Miracle-dz/UniFAR-FTUs huggingface dataset:
The quality of service in passenger transport terminals

The quality of service in transport terminals is differently perceived by engineers, economists, transport operators and sociologists. The traveler's perception is nevertheless decisive. The quality of service is well connected with the inside design of terminals, with the facilities in terminals and with the provided service standards. In order to provide a high level of service, the activities taking place in the public transport terminal and the maximum travelers flow size must be carefully analyzed and dimensioned. The purpose of modelling is to find the best route for each traveler from origin (entrance) to destination (exit) through all the intermediate service points, taking into consideration the instant network conditions. In developing the model we consider the walking, the waiting and the serving time. Using a simulation program written in ARENA we determine the waiting time. For validation, the model is used to evaluate the performance level in Bucharest Basarab station. By comparing the total walking distance for the possible routes and the utility function that describes the utility of all activities from entrance to exit we can find the optimal route.
Fighting Violence Against Women: The Role of Female Political Representation

We investigate the eﬀect of women’s political leadership in local government on homicide and violence against women. Using a regression discontinuity design we compare Brazilian municipalities where a female candidate barely won to those where a female candidate barely lost mayoral elections. Having a female mayor reduces intentional homicide rate of women by 20% and violence (physical, psychological, sexual) by 40%. These results are not due to pre-existing municipalities characteristics or other observable mayor characteristics. Overall, our ﬁndings provide compelling evidence that women holding oﬃce are eﬀective in addressing violence against women.
Multi-Band Transparent Optical Network Planning Strategies for 6g-Ready European Networks

As long-haul network operators upgrade core optical networks to support 5G services, research and standardization efforts for 6G have begun. Keeping in mind the ever-growing demand for high-bandwidth services, our work proposes a novel core-network traffic model which exploits the network’s graph and population metric to estimate demands. We further develop a multi-period Routing, Configuration, and Spectrum Assignment (RCSA) algorithm which takes as input the traffic model demands, and provides network metrics spread across all planning periods. A multi-period multi-band transparent optical network planning study on three European networks is undertaken, using two deployment approaches; namely, flexible bandwidth variable transceivers (Flex-BVTs) and transparent IP over wavelength division multiplexing (IPoWDM). We show that Flex-BVT places up to 15% lesser equipment into the network while carrying up to 20% higher traffic. Using a simple Capital Expenditure (CAPEX) calculation, we also show that the CAPEX-per-bit of deploying Flex-BVTs is up to 12% lower, as compared to transparent IPoWDM solutions.
The Electrical Response of Real Dielectrics: Using the Voltage Ramp Method as a Straightforward Diagnostic Tool for Polymeric Composites

An experimental method exploiting the capacitive response of most materials is here revised. The procedure called the “Voltage Ramp Method” (VRM) is based on applying proper voltage ramp cycles over time and measuring electrical current intensity flowing through the material sample. In the case of an ideal capacitor, a current plateau should be easily measured, and the capacitance value precisely determined. However, most media, e.g., semiconductors and insulating polymers, show dielectric absorption and hence electric leakage effects. Therefore, the VRM method allows simultaneous determination of their equivalent capacitance and resistance. Some case studies are discussed as concerning the application of VRM to both standard and actual media. A figure of merit of the method is the percentage difference between 2.5% and 1.5% with respect to the nominal values of a commercial capacitor and resistor, respectively. The simulation modeling of the material electrical response is compared to the experimental data also on polymer nanocomposites suitable for energy harvesting.
Fungus–Fungus Association of Boletus griseus and Hypomyces chrysospermus and Cadmium Resistance Characteristics of Symbiotic Fungus Hypomyces chrysospermus

Fungi bioaccumulation of heavy metals is a promising approach to remediate polluted soil and water. Boletus griseus could accumulate high amounts of Cd, even in a natural habitat with low Cd contents. This study found a symbiotic association of B. griseus with a fungus. The symbiotic fungus was isolated and identified as Hypomyces chrysospermus. The isolated strain had a strong ability to tolerate Cd. The minimum inhibitory concentration of Cd of fungal growth was 200 mg·L−1. The Cd bioaccumulation capacity of the fungus reached 10.03 mg·g−1. The biomass production of the fungus was promoted by 20 mg·L−1 Cd. However, high concentrations of Cd suppressed fungal growth and significantly altered the morphology and fine texture of fungal hyphae and chlamydospores. The immobilization effects of the cell wall and acid compounds and antioxidant enzymes were employed by the fungus to alleviate the toxic effects of Cd. The results not only demonstrate a new insight into the Cd bioconcentration mechanisms of B. griseus but also provide a potential bioremediation fungus for Cd contamination.
Deep learning-based hyperspectral image reconstruction from emulated and real computed tomography imaging spectrometer data

