Miracle-dz/UniFAR-FTUs huggingface dataset:
Data‐driven storage operations: Cross‐commodity backtest and structured policies

Storage assets are critical for physical trading of commodities under volatile prices. State‐of‐the‐art methods for managing storage facilities such as the reoptimization heuristic (RH), which are part of commercial software, approximate a Markov Decision Process (MDP) assuming full information regarding the state and the stochastic commodity price process and hence suffer from informational inconsistencies with observed price data and structural inconsistencies with the true optimal policy, which are both components of generalization error. Focusing on spot trades, we find via an extensive backtest that this error can lead to significantly suboptimal RH policies. We develop a forward‐looking data‐driven approach (DDA) to learn policies and reduce generalization error. This approach extends standard (backward‐looking) DDA in two ways: (i) It represents historical and estimated future profits as functions of features in the training objective, which typically includes only past profits; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang‐bang and base‐stock policies, respectively, using linear‐ and mixed‐integer programs, thereby extending known DDAs that parameterize decisions as functions of features without policy structure. We backtest the performance of RH and DDA on six major commodities, employing feature selection across data from Reuters, Bloomberg, and other public data sets. DDA can improve RH on real data, with policy structure needed to realize this improvement. Our research advances the state‐of‐the‐art for storage operations and can be extended beyond spot trading to handle generalization error when also including forward trades.
METAMATERIALS APPLIED TO INDUCTIVE WIRELESS POWER TRANSMISSION

Over recent years, the interest in using inductive wireless power transmission for many applications has grown. One of the major limitations of this technology is the reduced operating distance. Some recent works have suggested using artificial materials known as metamaterials to improve the power transfer efficiency over distance. Due to their unique electromagnetic properties, such as negative permeability, metamaterials can be used to enhance the evanescent waves of the near field. In the present work, the usage of an electromagnetic metamaterial to increase the inductive coupling by means of enhanced evanescent waves is studied. Analytical calculations and numerical simulations of the proposed metamaterial are presented. The improvement of the power transfer efficiency is supported by empirical evidences. This work is written integrally in the English language.
A Review on Control Strategies of Dual Active Bridge-Isolated Bidirectional DC-DC Converter for High Frequency Power Conversion Systems

Dual active bridge-isolated bidirectional DC-DC converters (DAB-IBDCs) are the core circuits of high frequency power conversion systems. These have the advantages of high power density with reduced weight and size but without compromising the cost, efficiency and reliability of the system. This paper provides a comprehensive review on the control strategies for DAB-IBDCs. It deals with different modulation techniques for DAB-IBDCs and thus enabling the integrated solutions for their increased performance in terms of efficiency for variation in supply voltages and load conditions. As far as modelling and optimization of the DAB-IBDCs with high frequency isolation in power conversion systems are concerned, stateof-the-art control strategies as modulation techniques is trending as the research direction which has been discussed here. The advances in the semiconductor devices, electromagnetic materials as well as nano-electronic technologies have increased their expectations in various applications. Index Terms Dual-active-bridge, Isolated bidirectional DC-DC converter, single-phase-shift modulation, extended-phaseshift modulation, double-phase-shift modulation, triple-phase-shift modulation, power conversion systems.
Smart crowding in poly-Acrylamide/Poly(N-isopropylacrylamide) composite hydrogels, studied by two-focus fluorescence correlation spectroscopy

IX Kurzfassung . XII 1. Motivation and Aim of the Thesis . 1 2. Theory . 5 2.1. Diffusion . 5 2.2. Brownian motion . 7 2.3. Fluorescence . 10 2.4. Hydrogels. 12 2.5. Fluorescence Correlation Spectroscopy . 14 2.5. 1. Basic working principle . 15 2.6. Two-focus Fluorescence Correlation Spectroscopy . 18 2.6. 1. Basic working principle . 18 2.6. 2. The molecule detection function (MDF) and the resulting correlation functions . 19 2.7. References . 22 3. Refractive index mismatch can misindicate anomalous diffusion in single-focus fluorescence correlation spectroscopy . 25 3.
Hybridization of Evolutionary Techniques for the TSP with a Genotypic Termination Criterion

