Miracle-dz/UniFAR-FTUs huggingface dataset:
A LiDAR-Aided Inertial Positioning Approach for a Longwall Shearer in Underground Coal Mining

The absolute three-dimensional position of a longwall shearer is fundamental to longwall mining automation. The positioning of the longwall shearer is usually realized by the inertial navigation system (INS) and odometer (OD). However, the position accuracy of this positioning approach gradually decreases over time due to the gyro drift. To further increase the positioning accuracy of the shearer, this paper proposes a positioning approach based on the INS and light detection and ranging (LiDAR). A Kalman filter (KF) model based on the observation provided by detecting hydraulic supports which are part of the longwall face, using the LiDAR, is established. The selection scheme of the point features is studied through a set of simulations. In addition, compared with that of the approach based on the INS and OD, the shearer positioning accuracy obtained using the proposed approach is higher. When the shearer moves along a 350 m track for 6 cutting cycles and lasts about 7.1 h, both east and north position errors can be maintained within 0.2 m and the height error within 0.1 m.
URBAN CONFIGURATION AND WATER BALANCE WITH AQUACYCLE MODEL IN THE BELÉM CATCHMENT, SOUTHERN BRAZIL

The influence of anthropogenic activities on drainage basins increases the complexity of hydrological systems in urban watersheds due to the increased amount of impervious surfaces and the interaction between natural and constructed drainage systems. Herein, the aim is to verify how water balance components respond to anthropogenic changes in different configurations of urban space. The study focuses on the Belém catchment, located in Curitiba, Southern Brazil city. Simulations were performed using the Aquacycle model to evaluate the water balance of fourteen Hydrologic Similarity Areas, which were delimited based on the integrated analysis of zoning law, and demographic density data. The results show a direct and linear interaction between an increase in impervious areas with an increase in surface runoff and decreases in infiltration and evapotranspiration, demonstrating the relationship among urban configuration and components of hydrological cycle. Linear regression analysis indicates R2 of 0.986 between impervious surfaces and surface runoff, 0.956 for evapotranspiration and 0.934 for groundwater recharge. However, streamflow and baseflow parameters showed no sensitivity to the percentage of impervious surfaces, with R2 0.557 and 0.244, respectively. Finally, the results indicate a significant contribution to the water balance resulting from water-supply system leakage and wastewater discharge into the drainage system.
University of Southern Denmark Internet-Delivered Cognitive Behavioral Therapy for Anxiety Disorders in Open Community Versus Clinical Service Recruitment

Background: Ample studies have shown the effectiveness of internet-delivered cognitive behavioral therapy (iCBT) for anxiety disorders. These studies recruited their participants mainly from the community and, to a lesser extent, from within routine care services. Little is known about whether different recruitment strategies lead to different treatment effects. Objective: This meta-analysis compared clinical results obtained in trials with recruitment from the community versus results obtained in trials with clinical service recruitment and explored factors that may mediate differences in treatment outcome. Methods: We included randomized controlled trials in which the clinical effects of iCBT for anxiety disorders were compared with a control condition (waitlist controls or face-to-face cognitive behavioral therapy). We classified trials as open recruitment trials (recruitment from the community) or clinical service recruitment trials (recruitment through outpatient clinics). Pooled effect sizes based on measures examining anxiety symptoms, depressive symptoms, and quality of life were computed for each type of trial. Subgroup analyses examined whether clinical results from open recruitment trials differed from those obtained in clinical service recruitment trials. Additional analyses explored which demographic, clinical, and treatment-related factors contributed to differences in effect sizes of open recruitment versus clinical service recruitment trials. Results: We included 42 studies with 53 comparisons (43 open recruitment comparisons and 10 clinical recruitment comparisons). Analyses of anxiety measures revealed, first, that iCBT open recruitment studies with waitlist control comparators showed a significantly higher effect size for decrease in anxiety symptoms than did those with clinical recruitment (Q=10.09; P=.001). This association between recruitment method and effect size was no longer significant in a multivariate metaregression with treatment adherence and exclusion of patients with depressive symptoms entered as additional predictors of effect size. Second, effect size for decrease in anxiety symptoms did not differ significantly between clinical recruitment and open recruitment studies with face-to-face cognitive behavioral therapy comparators. The effects of open recruitment trials and clinical recruitment trials J Med Internet Res 2019 | vol. 21 | iss. 4 | e11706 | p. 1 http://www.jmir. org/2019/4/e11706/ (page number not for citation purposes) Romijn et al JOURNAL OF MEDICAL INTERNET RESEARCH
S AMPLE EFFICIENT Q UALITY D IVERSITY FOR NEURAL CONTINUOUS CONTROL

