Miracle-dz/UniFAR-FTUs huggingface dataset:
The Changing Distribution of Working Hours in Australia

This paper presents statistical evidence on the nature of working time arrangements in Australia, and especially their distribution. More specifically, the paper analyses: (i) the distribution of weekly working hours in Australia and how that has changed over time; (ii) the extent of mismatch between usual and preferred hours of work, and the degree of persistence in such mismatch; (iii) annual leave usage and its correlation with weekly hours of work; and (iv) how working time arrangements in Australia compare with that in other industrial nations.
A dielectric-lens aerial for wide-angle beam scanning

The paper deals with the design of dielectric lenses which produce, when illuminated by a suitable source of radiation, a beam capable of being scanned without distortion through a fairly large angle by moving the source. The treatment is restricted to aerial systems in which the radiation is guided between parallel plates to a long narrow rectangular aperture, the system being symmetrical about a central axis. It is shown that to avoid distortion when the beam is scanned, the rays from the focus and the final rays must meet on a circle about the focus (the sine condition). A dielectric lens which satisfies this condition and produces a beam of maximum gain in the direction of the axis can be designed. This necessitates the numerical integration of a differential equation which is discussed in the Appendix. It is also shown that for acertain range of values of the refractive index this lens can in practice be replaced by a plano-convex lens.
Interface Layer Tack Coat Optimization

Interface bonding is a key factor affecting pavement performance life. This study focused on optimizing in situ tack coat application rate and field installation. The objective was to validate the laboratory-determined optimum residual application rate and evaluate the field performance of tack coat materials. The parameters analyzed included two cleaning methods (broom and air blast), two paving procedures (spray paver and conventional paving with use of a distributor and a regular paver), tack coat type (SS-1h, SS-1hp, and SS-1vh), and existing pavement surface. Projects were conducted on I-80 and Illinois Route 98. Cores were tested by using the interface shear test device. A life-cycle cost analysis that considered construction materials and paving methods was performed. Results showed similar bond strength for both cleaning methods; however, air-blast cleaning reduced the required optimum residual application rate. The resulting interface bond strength was similar when using either of the paving procedures considered. SS-1vh, a nontrack tack coat, performed better than any other material studied. Identification of the optimum tack coat application rate will help ensure cost-effective and efficient tack coat application in the field.
Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Cancer still ranks as a leading cause of mortality worldwide. Although considerable efforts have been dedicated to anticancer therapeutics, progress is still slow, partially due to the absence of robust prediction models. Multicellular tumor spheroids, as a major three-dimensional (3D) culture model exhibiting features of avascular tumors, gained great popularity in pathophysiological studies and high throughput drug screening. However, limited control over cellular and structural organization is still the key challenge in achieving in vivo like tissue microenvironment. 3D bioprinting has made great strides toward tissue/organ mimicry, due to its outstanding spatial control through combining both cells and materials, scalability, and reproducibility. Prospectively, harnessing the power from both 3D bioprinting and multicellular spheroids would likely generate more faithful tumor models and advance our understanding on the mechanism of tumor progression. In this review, the emerging concept on using spheroids as a building block in 3D bioprinting for tumor modeling is illustrated. We begin by describing the context of the tumor microenvironment, followed by an introduction of various methodologies for tumor spheroid formation, with their specific merits and drawbacks. Thereafter, we present an overview of existing 3D printed tumor models using spheroids as a focus. We provide a compilation of the contemporary literature sources and summarize the overall advancements in technology and possibilities of using spheroids as building blocks in 3D printed tissue modeling, with a particular emphasis on tumor models. Future outlooks about the wonderous advancements of integrated 3D spheroidal printing conclude this review.
Critical role of H3K27 methylation in cell fate determination of two cell lineages in male gametophyte

During angiosperm male gametogenesis, microspores divide to produce a vegetative cell (VC) and a male germline (MG), each with a distinct cell fate. How the MG cell/VC fate is determined remains largely unknown. Here, we report that H3K27me3 is essential for VC fate commitment and H3K27me3 erasure contributes to the MG cell fate initiation in male gametophyte of Arabidopsis. The VC-targeted H3K27me3 erasure disturbed the VC development and resulted in the VC fate shifting towards a gamete destination, which suggests that MG cells require H3K27me3 erasure for triggering the gamete cell fate. Multi-omics and cytologic analysis confirmed the occurrence of extensive cell identity transition due to H3K27me3 erasure. Therefore, we experimentally confirm that the MG cell/VC fate is epigenetically regulated. The H3K27 methylation plays a critical role in the guidance of MG cell/VC fate determination for pollen fertility in Arabidopsis. Our work also provides new evidences for two previous hypotheses that the germline cell fate is specified by the differential distribution of yet unknown determinant, and VC maintains the microspore’s default program, namely the H3K27me3 setting, whereas MG requires reprogramming.
Intrusion Detection Systems by Applying An Improvement in Apriori Algorithm