: - In this paper we present new evolutionary algorithms to solve the Travelling Salesman Problem (TSP). The new hybrid algorithms combine classical representations of the TSP with two different crossover methods, namely Multiple Crossover Per Couple (MCPC) and Multiple Crossovers between Multiple Parents (MCMP). A genotypic termination condition was imposed in order to gain proper insight into some behavioural features. The performance analysis revealed that the MCPC algorithms with path representation lead to the best results.
An Impact Assessment of Betani Irrigation Dam on Fish Diversity of Damak Municipality, Jhapa, Nepal

1.Nature Conservation and Health Care Council, Biratnagar, Nepal 2. Damak Multiple Campus, Tribhuvan University, Damak, Jhapa, Nepal 3. Dhankuta Multiple Campus, Tribhuvan University, Dhankuta, Nepal 4. Central Department of Zoology, Tribhuvan University, Kirtipur, Kathmandu, Nepal 5. Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, Nepal *Correspondence: limbujash@gmail.com _____________________________________________________________________________________
Development of a standardized MRI protocol for pancreas assessment in humans

Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes.
White matter abnormalities in active elite adult rugby players

Abstract The recognition, diagnosis and management of mild traumatic brain injuries are difficult and confusing. It is unclear how the severity and number of injuries sustained relate to brain injuries, such as diffuse axonal injury, diffuse vascular injury and progressive neurodegeneration. Advances in neuroimaging techniques enable the investigation of neuropathologies associated with acute and long-term effects of injury. Head injuries are the most commonly reported injury seen during professional rugby. There is increased vigilance for the immediate effects of these injuries in matches, but there has been surprisingly little research investigating the longer-term effects of rugby participation. Here, we present a longitudinal observational study investigating the relationship of exposure to rugby participation and sub-acute head injuries in professional adult male and female rugby union and league players using advanced MRI. Diffusion tensor imaging and susceptibility weighted imaging was used to assess white matter structure and evidence of axonal and diffuse vascular injury. We also studied changes in brain structure over time using Jacobian Determinant statistics extracted from serial volumetric imaging. We tested 41 male and 3 female adult elite rugby players, of whom 21 attended study visits after a head injury, alongside 32 non-sporting controls, 15 non-collision-sport athletic controls and 16 longitudinally assessed controls. Eighteen rugby players participated in the longitudinal arm of the study, with a second visit at least 6 months after their first scan. Neuroimaging evidence of either axonal injury or diffuse vascular injury was present in 23% (10/44) of players. In the non-acutely injured group of rugby players, abnormalities of fractional anisotropy and other diffusion measures were seen. In contrast, non-collision-sport athletic controls were not classified as showing abnormalities. A group level contrast also showed evidence of sub-acute injury using diffusion tensor imaging in rugby players. Examination of longitudinal imaging revealed unexpected reductions in white matter volume in the elite rugby players studied. These changes were not related to self-reported head injury history or neuropsychological test scores and might indicate excess neurodegeneration in white matter tracts affected by injury. Taken together, our findings suggest an association of participation in elite adult rugby with changes in brain structure. Further well-designed large-scale studies are needed to understand the impact of both repeated sports-related head impacts and head injuries on brain structure, and to clarify whether the abnormalities we have observed are related to an increased risk of neurodegenerative disease and impaired neurocognitive function following elite rugby participation.
MiRNA-1202 promotes the TGF-β1-induced proliferation, differentiation and collagen production of cardiac fibroblasts by targeting nNOS

