Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of iron ore mineral wastes for sulfate removal from groundwater wells: a case study

To reduce environmental risks of mining activities, it is important to find eco-friendly and efficient ways of using mine waste. One way to utilize mine waste is to reuse it as an adsorbent in environmental decontamination. This study describes the efficiency of Choghart iron ore mineral wastes (namely quartz-albitophire and metasomatite) as efficient adsorbents of sulfate in contaminated groundwater from wells of the Bafq district in the center of Iran. At first, iron ore mineral wastes were characterized using XRD, XRF, and FTIR spectroscopy and petrographic observations of thin sections. The main parameters such as pH, contact time, initial sulfate concentration and amount of adsorbent have been optimized for maximum sulfate removal, using response surface methodology (RSM) based on the central composite design (CCD) method. Based on RMS analysis, the sulfate removal models proved to be in good agreement with the experimental values, with very low probability values (<0.0001). From the predicted model, maximum sulfate adsorption onto metasomatite and quartz-albitophire was 31.07 and 20.27 mg g−1, respectively. This adsorption rate indicates that the seemingly worthless iron ore mineral waste can be useful to eliminate one of the most hazardous environmental pollutants. To identify the mechanism of adsorption, the equilibrium of the adsorption process was examined using non-linear isotherm models (Langmuir, Freundlich, Temkin, Dubinin–Radushkevich, Redlich–Peterson, Toth and Koble–Corrigan). These studies uncovered that sulfate adsorption onto quartz-albitophire has a complex character, and it seems that a combination of heterogeneous and homogenous adsorption occurs on the surface. The heterogeneous sulfate adsorption onto metasomatite has been confirmed by the Toth and Koble–Corrigan models. According to optimum conditions of sulfate adsorption, the maximum removed amounts of sulfate from Bafq groundwater wells using quartz-albitophire and metasomatite were 15.62 and 29.33 mg g−1, respectively.
Structure and Properties of the Amorphous Lipon Electrolyte By First-Principles Simulations

Amorphous materials belonging to the family of Lithium Phosphorus Oxynitrides (LiPON) are increasingly popular solid electrolytes for thin-film Li-ion batteries. RF-sputtering of a lithium orthophosphate target in a nitrogen plasma leads to the deposition of amorphous glasses [1]. Besides a fairly high ionic conductivity and very low electronic conductivity, LiPON exhibits remarkable chemical and physical stability to such an extent that its use as a protective layer for cathode materials has been suggested [2]. Rationalization of the transport properties rests on a valid structural model for these glassy structures, whose simulation represents a main challenge from a computational point of view also because of their non-trivial compositions. The motivation for this study is the desire to assess the structural, electronic and transport properties of an amorphous member of the LiPON family with non-trivial composition and cross-linking. The issue of structure prediction for a given composition is a longstanding problem even for the simplest crystalline solids. The problem is further complicated in the case of an amorphous material, as the potential energy landscape that describes the glassy region exhibits a large number of minima of varying depths. Researchers Sabrina Sicolo and Prof. Dr. rer. nat. Karsten Albe
Distinguishing Original and Non-Original Stands at the Zhanjiang Mangrove National Nature Reserve (P.R. China): Remote Sensing and GIS for Conservation and Ecological Research

The present research developed a novel methodological framework to differentiate natural mangrove stands (i.e., original), from stands which were planted and stands naturally established after interaction between planted and non-planted stands (e.g., through pollination, i.e., non-original). Ground-truth and remote sensing data were collected for Zhanjiang Mangrove National Nature Reserve (ZMNNR) in P.R. China. First, satellite images of Corona (1967) and GeoEye-1 (2009) were overlaid to identify original (1967) and non-original (2009) mangrove stands. Second, in both stands a total of 75 in situ plots (25 m2) were measured for ground-truthing of tree structural parameters including height, density, basal area and Complexity Index (CI). From temporal satellite data, we identify 236.12 ha of original mangrove and 567.88 ha of non-original mangrove in the reserve. Averaged measurements of the original mangrove stands, i.e., stem density (1164 nos. 0.1 ha−1), basal area (90.3 m2 0.1 ha−1) and CI (100.59), indicated that they were in a state of maturity and less disturbed compared to the non-original mangroves (density, 1241 nos. 0.1 ha−1; basal area, 4.92 m2 0.1 ha−1 and CI, 55.65). The Kruskal–Wallis test showed significant differentiation between the original and non-original mangrove tree structural parameters: Kandelia obovata’s density, X2 = 34.78, d.f. = 1, p = 0.001; basal area, X2 = 108.15, d.f. = 1, p = 0.001; Rizhopora stylosa’s density, X2 = 64.03, d.f. = 1, p = 0.001; basal area, X2 = 117.96, d.f. = 1, p = 0.001. The latter is also evident from the clustering plots generated from the Principal Component Analysis (PCA). Vegetation dynamics at the ZMNNR also enabled us to compare the species composition and distribution patterns with other Indo-West Pacific regions. Overall, the present study not only highlights the advantage of >50 years old satellite data but also provide a benchmark for future ecological research, conservation and management of the ZMNNR.
Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

