks to generate enhanced visual features from video content. In GLAM, the Global Attention (GA) is used to catch contextual scene-oriented information via assigning channels with various weights while the Local Attention (LA) is employed to learn salient object-oriented features via allocating different weights for spatial regions. Moreover, GLAM can be extended to ones with multiple GAs and LAs for further visual enhancement. These two types of features respectively captured by GAs and LAs are integrated via convolution layers, and then delivered into convolutional Long Short-Term Memory (convLSTM) to generate spatial-temporal representations, constituting the content stream. In addition, video motions are explored to learn long-term movement variations, which also contributes to video venue prediction. The content and motion stream constitute our proposed Hybrid-Attention Enhanced Two-Stream Fusion Network (HA-TSFN). HA-TSFN finally merges the features from two streams for comprehensive representations. Extensive experiments demonstrate that our method achieves the state-of-the-art performance in the large-scale dataset Vine. The visualization also shows that the proposed GLAM can capture complementary scene-oriented and object-oriented visual features from videos. Our code is available at: https://github.com/zhangyanchao1014/HA-TSFN.
Network structure detection using convergent cross mapping on multivariate time series

: The recent developments in measurement techniques have allowed us to observe multidimensional time series data in various ﬁelds. Thus, detecting causal relations between elements from multidimensional time series data is useful for prediction, model generation, and system control. In addition, causal relations between elements can be detected to estimate network connections. In other words, the network structure can be estimated from multidimensional time series data by using causality detection methods. Among the several methods for detecting causality, Granger causality is a well-known method that is widely used for causal estimation between time series. On the other hand, a method called convergent cross mapping (CCM) has been proposed, which can distinguish causality from pseudo-correlation. It is important to investigate whether CCM will be eﬀective with increased number of elements, even though the evaluation of its performance with a few elements has been reported in the literature. Moreover, it is important to evaluate the performance with changing dynamics of the elements. In this study, to estimate the connectivity between elements in complex networks, we apply CCM to mathematical models of complex networks, or the Watts-Strogatz model. In particular, we investigate how complex network structures aﬀect causal estimation, by applying CCM to multidimensional time series data produced from complex networks. According to the results, we ﬁnd the connectivity estimation accuracies in the regular ring-lattice network to be slightly higher than those in random networks. Furthermore, we reveal that it is easier to perform connectivity estimation for a network with a community structure than a random structure.