Background Atrial fibrillation (AF) is a clinically common arrhythmia that affects human health. Myocardial fibrosis serves as an important contributor to AF. Recently, miRNA-1202 have been reported to be up-regulated in AF. However, the role of miRNA-1202 and its mechanism in myocardial fibrosis remain unclear. Methods Human cardiac fibroblasts (HCFs) were used to construct a fibrosis model by TGF-β1 induction. The expression of miR-1202 was measured by qRT-PCR. Cell proliferation was assessed by CCK-8 assays. Protein expression levels were measured by western blot. Collagen accumulation was measured by ELISA. The relationship between miR-1202 and nNOS was investigated by luciferase reporter assays. Results MiR-1202 expression was obviously increased in HCFs and was both time- and dose-independent. MiR-1202 could increase the proliferation and collagen I, collagen III, and α-SMA levels with or without TGF-β1. MiR-1202 could also increase TGF-β1 and p-Smad2/3 protein levels in comparison to the control group. However, they were obviously decreased after inhibitor transfection. MiR-1202 targets nNOS for negative regulation of HCFs fibrosis by decreasing cell differentiation, collagen deposition and the activity of the TGF-β1/Smad2/3 pathway. Co-transfection of miR-1202 inhibitor and siRNA of nNOS inhibited nNOS protein expression, thereby enhancing the HCFs proliferation. Furthermore, co-transfection of the miR-1202 inhibitor and siRNA of nNOS significantly promoted collagen I, collagen III, TGF-β1, Smad2/3 and α-SMA protein expression and Smad2/3 protein phosphorylation. These findings suggested that miR-1202 promotes HCFs transformation to a pro-fibrotic phenotype by targeting nNOS through activating the TGF-β1/Smad2/3 pathway.
Enabling qualitative research data sharing using a natural language processing pipeline for deidentification: moving beyond HIPAA Safe Harbor identifiers

Abstract Objective Sharing health research data is essential for accelerating the translation of research into actionable knowledge that can impact health care services and outcomes. Qualitative health research data are rarely shared due to the challenge of deidentifying text and the potential risks of participant reidentification. Here, we establish and evaluate a framework for deidentifying qualitative research data using automated computational techniques including removal of identifiers that are not considered HIPAA Safe Harbor (HSH) identifiers but are likely to be found in unstructured qualitative data. Materials and Methods We developed and validated a pipeline for deidentifying qualitative research data using automated computational techniques. An in-depth analysis and qualitative review of different types of qualitative health research data were conducted to inform and evaluate the development of a natural language processing (NLP) pipeline using named-entity recognition, pattern matching, dictionary, and regular expression methods to deidentify qualitative texts. Results We collected 2 datasets with 1.2 million words derived from over 400 qualitative research data documents. We created a gold-standard dataset with 280K words (70 files) to evaluate our deidentification pipeline. The majority of identifiers in qualitative data are non-HSH and not captured by existing systems. Our NLP deidentification pipeline had a consistent F1-score of ∼0.90 for both datasets. Conclusion The results of this study demonstrate that NLP methods can be used to identify both HSH identifiers and non-HSH identifiers. Automated tools to assist researchers with the deidentification of qualitative data will be increasingly important given the new National Institutes of Health (NIH) data-sharing mandate.
Multipliers waveform inversion

The full-waveform inversion (FWI) addresses the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms in the frame of nonlinear optimization. We formulate FWI as a nonlinearly constrained optimization problem, for which a regularization term is minimized subject to the nonlinear data matching constraint. Unlike FWI which is based on the penalty function, the method of multipliers solves the resulting optimization problems by using the augmented Lagrangian function; and leads to a two-step recursive algorithm. The primal step requires solving an unconstrained minimization problem like the traditional FWI with a difference that the data are replaced by the Lagrange multipliers. The dual step involves an update of the Lagrange multipliers. The overall performance of the algorithm is improved considering that this multiplier method does not require an exact solution of these primal-dual subproblems. In fact, convergence is attained when only one step of a gradient-based method is taken on both subproblems. The proposed algorithm greatly improves the overall performance of FWI such as convergence from inaccurate starting models and robustness with respect to the determination of the step length. Furthermore, it can be performed by the existing FWI engines with minimal change. We only have to replace the observed data at each iteration with the multipliers, thus all the nice properties of the traditional FWI algorithms are kept. Numerical experiments confirm that the multipliers waveform inversion can converge to a solution of the inverse problem in the absence of low-frequency data from an inaccurate initial model even with a constant step size.
Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation. However, almost all prior arts assume concurrent access to both labeled source and unlabeled target, making them unsuitable for scenarios demanding source-free adaptation. In this work1, we enable source-free DA by partitioning the task into two: a) source-only domain generalization and b) source-free target adaptation. Towards the former, we provide theoretical insights to develop a multi-head framework trained with a virtually extended multi-source dataset, aiming to balance generalization and specificity. Towards the latter, we utilize the multi-head framework to extract reliable target pseudo-labels for self-training. Additionally, we introduce a novel conditional prior-enforcing auto-encoder that discourages spatial irregularities, thereby enhancing the pseudo-label quality. Experiments on the standard GTA5→Cityscapes and SYNTHIA→Cityscapes benchmarks show our superiority even against the non-source-free prior-arts. Further, we show our compatibility with online adaptation enabling deployment in a sequentially changing environment.
Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning

