Miracle-dz/UniFAR-FTUs huggingface dataset:
The Risk Assessment of Pesticide Ingestion with Fruit and Vegetables for Consumer's Health

Pesticides are chemicals used in agriculture to protect crops from pests. In addition to protection during cultivation, they are also used after harvesting to extend the shelf life of products. Postharvest control stands out, especially when it comes to products imported from distant countries, resulting in increased concentration of pesticides and risk to human health consuming such products. In this study, analyses of pesticide residues were performed on 200 samples of fruits and vegetables. Pesticide residues were identified and quantified in 30 out of 200 samples. Study results revealed imazalil to be the most frequently detected pesticide. Risk assessment was performed on the obtained results, and it was carried out separately for adults and for children under 6 years of age. Imazalil showed the highest ARfD percentage for adults (max% ARfD 251%), and these values were especially high on risk assessment for children, where they amounted up to max% ARfD 1087%. The study of imazalil impact was performed on 16 Swiss albino mice divided into two groups and 4 subgroups. Experimental group animals were treated with the corresponding NOAEL dose of imazalil (10 mg/kg) for 28 days. Body weight was measured before each pesticide application on a digital electronic Sartorius scale. Peripheral blood analysis was performed after 28-day animal exposure to pesticides. Animals were anesthetized, blood samples were obtained by cardiac puncture, and red blood cell (RBC) count, hemoglobin (Hb) concentration, and white blood cell (WBC) count were determined by standard hematological methods. The organs for determination of imazalil concentration were extracted immediately upon animal sacrifice and stored in a freezer at -80°C until analysis. Results show difference in gain weight, and an increase in WBC count was recorded in the experimental group as compared with a control group of animals. The highest imazalil levels were recorded in adipose tissue (45.2‰) which proves tendency to accumulate.
Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.
Agro-industrial Wastes as Bioactive Molecules Source

Sustainable use of renewable natural resources, through value addition, using biological life processes, is an ideal transition from an oil-based economy to bioresource economy. Addressing climate change, renewable resources such as agro-industrial wastes, currently seen as low value materials, are in fact natural sources of value-added compounds that can be used, for example, as economic sources of bioactive substances of high interest for chemical, pharmaceutical and food industries. Moreover, agro-industrial by-products such as most of organic effluents have a high organic load content and can be vaporizable through a gaseous energy carrier production (methane/hydrogen) and a digested flow for agricultural purposes, using anaerobic digestion techniques. Once adopted, biological processes of these materials may contribute to reduce the environmental pollution burden.The present work refers to different procedures and methodologies applied to wastes valorisation that, within a biorefinery concept, had the achievement of valuable biomolecules as a core of the entire recovery line system. Several wastes/wastewaters mainly produced in the Mediterranean area were under study.
Fish Quality and Consumers: How Do Consumers’ Knowledge About and Involvement in Fish Quality Define Factors That Influence Fish Buying Behavior?

ABSTRACT Despite recommendations, fish consumption is low in several countries. One of the many relevant factors that influence fish consumption is the quality of fish products. However, consumers differ in involvement in and knowledge about fish quality. The aim of this study was to reveal differences in involvement and knowledge about fish quality among consumers and measure how these lead to variations in the importance of factors that influence consumers when buying fresh fish fillets. We aimed to explain these results based on subjective and objective knowledge, fish consumption, and social and demographic characteristics. This study was based on data collected from 738 Norwegian consumers who responded to a questionnaire. The survey included items regarding knowledge and involvement in fish quality, objective and subjective knowledge measures, factors important when buying fish, and finally sociodemographic characteristics. This article suggests that perceived quality is one of the most important factors that influence buying behavior when it comes to fish fillet products in Norway. Consumers can have a high or low involvement with and knowledge about fish quality. However, when confronted with the decision of what fish product they may buy, most consumers reported using fish quality as a main driver.
Malicious URL Detection : A Survey

Phishing is a type of fraud, in which two actors, phisher and victim, take part. The role of a phisher is to create a phishing webpage by mimicking as an authorized one and embed the website in an email or any other media. A victim may access to the phished link without consciousness or with lack of knowledge. Detecting malicious URLs (Uniform Resource Locators) is a challenging, yet interesting topic because phishers mostly generate URLs randomly and researchers have to detect them while considering the behaviors behind the generated phishing URLs. Although various detection schemes exist in anti-phishing area, URL-based scheme is safer and more realistic because of two perspectives: no need of access to the malicious webpage and an ability of zero-hour detection. Therefore, in our paper, we survey malicious URL detection by approaching a variety of existing technical countermeasures and analyze existing weakness. Eventually, we conclude our survey with potential opinion for more effective detection.
Effect of Tween 80 Surfactant on the Electropolymerization and Corrosion Performance of Polyaniline on Mild Steel