We propose a novel Deep Neuroevolution algorithm, QD-RL, that combines the strengths of off-policy reinforcement learning (RL) algorithms and Quality Diversity (QD) approaches to solve continuous control problems with neural controllers. The QD part contributes structural biases by decoupling the search for diversity from the search for high return, resulting in efficient management of the exploration-exploitation trade-off. The RL part contributes sample efficiency by relying on off-policy gradient-based updates of the agents. More precisely, we train a population of off-policy deep RL agents to simultaneously maximize diversity within the population and the return of each individual agent. QD-RL selects agents interchangeably from a Pareto front or from a Map-Elites grid, resulting in stable and efficient population updates. Our experiments in the ANT-MAZE and ANT-TRAP environments show that QD-RL can solve challenging exploration and control problems with deceptive rewards while being two orders of magnitude more sample efficient than the evolutionary counterpart.
Plantation forests as regional strength for development of rural bioeconomy

The establishment of plantation forests in areas not viable for agriculture can make a significant contribution to the economy. The yield from 1 ha of plantation forest depends on the management purpose obtaining of round wood (pulpwood, sawnlog, veneer log, tare), bioenergy and extraction of tree foliage (broadleaved and coniferous). In Latvia, based on 2019 data, plantation forests achieve 2760 ha of Scots pine, 7855 ha of Norway spruce, 7431 ha of Birch, 2123 ha of Grey alder, 1274 ha of Black alder and Populus spp. and 618 ha of Salix spp. Estimated and projected gains are calculated both as round wood over 20 to 50 years: pine 410-to 994 thou. m; spruce, 335 to 2.906 thou. m, birch 1.040 -2.452 thou. m. Accordingly, it is possible to obtain gross income from the whole plantation forest area in Latvia: pine-12.42-63.8 mln. EUR; spruce 40.1 -192.3 mln. EUR; for birch 32.2 -202.7 mln. Eur. Additionally to that, 18.6 -21.6 t ha and 24.0 -37.0 t ha of processed foliage can be obtained from 1 ha of pine and spruce forest plantations (40-50 years old). Alnus incana sp. (5-20 years), yielding 19.65-122.65 thou. Solid m and Salix spp. (3-5 years), yielding 58.71-84.97 thou. solid m, are used for energy production, furthermore Alnus spp. wood can be used than valuable raw material for plywood production. At the same time, it is possible to capture 106-1477 thou. tonnes of CO2 equivalent. Systematic investigations of chemical composition of above mentioned Latvian plantation trees, wood and bark, have shown that incorporation of extraction treatment in existing processing schemes will allow to manufacture high value added monomeric and oligomeric products which are of great demand for substitution of synthetic ones in different economy sectors (agriculture, including means for plant protection, food industry, polymer production, pharmacy etc.). Creation of small and medium-sized enterprises in rural region in close proximity to plantations opens the opportunity for the appearance of new working places, including organization of new nurseries, plantation services, private businesses for processing of various lignocellulosic waste into new special products / semi-products / feedstock for green industrial materials and chemicals, at the same time diminishing the logistics expenses.
Low Power Resource Efficient Ternary Content Addressable Memory Using Multiport SRAM

