Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects

Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.
Realization of homojunction PN AlN diodes

Aluminum nitride (AlN) is an insulator that has shown little promise to be converted to a semiconductor via impurity doping. Some of the historic challenges for successfully doping AlN include a reconfigurable defect formation known as a DX center and subsequent compensation that causes an increase in dopant activation energy resulting in very few carriers of electricity, electrons, or holes, rendering doping inefficient. Using crystal synthesis methods that generate less compensating impurities and less lattice expansion, thus impeding the reconfiguration of dopants, and using new dopants, we demonstrate: (a) well behaved bulk semiconducting functionality in AlN, the largest direct bandgap semiconductor known with (b) substantial bulk p-type conduction (holes = 3.1 × 1018 cm−3, as recently reported in our prior work), (c) dramatic improvement in n-type bulk conduction (electrons = 6 × 1018 cm−3, nearly 6000 times the prior state-of-the-art), and (d) a PN AlN diode with a nearly ideal turn-on voltage of ∼6 V for a 6.1 eV bandgap semiconductor. A wide variety of AlN-based applications are enabled that will impact deep ultraviolet light-based viral and bacterial sterilization, polymer curing, lithography, laser machining, high-temperature, high-voltage, and high-power electronics.
Image-Based Analysis Revealing the Molecular Mechanism of Peroxisome Dynamics in Plants

Peroxisomes are present in eukaryotic cells and have essential roles in various biological processes. Plant peroxisomes proliferate by de novo biosynthesis or division of pre-existing peroxisomes, degrade, or replace metabolic enzymes, in response to developmental stages, environmental changes, or external stimuli. Defects of peroxisome functions and biogenesis alter a variety of biological processes and cause aberrant plant growth. Traditionally, peroxisomal function-based screening has been employed to isolate Arabidopsis thaliana mutants that are defective in peroxisomal metabolism, such as lipid degradation and photorespiration. These analyses have revealed that the number, subcellular localization, and activity of peroxisomes are closely related to their efficient function, and the molecular mechanisms underlying peroxisome dynamics including organelle biogenesis, protein transport, and organelle interactions must be understood. Various approaches have been adopted to identify factors involved in peroxisome dynamics. With the development of imaging techniques and fluorescent proteins, peroxisome research has been accelerated. Image-based analyses provide intriguing results concerning the movement, morphology, and number of peroxisomes that were hard to obtain by other approaches. This review addresses image-based analysis of peroxisome dynamics in plants, especially A. thaliana and Marchantia polymorpha.
FAHP-Based Reliability Evaluation of Distributed IoT Devices in a Distribution Power Grid

Reliable operation of distributed internet of things (IoT) devices is essential for supporting two-way interaction between energy flow and information flow in a distribution power grid. Traditional reliability evaluation methods suffer from several challenges including complex evaluation indicator selection and inadaptability of fixed weights with actual reliability requirements. In this paper, we first establish a multilevel reliability evaluation indicator system, where the indicators are selected through joint consideration of comprehensiveness and effectiveness. Then, we propose a fuzzy analytic hierarchy process- (FAHP-) based reliability evaluation method, where a three-layer reliability evaluation architecture is established. Specifically, a 0.1-0.9 scaling method is adopted to establish the fuzzy judgment matrix and scale the important relationship among state-layer elements, which can be dynamically adjusted to generate the weights adapting with actual operation state. The scores of distributed IoT devices and corresponding reliability levels can be obtained through the weighted summation of the scores of each layer. Simulation results verify the effectiveness and accuracy of the proposed method through comparing with actual reliability value and two existing evaluation methods.
Robust and high-sensitivity thermal probing at the nanoscale based on resonance Raman ratio (R3)