Abstract. The computed tomography imaging spectrometer (CTIS) is a hyperspectral imaging (HSI) approach where spectral and spatial information of a scene is mixed during the imaging process onto a monochromatic sensor. This mixing is due to a diffractive optical element integrated into the underlying optics and creates a set of diffraction orders. To reconstruct a three-dimensional hyperspectral cube from the CTIS sensor image, iterative algorithms were applied. Unfortunately, such methods are highly sensitive to noise and require high computational time for reconstruction thus hindering their applicability in real-time and high frame-rate applications. To overcome such limitations, we propose a lightweight and efficient deep convolutional neural network for hyperspectral image reconstruction from CTIS sensor images. Compared with classical approaches our model delivers considerably better reconstruction results on synthetic as well as real CTIS images in under 0.17 s, which is over 60 times faster compared with the standard iterative approach. In addition, the reshaping method we have developed enables a lightweight network architecture with over 100 times fewer parameters than previously reported.
Moving beyond Risk Quotients: Advancing Ecological Risk Assessment to Reflect Better, More Robust and Relevant Methods

Under standard guidance for conducting Ecological Risk Assessments (ERAs), the risks of chemical exposure to diverse organisms are most often based on deterministic point estimates evaluated against safety-factor-based levels of concern (LOCs). While the science and guidance for mechanistic effect models (e.g., demographic, population, and agent-based) have long been demonstrated to provide more ecologically relevant effect endpoints upon which risk can be evaluated, their application in ERAs has been limited, particularly in the US. This special issue highlights the state of the science in effect modeling for ERAs through demonstrated application of the recently published Population modeling Guidance, Use, Interpretation, and Development for ERA (Pop-GUIDE). We introduce this issue with a perspective on why it is critical to move past the current application of deterministic endpoints and LOCs. We demonstrate how the current, widely used approaches contain extensive uncertainty that could be reduced considerably by applying models that account for species life histories and other important endogenous and exogenous factors critical to species sustainability. We emphasize that it is long past time to incorporate better, more robust, and ecologically relevant effect models into ERAs, particularly for chronic risk determination. The papers in this special issue demonstrate how mechanistic models that follow Pop-GUIDE better inform ERAs compared to the current standard practice.
A Compound Control Method Based on the Adaptive Linear Extended State Observer and Global Fast Terminal Sliding Mode Control for Inertially Stabilized Platform

To realize high-performance control for the inertially stabilized platform, a compound control method based on the adaptive linear extended state observer and global fast terminal sliding mode control is proposed. For estimating the unknown disturbances of inertially stabilized platform, an adaptive linear extended state observer was developed. With the full use of the information of attitude and angular velocity, the adaptive bandwidth of an adaptive linear extended state observer can deal with the peaking phenomenon without introducing excessive noise. Furthermore, the adaptation law based on the global fast terminal sliding mode control for disturbance estimation compensation was developed, which can improve the disturbance estimation accuracy of the adaptive linear extended state observer, and the higher order terminal function in global fast terminal sliding mode control was replaced by the lumped disturbance estimation of adaptive linear extended state observer, which can improve the anti-interference ability of inertially stabilized platform, reduce the chattering problem, and improve the control performance. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The effectiveness of the proposed method was validated by a series of simulations and experiments.
DANC-Net: Dual-Attention and Negative Constraint Network for Point Cloud Classification

Convolutional neural networks, as a branch of deep neural networks, have been widely used in multidimensional signal processing, especially in point cloud signal processing. Nevertheless, in point cloud signal processing, most point cloud classification networks currently do not consider local feature correlation. In addition, they only adopt ground-truth as positive information to guide the training of networks while ignoring negative information. Therefore, this paper proposes a network model to classify point cloud signals based on feature correlation and negative constraint, DANC-Net (dual-attention and negative constraint on point cloud classification). In the DANC-Net, the dual-attention mechanism is utilized to strengthen the interaction between local features of point cloud signal from both channel and space, thereby improving the expression ability of extracted features. Moreover, during the training of the DANC-Net, the negative constraint loss function ensures that the features in the same categories are close and those in the different categories are far away from each other in the representation space, so as to improve the feature extraction capability of the network. Experiments demonstrate that the DANC-Net achieves better classification performance than the existing point cloud classification algorithms on synthetic datasets ModelNet10 and ModelNet40 and real-scene dataset ScanObjectNN. The code is released at https://github.com/sunhang1986/DANC-Net.
Effects of an Angle Droop Controller on the Performance of Distributed Generation Units with Load Uncertainty and Nonlinearity

