Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternative stable states and hydrological regime shifts in a large intermittent river

Non-perennial rivers and streams make up over half the global river network and are becoming more widespread. Transitions from perennial to non-perennial flow are a threshold-type change that can lead to alternative stable states in aquatic ecosystems, but it is unknown whether streamflow itself is stable in either wet (flowing) or dry (no-flow) conditions. Here, we investigated drivers and feedbacks associated with regime shifts between wet and dry conditions in an intermittent reach of the Arkansas River (USA) over the past 23 years. Multiple lines of evidence suggested that these regimes represent alternative stable states, including (a) significant jumps in discharge time series that were not accompanied by jumps in flow drivers such as precipitation and groundwater pumping; (b) a multi-modal state distribution with 92% of months experiencing no-flow conditions for <10% or >90% of days, despite unimodal distributions of precipitation and pumping; and (c) a hysteretic relationship between climate and flow state. Groundwater levels appear to be the primary control over the hydrological regime, as groundwater levels in the alluvial aquifer were higher than the stream stage during wet regimes and lower than the streambed during dry regimes. Groundwater level variation, in turn, was driven by processes occurring at both the regional scale (surface water inflows from upstream, groundwater pumping) and the reach scale (stream–aquifer exchange, diffuse recharge through the soil column). Historical regime shifts were associated with diverse pressures including network disconnection caused by upstream water use, increased flow stability potentially associated with reservoir operations, and anomalous wet and dry climate conditions. In sum, stabilizing feedbacks among upstream inflows, stream–aquifer interactions, climate, vegetation, and pumping appear to create alternative wet and dry stable states at this site. These stabilizing feedbacks suggest that widespread observed shifts from perennial to non-perennial flow will be difficult to reverse.
Fault detection, classification and protection in solar photovoltaic arrays

Fault analysis and fault detection are important to the efficiency, safety and reliability of solar photovoltaic (PV) systems. Despite the fact that PV systems have no moving parts and usually require low maintenance, they are still subject to various fault conditions. Especially for PV arrays (dc side), it is difficult to shut down PV modules completely during faults, since they are always energized by sunlight in daytime. Furthermore, conventional series-parallel PV configurations increase voltage and current ratings, leading to higher risk of large fault currents or dc arcs. This dissertation reviews the challenges and limitations of existing fault detection and protection solutions in solar PV arrays. For the first time, a 35kW commercialscale PV laboratory is designed to study faults under real-working conditions and to discover the “blind spots” in conventional fault protection schemes. It is shown that the line-line fault may not be detectable by traditional overcurrent protection devices (OCPD) under certain conditions. Therefore, the fault may remain in the PV system as a safety concern. To eliminate the detection “blind spot,” outlier rules, such as statistical outlier detection rules (ODRs) and local outlier factors (LOFs) are proposed in PV-string monitoring systems. To further identify the fault types (or so-called fault classification), machine learning algorithms are studied in solar PV arrays. To overcome the drawbacks of supervised learning algorithms, a semi-supervised learning algorithm is proposed. The dissertation demonstrates the effectiveness in fault detection and classification in both simulation and experimental results. xiv
Sociopragmatic parameters of politeness strategies among the Sasak in the post elopement rituals

Arranged marriages in Asia involve the consent and approval of family members. However, marriages that condone elopement are shared among the Sasak people in Lombok. The elopement entails taking away the bride from her home without knowing her parents, close relatives, or other family members. As such, events that occur after discovering the elopement are the focus of this study. Post-elopement rituals are held to assuage both sides of the families, where the Pembayun (or adat leaders) are nominated to represent the families. The Pembayun were found to be bound by sociopragmatics in their use of politeness strategies. The analysis of sociopragmatics is based on the politeness theories of Brown and Levinson (1987). A qualitative approach that involved interviews and video recordings were used to collect data from participants and non-participant observation. The study revealed that sociopragmatics play a significant role in Pembayun’s choice of politeness strategies such as social distance, power distribution, rank, situational, and cultural parameters. These factors determine the weightiness of the act and become the basis for politeness strategies in the deliverances of specific acts during the post-elopement rituals.
Application of Laser Re-melting on selective laser melting parts

