Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure E-transaction system based on smart contract and InterPlanetary file system

As an integral part of our life in modern society, e-transaction under the third-party payment model is widely used. However, this model may cause some issues such as privacy exposure, data being stolen or tampered, and unsatisfactory fund supervision. To solve these problems, the paper proposes a secure transaction system based on the smart contract and the InterPlanetary file system. Blockchain technology is used to realize decentralized transactions and payments, ensure the anonymity of users and stores in the transaction process, and guarantee that transaction records and payment records cannot be tampered with. BF-IBE algorithm is introduced to encrypt transaction records to solve the problem of transaction information exposure and ensure its confidentiality. And InterPlanetary file is used for distributed storage of encrypted orders to improve the storage and query efficiency. Based on smart contract and blockchain technology, traceable supervision of capital flow is conducted to solve the problem of unsatisfactory capital supervision and ensure the correctness and integrity of capital flow. Smart contracts and digital signature technology are combined to authenticate users to ensure the integrity of logistics data, only allowing legitimate users to modify logistics information. Finally, the security proof and experimental simulation and testing are carried out on the new system, the off-chain and on-chain computing efficiency and resource consumption are analyzed in detail, and the results show that the solution is secure and efficient.
Multifunctional Milk-Derived Small Extracellular Vesicles and Their Biomedical Applications

In recent years, small extracellular vesicles (sEVs) have been regarded as the next generation of novel delivery systems after lipid nanoparticles because of their advantages and huge prospects in drug delivery. Studies have shown that sEVs are abundant in milk and therefore can be a large and economical source of sEVs. Natural milk-derived small extracellular vesicles (msEVs) have important functions such as immune regulation, anti-bacterial infection, anti-oxidative, etc., and play a beneficial role in human health at multiple levels, including intestinal health, bone/muscle metabolism, and microbiota regulation. In addition, because they can pass the gastrointestinal barrier and have low immunogenicity, good biocompatibility, and stability, msEVs are considered a crucial oral drug delivery vehicle. Moreover, msEVs can be further engineered for targeted delivery to prolong the circulation time or enhance local drug concentrations. However, msEVs separation and purification, complex contents, and quality control hinder their application in drug delivery. This paper provides a comprehensive review of the biogenesis and characteristics, isolation and purification, composition, loading methods, and function of msEVs, based on which their applications in biomedical fields are further explored.
Distributed Rendezvous placement for producer mobility support in NDN-IoT

Named Data Networking is a new networking architecture proposed as a potential design for the next generation of networks. However, supporting the mobility of producers in NDN-based IoT systems remains an open challenge for researchers to address. To tackle this problem, the NDN research group introduced a solution called KITE [1]. Since this solution is an initial design, some issues to be resolved remain, including the rendezvous placement selection. The location of the rendezvous servers can significantly reduce the path stretch efficiently. This paper focuses on improving the KITE solution by addressing two specific issues. First, we aim to determine the optimal number of rendezvous servers to use in the network. Second, we seek to identify the best locations for the rendezvous servers to minimize the path stretch efficiently. We propose an evaluation metric called average hop count based on network centrality measures to select the number and the location for the rendezvous. We evaluate the Average hop count under different network centrality measures using random scale-free network topology. Our results show that the betweenness centrality measure performs better than other measures, and we, therefore, suggest using it as a metric for selecting the number of rendezvous servers and their locations.
A Fuzzy logic based objective function to improve reliability of RPL routing protocol in LLNs

In wireless networks, a routing protocol is considered critical for the efficient transmission of packets along with MAC protocol. RPL protocol is introduced as a routing protocol for LLNs. RPL protocol is designed to offer the best path for data packet transmission in the specific resource constraint environment, such as the limitation of battery power, processing, and storage. It uses an objective function to choose the best routing paths for data packet transmission in the wireless network. However, the key limitation of these objective functions is that they use a single routing metric namely, Expected Transmission Count (ETX) or Hop Count. The use of single routing metric results in the degradation of the overall performance of the RPL protocol in the LLNs. In order to address this issue, various schemes are proposed by different researchers. This paper provides a thorough analysis of the composite routing metric whose objective is to mitigate the limitation of a single routing metric. A fuzzy logic-based scheme framework is also proposed in this paper to resolve this single routing metric limitation.
Financial development, interest rate pass-through and interest rate channel of monetary policy