Raman spectroscopy-based temperature sensing usually tracks the change of Raman wavenumber, linewidth and intensity, and has found very broad applications in characterizing the energy and charge transport in nanomaterials over the last decade. The temperature coefficients of these Raman properties are highly material-dependent, and are subjected to local optical scattering influence. As a result, Raman-based temperature sensing usually suffers quite large uncertainties and has low sensitivity. Here, a novel method based on dual resonance Raman phenomenon is developed to precisely measure the absolute temperature rise of nanomaterial (nm WS2 film in this work) from 170 to 470 K. A 532 nm laser (2.33 eV photon energy) is used to conduct the Raman experiment. Its photon energy is very close to the excitonic transition energy of WS2 at temperatures close to room temperature. A parameter, termed resonance Raman ratio (R3) Ω=IA1g/IE2g is introduced to combine the temperature effects on resonance Raman scattering for the A1g and E2g modes. Ω has a change of more than two orders of magnitude from 177 to 477 K, and such change is independent of film thickness and local optical scattering. It is shown that when Ω is varied by 1%, the temperature probing sensitivity is 0.42 K and 1.16 K at low and high temperatures, respectively. Based on Ω, the in-plane thermal conductivity (k) of a ∼25 nm-thick suspended WS2 film is measured using our energy transport state-resolved Raman (ET-Raman). k is found decreasing from 50.0 to 20.0 Wm−1 K−1 when temperature increases from 170 to 470 K. This agrees with previous experimental and theoretical results and the measurement data using our FET-Raman. The R3 technique provides a very robust and high-sensitivity method for temperature probing of nanomaterials and will have broad applications in nanoscale thermal transport characterization, non-destructive evaluation, and manufacturing monitoring.
Framework for selecting occupancy-focused energy interventions in buildings

Energy policy tools aimed at achieving energy savings for buildings have often yielded less than the predicted savings. One reason for these results is that the design of these tools often neglects the actions and behaviours of the building occupants, and focuses more on the cost and ease of implementation. This research highlights the importance of identifying the diverse characteristics of occupants that significantly contribute to environmental problems, and the factors that make sustainable behavioural patterns attractive. A multilevel intervention strategy tailored to various occupants’ characteristics to produce and maintain large-scale energy savings for buildings over time is proposed. To achieve this, the framework adopts a motivation/opportunity/ability (MOA) approach from the consumer and social marketing fields, where intervention strategies can be regarded as advertisements enticing the building occupants to adopt certain energy-use characteristics. The conceptual framework involves: measuring occupants’ pre- and post-intervention exposure MOA level and energy-use profiles; clustering occupants based on identified characteristics; and accordingly choose energy-efficiency intervention strategies. The results of a case study of an actual building highlight the capabilities of the proposed framework in the selection of effective intervention strategies to achieve the required energy reductions.
Social Spammer Detection via Convex Nonnegative Matrix Factorization

With the increasing popularity of social network platforms such as Twitter and Sina Weibo, a lot of malicious users, also known as social spammers, disseminate illegal information to normal users. Several approaches are proposed to detect spammers by training a classifier with optimization methods and mainly using content and social following information. Due to the development of spammers’ strategies and the courtesy of some legitimate users, social following information becomes vulnerable to fake by spammers. Meanwhile, the possible social activities and behaviors vary significantly among different users, which leads to a large yet sparse feature space to be modeled by existing approaches. To address issues, in this paper, we propose a new approach named CNMFSD for spammer detection in social networks, which exploits both content information and users interaction relationships in an innovative manner. We have empirically validated the proposed method on a real-world Twitter dataset, and experimental results show that the proposed CNMFSD method improves the detection performance significantly compared with baselines.
Graphene Oxide Molecularly Imprinted Polymers as Novel Adsorbents for Solid-Phase Microextraction for Selective Determination of Norfloxacin in the Marine Environment

In this study, a novel sample pretreatment strategy of solid-phase microextraction using graphene oxide molecularly imprinted polymers as adsorbents coupled with high-performance liquid chromatography was developed to detect norfloxacin in the marine environment. As a carrier, the imprinted polymers were synthesized by precipitation polymerization with graphene oxide. Compared with graphene oxide non-imprinted polymers, the graphene oxide molecularly imprinted polymers exhibited higher adsorption capacity towards norfloxacin. The synthesized polymeric materials were packed into a molecularly imprinted solid-phase microextraction cartridge, and critical parameters affecting the extraction process were optimized. Under the optimized molecular imprinted solid-phase microextraction condition, the proposed method was applied to the analysis of norfloxacin for seawater and fish with satisfactory recovery (90.1–102.7%) and low relative standard deviation (2.06–5.29%, n = 3). The limit of detection was 0.15 μg L−1 and 0.10 μg kg−1 for seawater and fish, respectively. The study revealed that the proposed molecularly imprinted solid-phase microextraction represents an attractive sample pretreatment strategy for the analysis of norfloxacin in the marine environment.
PREDICTING METHOD OF THE MAINTENANCE CYCLE FOR REPAIRABLE SYSTEM BASED ON THE FAULT STATISTICS

