Miracle-dz/UniFAR-FTUs huggingface dataset:
Conceptualising and measuring levels of risk by immigration status for children in the UK

ABSTRACT Extensive evidence exists on how characteristics and circumstances of children shape their lifepaths and outcomes, and on the scale of resulting need. However, little research exists assessing the numbers of children who may be at risk of harm or disadvantage due to their immigration status. In this paper, we sought to establish the degree to which it is possible to monitor the aggregate vulnerability to risk of children in the UK by virtue of immigration status. First, we developed an observable set of immigration risk and vulnerability factors through workshop consultations that were analysed to produce a core set of variables that might be measured to assess aggregate need by virtue of immigration status. Second, we assessed through an administrative data review what is known statistically about the numbers of children at risk by virtue of immigration status in the UK. This research indicates a considerable gap in statistical knowledge of the level of vulnerability of children in the UK by virtue of immigration status. The approach we have developed provides a framework for future statistical work that might address this gap.
Numerical Simulation and Seismic Fragility Analysis of a Self-Centering Steel MRF with Web Friction Devices

This article presents the seismic fragility analysis of a self-centering steel moment-resisting frame (SC-MRF) with web friction devices. A detailed numerical model of the SC frame was developed using the Open System for Earthquake Engineering Simulation (OpenSees) and the elastoplastic responses of the SC-MRF were studied, including the strength degradation under cyclic loading, tendon rupture, beam buckling, bolt bearing and friction loss, etc. The proposed simulation approach is validated by comparing the simulated results with those in existing hybrid-simulation tests, quasi-static pushover test and low cyclic tests, where good agreement is observed. In addition to the well-established performance limit states (i.e., immediate occupancy, collapse prevention and global dynamic instability), two unique performance limit states (i.e., the recentering and repairable limit states) are defined for the SC-MRF. Finally, incremental dynamic analyses are conducted to evaluate the seismic fragilities regarding the five performance limit states.
Road Surface Wetness Variations: Measurements and Effects for Winter Road Maintenance

Road wetness is a critical parameter when it comes to preventing road slipperiness. It has so far been difficult to measure, but new measurement techniques on the market make it useful to investigate the savings opportunities within road maintenance operations. The present study tests a new sensor mounted on a road maintenance vehicle run in a maintenance district in central Sweden. The sensor that measures road surface water depth is connected to a global positioning system (GPS) system and a global system for mobile communications (GSM) phone modem. Potential savings are calculated based on the measured road surface water depth and on the amount of applied salt needed to lower the freezing point to a certain degree. The results indicate variations in water depth along the measured roads, which implies that the salt concentrations along the roads could also vary. Though the variations were smaller along the tested highway, the calculated savings could still be up to 22%. Despite the many assumptions made in the calculations, considerable savings could potentially be realized by applying such wetness measurements.
Protective Effect of Stem Cells from Toxicity Induced by Gulf War Illness (GWI) Serum in N2A Neuroblastoma Cells

Page 26 of 31 Protective Effect of Stem Cells from Toxicity Induced by Gulf War Illness (GWI) Serum in N2A Neuroblastoma Cells Effie-Photini C. Tsilibary1,2, Danielle Carlson1,2, Apostolos P. Georgopoulos1,2* 1Brain Sciences Center, Department of Veterans Affairs Health Care System, Minneapolis, Minnesota, USA 2Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, USA
Therapeutic Effect of Darkling Beetle (Zophobas morio) Hemolymph on Skin Thermal Injury in Mice Infected by Staphylococcus haemolyticus

Staphylococci are the most common pathogens isolated from skin infections in livestock or companion animals. Antibiotic therapy is the best treatment for infections, but local or systemic use of antimicrobials increases the risk of bacterial resistance. Insects are rich in antimicrobial peptides, which can reduce bacterial resistance and can be used to treat bacterial infections after skin burns. We propose that the use of the darkling beetle (Z. morio) hemolymph to treat skin infections in mice by Staphylococcus haemolyticus is one of the alternatives. Z. morio hemolymph alleviated the increase in wound area temperature in mice with a skin infection, reduced the bacterial load of the wound, and accelerated the wound healing speed significantly. Pathological sections showed that Z. morio hemolymph can significantly reduce inflammatory cell infiltration, and promote skin tissue repair. Real-time fluorescent quantitative polymerase chain reaction (PCR) revealed that the Z. morio hemolymph can significantly reduce the levels of pro-inflammatory cytokines, including interleukin-1 beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and chemokine interleukin-8 (IL-8). Our findings suggest that Z. morio antibacterial hemolymph can promote wound contraction, relieve local inflammatory responses and promote wound healing in mice infected with a heat injury, which has a positive therapeutic effect and enormous potential for skin thermal injury.
Widespread azendohsaurids (Archosauromorpha, Allokotosauria) from the Late Triassic of western USA and India