Abstract The paper examined the interest rate operations and processes in Nigeria and examined the role of financial development in incentivizing central bank monetary policies from the monetary policy rate to the money market rates, lending rates, and deposit rates. The analysis covered the period from 1981 to 2021 with sub-samples for 1981 to 2011 and 1991 to 2021 to test the consistency of the findings. The findings confirmed that interest rate pass-through is incomplete for Nigeria, albeit to a lower degree in the short run compared to the long run. The reasons for this may be due to interest rate stickiness, problems of asymmetric information, and bank switching costs. Also, the findings confirmed that financial development weakens the impact of monetary policy on the interest rate pass-through process, while the analysis of the asymmetric mean adjustment lags confirmed that changes in the policy rate are transmitted to the deposit and lending rates within the year it was announced. The analysis confirmed that the results across each sub-sample and the robustness tests are consistent with the main analysis. Therefore, it is imperative that policymakers should account for financial development when designing monetary policy effectiveness since it can hinder or strengthen the interest rate monetary policy channel.
XAMS - development of liquid xenon detector technology for dark matter searches

One of the most promising detector technologies to directly detect weakly interacting massive particle (WIMP) dark matter are time projection chambers (TPCs) filled with dual-phase (liquid and gaseous) xenon. The hypothetical WIMP could scatter with atoms in the liquid and the transferred recoil energy would lead to a prompt light signal (S1) and, after a drift time of ionisation electrons into the gas phase, to a secondary signal (S2). Only a detection of a dark matter particle interaction can provide evidence beyond doubt for its existence and could potentially solve a puzzle that combines astroparticle physics and cosmology. To date, even the most sensitive detectors (filled with more than 100 kg of liquid xenon) could not produce indisputable proof of dark matter. In order to improve the sensitivity of future dual-phase xenon TPCs this thesis investigated the GridPix detector as an alternative for the detection of the S2 signal. Unlike the typically used photomultiplier tubes, GridPix offers a lower radioactive content. This could reduce the intrinsic background of the TPC, hence making it more sensitive to small energy deposits. However, the studies showed that GridPix does not operate stably enough at the challenging conditions of high-purity, low-temperature gas. To test other detector technologies as well as to study xenon properties and data acquisition techniques, a small-scale dual-phase xenon TPC was built and commissioned at Nikhef: XAMS, a xenon facility at Amsterdam. Calibration measurements showed the basic functioning of the TPC and led to improvements that make XAMS a valuable testing ground for future dark matter search experiments.
A Voltage Controlled Attenuator Based on Multilayer Graphene

This letter introduces a graphene voltage-adjustable attenuator with an equalization effect. The attenuator consists of a pair of LC bias circuits and microstrip lines. Graphene was located in the microstrip line gap and the bias voltage at both ends of the graphene is adjusted to achieve dynamically tunable attenuation. At the same time, the symbolically defined device (SDD) model is established based on the conductivity formula of graphene. By equivalent graphene to a resistor and inductor in a series circuit, the voltage adjustable and nonlinear characteristics of graphene are simulated well. In addition, a prototype attenuator operating in the frequency band from 0.1 to 5 GHz has been fabricated and tested. When the bias voltage increases from 0 to 5.5 V, the attenuation range measured at 3 GHz is 7.0–17.9 dB, and the equalization range measured at 0.1 GHz is 20.2–25.1 dB. The simulation results are in good agreement with the measured results.
U-Net-Based CNN Architecture for Road Crack Segmentation

Many studies on the semantic segmentation of cracks using the machine learning (ML) technique can be found in the relevant literature. To date, the results obtained are quite good, but often the accuracy of the trained model and the results obtained are evaluated using traditional metrics only, and in most cases, the goal is to detect only the occurrence of cracks. Particular attention should be paid to the thickness of the segmented crack since, in road pavement maintenance, the width of the crack is the main parameter and is the one that characterizes the severity levels. The aim of our study is to optimize the crack segmentation process through the implementation of a modified U-Net model-based algorithm. For this, the Crack500 dataset is used, and then the results are compared with those obtained from the U-Net algorithm, which is currently found to be the most accurate and performant in the literature. The results are promising and accurate, as the findings on the shape and width of the segmented cracks are very close to reality.
Popov–Belevitch–Hautus type tests for the controllability of linear complementarity systems

It is well-known that checking certain controllability properties of very simple piecewise linear systems are undecidable problems. This paper deals with the controllability problem of a class of piecewise linear systems, known as linear complementarity systems. By exploiting the underlying structure and employing the results on the controllability of the so-called conewise linear systems, we present a set of inequality-type conditions as necessary and sufficient conditions for controllability of linear complementarity systems. The presented conditions are of Popov–Belevitch–Hautus type in nature.
Development of Functional Prebiotic Nutraceutical Supplement Mix for Geriatrics

