Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Homomorphic Authentication in Cloud-Assisted VANETs with Blockchain-Based Healthcare Monitoring for Pandemic Control

Currently, the outbreak of COVID-19 pandemic has caused catastrophic effect on every aspect of our lives, globally. The entire human race of all countries and regions has suffered devastating losses. With its high infectiousness and mortality rate, it is of great significance to carry out effective precautions and prevention of COVID-19. Specifically, the transportation system has been confirmed as one of the crucial spreading routes. Hence, enhancing healthcare monitoring and infection tracking for high-mobility transportation system is infeasible for pandemic control. Meanwhile, due to the promising advantages in the emerging intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) is able to collect and process relevant vehicular data for improving the driving experience and road safety, which provide a way for non-contact automatic healthcare monitoring. Furthermore, the proliferating cloud computing and blockchain techniques enable sufficient processing and storing capabilities, along with decentralized remote auditing towards heterogenous vehicular data. In this case, the automated infection tracking for pandemic control could be achieved accordingly. For the above consideration, in this paper we develop a practical homomorphic authentication scheme for cloud-assisted VANETs, where the healthcare monitoring for all involving passengers is provided. Notably, the integrated cloud-assisted VANET infrastructure is utilized, where the hybrid medical data acquisition module is attached. In this way, timely, non-contact measurement on all passengers’ physical status can be remotely done by vehicular cloud (VC), which could also drastically improve the efficiency and guarantee safety. Vulnerabilities of the employed dedicated-short-range-communication (DSRC) technique could be properly addressed with the applied homomorphic encryption design. Additionally, the decentralized blockchain-based vehicle recording mechanism is cooperatively performed by VC and edge units. Infection tracking on specific vehicle and individual can be offered in this way. Each signature sequence is collaboratively maintained and verified by the current roadside unit (RSU) and its neighbor RSUs. The security analysis demonstrates that the proposed scheme is secure against major attacks, while the performance comparison with the state-of-the-arts relevant methods are presented for efficiency discussion.
Effect of newly found polymorphisms in the promoter region of the CAPN1 gene on transcript abundance in broiler chicken breast muscle

The calpain family includes proteolytic enzymes, which have a high capacity to degrade cytoskeletal and muscle fibre proteins. Thus they play an important role in the fusion of myoblasts and in cell proliferation and growth. The CAPN1 gene has been selected as a ‘candidate gene’ for meat quality in many domestic animals, including chickens. Consequently, the aim of our study was to identify new polymorphisms in the promoter region of the CAPN1 gene in broilers and to investigate their impact on CAPN1 transcript abundance in breast muscles. The experiment used broilers of two genetic lines, fastand slow-growing. Five new polymorphisms in the promoter region of the CAPN1 gene were identified, all of them in linkage disequilibrium (P<0.05). However, the results obtained for their association with expression level were doubtful. Therefore, we surmise that the newly discovered polymorphisms, although they alter the potential sequence binding of transcription factors, probably have just a weak effect on the level of CAPN1 expression in broiler chickens at the investigated stage of ontogenesis.
Season length, body size, and social polymorphism: size clines but not saw tooth clines in sweat bees

1. Annual insects are predicted to grow larger where the growing season is longer. However, transitions from one to two generations per year can occur when the season becomes sufficiently long, and are predicted to result in a sharp decrease in body size because available development time is halved. The potential for resulting saw‐tooth clines has been investigated only in solitary taxa with free‐living larvae.
Test-day genetic analysis of Murrah buffalo sires for milk production

