Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

Abstract As the modular multi-level converter is now developing rapidly, especially in high power applications, and the large amount of sub-modules in the modular multi-level converter will increase the probability of failures, fault detection and diagnosis of the sub-modules are of importance for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good generalization, as well as the time-saving characteristic is verified by conducting a comparison with the neural network method.
Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.
Review Of Geophysical Applications in Australian archaeology

Abstract Multidisciplinary approaches are now commonplace in the investigation of archaeological sites worldwide. Consequently, geophysics has become an increasingly important tool for reconstructing past landscapes and investigating research questions. However, despite their acceptance internationally, in Australia the use of geophysical techniques on archaeological sites has been underutilised. This paper examines the history of archaeological geophysics in Australia and seeks to understand, given their potential advantages, the role that factors such as costs, time, instrument availability and lack of theoretical knowledge have played in the underrepresentation of these methods in local archaeological investigations to date. With the recent introduction of short courses in archaeological geophysics to at least one Australian tertiary institution, this review is a timely overview of where this discipline has been, what it has to offer and whether there is potential for Australian archaeologists to develop the skills necessary to conduct archaeological geophysic investigations, as their international counterparts do already, in the future.
Analysis and design of tunable fiber Fabry-Perot filter

The effective reflectivity of planar tunable filter fiber Fabry-Perot (TF-FFP) caused by coupling losses would led to the change of tuning range and the full width half maximum (FWHM) in the process of gap distance tuning. The general transfer function expression of FFP is developed by Gaussian beams model and coupling losses. A method that is by appropriate unsymmetrical reflectivity of fiber end can increase the tuning range and reduce the FWHM is proposed, the obtained parameters may be useful for the analysis and design of other TF-FFP. At last the initial gap position and reflectivities of both ends of TF-FFP are designed theoretically for C-band and C+L-band respectively.
IMPACT OF HARVESTING MACHINERY ON SOIL PHYSICAL PARAMETERS: EVALUATION OF PROFOR MODEL IN THREE MAIN FORESTRY REGIONS OF SOUTH AFRICA.

Timber harvesting operations in plantation forestry in South Africa are rapidly being mechanised. However, movement of forest machines over the soil increases the potential for soil compaction and disturbance. In an effort to prevent forest soil damage, the Technical University of Munich developed the “ProFor” software. This software enables the calculation of critical soil water content for a given machine and physical soil characteristics. The applicability of ProFor in the South African forestry industry was assessed through the comparison of the evaluation of the impact of forest harvesting machines on soil properties with ProFor predictions. The study was conducted in four harvesting sites located in three of the major plantation forestry regions of South Africa namely: KwaZulu-Natal; Eastern Cape and the Western Cape. The impact of forest harvesting machines on soil physical properties was assessed through the evaluation of changes in soil saturation, soil bulk density and rut depth. The impacts of machine movements on soil physical properties were then compared with ProFor’s predictions. The study has indicated that ProFor gave good predictions of the critical water contents for most of the studied soils except for sandy soils. The study has also indicated that in more than 75% of the observed cases (r = 0.76) ProFor gave valid predictions of rut formation. However, ProFor predictions poorly correlated (r = -0.1) with the observed soil compaction. The model can be adopted for the South African forestry industry for use in the management of wet spots of a plantation. However, ProFor can be of even greater importance if a separate algorithm was built to be used for the prediction of soil compaction which is a common hazard in most South African forestry.
Pleistocene Megaflood Discharge in Grand Coulee, Channeled Scabland, USA