The paper puts forward a fault statistics model for predicting the maintenance cycle of repairable system. The new method can not only be used to make multi sample statistical analysis for a large number of epuipments produced in batches, but also be used for single sample state estimation of complex system produced singly. Farthermore, this paper introduces the estimating method of the new model parameter and discusses its application in practical situation.
A Review on Face Detection Based on Convolution Neural Network Techniques

Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.
COVID-19 mortality is associated with pre-existing impaired innate immunity in health conditions

COVID-19 can be life-threatening to individuals with chronic diseases. To prevent severe outcomes, it is critical that we comprehend pre-existing molecular abnormalities found in common health conditions that predispose patients to poor prognoses. In this study, we focused on 14 pre-existing health conditions for which increased hazard ratios of COVID-19 mortality have been documented. We hypothesized that dysregulated gene expression in these pre-existing health conditions were risk factors of COVID-19 related death, and the magnitude of dysregulation (measured by fold change) were correlated with the severity of COVID-19 outcome (measured by hazard ratio). To test this hypothesis, we analyzed transcriptomics data sets archived before the pandemic in which no sample had COVID-19. For a given pre-existing health condition, we identified differentially expressed genes by comparing individuals affected by this health condition with those unaffected. Among genes differentially expressed in multiple health conditions, the fold changes of 70 upregulated genes and 181 downregulated genes were correlated with hazard ratios of COVID-19 mortality. These pre-existing dysregulations were molecular risk factors of severe COVID-19 outcomes. These genes were enriched with endoplasmic reticulum and mitochondria function, proinflammatory reaction, interferon production, and programmed cell death that participate in viral replication and innate immune responses to viral infections. Our results suggest that impaired innate immunity in pre-existing health conditions is associated with increased hazard of COVID-19 mortality. The discovered molecular risk factors are potential prognostic biomarkers and targets for therapeutic intervention.
ENGAGING VIRTUAL REALITY TECHNOLOGY TO DETERMINE PRO-ENVIRONMENTAL BEHAVIOUR: THE INDIAN CONTEXT

The main purpose of this study is to capture key pro-environmental behaviours that are triggered when individuals are subjected to a virtual environment simulation of a pristine tourism destination. The study made use of virtual reality headsets to gauge potential predictors of pro-environmental behaviour. Pre and post intervention response differentials were recorded through a structured questionnaire on 100 individuals. The study was divided into two stages. The first stage comprised of the PLS-SEM algorithm which empirically tested postulations anchored on the Pro-Environmental Behaviour framework. The second phase of the study deployed the PLS-MGA algorithm to observe changes in propensities. Findings reveal that, virtual reality interventions partially effect how individuals perceive pro-environmental behaviour. The study advices policy makers and practitioners to cultivate industry 4.0 technologies like virtual reality to raise awareness about climate action among tourists. For academicians, the study expands the utility sprectrum of the Pro-Environmental Behaviour (PEB) framework and it is suggested that future studies inculcate virtual/augmented/extended reality competencies in experiment based investigations. The study maybe repeated in the context of other developing economies where sustainable tourism development remain a challenge.
Air flow distribution for different wind pressure coefficient values in a naturally ventilated room

The objective of this research is to investigate airflow distribution inside a test room, which is naturally ventilated through adjustable louvres for summer conditions. The test room is located in a sheltered area. Indoor air temperature and velocity across the room at four locations and six different levels are measured. The air pressures and velocities across the opening are measured in order to allow an accurate estimation of wind pressure coefficients. The VENT simulation program is used to predict the spatial distribution of the airflow. The test room is fictitiously divided into 27 zones of equal size, each with a volume of 0.8 m 3 . The program calculates temperature and air speed within a single zone using a network airflow model. The numerical model used is based on an explicit finite difference formulation for unsteady heat flows within the building fabric. One of the program inputs is the wind pressure coefficient (c p ). The simulation was performed for C P values calculated using a published parametric model and for the measured values. In general the measurements demonstrate considerable air movement inside the room. The air enters the test room from the bottom and exits from the top. The predicted flow showed a similar trend and the simulation results were in close agreement with the measured data.
Development of Collision Avoidance System in Slippery Road Conditions