The aim of the study was to investigate efficiency of test-day model (TDM) compared to lactation model (LM) for genetic evaluation of Murrah buffalo bulls. Use of TDM instead of LM is of more interest in genetic evaluation because of variability of lactation days in dairy animals. Data pertaining to first lactation monthly test-day milk yield (FLMTDMY) and first lactation 305-days or less milk yield (FL305DMY) of 1105 Murrah buffaloes during 1993 to 2010 were collected and adjusted against significant environmental influences. It was found that test-day6 milk yield (FLMTD6MY) had the highest genetic and phenotypic correlation with FL305DMY. An attempt is being made in the present investigation to compare the estimated breeding values (EBVs) of Murrah bulls through contemporary comparison method for FL305DMY and FLMTD6MY. The rank correlations between two traits were highly statistically significant indicating that FLMTD6MY equally effective to discriminate amongst sires.
A comprehensive review on hybrid network traffic prediction model

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier

Abstract Subglacial topography is an important feature in numerous ice-sheet analyses and can drive the routing of water at the bed. Bed topography is primarily measured with ice-penetrating radar. Significant gaps, however, remain in data coverage that require interpolation. Topographic interpolations are typically made with kriging, as well as with mass conservation, where ice flow dynamics are used to constrain bed geometry. However, these techniques generate bed topography that is unrealistically smooth at small scales, which biases subglacial water flowpath models and makes it difficult to rigorously quantify uncertainty in subglacial drainage patterns. To address this challenge, we adapt a geostatistical simulation method with probabilistic modeling to stochastically simulate bed topography such that the interpolated topography retains the spatial statistics of the ice-penetrating radar data. We use this method to simulate subglacial topography using mass conservation topography as a secondary constraint. We apply a water routing model to each of these realizations. Our results show that many of the flowpaths significantly change with each topographic realization, demonstrating that geostatistical simulation can be useful for assessing confidence in subglacial flowpaths.
Can Geometry Achievement and Geometric Habits of Mind Be Improved Online? Reflections From a Computer-Aided Intervention

This article aims to examine the impact of an online learning environment enriched with computer activities on geometry achievement and geometric habits of mind (GHoM). The study includes reflections from an online application of a one-term computer-aided mathematics teaching course in which the participants were preservice teachers (PTs) enrolled in a faculty of education. A test was used to measure all participating PTs’ learning outcome and determine their GHoM. In addition, clinical interviews were administered before and after the intervention to examine the opinions of the PTs regarding the online learning environment. The results revealed that the design provided for positive development in terms of both geometry achievement and GHoM. However, the impact was found to be very limited for some components of GHoM. The article presents a discussion of the effective aspects of the intervention.
Range‐only control for cooperative target circumnavigation of unmanned aerial vehicles

This article deals with cooperative target circumnavigation control strategy for a team of unmanned aerial vehicles (UAVs) when only range measurements are available. First, we propose novel guidance laws based solely on range measurements for tracking and cooperation. Then, convergence and stability of the proposed guidance laws are proved by a rigorous inference of mathematics. Moreover, an illustrative simulation shows that the controlled UAVs orbit around a target and maintain even spacing along the common circle, which confirms the performance of the proposed guidance algorithms.
Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.
The Changes in Yield, Biochemica l Properties and Essential Oil Compounds of Saffron (Crocus Sativus L.) Plants Treated with Organic and Inorganic Fertilizers Under Dryland Farming System

The use of environmental friendly fertilizers is the main agricultural practice to improve the plant quality and quantity. The present study aims to assess the effect of organic and inorganic fertilizers on flower and stigma yield, biochemical properties and essential oil (EO) composition of saffron (Crocus Sativus L.) in research station of Hamand Absard, Iran. The plants were treated with cattle manure (20 t ha-1) and foliar application of delfard (7 kg ha-1) and floral P (2.5 kg ha-1) in a completely randomized block design (CRBD) with three replications. The results showed significant increases of flower number and yield under all fertilizers compared to control. The yield of fresh and dry stigma under manure application was higher than floral and delfard. The response of chlorophyll (Chl) concentration was different under fertilizers. Floral and manure application led to higher Chl a compared to control. Chl b under manure and delfard was significantly higher than control and floral. TPC and TFC were significantly improved by organic and inorganic fertilizers. The main improvement of TPC and TFC was obtained under manure application followed by floral and delfard. GC/MS analysis showed the main EO composition were Methyl pentanoate (25.8- 62.15%) followed by Dihydro- β- ionol (17.7-34.79%), and hexadecyl acetate (4.9-9.4%), representing different responses to fertilizers. To sum up, the best fertilizer to improve the yield of saffron was manure followed by floral and delfard.
A review on underwater shockwave processing and its application in food technology