A Ternary Content addressable memory (TCAM) performs search operation by comparing input string with the stored data at high speed. TCAM is popular in high speed packet classification, forwarding and network switches. TCAM functionality is emulated with SRAM to overcome the limitations of TCAM such as small storage quantity, complex memory structure and high energy consumption. Resource Efficient SRAM based TCAM (REST) architecture utilize the functionalities of TCAM using optimal resource. The low power REST architecture has been proposed with 72X28 multiport SRAM to improve the speed and to reduce the power consumption of TCAM. In this proposed architecture, the virtual blocks of multiport SRAM unit is used to store the address information given in the TCAM. A72x28 low power REST architecture is simulated using modelsim and Xilinx Vivado.
Technical Note: Stability of tris pH buffer in artificial seawater 
stored in bags

Abstract. Equimolar tris (2-amino-2-hydroxymethyl-propane-1,3-diol) buffer in artificial seawater is a well characterized and commonly used standard for oceanographic pH measurements. We evaluated the stability of tris pH when stored in flexible, gas impermeable bags across a variety of experimental conditions, including bag type, tris batch, and storage in air vs. seawater over 300 days. Bench-top spectrophotometric pH analysis revealed that the pH of tris stored in bags drifted at a rate of −0.0058 ± 0.0008 yr−1 (mean slope ± 95 % confidence interval of slope). Analyses of total dissolved inorganic carbon confirmed that a combination of CO2 infiltration and/or microbial respiration led to the observed decrease in pH. Eliminating bagged tris pH drift remains a goal, yet the pH drift rate of 0.006 yr−1 is lower than many processes of interest and demonstrates the value of bagged tris to sensor calibration and validation of autonomous in situ pH measurements.
Vulnerability of Road Pavements to Climate Change in Abia and Imo States of Nigeria

Around the world today, road pavement is increasingly vulnerable and Southeast Nigeria is no exception. To this end, the Delphi technique is used in this study to ascertain experts opinions in order to identify and prioritize road pavements vulnerable to climate change in Abia and Imo states of Southeast Nigeria. As a measure of stability, standard deviation of two rounds of questionnaire responses was used to assess the level of convergence of opinions of 10-member Delphi panel. The Delphi panel achieved acceptable level of agreement at threshold values of standard deviation = 0.10 for stability and mean = 0.20 for consensus. Out of 14 federal roads considered in the study, 1 road had high vulnerability status, 5 roads had medium vulnerability status, 5 roads were rated low vulnerability and 2 roads were rated very low vulnerability. The only highly vulnerable road is from Abia state. This study has shown that the vulnerability of road pavements to climate change can be assessed comprehensively using collective and subjective judgement in the form of Delphi technique. Government should therefore apply adaptive maintenance plans that reflect the relative vulnerability rating of these roads.
Analytical Concentrations of Some Elements in Seeds and Crude Extracts from Aesculus hippocastanum, by ICP-OES Technique

The metal content in some samples of horse chestnut seeds (Aesculus hippocastanum) was monitored over time (years 2016–2019) considering the two most common and representative Mediterranean varieties: the pure species (AHP, which gives white flowers) and a hybrid one (AHH, which gives pink flowers). The selected elemental composition of the samples was determined by applying the Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) technique. Several samples obtained from different preliminary treatments of the peeled seeds were examined, such as: (i) floury samples (wild-type) mineralized with the wet method; (ii) the ashes of both AHP and AHH varieties; (iii) the fraction of total inorganic soluble salts (TISS). Furthermore, the hydroalcoholic crude extracts (as a tincture) were obtained according to the official Pharmacopoeia methods, and the relevant results were compared with those of a commercial sample, an herbal product-food supplement of similar characteristics. The main characteristics of this research work underline that the two botanical varieties give different distinctive characters, due to the Fe content (80.05 vs. 1.42 mg/100 g d.s., for AHP and AHH wild-type flour samples, respectively), along with K, Ca, Mn, Ni and Cu, which are more abundant in the AHP samples. Furthermore, the Principal Component Analysis (PCA) was applied to the experimental dataset in order to classify and discriminate the samples, in relation to their similar botanical origin, but different for the color of the bloom. These results can be useful for the traceability of raw materials potentially intended for the production of auxiliary systems of pharmacological interest.
ANALYTICAL STUDY OF SEDIMENT EVOLUTION IN THE LAKE OF THE FOUM EL GHERZA DAM (BISKRA, ALGERIA) ETUDE ANALYTIQUE DE L’EVOLUTION SEDIMENTAIRE DANS LE LAC DU BARRAGE FOUM EL GHERZA (BISKRA, ALGERIE)