This study aims to develop a functional prebiotic nutraceutical supplement mix for geriatrics and evaluate its nutritional and sensory characteristics. Prebiotics are non-digestible food nutrients that selectively stimulate the activities of beneficial probiotic organisms in the colon and maintain intestinal microbial balance. Nutraceutical includes functional foods, dietary supplements, and medical foods. Geriatrics is a medical practice that addresses older patients' complex needs and maintains functional independence even in the presence of chronic diseases. Elderly people are at risk for macro and micronutrient deficiencies due to various social, physical, and economic obstacles. This research product aims to promote the ideal blend of grains, legumes, and nuts for elderly people and enhance their nutritional energy. Foxtail millets, oats, garlic, almonds & cashew nuts possess high prebiotic content. This study formulated ten formulations using Response Surface Method – Design Expert 12.0. The third sample (Trial 3) containing foxtail millet (25%), green pea protein isolate (10%), oats (15%) and carrot (15%), pomegranate (15%), garlic (5%), almond & cashew nut (5%), skim milk solids (10%) had the highest score for sensory attributes. The developed combination was high in nutrients and provide ample protein and energy for daily needs. Its nutritional value for 100g is of total protein (11.5%), fat (4.36), carbohydrate (67.57), vitamins, minerals, and total energy (360.17 K/Cal).
Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

Stress is a natural human reaction to demands or pressure, usually when perceived as harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it increases the risk of mental health and physiological uneasiness. Furthermore, chronic stress raises the likelihood of mental health plagues such as anxiety, depression, and sleep disorder. Although measuring stress using physiological parameters such as heart rate variability (HRV) is a common approach, how to achieve ultra-high accuracy based on HRV measurements remains as a challenging task. HRV is not equivalent to heart rate. While heart rate is the average value of heartbeats per minute, HRV represents the variation of the time interval between successive heartbeats. The HRV measurements are related to the variance of RR intervals which stand for the time between successive R peaks. In this study, we investigate the role of HRV features as stress detection bio-markers and develop a machine learning-based model for multi-class stress detection. More specifically, a convolution neural network (CNN) based model is developed to detect multi-class stress, namely, no stress, interruption stress, and time pressure stress, based on both time- and frequency-domain features of HRV. Validated through a publicly available dataset, SWELL–KW, the achieved accuracy score of our model has reached 99.9% (Precision = 1, Recall = 1, $F1-$ score = 1, and MCC = 0.99), thus outperforming the existing methods in the literature. In addition, this study demonstrates the effectiveness of essential HRV features for stress detection using a feature extraction technique, i.e., analysis of variance.
Fast marching based on adaptive mesh

A fast marching algorithm is proposed to solve shape reconstruction problems and the general Eikonal equations.The mesh is dynamically refined according to the surface curvature.Hash table is used to quickly locate neighboring nodes.The causality condition is proven theoretically for first order differential.Experimental results show that the algorithm can improve numerical accuracy,intensify noise immunity and solve shape from shading problems.
Human-Riding Inspired Acceleration Control of a Wheel-Legged Humanoid Robot

Wheel-based robots and leg-based robots are two major types of ground mobile robots. It is well known that their performance properties are complementary. With the combination of the two, several of wheel-legged humanoid robots (WLR) are proposed to overcome obstacles on broken pavement and move at high speed on flat pavement. However, WLR’s high-speed performance, particularly, during acceleration phase is still not good enough compared with traditional wheeled mobile robots. To be detailed, WLR always response slowly and get large velocity tracking error under the influence of its specific dynamic characteristics. In this case, this paper analyses the conflict between the system’s nonlinearity and its inertial dynamics of WLR. Besides, to solve the problem, we present a human-riding inspired acceleration control (HIAC) of a wheel-legged humanoid robot. HIAC contains a partial feedback linearization controller and a human-riding inspired body swing motion. During acceleration phase, it can response quickly and smoothly, also with less error. We demonstrate HIAC’s superiority in acceleration by simulation.
Coronary Artery Anomalies: A Short Case Series and Current Review

Coronary artery anomalies (CAAs) are rare congenital cardiovascular defects that can present in various ways depending on the origin, course, and termination of the abnormal coronary artery fistula. It is sometimes detected incidentally during procedures such as coronary angiography or autopsies. While adults with this condition are often asymptomatic, some may experience angina, congestive heart failure, myocardial infarction, cardiomyopathy, ventricular aneurysms, or sudden cardiac death (SCD). In fact, it is the second leading cause of SCD among young athletes and requires more studies to handle such patients efficiently. To illustrate the many possible manifestations of this unusual diagnosis, we present a series of five cases. We have also reviewed the different varieties of this rare congenital anomaly and discussed the latest diagnostic tests and treatment plans.
A Metaheuristic-Based Modularity Optimization Algorithm Driven by Edge Directionality for Directed Networks

