Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic Nondestructive Testing Techniques of Constructional Steel

Steel is a kind of ferromagnetic material, which is extensively applied in such fields as buildings, bridges, railways, machines and lifeline engineering etc. Those engineering structures built of constructional steel will unavoidably experience some damages during their service lifetime, thus which will influence the distribution regularity of internal forces in structures, result in over-stresses, cause the local failure of structures, and even lead to collapse of the whole structure. Therefore, it is a pressing topic to study how to directly evaluate the real-time stressed states of structural members, damages and steel characteristics in present structural health monitoring and diagnosing fields. And the achievements of this research will be of theoretical significance and of application value of engineering. This paper summarizes varieties of new magnetic nondestructive testing techniques used in constructional steel, respectively investigates the testing principles, characteristics and application for the magnetic Barkhausen noise technique, magnetic acoustic emission technique, magnetic flux leakage technique, magnetic memory technique and magnetic absorption technique, and points out the problems present in the application of these new techniques to actual testing and the further research objective.
Use of Different Types of Alternative Medicine in the Treatment of Diabetes Mellitus

Introduction: Diabetes is a chronic metabolic disorder whose hallmarks are hyperglycemia accompanied by persistent damage, dysfunction and failure of various organ systems. Objectives of the Study: To assess the frequency of use alternative medicine for Diabetes Mellitus. Methodology of the Study: This cross sectional study was conducted during June 2019 to April 2020 at Sahiwal Medical College, Sahiwal. Data regarding prevalence of alternative medicines usage in Pakistan, is not available. Thus, to calculate sample size, prevalence of India is being used which is 67%. Results: The data was collected from 400 patients and there was 167 male and 233 female. The mean age was 52.93±12.97 years. There are 113 patients who was suffering from diabetes from less than 5 years and almost 187 patients who was suffering more than 10 years. There were 189 patients who have good diabetic control as compared to others. There are 158 patients who used CAM for diseases control. Conclusion: It is concluded that patients might be less compelled to seek a CAM therapy or any other medical therapy if they have relatively few symptoms.
A project for the creation of an underwater archaeological park at Apollonia, Libya

In 2008 and 2009 the Archeotema society (Venice, Italy) was contracted by the Libyan government to outline a project for the management of the underwater archaeological sites of Libya as places for underwater tourism. The project was part of a general remit of Evaluation and Conservation of the Cultural Libyan Heritage. A selected site was Apollonia in Cyrenaica. The ancient harbour with all its structures allowed for the visualisation of a real park with an itinerary for divers and a glass-bottom boat for people to see from the surface.
A tool to evaluate accessibility due to sea-ice cover: a case study of the Weddell Sea, Antarctica

Abstract Sea ice is the major constraint on human activities in the Southern Ocean. Depending on a vessel's ice class, human mobility may be restricted or even prevented altogether by sea-ice conditions. This may imply limited access to research or monitoring stations, preferred fishing grounds or attractive tourist sites. Here, we introduce a statistical model that evaluates the sea-ice cover with two measures: 1) accessibility (i.e. the probability that a given area is navigable by vessels at a given time) and 2) repeated accessibility (i.e. the probability that a given area is navigable by vessels at a given time and again at least once within a defined timespan). We use daily sea-ice concentration data from 2002 to 2020 to demonstrate this tool and its functioning regarding the spatiotemporal variability of sea-ice cover in the wider Weddell Sea region. These findings reflect known characteristics of sea-ice distribution and dynamics in the Weddell Sea, confirming the functionality of our simple tool for determining repeated accessibility of certain areas. Such a tool may facilitate the planning of research and monitoring activities in the Southern Ocean, as well as in Arctic seas.
Food Insecurity Is Associated with Mental–Physical Comorbidities among U.S. Adults: NHANES 2013 to 2016

The co-occurrence of mental and physical conditions has increased significantly during the last decade. However, research examining the influence of social factors such as food insecurity is limited. The purpose of this study was to examine the association between food insecurity and mental–physical comorbidity status among U.S. adults. Data for this analysis were drawn from the National Health and Nutrition Examination Survey (NHANES) for the years 2013–2016. Respondents ages 18 and older who reported at least one of three chronic conditions (i.e., type 2 diabetes mellitus, hypertension, and hyperlipidemia) and responded to a nine-item depression scale were included in the analytic sample. The prevalence of food insecurity among those with depression and a cardiometabolic condition was 34% compared to 13% among those with a cardiometabolic condition only. Findings from multinomial logistic regression models indicated that food insecurity was associated with higher risk of mental–physical comorbidity (OR: 3.6, 95% CI: 2.26–5.76). Respondents reporting poor diet and poor self-reported health had higher odds of comorbid depression and cardiometabolic conditions. Female respondents had increased odds of comorbid depression and cardiometabolic conditions. Food insecurity is associated with co-occurring depression and cardiometabolic disease and may have implications for disease management.
ANT at the Mirror: A Reflexive Analysis of the Theory and the Rise of a ‘New’ Action