Abstract Underwater shockwave processing (USP) is a non-thermal food processing method where a high-energy impulse is generated near a food product submerged in a liquid. The resulting shockwave transfers energy to the food, and is used to improve quality, safety, and nutritional aspects. This review presents the origin and evolution of the technology, principles of shockwave generation, mechanism of action, and applications in the food industry. The most common food application of USP is currently meat tenderization, where it is used to improve the sensory characteristics of meat as a value-added process. The use of USP as a pretreatment process has also been investigated to increase the yield and nutritional value of extracted juice and oil via softening of plant tissues. This technique also has an impact on food-borne pathogens and spoilage microorganisms in food, however, it is more effective when combined with other hurdles. Major challenges facing the industrial implementation of underwater shockwave technology include the lack of appropriate packaging materials resistant to the disruptive effects of shockwaves, the capital investment required, and a lack of regulatory information pertaining to USP. So far, most studies of underwater shockwaves on food are at the laboratory scale and validation stage. Further research endeavors and collaboration between food scientists, engineers, and regulators are necessary to scale up this technology to industrial implementation.
Effects of 4 Thiopurine Compounds on Nitric Oxide Production and Cell Viability of HIG-82 Synoviocytes and RAW 264.7 Macrophages

1Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia. 2Faculty of Medicine and Health Sciences, UniversitiSains Islam Malaysia, Kuala Lumpur, Malaysia. 3Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia. 4Laboratory of UPM-MAKNA Cancer Research (CANRES), Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. *Correspondence Author E-mail: nazrulh@upm.edu. my
Influence of Deficit Irrigation and Nitrogen Fertilization on Potato Yield, Water Productivity and Net Profit

Potato is described as a sensitive crop to short periods of irrigation deficit and Nitrogen deficiency. Farmers usually use excessive amounts of irrigation water and fertilizers to obtain higher yields. Recently, there is a shortage of water resources and therefore it is fundamentally important to employ better irrigation and fertilization management of such crops. A field experiment was undertaken to assess the effects of irrigation regime and Nitrogen fertilization on potato tuber yield, Chlorophyll content and irrigation water productivity of potato crop. Four levels of applied water 1.25, 1.00, 0.75 and 0.50 of the crop evapotranspiration (ET c ) were examined with three amounts of Nitrogen (N) 50, 125, and 200 kg ha -1 . The results clearly demonstrated a significant influence of both factors on potato yield. The highest potato yield of 29.3 Mg ha -1 was obtained with the treatment of 1.25 ET c and 200 kg N ha -1 . Water productivity was conversely proportion with increasing amount of applied water since the highest average record of 27.2 kg m -3 obtained with lowest amount of applied water. The results further showed an inversely proportional between irrigation regime and water productivity which was reflected on the total profits. Consequently, the results suggest that potato can be grown in arid and semi-arid environments with acceptable potato yield and quality while saving water and using Nitrogen fertilization more efficiently.
Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Routing remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.
Audit Lag Criteria Report as a Determination of the Reliability and Quality of Auditor's Report in Indonesia