Community detection of directed networks has been demonstrated to be able to reveal network function and dynamic characteristics, due to the special information brought by asymmetric edge directionality, but the critical issue of improving the quality of detected communities has not been fully addressed. In this study, we propose a metaheuristic-based modularity optimization method for directed network community detection (MMDCD), which comprehensively leverages nature-inspired metaheuristics and edge directionality information. Specifically, a nature-inspired metaheuristic algorithm, termed diversified farmland fertility (DFF), is improved to provide an efficient optimization strategy for directed modularity maximization. More importantly, the information induced by edge directionality, including edge density, edge pattern, and node importance, are fully used to improve the quality of optimized partitions. Experimental results on synthetic and real-life networks prove the effectiveness of MMDCD, which is superior to state-of-the-art community detection algorithms in terms of quality and accuracy.
WasteMiner: An Efficient Waste Collection System for Smart Cities Leveraging IoT and Data Mining Technique

An efficient and automated waste collection system can facilitate the overall waste management of any smart city. Many IoT-based techniques are developed to make waste collection system optimized and autonomous. However, none of them considers any situation when IoT-based technique could fail to transfer data due to any unforeseen reasons (e.g, natural calamity, poor maintenance, device failure, connection outage, etc.). In this work, we propose an IoT-based technique named WasteMiner for collecting waste in an efficient way where we also calculate the shortest distances between the waste bins for making the system fuel and time efficient. Moreover, we collect data of waste levels of the bins and apply data mining technique to the collected data for extracting important information. This information help us collecting waste in an efficient way in the case of any system failure in the IoT-based network of WasteMiner. Our experimental results show that WasteMiner is effective and efficient when, i) IoT-based system functions properly, ii) any system failure or communication problem occurs, and iii) if scaled up properly, can be applicable to real-world systems by integrating WasteMiner as one of the key smart city components.
Dietary effects of melatonin on growth performance by modulation of protein bioavailability and behavior in early weaned rats and pigs

Abstract Melatonin, which is produced from tryptophan, exerts various biological functions, including the regulation of circadian rhythm, sedative agents, and antioxidant ability. Therefore, we conducted two experiments with early-weaned rats and pigs to investigate the antioxidant and sedative effects of melatonin. In the rat experiment, a total of 42 rats (21 days old) were used, and the antioxidant capacity was determined. Next, we used 120 early-weaned piglets (21 days old) to conduct a 5-week experiment to evaluate the reductive effect of melatonin on energy-wasting movement, including roaming and fight states. Dietary melatonin supplementation significantly improved growth in both rats and pigs compared to the control groups. Additionally, rats fed a melatonin-supplemented diet showed advanced antioxidant capacity with a decrease in hepatic malondialdehyde concentration compared to rats fed a basal diet. Moreover, dietary melatonin ingestion increased resting and feeding behaviors and reduced roaming and fight behaviors during Days 8–21 compared to the control diet group. Collectively, early weaned animals given dietary melatonin supplementation showed improved growth through upregulation of hepatic antioxidant capacity and minimization of energy-wasting behavior, including roaming and fight states, after pigs’ social hierarchy establishment.
Effect of wall suction on rotating disk absolute instability

This research investigates the effect of uniform suction on the absolute instability of Type I cross-flow modes in the boundary layer on a rotating disk. Specifically, it is designed to investigate whether wall suction would transform the absolute instability into a global mode, as first postulated in the numerical simulations of Davies & Carpenter (J. Fluid Mech., vol. 486, 2003, pp. 287–329). The disk is designed so that with a suction parameter of $0.2$ , the radial location of the absolute instability critical Reynolds number, $Re_{c_{A}}=650$ , occurs on the disk. Wall suction is applied from $Re=317$ to 696.5. The design for wall suction follows that of Gregory & Walker (J. Fluid Mech., 1960, pp. 225–234) where an array of holes through the disk communicate between the measurement side of the disk and the underside of the disk which is inside of an enclosure that is maintained at a slight vacuum. The enclosure pressure is adjustable so that a range of suction or blowing parameters can be investigated. The holes in the measurement surface are covered by a compressed wire porous mesh to aid in uniformizing the suction on the measurement surface of the disk. The mesh is covered by a thin porous high-density polyethylene sheet featuring a $20~{\rm\mu}\text{m}$ pore size which provides a smooth finely porous surface. A companion numerical simulation is performed to investigate the effect that the size and vacuum pressure of the underside enclosure have on the uniformity of the measurement surface suction. Temporal disturbances are introduced using the method of Othman & Corke (J. Fluid Mech., 2006, pp. 63–94). The results document the evolution of disturbance wavepackets in space and time. These show a temporal growth of the wavepackets as the location of the absolute instability is approached which is in strong contrast to the temporal evolution without suction observed by Othman and Corke. The results appear to support the effect of wall suction on the absolute instability postulated by Thomas (PhD thesis, 2007, Cardiff University, UK) and Thomas & Davies (J. Fluid Mech., vol. 663, 2010, pp. 401–433).