Miracle-dz/UniFAR-FTUs huggingface dataset:
UV-C Disinfection Robotic

The authors and team developed a disinfection robot to achieve quick and accurate sanitation under a designated disinfection area. This technology has issues with robots that work at low levels and ignore disinfection zones. The disinfection robot is equipped with a digital system to ensure regular operation. The study aims to achieve a safe and high-efficiency disinfection system using a mobile robot. The designed robot comprises a mobile platform and an ultraviolet-C (UV-C disinfection batten). The UV-C lamps are mounted on the end-effector to provide large-scale, accurate manipulation. It is possible to enable wireless control, custom disinfection, and data monitoring by utilizing a range of ultrasonic sensors and creating an intuitive client interface. We tested the feasibility of the development in the fabrication lab by disinfecting surfaces in three different locations, observing the dose distribution, and observing its effectiveness. The robot can disinfect an area quickly, precisely, and thoroughly.
Network Security with Network Intrusion Detection System using Machine Learning Deployed in a Cloud Infrastructure

A software/device which monitors the network or the internet for any cyber-attack or malicious activity is called Network Intrusion Detection System (NIDS). The conventional detection systems are limited to slow detection and have to be monitored by a network engineer. This research study has attempted to improve the conventional detection systems by including powerful machine learning algorithm to the software thereby increasing its accuracy and preventing the need of a network engineer. The proposed study has used NSLKDD dataset, which has many independent features for different cyber-attacks. The data is pre-processed in order to make the data fit for the algorithm. The most important features are selected using feature selection. The model is trained using different machine learning algorithms. The accuracies of these algorithms are evaluated, and the validation metrics are calculated for the models. The trained model with best accuracy is containerized using Docker to make it machine/operating-system independent. This containerized model is stored in Docker repository. It is deployed into a cloud service provider which has many machine learning dependencies assisting the detection system. In the cloud, a virtual machine is created with required memory and processing unit. The detection system which is in Docker repository as a container is deployed into the cloud. After successful deployment, the user is provided with an Application Programming Interface (API). The user can connect to the internet and use the Network Intrusion Detection System via API to detect any attacks present in the network and secure the devices/software in the network.
Stratus over rolling terrain: Large‐eddy simulation reference and sensitivity to grid spacing and numerics

The formation of low stratus cloud over idealized hills is investigated using numerical model simulations. The main driver for the cloud formation is radiative cooling due to outgoing longwave radiation. Despite a purely horizontal flow, the advection terms in the prognostic equations for heat and moisture produce vertical mixing across the upper cloud edge, leading to a loss of cloud water content. This behavior is depicted via a budget analysis. More precisely, this spurious mixing is caused by the diffusive error of the advection scheme in regions where the sloping surfaces of the terrain‐following vertical coordinate intersect the cloud top. This study shows that the intensity of the (spurious) numerical diffusion depends strongly on the horizontal resolution, the order of the advection schemes, and the choice of scalar advection scheme. A large‐eddy simulation with 4‐ m$$ \mathrm{m} $$ horizontal resolution serves as a reference. For horizontal resolutions of a few hundred meters and simulations carried out with a model setup as used in numerical weather prediction, a strong reduction of the simulated liquid‐water path is observed. In order to keep the (spurious) numerical diffusion at coarser resolutions small, at least a fifth‐order advection scheme should be used. In the present case, a weighted essentially nonoscillatory scalar advection scheme turns out to increase the numerical diffusion along a sharp cloud edge compared with an upwind scheme. Furthermore, the choice of vertical coordinate has a strong impact on the simulated liquid‐water path over orography. With a modified definition of the sigma coordinate, it is possible to produce cloud water where the classical sigma coordinate does not allow any cloud formation.
Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles

A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from Daphnia magna, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.
A Hybrid Compensator for Unbalanced AC Distribution System With Renewable Power