Polyaniline conducting polymer have been electropolymerized from aniline monomer on mild steel surface by cyclic voltametry method in aqueous oxalic acid solution and different concentrations of Tween 80 as surfactant. Characterization of coatings was carried out by scanning electron microscope SEM. Corrosion behavior of the polymer coated mild steel electrodes was investigated in 0.5 M H2SO4 solution by potentiodynamic polarization and electrochemical impedance spectroscopy EIS. Adsorption of the polymer on the metal surface was examined by applying the Langumir adsorption isotherm to fit the experimental data. The mechanism of action of the surfactant in the electropolymerization process has been investigated and discussed.
Supporting Information Binary choice : Task description

This data was initially reported by Krajbich et al. in 2010. For the convenience of the reader, we include the task description from the original paper [1]. The experiment consisted of 39 Caltech students. Only subjects who self-reported regularly eating the snack foods (for example, potato chips and candy bars) used in the experiment and not being on a diet were allowed to participate. These steps were taken to ensure that the food items we used would be motivationally relevant. This would not have been the case if the subjects did not like junk food. Subjects were asked to refrain from eating for 3 h before the start of the experiment. After the experiment they were required to stay in the room with the experimenter for 30 min while eating the food item that they chose in a randomly selected trial (see below). Subjects were not allowed to eat anything else during this time. In an initial rating phase subjects entered liking ratings for 70 different foods using an on-screen slider bar (how much would you like to eat this at the end of the experiment? , scale -10 to 10). The initial location of the slider was randomized to reduce anchoring effects. This rating screen had a free response time. The food was kept in the room with the subjects during the experimental session to assure them that all the items were available. Furthermore, subjects briefly saw all the items at this point so that they could effectively use the rating scale. In the choice phase, subjects made their choices by pressing the left or right arrow keys on the keyboard. The choice screen had a free response time. Food items that received a negative rating in the rating phase of the experiment were excluded from the choice phase. The items shown in each trial were chosen pseudo-randomly according to the following rules: (i) no item was used in more than 6 trials; (ii) the difference in liking ratings between the two items was constrained to be 5 or less; (iii) if at some point in the experiment (i) and (ii) could no longer both be satisfied, then the difference in allowable liking ratings was expanded to 7, but these trials occurred for only 5 subjects and so were discarded from the analyses. The spatial location of the items was randomized. After subjects indicated their choice, a yellow box was drawn around the chosen item (with the other item still on-screen) and displayed for 1 s, followed by a fixation screen before the beginning of the next trial. Subjects fixation patterns were recorded at 50 Hz using a Tobii desktop-mounted eyetracker. Before each choice trial, subjects were required to maintain a fixation at the center of the screen for 2 s before the items would appear, ensuring that subjects began every choice fixating on the same location.
Supplementary Material

Dynamical movement primitives (DMPs) are an effective approach towards constructing the desired oscillators for rhythmic movement control [1, 2]. DMPs can imitate the outputs of any type of central pattern generator (CPG) efficiently via an optimization technique (e.g., locally weighted regression (LWR)) [3]. In this supplementary material, we integrate the DMPs-based control with the DFRL. Here, the DMPs serve as CPGs, generating periodic commands for driving the joint movements of a quadruped robot. The DFRL is directly integrated with the DMPs-based control, to adapt the offsets of the DMP outputs. As shown in Fig. S.1, under the DMPs-based control, the DMPs act as CPGs instead of the SO(2) oscillators. The DMPs consist of four DMP units, each DMP unit (featuring two DMP oscillators) is responsible for the two joint movement controls of a leg. The DMP oscillator model can be described as [2, 4]
Micro-PIXE elemental mapping for ionome studies of crop plants

In order to maintain homeostasis and consequent optimal cell functioning and integrity and/or to avoid toxicity, proper allocation of elements at organ, tissue, cellular and subcellular level is needed. Studies of element localization are therefore crucial to reveal the mechanisms of element trafficking and also tolerance and toxicity. Moreover, studies of localization and speciation of trace elements in grains of staple crops are also of high applicative value, allowing one to determine major and trace element concentrations in different grain tissues without possible contamination. In the last decade, a remarkable progress has been made in the development and application of different 2D imaging techniques in complex biological systems, especially in the sense of improved lateral resolution and sensitivity. The superiority of micro-PIXE over other 2D imaging techniques lies in its wide elemental range (from sodium (Na) to uranium (U)), high elemental sensitivity below micron spatial resolution and fully quantitative element concentration analysis. The aim of this review is to summarize the latest development of micro-PIXE for imaging of the distribution of major and trace elements in crop plants with emphasis on sample preparation methodologies and post-imaging analysis. Case studies of element localization in the grains of major crop plants are also presented.
Rapid Analysis of Pesticide Mixture by Gas Chromatography-Mass Spectrometry with a New Alternative Iterative Algorithm