Archosauromorph reptiles underwent rapid lineage diversification, increases in morphological and body size disparity, and expansion into new adaptive landscapes. Several of the primary early archosauromorph clades (e.g. rhynchosaurs) are easy to differentiate from others because of their characteristic body types, whereas the more lizard‐like and carnivorous forms with long necks (e.g. tanystropheids) were historically all relegated to the groups Protorosauria or Prolacertiformes. However, it is now clear that these groups are polyphyletic and that a lizard‐like, carnivorous form is plesiomorphic for Archosauromorpha, and multiple subclades started with that body plan. Among these early forms is Malerisaurus from the Upper Triassic of India (M. robinsonae) and the Upper Triassic of south‐western USA (M. langstoni). In this paper, we critically re‐evaluate the genus. We find both species of Malerisaurus as valid, and identify Malerisaurus as an early diverging, but late‐surviving, carnivorous member of Azendohsauridae within Allokotosauria. Our histological analysis and assessment of ontogenetic changes of limb bones of small and large individuals demonstrate that the skeletons of the small forms grew slowly and became more robust through ontogeny, and that the larger recovered bones are at or near the maximum size of the taxon. Malerisaurus and Malerisaurus‐like taxa were common members of the Otischalkian–Adamanian (late Carnian to mid‐Norian) faunal assemblages from Upper Triassic strata of the south western USA, but they are absent from the younger Revueltian holochronozone. Specimens from western North America show that Allokotosauria had a near‐Pangaean distribution for much of the Middle Triassic to Late Triassic.
Simulation of magnetrons and crossed-field amplifiers

Describes a computer program for simulation of the magnetron oscillator and crossed-field amplifiers. Emphasis is placed on accuracy and efficient computation with moderate numbers of electrons and moderately large time steps. Distinctive features are (1) space-charge field evaluation by Buneman's cycle reduction method and separate treatment of electrons near the cathode; (2) circuit field and electronic induction calculation by use of a Ramo (Green's) function, accounting for space-harmonics; (3) calculation of RF network response by means of Green's functions; (4) predictor/corrector evaluation of average RF signals; and (5) use of fifth-degree power series calculation of electron trajectories. The simulation is illustrated in the starting of oscillations from computer noise. >
Pre- and poststrike atmospheric assessment system

The tactical motivation for a compact and easily deployable remote sensing unit for hazardous material releases is described. The most important measurements such a unit could make for accurately determining the extent of a plume from a target or potential target is a range resolved profile of the wind within the vicinity of the strike zone. However, to make such measurements a new type of instrument is required which is inherently robust, compact, and efficient. To demonstrate that such an instrument is feasible, we have designed, built, tested, and performed an initial laboratory evaluation of a compact Doppler lidar system for tactical applications.
Robot Coordination with Ad-hoc Team Formation (Extended Abstract)

Coordinating multiagent systems to maximize global information collection both presents scientic challenges (what should each agent aim to achieve? ) and provides application opportunities (planetary exploration, search and rescue). In particular, in many domains where communication is expensive (for example, because of limited power or computation), the coordination must be achieved in a passive manner, without agents explicitly informing other agents of their states and/or intended actions. In this work, we extend results on such multiagent coordination algorithms to domains where the agents cannot achieve the required tasks without forming teams.
Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection

DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity and confidentiality of computer networks and systems, which are essential assets in today’s world. Detecting DDoS attacks is a difficult task that must be accomplished before any mitigation strategies can be used. The identification of DDoS attacks has already been successfully implemented using machine learning/deep learning (ML/DL). However, due to an inherent limitation of ML/DL frameworks—so-called optimal feature selection—complete accomplishment is likewise out of reach. This is a case in which a machine learning/deep learning-based system does not produce promising results for identifying DDoS attacks. At the moment, existing research on forecasting DDoS attacks has yielded a variety of unexpected predictions utilising machine learning (ML) classifiers and conventional approaches for feature encoding. These previous efforts also made use of deep neural networks to extract features without having to maintain the track of the sequence information. The current work suggests predicting DDoS attacks using a hybrid deep learning (DL) model, namely a CNN with BiLSTM (bidirectional long/short-term memory), in order to effectively anticipate DDoS attacks using benchmark data. By ranking and choosing features that scored the highest in the provided data set, only the most pertinent features were picked. Experiment findings demonstrate that the proposed CNN-BI-LSTM attained an accuracy of up to 94.52 percent using the data set CIC-DDoS2019 during training, testing, and validation.
Excitation of large-amplitude parametric resonance by the mechanical stiffness modulation of a microstructure