For many years, researchers have used the discovery of intrusion technology to supply the security of networks and computer systems. Increasing Cyber threats has caused the need to have a flexible approach that can offer the efficiency of this type of attack and to deal with more complex and unknown attacks. On this basis, the systems of intrusion discovery based on data mining have been presented that derives the unknown patterns and Cyber attacks from the data network. What is presented in this paper is an improvement on Apriori algorithm to realize the intrusion pattern in less time in the network and take proper reaction against it. (Abstract)
An investigation on performance of XLPE compounds for high-voltage DC application

This paper presents the results of test procedures aiming at electrical characterization of polymeric materials for DC-insulation application. Electrical life tests at room temperature and space-charge observations were performed on specimens of two materials, i.e. a reference XLPE and a XLPE-based compound, in order to compare the behavior under both constant and periodically-inverted DC voltage. The compound differs from the base material due to the introduction of additives intended to modify conductivity, and thus space-charge trapping dynamics. The effect of additives is inferred coherently by both endurance estimation and space charge measurements, thus confirming that an increase of charge mobility can lead to an improvement of insulation performance in the presence of voltage inversion. The test procedures here discussed allow characterization to be achieved, and, thus, a feedback to be provided to insulating-material manufacturers, in reasonable times.
Formulation, Optimization and In Vivo Evaluation of Fucoidan-Based Cream with Anti-Inflammatory Properties

Fucoidan is a polysaccharide found in brown alga with glorious potential for pharmacological activities, among which its anti-inflammatory properties have gained meaningful attention. Due to several advantages of formulations for topical application, this study aimed to develop and optimize a fucoidan-based cream formulation and to investigate its anti-inflammatory potential after topical application in vivo. Fucoidan from Fucus vesiculosus L. was used. The cream base consisting of olive oil and Kolliphor RH40 was optimized followed by in vitro agar diffusion and drug release studies. The fucoidan-based cream with 13% Kolliphor P 407, 1% Transcutol P, and 5% PEG400 showed good spreadability, washability, and colloidal stability, and it did not irritate the skin. The kinetics of fucoidan release from the optimized cream exhibited the best fit to the Korsmeyer–Peppas and Higuchi models with R2 > 0.99. Fucoidan release was controlled by drug diffusion and anomalous transport provided by the optimized cream base. The formulation was stable and provided high fucoidan release after storage for 1 year. Topical application of the fucoidan-based cream dose-dependently inhibited carrageenan-induced edema and ameliorated mechanical allodynia in rats. The efficacy of the fucoidan-based cream at a high dose was comparable with the efficacy of diclofenac gel. The fucoidan-based cream could be considered a promising anti-inflammatory formulation.
The high-precision videometrics methods to determining absolute vertical benchmark

The mobile measurement equipment plays an important role in engineering measurement tasks and its measuring device is fixed with the vehicle platform. Therefore, how to correct the measured error in time that caused by swayed platform is a basic problem. Videometrics has its inherent advantages in solving this problem. First of all, videometrics technology is non-contact measurement, which has no effect on the target’s structural characteristics and motion characteristics. Secondly, videometrics technology has high precision especially for surface targets and linear targets in the field of view. Thirdly, videometrics technology has the advantages of automatic, real-time and dynamic. This paper is mainly for mobile theodolite.etc that works under the environment of absolute vertical benchmark and proposed two high-precision methods to determine vertical benchmark: Direct-Extracting, which is based on the intersection of plats under the help of two cameras; Benchmark-Transformation, which gets the vertical benchmark by reconstructing the level-plat. Two methods both have the precision of under 10 seconds by digital simulation and physical experiments. The methods proposed by this paper have significance both on the theory and application.
KINEMATICAL ANALYSIS OF SHOULDER JOINT DURING ELEVATION MEASURED BY ULTRASOUND-BASED MEASURING SYSTEM