Chemometric methods have been proved to be a powerful tool for resolution of overlapping signals. Immune algorithm (IA) is one of the chemometric approaches for analyzing multi-component GC-MS signals. The method extracts the information of the components by iteratively eliminating standard information (chromatogram or mass spectrum) from overlapping GC-MS signals. In the primary IA, however, the standard signal of each component possibly contained in the mixture must be provided by measuring the standard or by theoretical simulation. When there is difference between the measured signals of the standards and the mixture, distortion and negative values will appear in the resolved chromatograms. In order to conquer the problem, an algorithm based on an alternative iteration of least squares fitting and IA was proposed in this work. In the method, the measurement of GC-MS was achieved with a very fast temperature program to make the analytes to elute within a short retention time period, and then the chromatographic and mass spectral information of the components in the overlapping signal is calculated with the proposed algorithm. In the calculation, the algorithm takes random mass spectra of the components as the starting input, and then the information of each component is obtained with an alternating iterative process. In the iteration, the mass spectra are calculated by using the least squares fitting and the chromatographic profiles are resolved by IA. Furthermore, the non-negative and unimodality constraints are adopted in the calculation for improving the resolved results. The iteration stops when the remaining signal does not change. The feasibility of the method was validated by using a simulated GC-MS data matrix of a three-component mixture, and the practicability of the method was proved by resolving the GC-MS data of the 40-pesticide mixture. The results show that both the mass spectra and the chromatographic information of the components were extracted from the overlapping signals, and the pesticide mixture was analyzed within 10 min elution with the help of the proposed method.
Serum fructosamine concentration as measure of blood glucose control in insulin dependent diabetes

Are problem drinkers dangerous drivers? C Brewer, MRCPSYCH . 1075 Serum fructosamine concentration as measure of blood glucose control in insulin dependent diabetes W Luqman, FACP, and others; J Baker, MB. 1075 The rapid transit system for patients with fractures of the proximal femur R W Warne, FRACP . 1076 Children with asthma: will nebulised salbutamol reduce hospital admissions? D Jenkinson, MRCGP J P Osborne, MD, and S Cocksedge, DCH; J L Pearce, FRACP, and Helen M M Wesley, MRCP . 1076 Containing the use of diagnostic tests D P Davies, FRCP . 1077 Cardiac arrhythmias: theory and practice D Ward, MD, and J Camm, FRCP; J P Saunders, MFCM . 1077 Natural progesterone and antihypertensive action Katherina Dalton, FRCGP . 1078 Chemotherapy for small cell carcinoma Freda Festenstein, MRCS, LRCP, and R M Weatherstone, MRCP . 1078 Reducing errors in the accident and emergency department: using radiographers L Berman, MRCP . 1078 On the reputed decline in gastric carcinoma Flora Hartveit, MB . 1079 Domperidone D N Bateman, MRCP . 1079 Women doctors in urban general practice R M Ridsdill-Smith, FRCGP C M Richards, MRCGP, and others . 1079 Credit transfers M L Heath, FFARCS . 1079 Overview of randomised trials of diuretics in pregnancy Margaret Ounsted, DM, and C W G Redman, FRCP . 1079 Clinical budgeting: playing with fire? Caroline Miles . 1080 Screening for neural tube defects S Thome . 1080 Points Diagnosis of meningococcal infection by postmortem blood sampling (D P M Howells and others); Hyperlipidaemia (P G F Nixon); Listeria monocytogenes in a patient with AIDS (G E Forster); Abortion due to infection with Chlamydia psittaci in a sheep farmer's wife (M C Bateson); Lectins (A G Davison) . 1080
Entrepreneurial Learning and Disincentives in Crowdfunding Markets

Reward-based crowdfunding has enabled entrepreneurs to interact with consumers even before product launches. However, this market persistently suffers from a high failure rate; that is, entrepreneurs fail to launch and deliver their products as promised. We investigate the extent to which this high failure rate is because of information distortion—entrepreneurs have uncertainty about consumers’ evaluation of the new products. We model the product launch decisions of different types of entrepreneurs who raise funds through preselling on reward-based crowdfunding platforms and subsequently decide whether to continue with the product launch. To do so, we collect structured and unstructured data from Kickstarter’s digital video game category and classify attributes using supervised learning methods. We develop and estimate an integrated model of crowdfunding demand and entrepreneurs’ product launch decisions. We find that the information entrepreneurs gather from crowdfunding sales has sizable impacts on the product launch decisions of entrepreneurs with low managerial capital or new to the crowdfunding platform. Our counterfactual simulations suggest that platform policy regulating overfunded projects can reduce the product launch failure rate by about 13%. This paper was accepted by Matthew Shum, marketing.
Enhanced Carry: Prospective Interest Rate Differential and Currency Excess Returns