The present study proposes an angle droop controller for converter interfaced (dispatchable) distributed generation (DG) resources in the islanded mode of operation. Due to the necessity of proper real and reactive power sharing between different types of resources in microgrids and the ability of systems to respond properly to abnormal conditions (sudden load changes, load uncertainty, load current disturbances, transient conditions, etc.), it is necessary to produce appropriate references for all of the mentioned above conditions. The proposed control strategy utilizes a current controller in addition to an angle droop controller in the discrete time domain to generate appropriate responses under transient conditions. Furthermore, to reduce the harmonics caused by switching at converters’ output, a LCL filter is used. In addition, a comparison is done on the effects that LCL filters and L filters have on the performance of DG units. The performance of the proposed control strategy is demonstrated for multi islanded grids with various types of loads and conditions through simulation studies in the DigSilent Power Factory software environment.
Environmental Impacts of CO₂-EOR: The Offshore UK Context

The aim of this report is to identify the potential environmental impacts on the marine environment of offshore activities which are unique to CO2-EOR, or which may pose additional significant issues not encountered in normal oil and gas operations. These are therefore the potential incremental risks of offshore CO2-EOR in the UKCS. The first step of this assessment is to establish which activities (and therefore, risks) are new or unique to CO2-EOR compared with existing oil and gas operations. That is the aim of this section of the report. The following sections will briefly set out the basic environmental regulatory framework for the UKCS (Section 2) before describing in detail the potential environmental risks of these activities (Sections 3-5), and include conclusions drawn from experimental work carried out as part of this project (Section 6). A conceptual risk model can then be built to assess the potential risks associated with the CO2-EOR activities, and conclusions drawn thereafter (Section 7).
PCAPN: An Enhanced Feature Extraction Framework for Point Cloud

Point cloud is a widely used geometric data structure in the missions of 3D reconstruction, digital city and geologic survey etc. Extracting sufficient information from the point cloud is the key to deal with the aforementioned missions. However, huge number of points lead to computational complexity and inefficiency during the training process. To deal with this problem, this paper proposes a novel framework name Principal Component Analysis Point Net (PCAPN) for the feature extraction of point cloud. Firstly, a sampling module namely Component Point Sampling (CPS) is designed for generating several candidate sets of points by different scales, which defines the centroids of local regions. Secondly, a feature extraction framework based on MLP structure is adopted for extracting the feature vectors from the points set generated from the sampling module. Finally, the extracted feature vectors are concatenated together, and then put into a fully connected layer for classification. The proposed framework was evaluated on 2 benchmarks, i.e. ShapeNet part data set and ModelNet40. The experimental results show that our framework is efficient and robust. In particular, the results are significantly better than those obtained by the state-of-the-art frameworks. Our network is 4.6% more accurate than PointNet and 1.1% higher than PointNet++.
Microfluidic Organ-on-a-Chip System for Disease Modeling and Drug Development

An organ-on-a-chip is a device that combines micro-manufacturing and tissue engineering to replicate the critical physiological environment and functions of the human organs. Therefore, it can be used to predict drug responses and environmental effects on organs. Microfluidic technology can control micro-scale reagents with high precision. Hence, microfluidics have been widely applied in organ-on-chip systems to mimic specific organ or multiple organs in vivo. These models integrated with various sensors show great potential in simulating the human environment. In this review, we mainly introduce the typical structures and recent research achievements of several organ-on-a-chip platforms. We also discuss innovations in models applied to the fields of pharmacokinetics/pharmacodynamics, nano-medicine, continuous dynamic monitoring in disease modeling, and their further applications in other fields.
The role of strontium on the enhancement of photocatalytic response of TiO2 nanotubes – application in methylene blue and formic acid photodegradation under visible light and UV-A