A hybrid compensator with a new coordinated control is proposed for mitigating power quality aspects and integration of renewable power in an unbalanced three-phase four-wire distribution system. The proposed compensator comprises of thyristor switched capacitor (TSC) banks, delta connected thyristor controlled reactor (TCR), a 3rd and a 5th harmonic tuned passive LC filter & a low rating three-phase four-wire D-STATCOM. The TSC-TCR operates asymmetrically for the base var compensation and also eliminates negative sequence current during unbalanced loading at the point of common coupling (PCC). The D-STATCOM works in coordination with the TSC-TCR and the passive filters to compensate fundamental var, zero sequence current, voltage flicker & 7th harmonic currents at the PCC. The D-STATCOM thus serves as a supportive var compensator as well as an active filter in selective harmonic compensation mode, thereby improving the var compensation and harmonic current filtering at the PCC. A new coordination control system is proposed to control the operation of the D-STATCOM which is also employed to integrate a PV source & a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) to the PCC. This scheme with grid connected and islanded mode of operation using D-STATCOM exhibits low-voltage ride-through capabilities. Further, the chance of resonance of the passive filters used with source inductance is eliminated in the proposed scheme. Simulation results show that the proposed hybrid system effectively compensates var, negative & zero sequence current, reduces voltage flicker and harmonic currents at PCC.
High Temporal Variability in Late Blight Pathogen Diversity, Virulence, and Fungicide Resistance in Potato Breeding Fields: Results from a Long-Term Monitoring Study

Long-term site-specific studies describing changes in the phenotypic variability of Phytophthora infestans populations allow quantitative predictions of pathogen spread and possible outbreaks of epidemics, and provide key input for regional resistance breeding programs. Late blight samples were collected from potato (Solanum tuberosum) breeding fields in Estonia during a twelve-year study period between 2001 and 2014. In total, 207 isolates were assessed for mating type and 235 isolates for metalaxyl resistance and 251 isolates for virulence factors. The frequency of mating types strongly fluctuated across the years, whereas the later period of 2010–2014 was dominated by the A2 mating. Despite fluctuations, both mating types were recorded in the same fields in most years, indicating sustained sexual reproduction of P. infestans with oospore production. Metalaxyl-resistant and intermediately resistant strains dominated in the first years of study, but with the progression of the study, metalaxyl-sensitive isolates became dominant, reaching up to 88%. Racial diversity, characterized by normalized Shannon diversity index decreased in time, varying from 1.00 in 2003 to 0.43 in 2013. The frequency of several virulence factors changed in a time-dependent manner, with R2 increasing and R6, R8, and R9 decreasing in time. Potato cultivar resistance background did not influence the frequency of P. infestans mating type, response to metalaxyl, and racial diversity. However, the diversity index decreased in time among isolates collected from resistant and susceptible cultivars, and remained at a high level in moderately resistant cultivars. These data demonstrate major time-dependent changes in racial diversity, fungicide resistance, and virulence factors in P. infestans, consistent with alterations in the control strategies and popularity of potato cultivars with different resistance.
GPX3 Overexpression in Cumulus Cells Entails a Poor Prognosis for Uterine Implantation of Morphotype A Embryos

Simple Summary Despite the fact that human assisted reproduction has undergone extensive development with positive effects on pregnancy, the rates of success are still low. The morphokinetic score of preimplanted embryos is the common routine procedure for ART centres as a reliable predictor of implantation. However, the morphological quality of the embryo is not sufficient to fully ensure implantation success. Therefore, the identification of good predictors of implantation is mandatory to optimise assisted reproduction technology (ART). In this regard, the transcriptome study of the cumulus cells (CCs) is a non-invasive procedure that reflexes the physiological state of oocytes, given the molecular crosstalk between both types of cells. The present study shows a significant down-regulation of the GPX3 gene expression in the CCs isolated from oocytes in pregnant women compared to CCs from non-pregnant women who underwent assisted reproduction. Interestingly, CCs of the highest quality morphotype (A embryos), which achieved implantation success, expressed significantly lower levels of GPX3 expression compared to the embryo morphotype A with implantation failure. Therefore, our observations point to the expression of the GPX3 gene as a potential prognostic marker of bad implantation. Abstract Morphological embryo quality is an accurate prognostic tool for the success of assisted reproduction implantation, although complete certainty cannot be guaranteed. The transcriptome of the cumulus cells could be monitored as a faithful reflex of the physiological state of the oocytes, given the molecular crosstalk between both types of cells. Here, we compare the expression of specific genes related to oocyte competence, such as hyaluronic acid synthase 2 (HAS2), cell division control protein 42 (CDC42), connexin 43 (CX43), and glutathione peroxidase 3 (GPX3), in cumulus cells from implanted versus non-implanted embryos in 25 women, using RT-qPCR. After embryo transfer, two cohorts were differentiated: the pregnant group (women with the implantation of 100% of embryos transferred) versus the non-pregnant group (with an absence of embryo implantation), aiming to compare the possible differential expression of the selected genes in the cumulus cells of embryos from each group. HAS2, CDC42 and CX43 did not reveal differential expression between the two cohorts. However, GPX3 showed significantly reduced expression in the cumulus belonging to the pregnant group. Interestingly, even cumulus cells belonging only to morphotype A embryos showed a significantly lower expression of GPX3 in the pregnancy group. GPX3 overexpression in cumulus cells could be a poor prognostic indicator of implantation, discriminating beyond the capacity of the morphokinetic score. Unveiling the cumulus transcriptome could improve successful implantation in assisted reproduction treatments.
Real-Time Object Detection and Recognition Using Fixed-Wing LALE VTOL UAV