In order to analyse shoulder joint movements, the author uses a ZEBRIS CMS-HS ultrasound-based movement sensor and a related measurement program developed by the researchers in Biomechanical Laboratory in TUB. In essence, the measurement involves the determination of the spatial position of the 16 anatomical points specified on the basis of the coordinates of ultrasound-based triplets positioned on the upper limb, the scapula, and the thorax, measured in the course of motion. Kinematic characteristics of 74 shoulder joints of 50 healthy persons were identified during elevation. A specially developed processing program was used for the calculation and analysis of humerus elevation, scapulothoracal, and glenohumeral angles, defined as angles of spatial vectors; the glenohumeral, scapulothorocal and capuloglenoid ratio to be determined therefrom; and of scapulothoracal and glenohumeral rhythm during motion. Motion of the humerus and the scapula relative to each other was characterized by the rotation center of the two bones and rigid bodies as well as their absolute and relative displacement relative to each other. The biomechanical model of the shoulder joint during elevation can be described by analysing the results of the measurements performed.
Cutting on diesel, boosting PV: the potential of hybrid diesel/PV systems in existing mini-grids in the Brazilian Amazon

The hundreds of mini-grids fed by diesel gen sets operated by independent power producers (IPPs) and public power utilities in the Brazilian Amazon all make use of a government subsidy that covers 100% of the fuel. This subsidy has recently been extended for another 20 years. Most of the sites where these mini-grids operate are not easily accessible, increasing cost and decreasing reliability of supply. IPPs willing to invest in renewable generation that displaces diesel can currently claim the cost of the fuel consumption avoided. We demonstrate that hybrid diesel/PV systems without storage can be the most competitive option, if the introduction of PV in this sector is intended. We discuss some changes in the existing subsidies in order to make the PV-option viable, evidencing the potential of PV insertion to a level that can justify a local PV module manufacturing plant in the country. Extending the model to a next stage includes the introduction of hybrid PV/fuel cells to rely completely on the local solar resource.
Preoperative echocardiography as a prognostic tool for liver transplant in patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) presents with a hypertrophied left ventricle (LV). It is often associated with LV outflow tract obstruction (LVOTO) and a risk for sudden death. This study aimed to describe outcomes of patients with HCM who underwent liver transplant (LT).
Fast current controller in three-phase AC/DC boost converter using d-q axis crosscoupling

In this paper, a new simple current controller with both satisfactory steady-state characteristics and fast transient response is proposed. In this scheme, a reference modification part is incorporated with the generally used synchronous-frame proportional-integral (PI) controller for the fast transient response. Simulation and experimental results are presented and show the effectiveness of the proposed current controller. In the simulation and experimental results, both the current controller and DC-link regulator show conspicuous performance improvement. It is also observed in the simulation and experiment that the proposed current controller has a similar characteristic as the synchronous-frame PI controller in the steady state and as the minimum-time current controller in the transient state.
The Influence of Relative Humidity and Storage Conditions on the Physico-chemical Properties of Inhalation Grade Fine Lactose

Dry powder inhalers (DPIs) are favorable devices for the delivery of dry formulations to the lungs; still, they largely fail to deliver higher doses of active pharmaceutical ingredient (API) to the lower airways. Addition of fine particles of excipient (fines) to the blend of API and carrier was shown to improve aerosolization performance. Lactose monohydrate is ubiquitous excipient used for this purpose. Lactose exists in a thermodynamically stable crystalline form; however, processes like milling, sieving, or even mixing may induce alteration of crystalline structure and introduce amorphous domains, which could further affect the physico-chemical properties of the material. Therefore, the aim of this work is a detailed characterization of two commercially available types of inhalation grade fine lactose powders (Inhalac 400 and Inhalac 500) prepared using different air-jet milling parameters, with a focus on impact of storage conditions on material properties. We found that the different milling parameters resulted in variable particle size distribution (PSD), and thus surface areas, variable initial amorphous content, cohesivity, flowability, and moisture sorption of materials. In addition, exposure of fine powders to higher humidity reduced the amorphous content present in the materials, but also affected agglomeration tendency and dispersion behavior of both powders. We believe the obtained findings to be important for the aerosolization performance of carrier-based DPIs containing fines and thus need to be duly considered during formulation development.
Using a Systems Dynamics Model to Assess Skill Level Impact

Research activities have been performed to identify areas of complexity related to the product, process or operational tasks. The developed framework decouples the manufacturing complexity aspects using a systematic approach to decompose the problem into key impact factors. The result of this model provides insight into the system sensitivities when considering human characteristics. However, the model is a static model. Skill levels improve with experience and repetition. The actors within a system may have different levels of skills and knowledge, and how and where these resources are utilized within a system will impact inventory and throughput. As well, people have different learning characteristics. Both these static and dynamic elements impact the system performance. This research presents a systems dynamics model that contains production rules and rules to evaluate the impact of human skill level variations based on the complexity of a task / set of tasks. The impact of positioning a set of personnel with different skill levels on different positions in an assembly line is explored.
Adaptive nonlinear cancellation for high-speed fiber-optic systems