Is ANT an actor in its own ontology? This question summarises a number of issues that will be comprehensively investigated in this article. ANT will be analysed ‘reflexively’, that is, considering it as an actant of the ontology that it reclaims as a theory. The analysis of the concept of ‘description’ and the observation of the relational effects that ANT exerts and that are exerted on it reveal its asymmetric singularity in relation to the other actors. Therefore, we will argue whether ANT should actually be considered a ‘standing refutation’ of itself. The analysis of the concept of sociological relativity and its comparison with physical relativity reveal a possible reason for this self-contradictory behaviour, which will be the basis of our assumption concerning the emergence of a new type of action. Finally, a brief reinterpretation of ANT will be proposed, in the light of the newly introduced concepts of ontologisation and metaphysical action: it seems that this apparent contradiction can be ‘reabsorbed’ within the theory itself. Latour’s philosophical production will be broken down based on the applicability of the analysis.
Fast-HBNet: Hybrid Branch Network for Fast Lane Detection

As one of the fundamental visual tasks in the unmanned driving area, lane detection attracts increasing attention. In practical applications, lane points are very difficult to localize because they usually appear to be sparse and incomplete due to the influence of illumination and environment. Conventional lane detection methods rely on coarse features and carefully designed postprocessing to detect the lane lines. However, these methods are usually slow, and the stability and generalization ability are unsatisfactory. In this paper, we propose a novel lane detection network – Fast-HBNet(Fast-Hybrid Branch Network), which exploits both global semantic information and spatial contexts. To enlarge receptive fields and encode more detailed information, the compound transformation is employed and the proposed hybrid branch network extracts four diverse feature maps with different receptive fields and spatial contexts. Besides, we design a Hierarchical Feature Learning (HFL) module to learn lane features from the scale, channel, and spatial levels to enhance the generalization ability of our detector. These features are further selectively coalesced to generate unified lane feature maps with large receptive fields and rich detailed information. In other words, our network can encode the global semantic information from the high-resolution feature maps and the fine-grained details in the low-resolution feature maps. Experimental results conducted on the TuSimple (2017) and CULane [Pan et al. (2018)] datasets demonstrate that the proposed Fast-HBNet outperforms numerous state-of-the-art lane detectors in both speed and accuracy. Particularly, Fast-HBNet achieves an accuracy of 96.88% on the TuSimple dataset at a speed of 76 FPS.
Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm

In smart grids, a hybrid renewable energy system that combines multiple renewable energy sources (RESs) with storage and backup systems can provide the most cost-effective and stable energy supply. However, one of the most pressing issues addressed by recent research is how best to design the components of hybrid renewable energy systems to meet all load requirements at the lowest possible cost and with the best level of reliability. Due to the difficulty of optimizing hybrid renewable energy systems, it is critical to find an efficient optimization method that provides a reliable solution. Therefore, in this study, power transmission between microgrids is optimized to minimize the cost for the overall system and for each microgrid. For this purpose, artificial bee colony (ABC) is used as an optimization algorithm that aims to minimize the cost and power transmission from outside the microgrid. The ABC algorithm outperforms other population-based algorithms, with the added advantage of requiring fewer control parameters. The ABC algorithm also features good resilience, fast convergence, and great versatility. In this study, several experiments were conducted to show the productivity of the proposed ABC-based approach. The simulation results show that the proposed method is an effective optimization approach because it can achieve the global optimum in a very simple and computationally efficient way.
Thermal Management on 3D Stacked IC

Thermal management becomes serious in 3D stacked IC because of higher heat flux, increased power generation, extreme hot spot, etc. In this paper, we reviewed the recent developments of thermal management for 3D stacked IC which is a promising candidate to keep Moore`s law continue. According to experimental and numerical simulation results, Cu TSV affected heat dissipation in a thin chip due to its high thermal conductivity and could be used as an efficient heat dissipation path. Other parameters like bumps, gap filling material also had effects on heat transfer between stacked ICs. Thermal aware circuit design was briefly discussed as well.
Effects of Silver Diamine Nitrate and Silver Diamine Fluoride on Dentin Remineralization and Cytotoxicity to Dental Pulp Cells: An In Vitro Study