Sparse imaging relies on sparse representations of the target scenes to be imaged. Predefined dictionaries have long been used to transform radar target scenes into sparse domains, but the performance is limited by the artificially designed or existing transforms, e.g., Fourier transform and wavelet transform, which are not optimal for the target scenes to be sparsified. The dictionary learning (DL) technique has been exploited to obtain sparse transforms optimized jointly with the radar imaging problem. Nevertheless, the DL technique is usually implemented in a manner of patch processing, which ignores the relationship between patches, leading to the omission of some feature information during the learning of the sparse transforms. To capture the feature information of the target scenes more accurately, we adopt image patch group (IPG) instead of patch in DL. The IPG is constructed by the patches with similar structures. DL is performed with respect to each IPG, which is termed as group dictionary learning (GDL). The group oriented sparse representation (GOSR) and target image reconstruction are then jointly optimized by solving a l1 norm minimization problem exploiting GOSR, during which a generalized Gaussian distribution hypothesis of radar image reconstruction error is introduced to make the imaging problem tractable. The imaging results using the real ISAR data show that the GDL-based imaging method outperforms the original DL-based imaging method in both imaging quality and computational speed.
Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013-2020

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.
Replay Speech Answer-Sheet Detection on Intelligent Language Learning System Based on Power Spectrum Decomposition

Replay speech answer-sheet detection is an urgent problem to be solved for intelligent language learning system. Traditional features used in replay speech detection are often extracted from power spectrum. However, power spectrum may not be the optimal spectrum to extract feature for replay speech answer-sheet detection because it doesn’t consider the characteristic of replay speech. In order to solve this limitation, this paper proposes a method of power spectrum decomposition for replay speech answer-sheet detection on intelligent language learning system. Log frame-wise normalization spectrum (LFNS) and log spectral energy (LSE) which consider the characteristic of replay speech, are obtained by decomposing log power spectrum based on constant-Q transform. Next, the other two features are obtained at the base of LFNS and LSE. The first is constant-Q normalization octave coefficients (CNOC) which is obtained by combining LFNS and octave subband transform. The second is CNOC-LSE that is obtained by combining CNOC and LSE. Then LFNS, CNOC and CNOC-LSE are fed into frame- and utterance-based neural networks. Experimental results show that the proposed LFNS can outperform the conventional log power spectrum, CNOC and CNOC-LSE can perform better than most of commonly used features. We found that utterance-based neural network outperforms frame-based neural network with the same inputs. In addition, handcrafted features give worse performance than corresponding spectrum for the utterance-based neural network while the opposite conclusion can be obtained for the frame-based neural network.
Design and Evaluation of a System of Systems Architecture for the Optimization of a Cyclic Transport Operation

Cyclic transport missions involving fleets of vehicles are common in quarry and mining operations and have shown to have a significant potential for energy optimization. Vehicles such as articulated haulers and dump trucks utilized in transport missions can be of different brand, type and have different performance characteristics such as engine power, traction and load. Vehicles may also be operated and owned by different organizations as they can be subcontracted to an operation. The transport operation characteristics include stochastic behaviors and activity times that fluctuate over time, and hence real-time control is required for efficient optimization. As the vehicles are mobile, wireless communication also needs to be available. As the constituent systems (vehicles) have managerial and operational independence, a system of systems approach is applicable. This paper provides an overview of the key characteristics and requirements for such a system and discusses the pros and cons of acknowledged and directed system architectures. Further, a case study is presented where an acknowledged system of systems is implemented in a real world mine and evaluated through a qualitative assessment of an operator assistive optimization system. Key findings include the drawbacks and characteristics of the architecture approaches. Further challenges and potential for automation and production control of a larger process system are described.
Shape Memory Polymer-Based Insertable Electrode Array Towards Minimally Invasive Subdural Implantation