The authors propose techniques for adaptive nonlinear cancellation of intersymbol interference (ISI) in the electrical signal at the receiver in Gb/s lightwave systems and describe several demonstrations of these techniques. Techniques for adjustable nonlinear cancellations are discussed and demonstrations of these techniques using commercially available integrated circuits (ICs) at data rates as high as 1.7 Gb/s are described. Techniques for automatic adjustment are discussed, and a demonstration of adaptive nonlinear cancellation at 450 Mb/s is described. The authors discuss how these techniques can be integrated onto the detector IC for operations at 2.5 Gb/s and higher data rates. These techniques allow a single IC detector with adaptive nonlinear cancellation to be used in long-haul and undersea lightwave systems to optimize the detector threshold and compensate for the ISI. >
Visual Analytics for Transportation Incident Data Sets

Transportation systems are being monitored at an unprecedented scope, which is resulting in tremendously detailed traffic and incident databases. Although the transportation community emphasizes developing standards for storing these incident data, little effort has been made to design appropriate visual analytics tools to explore the data, extract meaningful knowledge, and represent results. Analyzing these large multivariate geospatial data sets is a nontrivial task. A novel, web-based, visual analytics tool called Fervor is proposed as an application that affords sophisticated, yet user-friendly, analysis of transportation incident data sets. Interactive maps, histograms, two-dimensional plots, and parallel coordinates plots are four featured visualizations that are integrated to allow users to interact simultaneously with and see relationships among multiple visualizations. Using a rich set of filters, users can create custom conditions to filter data and focus on a smaller data set. However, because of the multivariate nature of the data, finding interesting relationships can be a time-consuming task. Therefore, a rank-by-feature framework has been adopted and further expanded to quantify the strength of relationships among the different fields describing the data. In this paper, transportation incident data collected by the Maryland State Highway Administration's CHART program are used; however, the tool can be easily modified to accept other transportation data sets.
From Similarities to Probabilities: Feature Engineering for Predicting Drugs’ Adverse Reactions

Social media recently became convenient platforms for different groups with common concerns to share their experiences, including Adverse Drug Reactions (ADRs). In this paper, we propose a two stage intelligent algorithm which we call “Simi_to_Prob”, that utilizes social media forums; for ranking ADRs, and evaluating the ADRs prevalence considering different age and gender groups as its first stage. In the second stage, ADRs are predicted utilizing a different data set from the Food and Drug Administration (FDA). In particular, Natural Language Processing (NLP) is used on social media to extract ranked lists of ADRs, which are then validated using novel intrinsic evaluation methods. In the second stage, feature engineering is used to extend the input feature space, then a two stage supervised machine learning method is used to predict future ADRs incidences. Our results show correct ranked list of ADRs for three antihypertensive drugs, where high Spearman’s rank correlation coefficients (rs) of of 0.7458, 0.6678 and 0.5929 were obtained between SIDER database for drug ADRs, and our obtained lists from social media. Furthermore, Relatedness between ADRs and age and gender groups achieved high area under the ROC curve (AUC) reaching 0.959. The second stage results showed high AUCs of 0.96 and 0.99 for the prediction of future ADRs probabilities. The proposed algorithm shows that mining social media can provide reliable source of information, and additional features that can be used to boost supervised machine learning methods’ performance in different domains including Pharmacovigilance research.
rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at low level for the test datasets from structure prediction models or dependent on the “black-box” process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that, rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. Additionally, rsRNASP is also superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available at website https://github.com/Tan-group/rsRNASP. SIGNIFICANCE RNAs play crucial roles in catalyzing biochemical reactions and regulating gene expression, and the biological functions of RNAs are generally coupled to their structures. Complementary to experiments, developing computational models to predict RNA 3D structures can be very helpful for understanding RNA biology functions. For a computational model, a reliable energy function is essentially important either for guiding conformational folding or for structure evaluation. For this purpose, we developed a residue-separation-based distance-dependent statistical potential, named rsRNASP which distinguishes the short- and long-ranged interactions, for RNA 3D structure evaluation. Our rsRNASP were examined against extensive test sets and shows overall superior performance over existing top traditional statistical potentials and a recently developed scoring function through 3D convolutional neural networks, especially for realistic test set from various computational structure prediction models.