The research carried out as part of this work aims to provide answers to the rhythms and processes of sedimentation in the lake of the Foum El Gherza dam. Based on the bathymetric surveys carried out by the National Dams Agency in the Foum El Gherza dam, we have shown the temporal evolution of the silting up of the dam. As a result, the filling of the lake exceeded 70% of its initial capacity as of 2007. Despite the de-silting of the dam by dredging, the dam is in critical condition. Dredging operations must continue in the short term to extend the life of the structure.
Effecting of Metal Ions on the Interaction between Zidovudine and Bovine Serum Albumin

The interaction between zidovudine (ZDV) and bovine serum albumin (BSA) was investigated by fluorescence spectroscopy and ultraviolet-visible spectroscopy in aqueous solution (Tris-HCl buffer, pH 7.1). The effect of metal ions (Cu2+, Mg2+, Zn2+) on the interaction was also investigated. Results showed that the fluorescence intensity of BSA at 346 nm was quenched when zidovudine or metal ions were added. The quenching mechanism was a static quenching mechanism. A strong interaction exists between zidovudine and BSA. The thermodynamic parameters ΔH and ΔS were -10.2 kJ·mol-1 and 77.5 J·mol-1·K-1 at 298 K, respectively, indicating that electro-static forces played a major role. The binding constant, the number of binding site and the binding distance were 6.92×105 L·mol-1, 1.18, and 2.28 nm at 298 K, respectively. The binding constant and the number of binding sites decreased with the increase in temperature. The metal ions, Cu2+, Mg2+ and Zn2+, all decreased the binding constant and increased the binding distance for binding between ZDV and BSA.
VolumeVisual: Design and Evaluation of an Educational Software Tool for Teaching and Learning Volume Visualization

Visualizing three-dimensional volumetric data sets, often referred to as volume visualization, has been an essential topic in scientiﬁc visualization. Volume visualization is concerned with visualizing scalar ﬁeld data (e.g., temperature, pressure, intensity). Existing software tools can perform interactive visualization of volumetric data sets. However, these tools are often sophisticated and mainly geared towards researchers and developers. In this paper, we present VolumeVisual, a web-based educational software tool for teaching and learning volume visualization. VolumeVisual integrates the two most popular volume visualization techniques, direct volume rendering and isosurface rendering, to give students a comprehensive understanding of basic concepts and algorithms. The software tool includes a study component and a quiz component. Effectiveness of VolumeVisual is evaluated by conducting a formal user study consisting of an introduction and training session, a survey, followed by a quiz.
Map Coverage for Mobile Robot Implemented with Reinforcement Learning

This dissertation introduces a measure of navigation complexity for complete coverage path planning (CCPP). It also introduces a novel approach to CCPP using reinforcement learning (RL) to enable a mobile robot to cover an area, subject to constraints on time and environmental complexity. Target applications are those with many repeated obstacles and hard time constraints, such as cleaning an airline cabin during the gate time between flights. For navigation complexity measurement, the challenge is to consider various environmental factors together to measure the difficulty of navigating through the bounded space under consideration. For two-dimensional coverage problems, a low-complexity environment could be pictured as an area bounded by a rectangle or circle containing no obstacles, while a high complexity environment might be represented as a region bounded by a complex contour and filled with many randomly placed obstacles of random shapes and sizes. The size of environments and the numbers of corners on maps are commonly taken into consideration. However, the size of robots to conduct CCPP navigation, the number of obstacles and the location of obstacles on maps are factors that cannot be ignored. To address the aforementioned challenge, I propose a method consistent with the Shannon Entropy Formula. Four environmental factors are considered as inputs in our navigation complexity measurement. These four inputs include the spatial area to be covered, robot size, number of obstacles, and obstacle locations.
University of Birmingham Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network