Silver diamine nitrate (SDN) is expected to help control caries similar to silver diamine fluoride (SDF). The aim of this study was to determine the mineral precipitation in demineralized dentin and the cytotoxicity of SDN and SDF to dental pulp cells. Demineralized dentin specimens were prepared, and SDF, SDN, or water (control) was applied. The specimens were then remineralized in simulated body fluid for 2 weeks. The mineral precipitation in the specimens was examined using FTIR-ATR, SEM-EDX, and synchrotron radiation X-ray tomographic microscopy (SRXTM). Additionally, the cytotoxicity of SDF and SDN to human dental pulp stem cells was analyzed using an MTT assay. The increase in FTIR spectra attributable to apatite formation in demineralized dentin in the SDF group was significantly higher compared to the SDN and control groups (p < 0.05). Dentinal tubule occlusion by the precipitation of silver salts was detected in both SDF and SDN groups. The mineral density as shown in SRXTM images and cytotoxicity of both SDN and SDF groups were comparable (p > 0.05). In conclusion, SDF demonstrated superior in vitro apatite formation compared to SDN. However, the degree of mineral precipitation and cytotoxic effects of both were similar.
The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PDF
MultiRes-NetVLAD: Augmenting Place Recognition Training With Low-Resolution Imagery

Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors. However, commonly-used CNN-based methods either process multiple image resolutions after training or use a single resolution and limit multi-scale feature extraction to the last convolutional layer during training. In this paper, we augment NetVLAD representation learning with low-resolution image pyramid encoding which leads to richer place representations. The resultant multi-resolution feature pyramid can be conveniently aggregated through VLAD into a single compact representation, avoiding the need for concatenation or summation of multiple patches in recent multi-scale approaches. Furthermore, we show that the underlying learnt feature tensor can be combined with existing multi-scale approaches to improve their baseline performance. Evaluation on 15 viewpoint-varying and viewpoint-consistent benchmarking datasets confirm that the proposed MultiRes-NetVLAD leads to state-of-the-art Recall@N performance for global descriptor based retrieval, compared against 11 existing techniques. Source code is publicly available at https://github.com/Ahmedest61/MultiRes-NetVLAD.
Power transformer protection using chirplet transform

This study presents a novel differential protection algorithm (DPA) for power transformer using chirplet transform (ChT). The proposed method combines the features of biased restraint characteristic (BRC) of the conventional differential relay and out-turn of ChT in a two-stage algorithm. In the first stage, the BRC plane is divided into three zones: namely, high-set (HS), non-trip and vulnerable zones. The tripping decisions are carried out in the first two zones based on differential and biased current. However, if the operating condition of the power transformer falls in the vulnerable zone, then there is an ambiguity in discriminating internal fault, inrush current and current transformer saturation cases. Therefore, in the second stage, ChT is applied to differential current signal to obtain an energy distribution on the time-frequency plane with respect to time, frequency and chirp rate. Then, using the mean and standard deviation of the normalised energy, power transformer operating conditions are classified. Also, most of the DPAs available in the literature are system dependent. However, the proposed novel DPA can be effectively used for any system. The proposed scheme is validated for two power transformer systems using PSCAD to simulate various operating conditions and MATLAB to implement the algorithm.
PMOS-based Integrated Charge Pumps with Extended Voltage Range in Standard CMOS Technology

PMOS-based Integrated Charge Pumps with Extended Voltage Range in Standard CMOS Technology Jingqi Liu University of Guelph, 2012 Advisor: Dr. Stefano Gregori This thesis presents the design and implementation of PMOS-based integrated charge pumps with extended voltage range and their regulation circuits in a standard process. The performance of charge pumps are evaluated by their output resistances and power conversion efficiencies. Formulas which describe the charge pump characteristics are developed and presented. Existing charge pumps are analyzed and studied to understand their limitations in generating high voltages and achieving high performance. The proposed charge pump structures are designed to use PMOS switches to alleviate the high voltage stresses across transistors by biasing their bulk independently. The voltages across transistors and capacitors are kept within the suggested voltage rating (VDD) regardless of how high the output voltage is, thus the maximum voltage range is extended and no longer limited by the breakdown voltages of the devices. The charge pump circuits only need low-voltage devices and standard processes, and can be easily integrated in a digital or mixed-signal design. The proposed charge pump regulation circuits include a voltage divider, a voltage controlled ring oscillator and a feedback operational amplifier. The regulation circuits are able to adjust the clock frequency to regulate the charge pump to a steady output voltage (set by the reference voltage) under a large range of current loads. A test chip including the proposed charge pumps and regulation circuits was fabricated in a 0.18 μm digital CMOS process provided by Taiwan Semiconductor Manufacturing Company (TSMC). The proposed charge pumps were tested and demonstrated the reliable generation of output voltages up to 11.47 V using only low-voltage devices. The simulation and measurement results have been presented and compared, demonstrating the functionality and performance of the proposed circuits.
Development of Intelligent Mold Shop