Abstract The influence of ferroelectric strontium nanoparticles on the photocatalytic performance of 1 D TiO2 nanotubes (NT) was herein highlighted. The effect of adding various amounts of Sr nanoparticles onto NTs was therefore deeply characterized by means of Raman, UV-vis diffuse reflectance, photoluminescence (PL) and X-ray photoelectron spectroscopies, X-ray diffraction, N2 adsorption-desorption measurements and scanning electron microscopy coupled with energy-dispersive X-ray analysis as well as determining dielectric properties. The different xwt%Sr-NTs with x =(0.2, 0.4, 0.6, 0.8 and 1 wt%) were investigated for the photodegradation of Methylene Blue (MB) under visible light and formic acid under UV-A. Results emphasize that the kinetic study fits well with Langmuir-Hinshelwood (LH) model; for the same surface coverage (θ =0.99), the rate constant kLH increases versus Sr loading with an optimum at 0.8 wt% Sr. Under UV-A light, photodegradation increases versus Sr loading. As evidenced by PL, impedance and XPS measurements, the presence of ferroelectric strontium generates structural defects (Ti3+ and Ov) with strong opportunity to monitor the separation the photogenerated (electron- hole) pairs regardless of the synthesis limitations. Herein, the molar ratio O2-/Ti4+ increases vs strontium loading. Hence, Sr-NTs opens new route for “assisted” photocatalysts.
Complex Multimorbidity and Working beyond Retirement Age in Japan: A Prospective Propensity-Matched Analysis

Background: With the aging of populations worldwide, the extension of people’s working lives has become a crucial policy issue. The aim of this study is to assess the impact of complex multimorbidity (CMM) as a predictor of working status among retirement-aged adults in Japan. Methods: Using a nationwide longitudinal cohort study of people aged over 65 who were free of documented disability at baseline, we matched individuals with respect to their propensity to develop CMM. The primary outcome of the study was working status after the six-year follow-up. Results: Among 5613 older adults (mean age: 74.2 years) included in the study, 726 had CMM and 2211 were still working at the end of the follow-up. In propensity-matched analyses, the employment rate was 6.4% higher in the CMM-free group at the end of the six-year follow-up compared to the CMM group (725 pairs; 29.5% vs. 35.9%; p = 0.012). Logistic regression analysis showed that CMM prevented older people from continuing to work beyond retirement age and was a more important factor than socioeconomic factors (income or educational attainment) or psychological factors (depressive symptoms or purpose in life). Conclusions: Our study found that CMM has an adverse impact on the employment rate of older adults in Japan. This finding suggests that providing appropriate support to CMM patients may extend their working lives.
High-resolution three-dimensional imaging with compress sensing

LIDAR three-dimensional imaging technology have been used in many fields, such as military detection. However, LIDAR require extremely fast data acquisition speed. This makes the manufacture of detector array for LIDAR system is very difficult. To solve this problem, we consider using compress sensing which can greatly decrease the data acquisition and relax the requirement of a detection device. To use the compressive sensing idea, a spatial light modulator will be used to modulate the pulsed light source. Then a photodetector is used to receive the reflected light. A convex optimization problem is solved to reconstruct the 2D depth map of the object. To improve the resolution in transversal direction, we use multiframe image restoration technology. For each 2D piecewise-planar scene, we move the SLM half-pixel each time. Then the position where the modulated light illuminates will changed accordingly. We repeat moving the SLM to four different directions. Then we can get four low-resolution depth maps with different details of the same plane scene. If we use all of the measurements obtained by the subpixel movements, we can reconstruct a high-resolution depth map of the sense. A linear minimum-mean-square error algorithm is used for the reconstruction. By combining compress sensing and multiframe image restoration technology, we reduce the burden on data analyze and improve the efficiency of detection. More importantly, we obtain high-resolution depth maps of a 3D scene.
Agile Project Management in the Travel Industry

Software is used in many areas of our daily lives. Optimal software development processes are essential for successful projects in the software sector which has become a gigantic industry. Traditional software development processes can result in failure in software projects. Some difficulties exist in traditional processes if they aren’t receptive to the customer‘s requirements, or they can’t deliver within the deadline and the budget. In the Travel industry requirements changes frequently during the time the team is developing the software because market conditions are continuously changing. Creating a full requirement document is time consuming and not necessary since requirements often change. Agile software development methods have recently emerged as a novel way of developing software compared with traditional methodologies. Agile software development aims at fast, light and effective development that supports the customers‘ business without being chaotic or rigorous. Agile project management is defined as a conceptual framework for undertaking software engineering projects. Several approaches of Agile Methods are used to control and measure a software project. Agile Methods are considered a family of development processes, not a single approach to software development. The most popular and widely practiced Agile project management methods include Scrum, XP (Extreme Programming) etc. This research study aimed to examine Agile project management in the travel industry and knowledge in different agile methodologies.