Minimally invasive implantation of subdural electrodes can dramatically benefit the patients with various neurological diseases. In modern clinical practice, the implantation procedure of the electrode arrays remains traumatic for patients and increases postoperative infection risk. Here we report a design and insertion technique of thermally activated shape-memory polymer-based electrode array that can recover up to ten times length deformation. The compressed four-centimeter wide array can be easily packed into a three-millimeter diameter tube and subsequently deployed thought five-millimeter opening in a restricted space between a brain phantom and a simulated skull. The mechanical properties of the developed array are comparable to the materials traditionally employed for the purpose, and the electrical and signal recording properties are preserved after shape deformation and recovery. Additionally, the array is biocompatible and exhibits conformability to a curvy brain surface. The results demonstrate that insertion of the electrode array through a small hole into a restricted space similar to subdural cavity is possible, which may inspire future solution of minimal invasive implantation for patients suffering from epilepsy, amyotrophic lateral sclerosis or tetraplegia.
Non-Intrusive Load Monitoring using Multi-Output CNNs

Non-Intrusive Load Monitoring (NILM) is generally framed as a supervised learning problem whose input is the time series for aggregated power load of a household and whose output is the time series for the consumption of an individual appliance. Often the interest lies in predicting whether an appliance is ON or OFF, rather than its power usage. In this paper we propose a modification of a state-of-the-art convolutional neural network architecture to allow for multi-output channels, solving the regression and classification problems with relative weights simultaneously. We analyze the performance of this multi-output model and study the interplay between the two approaches on NILM.
An Ultra-Wideband Circular Polarization-Maintaining Metasurface and Its Application in RCS Reduction

In this work, a circular polarized (CP) maintaining metasurface is proposed, which can realize ultra-wideband CP-maintaining reflection and make its co-polarization reflection coefficient under CP incidence close to 1.0 in the frequency band 6.2-26.4GHz; in addition, by rotating its unit cell structure, Pancharatnam-Berry (PB) phase can be generated in its co-polarized reflection coefficient under CP incidence. Thus based on the CP-maintaining metasurface, a 2-bit PB coding metasurface is further proposed for Radar Cross Section (RCS) reduction. The simulated results show that the proposed PB coding metasurface has excellent performance in RCS reduction, compared with a pure metal plate with the same size, its RCS can be reduced more than10dB under arbitrary polarization incidences in the frequency band 6.2-26.5GHz with relative band of 124.1%. Finally, an effective experimental validation is carried out.
Real-Time Non-Intrusive Load Monitoring: A Machine-Learning Approach for Home Appliance Identification

Non-intrusive load monitoring (NILM) is a topic that lately attracts both the academic and the industrial interest. NILM is used to reveal useful information regarding the consumption breakdown on appliance or activity level, thus can be a key solution to unlock various smart-home services and opportunities. To that end, deep learning has arisen as a prominent solution. Although most of the known NILM techniques so far focus on a predefined number of home appliances, this paper proposes a system which can detect in real-time any number of appliances. In the proposed solution switched-on appliances are identified by processing the measured active power transient response sampled at 100 Hz. The NILM system includes three stages; statistics-based event detection, convolutional neural network and k-nearest neighbors classifier. For future extensions, it is capable to automatically identify new appliances; thus, no retraining and additional modeling is required.
Data Center Isolated Parallel Ring Bus Differential Protection Scheme

UPSs are used in data centers and other critical power applications for power conditioning and back-up. In addition, to ensure reliability of the data center, a redundant architecture is needed. This increases capital expenses and decreases the utilization rate. One approach is to use the isolated parallel ring bus (IPRB) architecture with static UPS. This features load sharing with active and reactive power transferred through choke inductors to the ring bus. This architecture maximizes the utilization rate while maintaining the reliability of the system. The protection scheme in this IPRB is critical to guarantee the operation in case of any fault. In this paper, the differential protection scheme of the IPRB is explained with simulation and field-testing results. The differential protection can effectively identify and isolate faults to ensure uninterrupted service to the load.
Suppression Switching Ringing of SiC-MOSFET Inverters with Combined Design of DC Bus Snubber and Gate Drive

The SiC MOSFET device has superior characteristics such as high switch speed, lower switching loss etc. However, fast switching of SiC MOSFET generally brings severe switching ringing in the power circuit. In this paper, suppression switching ringing of SiC-MOSFET inverters with combined design of DC bus snubber and gate drive is presented. Root locus method is introduced to design the snubber parameters. The effect and limitation of gate drive design are discussed. Finally, the suppression effect of the combined design is investigated through experiment on SiC half-bridge and 6-in-1 SiC module testing boards.
A Cyber Threat Mitigation Approach For Wide Area Control of SVCs using Stability Monitoring