This research aimed to identify objects from a real-time video (30 Hz) stream transmitted from a low-altitude long-endurance (LALE) fixed-wing hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) designed for asset monitoring. Illumination, rotation, and scale variations complicate the video frames created by UAVs’ optical payload, requiring preprocessing. Detection, identification, and classification of different objects (or targets) from a real-time video feed require fast and reliable detection algorithms, models, and computation. The weight restrictions and the onboard power supply only allowed for a Raspberry Pi onboard the UAV, thereby seriously limiting computational power. Image frames were created from the video data after enhancing its quality using image processing algorithms. Then, anchor-based object detection algorithms were applied to the processed and enhanced image frames. The Raspberry Pi at airspeeds of 16–18 m/s requires fast object detection algorithms having minimal computational overhead. A supervised learning technique using a supporting library further augmented the real-time detection at the ground control station (GCS). Four scenarios trained the candidate algorithms, namely: 1) helipad; 2) green area; 3) burned area; and 4) under construction area. The single-shot multibox detector (SSD) aided the supervised learning process during trials utilizing the labeled image datasets. The comparison of detection and identification performance of the SSD with ResNet-50 and ResNet-101 backbones with other methods, such as Faster-RCNN with four backbone architectures, namely: 1) ResNet-101; 2) ResNet-50; 3) deformable ResNet; and 4) FPN in four detection scenarios indicated better precision and reduced detection delays.
Removal of High-Concentration Sulfate from Seawater by Ettringite Precipitation

Due to the worldwide scarcity of fresh water, seawater becomes an alternative base fluid in hydraulic fracturing for oil and gas production. However, the injection of seawater that contains high concentration of sulfate will induce the scale formation and thus reduce hydrocarbon production. One of the most effective ways to solve this problem is to remove sulfate ions from seawater before fracturing application. The objective of this study is to develop an effective and environment-friendly approach to remove sulfate ions from seawater based on coprecipitation of SO42− with NaAlO2 and CaO as ettringite (Ca6Al2(SO4)3(OH)12·26H2O). Residual sulfate concentration in treated seawater was determined when NaAlO2 and CaO dosed at different molar ratios to sulfate. Results showed the efficiency of sulfate removal was more than 90% (4290 ppm to ∼400 ppm) when Al : Ca : S = 2 : 6 : 1. It was found the sulfate precipitation completed in 15 mins with stirring under an alkaline condition (pH ≈ 12) and was not affected by temperature (15°C to 45°C). Increasing the Na+ concentration from 0 to 25,000 ppm in waters resulted in the increment of residual sulfate concentration from 250 to ∼600 ppm, decreasing the removal efficiency. Besides, the analysis of Ca2+ and Mg2+ in treated seawater showed the Ca2+ concentrations were on the similar level as that before the treatment and Mg2+ was removed in the precipitation process, which is beneficial to the application of the treated seawater. The morphology and element analysis of the collected precipitates showed that the ettringites were in a layered shape with composition between Ca6Al2(SO4)3(OH)12 and Ca4Al2(SO4)(OH)12 at the optimized chemical dosage; therefore, the developed ettringite precipitation method could effectively remove sulfate from seawater without toxic chemicals involved, which benefits seawater hydraulic fracturing in an economic way, and this contributes to water sustainability.
Geometric Error Analysis and Compensation in Spherical Generating Grinding of Hemispherical Shell Resonators