Selective Laser Melting (SLM) is an additive manufacturing process whereby a threedimensional part with almost full density and good mechanical properties is built in a layerwise manner. During the process, a laser source selectively scans a powder bed according to the CAD data of the part to be produced and the powder metal particles are completely melted. In this way, SLM is capable of producing functional metallic parts with a high geometrical freedom. However, there are some limitations to the process including insufficient surface quality, remnant porosity (~1-2%) and stair effect which is inherent to all layer manufacturing processes. In this study, laser re-melting is applied using a continuous wave Nd:YAG laser during SLM of AISI 316L stainless steel parts to overcome or to weaken these limitations. After each layer is fully molten, the same slice data is used to re-expose the layer for laser re-melting. In this manner, laser re-melting does not only improve the surface quality but also has the potential to improve density. The influence of laser re-melting on density enhancement, surface quality and stair-effect is studied varying the operating parameters such as scan speed, laser power and scan spacing. The laser re-melting method with optimized parameter settings greatly densifies the SLM part and improves the surface quality significantly at a cost of longer production times. The stair-effect is also greatly eliminated when laser re-melting is applied on the inclined surfaces after the process is completed and the surrounding powder particles are blown away.
A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF

High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [kbest, abest] could be obtained, in which the permutation entropy (PE) and fuzzy entropy (FE) were selected for MOPSO as fitness functions. Secondly, the accelerometer voltage output signals were decomposed by the improved VMD, then some intrinsic mode functions (IMFs) were achieved. Thirdly, sample entropy (SE) was introduced to classify those IMFs into information-dominated IMFs or noise-dominated IMFs. Then, the short-window TFPF was selected for denoising information-dominated IMFs, while the long-window TFPF was selected for denoising noise-dominated IMFs, which can make denoising more targeted. After reconstruction, we obtained the accelerometer denoising signal. The denoising results of different denoising algorithms in the time and frequency domains were compared, and SNR and RMSE were taken as denoising indicators. The improved VMD and TFPF denoising method has a smaller signal distortion and stronger denoising ability, so it can be adopted to denoise the output signal of the High-G MEMS accelerometer to improve its accuracy.
Enterprise Digital Transformation and Stock Price Crash Risk: Evidences from China

With unstoppable rise of digital economy globally, digital transformation has become an inevitable choice for enterprises to survive and develop; both the public and the government are paying increasing attention to digital transformation. At the same time, the Chinese government takes more serious attitude towards systemic financial risks, emphasizing the importance of controlling systemic risks such as abnormal stock price in many public occasions. In this context, enterprise digital transformation and stock price crash risk have gained unprecedented attention. It is of great value for both industry and the government to analyze the influence and mechanism of enterprise digital transformation (EDT) on stock price crash risks. This study measures the degree/level of EDT of Chinese A-share publicly owned enterprises through document study and crawler technology, examining the influence and mechanism of digital transformation on companies’ stock price crash risk. Conclusions of this paper may provide a theoretical and empirical basis for understanding digital transformation's consequences in capital market and provide a reference for the government and regulatory authorities to formulate support and disclosure policies for digital transformation.
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation

We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes. Unlike previous inter-slice imputation methods, which only focus on the smoothness in the axial direction, this study aims to improve the smoothness of the interpolated 3D medical volumes in all three directions: axial, sagittal, and coronal. The proposed multitask inter-slice imputation method, in particular, incorporates a smoothness loss function to evaluate the smoothness of the interpolated 3D medical volumes in the through-plane direction (sagittal and coronal). It not only improves the resolution of the interpolated 3D medical volumes in the through-plane direction but also transforms them into isotropic representations, which leads to better segmentation performances. Experiments on whole tumor segmentation in the brain, liver tumor segmentation, and prostate segmentation indicate that our method outperforms the competing slice imputation methods on both computed tomography (1\% Dice improvement for CT liver tumor segmentation) and magnetic resonance images volumes (over 2\% Dice improvement for MRI prostate segmentation) in most cases.
Inertial sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation

Sensing approaches for ambulatory monitoring of human motion are necessary in order to objectively determine a person’s level of functional ability in independent living. Because this capability is beyond the grasp of the specialized equipment available in most motion analysis laboratories, body-mounted inertial sensing has been receiving increasing interest in the biomedical domain. Crucial to the success of this certainly not new sensing approach will be the capability of wearable inertial sensor networks to accurately recognize the type of activity performed (context awareness) and to determine the person’s current location (personal navigation), IDEA GROUP PUBLISHING This paper appears in the publication, Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques edited by Rezaul Begg and Marimuthu Palaniswami © 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB12284
Identification of aflatoxin contamination in corn kernels using line-scan Raman imaging