This paper presents an autonomous vehicle’s path planning and tracking system optimized for collision avoidance on slippery roads. During path planning, a path that can induce the maximum possible lateral acceleration is generated through the fifth-order spline in consideration of the tire-road friction. The generated path is tracked based on the model predictive control (MPC), and the nonlinearity generated by the tire and low friction surface is reflected through the extended bicycle model and the combined brushed tire model. Inside the controller, a new type of yaw rate constraint considering side-slip angles is utilized to prevent the vehicle from becoming unstable on slippery roads and maximize lateral maneuver of the vehicle at the same time. The proposed system is verified by the vehicle dynamics software CarSim, and the simulation results show that it significantly increases the possibility of collision avoidance in slippery road conditions.
Microplastic Pollution Focused on Sources, Distribution, Contaminant Interactions, Analytical Methods, and Wastewater Removal Strategies: A Review

Plastics have been one of the most useful materials in the world, due to their distinguishing characteristics: light weight, strength, flexibility, and good durability. In recent years, the growing consumption of plastics in industries and domestic applications has revealed a serious problem in plastic waste treatments. Pollution by microplastics has been recognized as a serious threat since it may contaminate all ecosystems, including oceans, terrestrial compartments, and the atmosphere. This micropollutant is spread in all types of environments and is serving as a “minor but efficient” vector for carrier contaminants such as pesticides, pharmaceuticals, metals, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The need to deeply study and update the evolution of microplastic sources, toxicology, extraction and analysis, and behavior is imperative. This review presents an actual state of microplastics, addressing their presence in the environment, the toxicological effects and the need to understand their extent, their interactions with toxic pollutants, the problems that arise in the definition of analytical methods, and the possible alternatives of treatments.
Comparative Study of Iron, Manganese and Nickel Contamination Potential of Disc and Hammer Milling Equipment and their Toxic Effect on Rat Kidney

Aim: To investigate the effect of hammer and disc milling equipment on the levels of Iron (Fe), Manganese (Mn) and Nickel (Ni) contents in maize (Zea mays) flour and the consequent impact on the kidney of albino rats. Study Design: The rats were randomly divided into groups of five rats per group. Six of the groups were fed with maize flour milled with a hammer mill, while the other six groups were fed with maize flour milled with a disc mill, and the thirteenth group was fed with crushed flour in a mortar and pestle as a control. The rats were sacrificed by decapitation under anesthesia on the 14th and 28th days. Place and Duration of Study: Departments of Biochemistry and Histology laboratories of the University of Jos, Nigeria, between, January to June, 2021. Methodology: For six days, a portion of the maize grains was milled with a hammer mill, another with a disc mill, and a third fraction was crushed into flour with a wooden mortar. The inductive coupled plasma mass spectrophotometric method was used to determine the levels of Fe, Mn, and Ni in maize flour. To avoid lysing the blood, blood samples were carefully collected by allowing it to run down the test tube's wall. The blood was allowed to coagulate at room temperature before being centrifuged and the serum collected and frozen until it was time for biochemical analysis. The kidneys of the rats were excised, cleaned, and preserved in chloroform until histological examinations were required. Results: The results show that Fe (325.16 ± 30.00 mg/kg and 205.05 ± 30.20 mg/kg) and Ni (20.92 ± 5.92 mg/kg and 18.00 ± 2.70 mg/kg) levels were extremely high in both disc and hammer milling machines. The Fe and Mn values in disc milled maize flour were higher than those in hammer milled maize flour. Rats in all groups had significantly higher serum urea, creatinine, K+, Na+, and Cl- levels than the control group. The kidney tissues of all the rats in the control and hammer-milled flour groups were normal, except for group 1 of the disc mill, which showed mild damage, according to the histopathological analysis. Conclusion: The Day 1 group fed with disc-milled flour showed a dense cast, atrophy, and nuclei loss in their kidney tissue. According to the results of the study, hammer milled maize flour is less harmful than disc milled maize flour, as shown by kidney histopathology.