The geometric accuracy of a hemispherical shell resonator (HSR) affects the assembly accuracy and final performance of a hemispherical resonant gyroscope in many ways. During the precision grinding of a resonator, the tool-setting error and wear error affect the form and positional accuracy of the inner and outer spherical surfaces. In this study, a compensation method for generating grinding of the HSR is proposed to address this problem. The geometric errors of the inner and outer spherical surfaces are systemically analyzed and a geometric model of the tool setting and wheel wear is established for generating grinding of the HSR. According to this model, a mapping relationship between the wheel pose and size, form, and positional error of the HSR was proposed. Experiments regarding machining, on-machine measurements, and error compensation were performed using the mapping relationship. The results demonstrate that the proposed method can reduce the radius error of the inner and outer spherical surfaces from 10 μm to 1 μm, sphericity from 5 μm to 1.5 μm, and concentricity from 15 μm to 3 μm following grinding. The form and positional errors are simultaneously improved, verifying the effectiveness of the proposed method.
Planning for Lower-Income Households in Privately Developed High-Density Neighbourhoods in Sydney, Australia

In Australia, as in many other countries, private high-density housing is typically marketed as the domain of middle- and higher-income residents. But, in practice, it accommodates many lower-income households. These households often live in mixed-income communities alongside wealthier neighbours, but, because of constrained budgets, they rely more heavily on access to community services and facilities. This has implications for public infrastructure planning in high-density neighbourhoods where private property ownership dominates. In this article, we examine two neighbourhood case studies within the same local government area in Sydney that have sizable populations of lower-income households living in apartments, but which provide markedly different day-to-day experiences for residents. We consider the causes of these varying outcomes and implications for neighbourhood-scale planning and development. The article argues that coordinated and collaborative planning processes are key to ensuring that the needs of lower-income households are met in privately developed apartment neighbourhoods.
Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
Enhancing Motivation to Think Critically

In the recent few decades, critical thinking (CT) has been regarded as one of the main thinking skills utilised in academic fields and school learning. People usually need to think critically when they tend to solve a problem, assess an argument, decide about a belief, or make a decision (Valenzuela, Nieto, & Saiz, 2011). Critical thinking is a higher-order process, which requires self-determination, reflection, effort, self-control, and metacognition (Mertes, 1991, cited in Fahim & Hajimaghsoodi, 2014). CT also can help students organise their thinking structure, utilise supported standards and finally promote their thinking quality (Fahim & Hajimaghsoodi, 2014). This is why increasing numbers of universities and school establish critical thinking courses for students. Although critical thinking is a significant ability in academic learning, some students have not realise its importance. However, the element component skills of CT are deductive and inductive reasoning, problem-solving and decision making (Saiz & Rivas, 2008, cited in Valenzuela et al., 2011), which assist students when they are studying or doing researches. Thus, it is crucial to encourage students to think critically. For many students, although they have already required the skills of critical thinking, they are unwilling to use it in learning. Facione (2000) points out that some people may specialise in CT, but they are not interested in addressing an issue within critically way.
Transient Receptor Potential (TRP) Channels in Airway Toxicity and Disease: An Update

Our respiratory system is exposed to toxicants and pathogens from both sides: the airways and the vasculature. While tracheal, bronchial and alveolar epithelial cells form a natural barrier in the airways, endothelial cells protect the lung from perfused toxic compounds, particulate matter and invading microorganism in the vascular system. Damages induce inflammation by our immune response and wound healing by (myo)fibroblast proliferation. Members of the transient receptor potential (TRP) superfamily of ion channel are expressed in many cells of the respiratory tract and serve multiple functions in physiology and pathophysiology. TRP expression patterns in non-neuronal cells with a focus on TRPA1, TRPC6, TRPM2, TRPM5, TRPM7, TRPV2, TRPV4 and TRPV6 channels are presented, and their roles in barrier function, immune regulation and phagocytosis are summarized. Moreover, TRP channels as future pharmacological targets in chronic obstructive pulmonary disease (COPD), asthma, cystic and pulmonary fibrosis as well as lung edema are discussed.