In this work we report on an approach allowing efficient parametric excitation of large-amplitude stable oscillations of a microstructure operated by a parallel-plate electrode, and present results of a theoretical and experimental investigation of the device. The frame-type structure, fabricated from a silicon on insulator (SOI) substrate using deep reactive ion etching (DRIE), consists a pair of cantilever-type suspensions connected at their ends by a link. The time-varying electrostatic force applied to the link by a parallel-plate electrode is transformed into a periodic tension of the beams, resulting in the modulation of their flexural stiffness and consequently the mechanical parametric excitation of the structure. The lateral compliance of the beams allows for large-amplitude in-plane oscillations in the direction parallel to the electrode while high axial stiffness prevents undesirable instabilities. The lumped model of the device, considered as an assembly of geometrically nonlinear massless flexures and a rigid massive link and built using the Rayleigh–Ritz method, predicted the feasibility of the excitation approach. The fabricated devices were operated in ambient air conditions by a combination of a steady (dc) and time-dependent (ac) components of voltage and the large-amplitude responses, up to 75 µm, in the vicinity of the principal parametric and primary resonances were registered by means of video acquisition and image processing. The shapes of the experimental resonant curves were consistent with those predicted by the model. The location and size of the instability regions on the frequency–voltage plane (parametric tongues) were quantitatively in good agrement with the model results. Theoretical and experimental results indicate that the suggested approach can be efficiently used for excitation of various types of microdevices where stable resonant operation combined with robustness and large vibrational amplitudes are desirable.
Analysis of the benefits and costs of construction craft training in the United States based on expert perceptions and industry data

The case for or against craft training in construction from the perspective of constructors, owners, governments, workers, and other stakeholders is not completely clear, despite several studies to date. The business case for investing in construction craft training is examined from the employer’s perspective on a construction project. A survey of 93 training directors and construction managers from throughout the US construction industry was used to collect quantitative estimates regarding craft training benefits and learning rates. Using these data, a benefit to cost ratio was calculated for a hypothetical typical industrial project (the CII model plant). According to the analyses, the estimated benefit to cost ratios range from 1.5:1 to 3.0:1. While these ratios are based on expert opinion, the results were triangulated with actual data from company training and project performance records as well as results from other research studies. The estimated benefit to cost ratios on the study’s hypothetical industrial project are sensitive to the craft workers’ employment duration, assuming that the training is provided by a single employer. Training durations are likely to be longer under a ‘community training’ model in which firms collaborate to sponsor training and workers have greater opportunities to continue in their training programme as they move from firm to firm. Nevertheless, the results contribute to a growing body of evidence that an investment in craft training is economically attractive for an employer, even over a typical project life cycle.
Increasy eutrophication symptoms during a prolonged drought event in tropical semi-arid reservoirs, Brazil

ABSTRACT Eutrophication is a global problem and an important cause of the ecological health degradation of aquatic ecosystems. The aim was to evaluate the effect of prolonged drought period (2012 to 2019) on the trophic state of two Brazilian semi-arid reservoirs, Boqueirão de Parelhas (BOQ) and Passagem das Traíras (TRA). The work was carried out according to a drought classification system, based on the Standardized Precipitation Index (SPI), with an aggregated timescale of 36 months, defining three periods. In the Period I, higher values of Secchi and lower values of turbidity, solids, nutrients and chlorophyll-a were registered, when compared to the other periods, mainly in BOQ. The principal component analyses of both reservoirs reveled a showed a temporal trend of the sample units according to the consequences of the prolonged drought. The results indicated changes in limnological variables due to the reduction of accumulated water volume, and they demonstrated that prolonged droughts impact the intensification of eutrophication in both systems. The trophic state of the BOQ reservoir was changed from mesotrophic to eutrophic as a consequence of the drought, while in TRA the eutrophic state remained, but with more intensified symptoms, with higher concentration values of nutrients, solids and algal biomass.
Study on Zostera marina L. Seed Germination Ecological Characteristics in Variable Environmental

The paper is mainly concerned with two parts in morphological observation and tissue culture of Eelgrass (Zostera marina L.). The results areas follows: 1.The microstructure of the plant showed that the cell wall exposing to seawater in epidermal cells of leaves was highly thickened, but the other sides were normal. It was also highly thickened in the side exposing to seawater in the epidermal cells of roots and stems. This structure may function as a barrier against toxic ions to enter the epidermal cells. Chloroplasts were found mainly in the epidermal cells, indicating that epidermal cells were main site where photosynthesis occurred. Aerenchyma was found everywhere in the plant which showed the ability of adaptation to submerged life. Anatomical observation of the inflorescence showed that eelgrass was a monoecious plant; the male flower and carpel were alternating arrangement on the inflorescence. SEM photographs of the stamen and the carpel indicated that the carpel had two stigmas; the pollens of the eelgrass were filamentous. All of the above observation showed that eelgrass is able to adapt to submerged life. The explants were dipped in 75%Alcohol for 10 seconds firstly, they were dipped in 1%NaClO 10 minutes, and this was the best way of sterilization than other treatments.
Solving optimum operation of single pump unit problem with ant colony optimization (ACO) algorithm