This paper aims to know the quality indicators of the financial statements which consist of profitability, solvency and reputation of Registered Public Accountant ( KAP ) to the audit lag with company size as a moderation variable either partially or simultaneously in LQ45 companies. This research is a comparative causal research with ex post facto approach. Purposive sampling technique is used in this research and there are 18 samples collected by this technique from LQ45 in Indonesia Company Issuer year 2010-2016 . T he data analyzed research is 126. Data analysis technique used M oderated R egression A nalysis (MRA) with the Application of Eviews Software . The study concluded that study showed that solvency, reputation of the public accounting firm and company size had a significant effect on Audit Lag, while profitability had no significant effect on Audit Lag. The size of a company able to moderate the effect of independent variables to the Audit Lag and not have to moderate the effect of the profitability to the Audit Lag.
THE INFLUENCE OF CRUDE OIL PRICES VOLATILITY, THE INTERNET AND EXCHANGE RATE ON THE NUMBER OF FOREIGN TOURIST ARRIVALS IN INDONESIA

This paper seeks to examine the influence of crude oil prices volatility, the internet, and exchange rate on the number of foreign tourist arrivals in Indonesia. Using a time-series dataset from 1995 to 2018 and employing an autoregressive distributed lag (ARDL) model plus an error correction model (ECM-ARDL), our research shows that in the long run, the internet has a positive influence on the number of foreign tourist arrivals. Every 1% rise in the internet, the number of foreign tourist arrivals rises by 0.49%. However, crude oil prices volatility and exchange rates do not significantly affect the number of foreign tourist arrivals. In the short run, there is a negative influence of crude oil price volatility on the number of foreign tourist arrivals. Meanwhile, the exchange rate positively affects the number of foreign tourist arrivals, meaning that the appreciation (depreciation) of the IDR exchange rate against the USD causes the number of foreign tourist arrivals to go down (up).
Convergence of user and service provider perspectives in mobile cloud computing environment: Taxonomy and challenges

Mobile cloud computing (MCC) has been introduced with the intuition to ascertain the computing requirements of resource constraint smart mobile devices (SMDs). Meanwhile, the research community has put forward a wide variety of solutions by independently considering the operational issues of cloud service user (CSU) side (e.g., when to offload) and cloud service provider (CSP) side (e.g., where to deploy the cloud data centers). As the MCC will evolve, the decision regarding the adoption of a solution at one end would likely rely on the features supported or required from the other side; for example, a CSU might decide to offload the execution of delay‐sensitive applications only if the CSP deploys the cloud data centers in his/her proximity. The identification and convergence of such dependencies are vital for flourishing the MCC. However, a cognitive representation of CSU and CSP side research proposals can only aid in the identification of such dependencies. In this paper, we develop a thematic taxonomy of the current CSU side as well as CSP side frameworks. Further, we review the current CSU side and CSP side frameworks by using the thematic taxonomy and analyze the implications as well as critical aspects of proposed solutions. We also outline the potential dependencies between the solutions of both sides. These dependencies are derived on the basis of elicited taxonomy. Finally, we put forward open research challenges that demand future efforts for the seamless integration of CSU side perspectives and CSP side perspectives.
Deep-Learning-Based Power Generation Forecasting of Thermal Energy Conversion

ORC is a heat to power solution to convert low-grade thermal energy into electricity with relative low cost and adequate efficiency. The working of ORC relies on the liquid–vapor phase changes of certain organic fluid under different temperature and pressure. ORC is a well-established technology utilized in industry to recover industrial waste heat to electricity. However, the frequently varied temperature, pressure, and flow may raise difficulty to maintain a steady power generation from ORC. It is important to develop an effective prediction methodology for power generation in a stable grid system. This study proposes a methodology based on deep learning neural network to the predict power generation from ORC by 12 h in advance. The deep learning neural network is derived from long short-term memory network (LSTM), a type of recurrent neural network (RNN). A case study was conducted through analysis of ORC data from steel company. Different time series methodology including ARIMA and MLP were compared with LSTM in this study and shows the error rate decreased by 24% from LSTM. The proposed methodology can be used to effectively optimize the system warning threshold configuration for the early detection of abnormalities in power generators and a novel approach for early diagnosis in conventional industries.