Bedrock erosion and canyon formation during extreme floods have dramatically altered landscapes on Earth and Mars. Grand Coulee was carved by outburst floods from Pleistocene glacial Lake Missoula and is the largest canyon in the Channeled Scabland, a megaflood‐scoured landscape in the northwestern USA. Quantifying paleo‐discharge is required to understand how landscapes evolve in response to extreme events, but there are few constraints on the magnitude of the floods that incised Grand Coulee; hence, we used hydraulic modeling and geologic evidence to quantify paleo‐flood discharges during different phases of canyon incision. When upper Grand Coulee was incising by headward waterfall retreat, the paleo‐discharge was 2.6 × 106 m3 s−1, which produced shear stresses great enough to cause the waterfall to retreat via toppling of basalt columns. The largest possible flood through upper Grand Coulee, a Missoula flood which raised glacial Lake Columbia to a stage of 750 m, produced a modeled discharge of 7.6 × 106 m3 s−1. The discharges associated with waterfall retreat and drainage of glacial Lake Columbia are >80% and ∼50% lower, respectively, than the 14–17 × 106 m3 s−1 discharge predicted by assuming the present‐day topography was inundated to the elevation of high‐water marks. Due to bedrock incision, high‐water marks may overestimate paleo‐flow depth in canyons carved by floods, hence bedrock erosion should be considered when estimating paleo‐discharge in flood‐carved canyons. Our results indicate that outburst floods with discharges and flow depths much lower than those required to inundate high‐water marks are capable of carving deep canyons.
Rethinking the Business Model in Construction by the Use of Off-Site System Deliverance: Case of the Shaft Project

AbstractThis paper presents a set of insights to be used in the development of business models for off-site system deliveries contributing to the development of off-site manufacturing (OSM) practices. The theoretical starting point for discussing the development of business models is the blue ocean strategy literature combined with theories on mass customization and platform development identifying the optimization of cost and value through the handling of complexity as the central process. This framework is developed to analyze a specific case on system deliverances—the prefabrication of installation shafts. Findings from the development and production of the installation shaft show that system deliveries represent a promising strategy for moving from a red ocean competitive environment with the predominant Cost+ business model to a blue ocean situation in which the competition emerges in the constant pursue of value creation and cost reduction. On the basis of that system, deliverances represent a promi.
Irrigation with municipal wastewater as a suitable solution for safflower cultivation in arid regions

In order to study the effect of different manure and chemical fertilizer levels on qualitative and quantitative characteristics of safflower in the condition of irrigation with municipal wastewater, an experiment was conducted as split plot basis of randomized complete design in Lakhshah region locate in Zahedan city, Iran. The treatments were comprised of two levels of irrigation, W1= Well water and W2= Treated wastewater, in main plots and sub plots consisted of F1: control (without consumption of manure and chemical fertilizer), F2: Recommended manure, F3: Half of recommended manure and chemical fertilizer (N, P and K), and F4: Recommended chemical fertilizer (N, P and K). The results showed that Treatment of treated wastewater had a positive and significant influence on all yield components, and the most influence was shown on 1000 seed weight. Also, irrigation with wastewater significantly increases the dry and fresh yield and grain yield of safflower than ordinary water. Among the fertilizer treatments, complete treatment of chemical fertilizer N, P and K had the greatest effect on increase of yield and grain yield components. In this experiment, water treatment hadn’t significant effect on accumulation of Cr, pb, Fe and Mn in safflower grain, and soil. While, fertilizer treatment had only a significant influence on the accumulation of Fe and Mn in safflower grain, but between these treatments wasn’t saw any significant difference on the accumulation of Cr and pb. In general, the results of this experiment showed that irrigation with wastewater and application of complete fertilizer with manure is recommended.
Endolysosomal Mesoporous Silica Nanoparticle Trafficking along Microtubular Highways

This study examines intra- and intercellular trafficking of mesoporous silica nanoparticles along microtubular highways, with an emphasis on intercellular bridges connecting interphase and telophase cells. The study of nanoparticle trafficking within and between cells during all phases of the cell cycle is relevant to payload destination and dilution, and impacts delivery of therapeutic or diagnostic agents. Super-resolution stochastic optical reconstruction and sub-airy unit image acquisition, the latter combined with Huygens deconvolution microscopy, enable single nanoparticle and microtubule resolution. Combined structural and functional data provide enhanced details on biological processes, with an example of mitotic inheritance during cancer cell trivision.
AI-driven Optimization in Healthcare: the Diagnostic Process