We propose a stability monitoring approach for the mitigation of cyber threats directed at the wide area control (WAC) system used for coordinated control of Flexible AC Transmission Systems (FACTS) used for power oscillation damping (POD) of active power flow on inter-area tie lines. The approach involves monitoring the modes of the active power oscillation on an inter-area tie line using the Matrix Pencil (MP) method. We use the stability characteristics of the observed modes as a proxy for the presence of destabilizing cyber threats. We monitor the system modes to determine whether any destabilizing modes appear after the WAC system engages to control the POD. If the WAC signal exacerbates the POD performance, the FACTS falls back to POD using local measurements. The proposed approach does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the SM approach for mitigation.
Effect of organic manure on nitrogen mineralization, nitrogen accumulation, nitrogen use efficiency and apparent nitrogen recovery of cauliflower (Braccica oleracea L., var. Botrytis)

Aims: The main aim of the study was to assess the effects of organic manure on nitrogen mineralization, uptake, use and recovery of cauliflower. Methodology: Nitrogen is one of the major yield limiting nutrients in cauliflower production. However, organic manure is applied to supplement soil nitrogen supply to maximize dry-matter yield. A field experiment was conducted at the Droevendaal Certified Organic Research and Demonstration Site of Wageningen University, Netherlands during 2012. The experiment was laid out in a randomized complete block design with four replications. The treatments comprised of four rates (0, 75, 150 and 225kgN.ha-1) each of lucerne pellets (LUP) and dry grass clover (DGC) organic manures. Results: Results showed that LUP amended plots exhibited highest initial N-mineralization compared to DGC and control plots. Nitrogen uptake in plant organs increased with increasing organic manure application. Nitrogen accumulation was highest in cauliflower leaf (38.28mg.kg-1), followed by the head (16.96mg.kg-1) and lowest in the stem (3.01mg.kg-1). Conclusion: Results suggest that timely and adequate nitrogen release from organic manures is important for the success of conservation and sustainable farming systems. Findings in this study could be utilized in improving efficiency of N use in crop management rotation systems.
Day-Ahead Optimal Management of Plug-in Hybrid Electric Vehicles in Smart Homes Considering Uncertainties

The plug-in hybrid electric vehicles (PHEVs) integration into the electrical network introduces new challenges and opportunities for operators and PHEV owners. On the one hand, PHEVs can decrease environmental pollution. On the other hand, the high penetration of PHEVs in the network without charging management causes harmonics, voltage instability, and increased network problems. In this study, a charging management algorithm is presented to minimize the total cost and flatten the demand curve. The behavior of the PHEV owner in terms of arrival time and leaving time is modeled with a stochastic distribution function. The battery model and hourly power consumption of PHEV are modeled, and the obtained models are applied to determine the battery's state of charge. The proposed method is tested on a sample demand curve with and without a charging management algorithm to verify the efficiency. The results verify the efficiency of the proposed method in decreasing the total cost using the management algorithm for PHEVs, especially when the PHEVs sell the electricity to the network.
A Method to Determine the Distance to the Critical Oscillatory Stability Limit in Terms of Active Power Injections

This article presents a method to derive meaningful margins that provide information regarding the distance to critical oscillatory stability limits. The proposed method relies on modal torque concepts which allows to reduce the eigenvalue problem to a problem involving only generator torque and speed oscillations. The reduced problem size and the sparsity of the involved matrices makes it suitable for the application to large power systems. The algorithm relies on deriving the first order eigenvalue sensitivities in respect to perturbations of generator machine active power injections based on power flow quantities. The viability of the method is demonstrated at the example of the Nordic power system, showing the capability of determining critical operation well away from the actual critical stability limit.
Software based support of curriculum mapping in education at medical faculties

The quality of education closely relates to the quality of curriculum and its management. This also applies to medical education, which is also changing with the development of modern technologies. Therefore, we summarised the best experience and knowledge of curriculum mapping and brought to our faculties a pioneering concept, including expertise in selected medical disciplines. The main results of our efforts include the delivery of methodologies and a unified ICT platform developed for optimisation of curricula. Based on users’ needs and requirements, the anatomy as one of the core preclinical study discipline and five different medical disciplines were completely mapped and optimised to prove modernizing effects as well as practical application of our platform. Thanks to the open and modular approach of platform's framework, the interdisciplinary study programmes can be easily designed, mapped and managed, and the redundancies and the gaps in curricula can be detected too. Curricula designers are equipped by the visual tools and feedbacks allowing them effective administration of particular study blocks as well as to communicate with teachers and educators to adapt and develop curricula to the recent trends and knowledge.