: Roller bearings form key components in many machines and, as such, their health status can directly influence the operation of the entire machine. Acoustic signals collected from roller bearings contain information on their health status. Hence, acoustic-based fault diagnosis techniques can provide novel solutions as condition monitoring tools for roller bearings. Traditionally, acoustic fault diagnosis methods have been based on conventional signal processing methods in which prior expert knowledge has been required in order to extract and interpret the health information contained within the collected acoustic signals. As an alternative, deep learning methods can be used to obtain heath information from the collected signals by constructing ‘end-to-end’ models that do not rely on prior knowledge. These approaches have been successfully applied in the condition monitoring of industrial machinery. However, conventional deep learning methods can only learn features from the vertices of input data and thereby ignore the information contained in the relationships (edges) between vertices. In this paper, which combines graph convolution operators, graph coarsening methods, and graph pooling operations; a deep graph convolutional network (DGCN) based on graph theory is applied to deliver acoustic-based fault diagnosis of roller bearings. In the proposed method, the collected acoustic signals are first transformed into graphs with geometric structures. The edge weights represent the similarity between connected vertices, which enriches the input information and hence improves the classification accuracy of the deep learning methods applied. To verify the effectiveness of the proposed system, experiments with roller bearings of varying condition were carried out in the laboratory. The experimental results demonstrate that the DGCN method can be used to detect different kinds and severities of faults in roller bearings by learning from the constructed graphs. The results have been compared to those obtained using other, conventional, deep learning methods applied to the same datasets. These comparative tests demonstrate improved classification accuracy when using the DGCN method.
Modelling of Redox Front and Uranium Movement in a Uranium Mine at Pocos de Caldas

A study of the natural migration of uranium at the Pocos de Caldas uranium mine in Brazil under the influence of the movement of oxidizing groundwaters has been performed. This has involved use of the coupled chemical equilibria/transport code CHEQMATE. Preliminary results give some encouraging agreements with field data which gives confidence in the validity of using such modelling techniques in other problems associated with the migration of radionuclides away from a nuclear waste repository. For particular aspects of the problem where good agreement with field data was not obtained, a number of suggestions have been made. These include incorrect choice of solubility-limiting mineral phases in the model, mineral precipitation reactions not being at equilibrium and possible errors in field measurements due to contamination of deep groundwater samples by surface waters.
Fog-based SDN for DDOS Attack Mitigation in IoT Systems: A Survey

Distributed Denial-of-Service (DDoS) attacks constitute a severe threat to academia and industry, with unfettered access by end-users to resources from cloud data center services. DDoS attacks, discussed and resolved with varying degrees of success by researchers in the recent past, have been detected and mitigated successfully in SDN environments. Given the rapid rise of edge devices like sensors, cloud storage access has become a serious concern, with fog computing stepping in to play a major role in sharing such resources as links between the cloud and edge computing devices. The fog is the middle layer of cloud and edge devices. This paper deals with DDoS attack detection and mitigation approaches between IoT devices and a cloud server via an SDN controller in a fog layer. It also discusses the roles and responsibilities of fog computing in an SDN-based testbed, as well as the challenges involved in DDoS attack detection using a fog-based SDN to protect the cloud server from edge devices.
Asymptotic approach to a rotational Taylor swimming sheet

The interaction of a viscous fluid and a circular, pre-stressed active shell is studied in the limit of low Reynolds numbers. A seminal paper of Taylor represents a benchmark for this class of problems. Here, inspired by the same approach, we determine with asymptotic techniques the possible swimming motions of the shell for the particular changes of curvature that it can achieve when actuated. We confirm numerical results obtained previously, and highlight the structure of a problem that turns out to be similar to that of Taylor, and as such represents a simple example of Stokesian swimming.