Purpose: Process optimization in healthcare using artificial intelligence (AI) is still in its infancy. In this study, we address the research question “To what extent can an AI-driven chatbot help to optimize the diagnostic process? ” Design / Method / Approach: First, we developed a mathematical model for the utility (i.e., total satisfaction received from consuming a good or service) resulting from the diagnostic process in primary healthcare. We calculated this model using MS Excel. Second, after identifying the main pain points for optimization (e.g., waiting time in the queue), we ran a small experiment (n = 25) in which we looked at time to diagnosis, average waiting time, and their standard deviations. In addition, we used a questionnaire to examine patient perceptions of the interaction with an AI-driven chatbot. Findings: Our results show that scheduling is the main factor causing issues in a physician’s work. An AI-driven chatbot may help to optimize waiting time as well as provide data for faster and more accurate diagnosis. We found that patients trust AI-driven solutions primarily when a real (not virtual) physician is also involved in the diagnostic process. Practical Implications: AI-driven chatbots may indeed help to optimize diagnostic processes. Nevertheless, physicians need to remain involved in the process in order to establish patient trust in the diagnosis. Originality / Value: We analyze the utility to physicians and patients of a diagnostic process and show that, while scheduling may reduce the overall process utility, AI-based solutions may increase the overall process utility. Research Limitations / Future Research: First, our simulation includes a number of assumptions with regard to the distribution of mean times for encounter and treatment. Second, the data we used for our model were obtained from different papers, and thus from different healthcare systems. Third, our experimental study has a very small sample size and only one test-physician. Paper type: Empirical
Control system for fuel consumption minimization–gas emission limitation of full electric propulsion ship power systems

Environmental pollution caused by ships’ green house gas emissions and worldwide concern about air quality and oil supplies have led to stricter emissions regulations and fuel economy standards. In this regard, respective limits are set, while efforts to provide general guidelines for the achievement of economic and green ship operation with an urge to ship operators to apply them and return feedback. Also, specific design and operation indicators have been proposed in order to ensure compliance with new emissions regulations and fuel economy standards. Up to now, these indices are limited to ships comprising conventional propulsion systems, while full electric propulsion systems are not examined. In this article, an integrated control system that attains economically optimized and environmentally friendly operation is proposed. Moreover, appropriate reformulation of energy efficiency operation indicator is proposed for real-time assessment of gas emissions. The study is supported with the presentation of results obtained from the simulation of the operation of a ship power system comprising full electric propulsion.
Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.
Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers

Data races are often hard to detect in device drivers. According to our study of Linux driver patches that fix data races, about 39% of patches involve a pattern that we call inconsistent locking discipline. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held around each access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a data race may occur. In this paper, we present a hybrid static-dynamic analysis approach, named SDILP, to detect data races caused by inconsistent locking discipline in device drivers. SDILP has a dynamic lockset analysis to detect data races at runtime, and a static lockset analysis to detect more data races based on the dynamic-analysis results. It also performs a static taint analysis to reduce the number of variable accesses monitored by the dynamic analysis. Compared to our previous dynamic approach DILP (Chen et al., 2019), introducing static analysis allows SDILP to achieve better performance and find more data races. We evaluate SDILP on 12 drivers in Linux 5.4, and find 117 real data races, 50 of which have been confirmed by driver developers.
9-cis and all-trans beta-carotene isomers of super critical CO2-extracted Dunaliella oil are absorbed and accumulated in mouse tissues

ABSTRACT Animals cannot produce β-carotene, and therefore they exclusively depend on its dietary availability. We previously showed that a 9-cis β-carotene-enriched diet, provided as Dunaliella powder, has a beneficial effect on atherosclerosis and diabetes mellitus in animal models and on lipid profiles, retinitis pigmentosa, and psoriasis in human trials. Therefore, the current study aimed to investigate the bioavailability of 9-cis β-carotene from super-critical CO2-extracted Dunaliella oil in mice. In total, ten 12-week old mice were allocated into two groups: (1) a control-received unfortified diet; (2) a Dunaliella oil diet enriched with Dunaliella oil. Four-week dietary supplementation with Dunaliella oil led to a substantial accumulation of both all-trans and 9-cis β-carotene in the plasma, liver and white adipose tissue. β-carotene was stable in oil preparation, and the 9-cis to all-trans β-carotene ratio was constant (60:40, g/g) after two years of storage at 4°C. These results suggest that supercritically extracted Dunaliella oil can potentially be used as a food supplement.
Three-Party Evolutionary Game Model of Stakeholders in Mobile Crowdsourcing