Aflatoxins are among the most carcinogenic mycotoxins and are known to contaminate a wide variety of agricultural and food commodities. This study aims to explore the effectiveness of Raman hyperspectral imaging in detecting aflatoxin contamination in corn kernels in a rapid and non-destructive manner. Four hundred kernels were used with 2 treatments, namely, 200 kernels inoculated with the AF13 fungus (aflatoxigenic), and 200 kernels inoculated with sterile distilled water as control. One hundred kernels from each treatment were incubated at 30 °C for 5 and 8 days, separately. On the specified post-inoculation day, the kernels were dried and wiped free of surface mold prior to imaging. The Raman images of kernels were acquired over the endosperm side over the 103-2831 cm-1 wavenumber range. The standard aflatoxin concentration in each kernel was determined by the VICAM AflaTest method. The original mean spectra of single kernels were extracted and preprocessed by adaptive iteratively reweighted penalized least squares, Savitzky Golay smoothing and min-max normalization. On basis of the calculated “reference” mean spectra of the aflatoxin negative and -positive categories, 14 and 17 local peaks were determined, separately. After removing the identical peaks from both peak sets, a total of 24 unique peaks were extracted and used as inputs for further discriminant model development. With 20 ppb and 100 ppb as the classification thresholds, the 2-class discriminant models established with the principal component analysis-linear discriminant analysis and partial least-squares discriminant analysis methods, obtained mean overall prediction accuracies between 77.9% and 82.0%. Further investigation is ongoing to include more diverse samples and execute different types of computation algorithms, seeking solutions to improve the discriminant models in identifying aflatoxin contamination in corn kernels.
Peer Effects from Friends and Strangers: Evidence from Random Matchmaking in an Online Game

In this paper, we quantify the social interaction (peer) effect from friends' and strangers' product adoption decisions. While a large literature has separately documented the importance of the observed adoptions of friends [1,2,8,9] and strangers [3,4,6,7] for spurring further product adoption, by analyzing both together we are able to benchmark their relative magnitudes, evaluate how these two types of peer effects interact, and provide managerial guidance on how firms can jointly leverage both types of peer effects in product seeding campaigns. Our context is an online multiplayer gaming app, where product adoptions correspond to microtransactions for in-game cosmetic items. We observe a rich panel of players' in-app purchases, friendship networks, interactions with friends, and, unique to this setting, interactions with strangers. Correlated adoptions may represent correlated unobserved shocks and not peer effects; we leverage the game's quasi-random assignment of strangers onto players' teams during matchmaking to generate conditionally exogenous exposure to strangers' adoptions. For peer effects from friends, we use the panel structure of our data to account for homophily and the reflection problem [5], and conduct placebo tests to check causality. Our baseline result shows that observed adoptions by friends and strangers both have positive effects on focal consumers' purchasing, and that the marginal peer effect from friends is nearly twice as large as the effect from strangers. We show that peer effects from friends and strangers are substitutes. More encounters with strangers who have adopted the product diminish the marginal peer effect from friends who have adopted the product, and vice versa. The substitution is not symmetric: encounters with friends can completely substitute for encounters with strangers. We analyze the mechanism behind the transmission of peer effects, and find evidence that while encounters with friends and strangers both raise awareness of the product, encounters with friends also lead to a mere exposure effect. We rule out learning about unobserved quality as a primary mechanism, but find that visibility of the product is important. To analyze how peer effects inform counterfactual product seeding strategies, we estimate a model of product diffusion using the observed in-app social network. We evaluate two seeding strategies which are designed to leverage peer effects from strangers and friends respectively: the first strategy seeds products to individuals who are more active on the app and therefore interact with more strangers, and the second strategy seeds products to individuals who have more friends. While both strategies improve on simple random seeding, seeding to well-connected individuals substantially outperforms seeding to active individuals, confirming the value of collecting social network data for marketing campaigns. The full paper is available at: https://papers.ssrn. com/sol3/papers.cfm? abstract_id=4116806
Implementation and Experimental Demonstration of Onboard Powered-Descent Guidance

Onboard, fuel-optimal, constrained powered-descent guidance based on the theory of lossless convexification has been implemented as the Guidance for Fuel-Optimal Large Diverts (G-FOLD) algorithm. Here, “guidance” means generating feedforward reference trajectories for control systems. This paper presents terrestrial flight-test demonstrations of large diverts planned by G-FOLD onboard a vertical-takeoff/vertical-landing rocket. The G-FOLD parser, which transforms the guidance problem into a second-order cone program and so encodes the divert constraints, is described at an engineering level, including new and modified constraints incorporated for these flight tests. Several practical issues, such as discretization effects, are addressed, and the flight-test architecture is presented. A total of eight flight tests were performed. In the first three, the rocket executed diverts of increasing size preplanned by G-FOLD on the ground. Then G-FOLD was demonstrated running onboard five times. Three qualitatively.
Just Breathe: Improving LEP Outcomes through Long Interval Breathing