Presented in the paper is an approach to developing an intelligent mold shop as a means to overcome the difficulties faced by mold-makers due to skill shortages and increased global competition. A machine shop where as much as of the human skills are replaced by a set of intelligent systems is called an intelligent machine shop , and an intelligent mold-making machine shop is called an intelligent mold shop (IMS) . By analyzing the contents of operator’s skill, three intelligent S/W stations have been designed: Technical Data Processing (TDP) Station, Loading Schedule Station, and Real-time Monitoring Station. A detailed architecture of the TDP station is described, and measures of effectiveness of IMS are elaborated.
Direct Cell-to-Cell Equalizer for Series Battery String Using Switch-Matrix Single-Capacitor Equalizer and Optimal Pairing Algorithm

In series battery strings, cell-inconsistency is caused by the state-of-charge (SOC) mismatch, nonidentical battery impedance, or different self-discharging rates, and this leads to overcharge and overdischarge. Practically, switched-capacitor equalizers are the most promising means to eliminate the cell inconsistency by virtue of automatic equalization, but the performance is heavily dependent on the initial cell voltage distribution and the number of series connections due to inefficient switch utilization. This article proposes a direct cell-to-cell equalizer for a series-connected battery using a switch-matrix single-capacitor converter to further improve the switched-capacitor equalizer in term of performance consistency and Coulomb efficiency. By adopting one extra current sensor and an optimal pairing algorithm, energy is transferred directly between the highest-SOC cell and the lowest-SOC cell to eliminate the impact of battery-impedance difference and false voltage measurement in battery monitoring integrated circuit caused by polarization effect. The experimental results verify the feasibility of the proposed scheme. Real-time tests are also implemented to fairly compare the proposed with the conventional methods. It is found that the performance of the proposed method is independent from initial voltage distribution in a series string, where the performance indices are consistent regardless of initial conditions. Besides, the energy loss of the proposed equalizer is further reduced and its overall efficiency is high in all test scenarios.
Solutions of Inverse Kinematics of Articulated Robot by Using Repeatedly Direct Kinematics.

In this paper, a method of repeatedly direct kinematics is introduced. By using this method, a flowchart serving for the solution of inverse kinematical problem of an articulated robot arm is investigated. The performance of the flowchart is analyzed in a robot arm whose inverse kinematical answers cannot be directly obtained in formula because of the existence of an offset in its wrist. In the flowchart, because the position error and the posture error are evaluated separately the computing efficiency becomes very high. In addition, another flowchart dealing with the plural answers of a robot hand with the same position and posture is put forward. The solution of the plural answers makes it possible for a robot arm to avoid the angular limitations of its joints or to bypass the obstacles while working.
Drug Repurposing Targeting Pseudomonas aeruginosa MvfR Using Docking, Virtual Screening, Molecular Dynamics, and Free-Energy Calculations

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium responsible for acute and chronic infections in planktonic state or in biofilms. The sessile structures are known to confer physical stability, increase virulence, and work as a protective armor against antimicrobial compounds. P. aeruginosa can control the expression of genes, population density, and biofilm formation through a process called quorum sensing (QS), a rather complex and hierarchical system of communication. A recent strategy to try and overcome bacterial resistance is to target QS proteins. In this study, a combined multi-level computational approach was applied to find possible inhibitors against P. aeruginosa QS regulator protein MvfR, also known as PqsR, using a database of approved FDA drugs, as a repurposing strategy. Fifteen compounds were identified as highly promising putative MvfR inhibitors. On those 15 MvfR ligand complexes, molecular dynamic simulations and MM/GBSA free-energy calculations were performed to confirm the docking predictions and elucidate on the mode of interaction. Ultimately, the five compounds that presented better binding free energies of association than the reference molecules (a known antagonist, M64 and a natural inducer, 2-nonyl-4-hydroxyquinoline) were highlighted as very promising MvfR inhibitors.