As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This model also considers the collusion between crowd workers and the platform to make it more realistic. Then, the replication dynamics method is utilized to analyze the evolutionary stability strategy. The strategies of rewards and penalties are given to avoid free-riding and false-reporting problems. Finally, the stability of the equilibrium point in the three-party game system is verified through simulation experiments, and the effective methods to motivate each player to choose a trusted strategy are given.
Validation of a Tranquillity Rating Prediction Tool

Summary In both urban and rural environments, ‘tranquil space’ is predominantly constructed through the sensory information received by the auditory and visual modalities. This paper reports the results of a study that set out to validate and further develop a ‘Tranquillity Rating Predication Tool’, previously proposed by the authors [Pheasant et al. JASA 123(3), 1446‐1457 (2008)], which utilises the percentage of natural features contained within a scene and either of the noise indices LAmax or LAeq, as the key components. The results of the validation study show that when responding to combined audio-visual stimuli the A-weighted sound pressure level LAeq is most significantly related to the Tranquillity Rating. It is suggested that a modified measure of the visual composition of a scene incorporating contextual aspects is required. The findings of this work should be of particular interest to those charged with landscape management, such as the National Park Authorities, Regional Councils, and other agencies concerned with providing and maintaining public amenity.
Low-cost antenna array with wide scan angle property

The scan range of an antenna array can be increased by providing a degree of curvature to its structure. However, achieving a wide scan range with an antenna array with limited number of elements is a challenging design issue. In this study, a novel 8-element linear array of polarisation reconfigurable antennas conformed to four different array structures; namely 2-faceted, 3-faceted, 4-faceted and 8-faceted are analysed and synthesised. Observations are made on the influence of the tilting angle of the faceted structures to the geometrical characteristics and the scan range of the array. It is shown that the 3-faceted array can achieve the widest scan range with the smallest dimension compared with the other faceted structures. Furthermore, the results show that the 3-faceted structure achieves ±69± scan range, compared to ±56± achieved with a linear array.
LINE-1 and Alu methylation Signatures in Autism Spectrum Disorder and Their Function in The Regulation of Autism-Related Genes

BackgroundLong interspersed nucleotide element-1 (LINE-1) and Alu elements are retrotransposons whose abilities cause abnormal gene expression and genomic instability. Several studies have focused on DNA methylation profiling of gene regions, but the locus-specific methylation of LINE-1 and Alu elements has not been identified in autism spectrum disorder (ASD). MethodsHere, DNA methylation age was predicted using Horvath’s method. We interrogated locus- and family-specific methylation profiles of LINE-1 and Alu elements (22,352 loci) in ASD blood using publicly-available Illumina Infinium 450K methylation datasets from heterogeneous ASD (n = 52), ASD with 16p11.2 del (n = 7), and ASD with Chromodomain Helicase DNA-binding 8 (CHD8) variants (n = 15). The differentially methylated positions of LINE-1 and Alu elements corresponding to genes were combined with transcriptome data from multiple ASD studies. ROC curve was conducted to examine the specificity of target loci.ResultsEpigenetic age acceleration was significantly decelerated in ASD children over the age of 11 years. DNA methylation profiling revealed LINE-1 and Alu methylation signatures in each ASD risk loci by which global methylation were notably hypomethylated exclusively in ASD with CHD8 variants. When LINE-1 and Alu elements were clustered into subfamilies, we found methylation changes in a family-specific manner in L1P, L1H, HAL, AluJ, and AluS families in the heterogeneous ASD and ASD with CHD8 variants. Our results showed that LINE-1 and Alu methylation within target genes is inversely related to the expression level in each ASD variant. Moreover, LINE-1 and Alu methylation signatures can be used to predict ASD individuals from non-ASD.LimitationsIntegration of methylome and transcriptome datasets was performed from different ASD cohorts. The small sample size of the validation cohort used post-mortem brain tissues and necessitates future validation in a larger cohort.ConclusionsThe DNA methylation signatures of the LINE-1 and Alu elements in ASD, as well as their functional impact on ASD-related genes, have been studied. These findings are considered for further research into DNA methylation profiles and the expression of the LINE-1 and Alu elements in post-mortem brain tissue, which has been linked to ASD pathogenesis.