Background: Laser-evoked potentials (LEPs) constitute an objective clinical diagnostic method used to investigate the functioning of the nociceptor system, including signaling in thin peripheral nerve fibers: Aδ and C fibers. There is preliminary evidence that phase locking LEPs with the breathing cycle can improve the parameters used to evaluate LEPs. Methods: We tested a simple breathing protocol as a low-cost improvement to LEP testing of the hands. Twenty healthy participants all underwent three variants of LEP protocols: following a video-guided twelve-second breathing instruction, watching a nature video, or using the classic LEP method of focusing on the hand being stimulated. Results: The breath protocol produced significantly shorter latencies as compared with the nature or classic protocol. It was also the least prone to artifacts and was deemed most acceptable by the subjects. There was no difference between the protocols regarding LEP amplitudes. Conclusions: Using a breathing video can be a simple, low-cost improvement for LEP testing in research and clinical diagnostics.
International Conference on System-integrated Intelligence : New Challenges for Product and Production Engineering , SysInt 2016 Disturbance observer design for utilizing of time-delayed vision measurements in high dynamical Systems

The utilization of vision information for control in intelligent technical applications with high dynamics has been a central issue. The main obstacles are the low sampling rate of the cameras (about 60Hz of a commercial camera) compared to the required sampling rate of the high dynamic applications (over 1 kHz) and the time-delayed measurements due to the image processing. A model-based Event-Triggered Observer (ETO) [12] has been proposed for linear systems to observe and predict the undelayed continuous state variables of the system from the sampled and delayed measurements where the sampling rate and the delaytime can be constant or variable. However, the disturbances like the sensor noise and the perturbations of the environment were not handled within [12]. As is well known, the disturbances decline the performance of the model-based observer. To keep the performance of the state-estimation and prediction, an effective solution is to adopt into account the unknown dynamic effects due to the disturbances by integrating them into the state-estimation. A survey [13] has shown different methods of dealing with the disturbance estimation to improve the accuracy of the estimation of the system internal states. Unlike other mentioned methods in [13], the so-called Unknown Input Observer (UIO), shows significant advantages with the ability to estimate the unknown input disturbances and the system internal states simultaneously by augmenting the state space of a classical state observer like Luenberger-Observer with a disturbance model [14,15]. In this paper, an Extended Event-Triggered Observer (EETO) is proposed, which based on the proposed ETO in [12] and is extended with an UIO. The new proposed EETO can estimate undelayed continuous state variables from the time-delayed measurements under the disturbances. An example system with a second-order delay behavior is simulated, whose output is sampled and delayed. The simulation results show the performance of the proposed Extended Event-Triggered Observer. c © 2016 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of SysInt 2016.
Biorefinery of Tomato Leaves by Integrated Extraction and Membrane Processes to Obtain Fractions That Enhance Induced Resistance against Pseudomonas syringae Infection

Tomato leaves have been shown to contain significant amounts of important metabolites involved in protection against abiotic and biotic stress and/or possessing important therapeutic properties. In this work, a systematic study was carried out to evaluate the potential of a sustainable process for the fractionation of major biomolecules from tomato leaves, by combining aqueous extraction and membrane processes. The extraction parameters (temperature, pH, and liquid/solid ratio (L/S)) were optimized to obtain high amounts of biomolecules (proteins, carbohydrates, biophenols). Subsequently, the aqueous extract was processed by membrane processes, using 30–50 kDa and 1–5 kDa membranes for the first and second stage, respectively. The permeate from the first stage, which was used to remove proteins from the aqueous extract, was further fractionated in the second stage, where the appropriate membrane material was also selected. Of all the membranes tested in the first stage, regenerated cellulose membranes (RC) showed the best performance in terms of higher rejection of proteins (85%) and lower fouling index (less than 15% compared to 80% of the other membranes tested), indicating that they are suitable for fractionation of proteins from biophenols and carbohydrates. In the second stage, the best results were obtained by using polyethersulfone (PES) membranes with an NMWCO of 5 kDa, since the greatest difference between the rejection coefficients of carbohydrates and phenolic compounds was obtained. In vivo bioactivity tests confirmed that fractions obtained with PES 5 kDa membranes were able to induce plant defense against P. syringae.