For pumping stations, the effective scheduling of daily pump operations from solutions to the optimum design operation problem is one of the greatest potential areas for energy cost-savings, there are some difficulties in solving this problem with traditional optimization methods due to the multimodality of the solution region. In this case, an ACO model for optimum operation of pumping unit is proposed and the solution method by ants searching is presented by rationally setting the object function and constrained conditions. A weighted directed graph was constructed and feasible solutions may be found by iteratively searching of artificial ants, and then the optimal solution can be obtained by applying the rule of state transition and the pheromone updating. An example calculation was conducted and the minimum cost was found as 4.9979. The result of ant colony algorithm was compared with the result from dynamic programming or evolutionary solving method in commercial software under the same discrete condition. The result of ACO is better and the computing time is shorter which indicates that ACO algorithm can provide a high application value to the field of optimal operation of pumping stations and related fields.
MRI Brain Abnormality Detection using MLRPNN.

In our proposed method, an automatic brain tumour segmentation and classification system is developed. The input image is preprocessed, segmented and features are extracted. Based on the extracted features, the input image is classified as cancerous or non-cancerous image using multilayer resilient propagation neural network classifier. In the preprocessing stage, noise is removed using median filter and the skull is stripped using morphological operators. Using thresholding technique and orthogonal polynomial transform, the skull stripped image is segmented into gray matter, white matter, cerebrospinal fluid and tumour. Then features like mean, variance, energy, and entropy are calculated. Later, multilayer resilient propagation neural network (MLRPNN) is trained with extracted features. A total of 150 images have been used, out of which 60 are used for training and remaining 90 images for testing. MLRPNN classifier classifies the input image to be cancer affected or normal based on features extracted. If the image is cancer affected, then type of cancer is detected as malign tumor or benign tumor using another MLRPNN Classifier. The performance of the proposed technique is validated and compared with the standard evaluation metrics such as sensitivity, specificity and accuracy values for neural network. The proposed method is compared with two standard methods KNN and FCM+NN. The obtained result depicts that the proposed classification method yields better results.
Užití programovatelných hradlových polí v systémech průmyslové automatizace

This doctoral thesis deals with the usage of Field Programmable Gate Arrays (FPGAs) in a diagnosis of power inverters which use the IGBTs transistors as switching devices. It is focused on the IGBT gate drives and their structures. As long as the transient phenomena and other quantities such as IG, VGE, VCE shows the IGBT degradation during the switching process (turn-on, turn-off), a new IGBT gate driver architecture is proposed for measuring and monitoring these quantities. Quick measurements and monitoring during the IGBT switching process require high sampling frequencies. Therefore, high speed parallel ADC converters (> 50 MSPS) are proposed. The thesis is focused on the FPGA design (hardware, software). A new FPGA board is designed for desired functions implementation such as IGBT driving using multiple stages, IGBT monitoring and diagnosis, and interfacing to inverter controller.
The Application of R.E.N.A.L. Nephrometry Score In Laparoscopic Partial Nephrectomy With Zero Ischemia And Sutureless

Background: To evaluate the application of R.E. N.A. L. nephrometry score (RNS) in laparoscopic partial nephrectomy (LPN) with zero ischemia and sutureless surgery, and to explore the efficacy and safety of zero ischemia and seamless LPN in the treatment of renal cell carcinoma.Methods: The clinical data of 67 patients with renal cell carcinoma treated by LPN in the affiliated Hospital of Guizhou Medical University from January 2016 to July 2020 were analyzed retrospectively. The patients were divided into renal artery occlusion group (n=31) and non-occlusion group (n=36). All cases were divided according to their RNS (low, moderate, and high), and the perioperative condition, postoperative complications, postoperative recovery and changes of renal function in the two groups were analyzed.Results: According to the RNS, all cases were classified in low-complex. Both groups successfully completed the operation without operative complications. Compared with the renal artery occlusion group, the non-occlusion group had a shorter operation time (35.51±20.48 min), shorter hospital stay (6.72±4.39 d), and no significant difference in intraoperative blood loss(50.39±30.19 ml). During the 6-month follow-up, the creatinine value of the renal function in the non-occlusion group (78.47±10.98μmol/L) was lower than that in the occlusion group(98.21±8.06μmol/L). Conclusion: Zero-ischemia sutureless LPN technique can effectively reduce the time of ischemia and avoid renal ischemia-reperfusion injury. This surgical technique may be a feasible surgical method for the treatment of low RNS renal cell carcinoma.