Following Engel (2011), we use a Beveridge-Nelson decomposition to link expected foreign currency excess returns to `prospective interest rate differential' -- the infinite sum of expected future interest rate differentials. Empirically, we find that prospective interest rate differential is a stronger predictor of currency excess returns than carry, in both portfolio sorts and Fama-MacBeth regressions. A factor based on prospective interest rate differential is also useful in explaining the returns of currency carry and momentum portfolios.
Crystal structure of catena-poly-[aqua(μ2-4,4′-bipyridine-κ2N:N′)bis(3′,5,5′-tricarboxybiphenyl-2-carboxylato-κ2O,O′)cadmium(II)], C42H28N2O17Cd

Abstract C42H28N2O17Cd, monoclinic, P2/n, a = 11.8533(4) Å, b = 7.1975(3) Å, c = 23.5490(7) Å, β = 115.181(2)°, V = 1818.14(11) Å3, Z = 2, Rgt(F) = 0.0220, wRref(F2) = 0.0615, T = 293(2) K.
SNPE-SRGAN: Lightweight Generative Adversarial Networks for Single-Image Super-Resolution on Mobile Using SNPE Framework

An image resulting from a low-resolution (LR) camera on the mobile phone has lower quality than a high-resolution(HR) camera on a DSLR. Meanwhile, the HR camera is pricing if compared with the LR camera. How to achieve a single-image quality on LR camera likewise on HR camera becomes essential research in the past years. Addressing this issue can be done by upscaling a single LR image. Recently, the super-resolution generative adversarial network (SRGAN) model is one of the state-of-the-art super-resolution(SR)models employed on single-image SR. However, implementing a deep learning model like SRGAN on a mobile device is challenging in computation power and resources. This study aims to develop a smaller and lower resources model while preserving single-image SR quality on mobile devices. To meet these objectives, we convert, quantize, and compress the SRGAN model on Snapdragon Neural Processing Engine (SNPE) as an example. We then validate the SRGAN on the DIV2K dataset on which improves the model performances. Besides, we conduct experiments on GPU, DSP environment. The experimental result confirmed that SNPE-SRGAN capable of achieves not only HR images’ quality but also low latency by 0.06 second and smaller model by 1.7 Mb size running on DSP. Also, the SRGAN-DLC-Quantized running on GPU has a smaller size by 1.7 Mb and lower latency by 1.151 seconds compared with Non-quantized SRGAN-TensorFlow by 9.1 Mb and 1.608 seconds latency.
Antidote-induced armchair graphene nanoribbon based resonant tunneling diodes

Resonant tunneling phenomena are explored using armchair graphene nanoribbon (AGNR), which eliminates the lattice mismatch and electron mobility degradation problems of conventional heterostructure resonant tunneling diodes (RTDs). Eight antidote topologies are proposed in this paper. These antidote topologies significantly increase or decrease the band gap of AGNR. Both double barrier quantum well and single barrier quantum well structures have been achieved by putting the antidote-induced AGNRs and pristine AGNRs. A numerical approach with a tight binding model and non-equilibrium Green’s function formalism has been used to simulate the quantum phenomena of the device. Current–voltage characteristics of these proposed RTDs show a high peak to valley ratio and low power dissipation with respect to different antidote topologies. Channel length variation effects are investigated in the proposed RTDs, and it is found that the peak to valley ratio, valley current, valley voltage, and power dissipation can be improved by tuning the channel length. These graphene-based RTDs are easy to fabricate and offer more flexibility in terms of peak to valley ratio, valley current, valley voltage, and power dissipation.
Securing IoT Network against DDoS Attacks using Multi-agent IDS

The crucial issue of Internet of Things (IoT) is vulnerable towards various kinds of security threats, especially on denial-of-service (DoS) attacks as IoT network consists of millions of devices attached to the network. There are plenty of intrusion detection systems (IDSs) available for IoT networks, however, the accuracy detection yet still a big problem. To address the problem, this paper proposes an ensemble IDS for IoT networks. The proposed IDS uses Multi-agent System (MAS) concept and ensemble the Information Gain feature selection algorithm with J48 classifier algorithm for the proposed IDS. Experimental results show the proposed MAS-J48-IDS provides the best accuracy performance of 99.8% for all selected features compared to IDSs that use Naïve Bayesian (NB) and Random Forest (RF) classifiers. Besides, the use of MAS contributes towards the load balancing among the nodes in the network itself.