With the underlying aim of increasing efficiency of computational modelling pertinent for managing&protecting the Great Barrier Reef, we perform a preliminary investigation on the use of deep neural networks for opportunistic model emulation of APSIM models by repurposing an existing large dataset containing outputs of APSIM model runs. The dataset has not been specifically tailored for the model emulation task. We employ two neural network architectures for the emulation task: densely connected feed-forward neural network (FFNN), and gated recurrent unit feeding into FFNN (GRU-FFNN), a type of a recurrent neural network. Various configurations of the architectures are trialled. A minimum correlation statistic is used to identify clusters of APSIM scenarios that can be aggregated to form training sets for model emulation. We focus on emulating 4 important outputs of the APSIM model: runoff, soil_loss, DINrunoff, Nleached. The GRU-FFNN architecture with three hidden layers and 128 units per layer provides good emulation of runoff and DINrunoff. However, soil_loss and Nleached were emulated relatively poorly under a wide range of the considered architectures; the emulators failed to capture variability at higher values of these two outputs. While the opportunistic data available from past modelling activities provides a large and useful dataset for exploring APSIM emulation, it may not be sufficiently rich enough for successful deep learning of more complex model dynamics. Design of Computer Experiments may be required to generate more informative data to emulate all output variables of interest. We also suggest the use of synthetic meteorology settings to allow the model to be fed a wide range of inputs. These need not all be representative of normal conditions, but can provide a denser, more informative dataset from which complex relationships between input and outputs can be learned.
Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters.

We present a study which shows encouraging stability of the response linearity for a simulated high granularity calorimeter module reconstructed by a CNN model to miscalibration, bias, and noise effects. Our results also show an intuitive, quantifiable relationship between these factors and the calibration parameters. We trained a CNN model to reconstruct energy in the calorimeter module using simulated single-pion events; we then observed the response of the model under various miscalibration, bias, and noise conditions that affected the model input. From these data, we estimated linear response models to calibrate the CNN. We also quantified the relationship between these factors and the calibration parameters by regression analysis.
Development of strategic social information seeking: Implications for cumulative culture

Human learners are rarely the passive recipients of valuable social information. Rather, learners usually have to actively seek out information from a variety of potential others to determine who is in a position to provide useful information. Yet, the majority of developmental social learning paradigms do not address participants’ ability to seek out information for themselves. To investigate age-related changes in children’s ability to seek out appropriate social information, 3- to 8-year-olds (N = 218) were presented with a task requiring them to identify which of four possible demonstrators could provide critical information for unlocking a box. Appropriate information seeking improved significantly with age. The particularly high performance of 7- and 8-year-olds was consistent with the expectation that older children’s increased metacognitive understanding would allow them to identify appropriate information sources. Appropriate social information seeking may have been overlooked as a significant cognitive challenge involved in fully benefiting from others’ knowledge, potentially influencing understanding of the phylogenetic distribution of cumulative culture.
Impact of collaborative innovation on green total factor productivity in Yangtze River Economic Belt: Analysis based on endogenous spatial-temporal weight matrix

6 Technological innovation can promote high-quality economic growth. This paper discusses the promotion of green 7 total factor productivity from the perspective of collaborative innovation in the Yangtze River Economic Belt. 8 Firstly, the evaluation index system of collaborative innovation level is constructed from two aspects of 9 collaborative innovation elements and collaborative innovation environment. Then the entropy method is used to 10 measure its development level. The results show that the collaborative innovation level of provinces in the Yangtze 11 River Economic Belt presents an increasing trend year by year. Meanwhile, there are regional differences, which is 12 characterized by 'high in the middle reaches, middle in the downstream and low in the upstream' Secondly, the 13 SDM model based on endogenous spatio-temporal weight matrix is constructed to analyze the influencing factors 14 of green total factor productivity. The results show that collaborative innovation in the Yangtze River Economic 15 Belt has significant negative impact on green total factor productivity in terms of spatial interaction and fiscal 16 expenditure also has a negative impact. The spatial interaction between environmental protection and opening up 17 has a significant positive impact on green total factor productivity. However, the spatial interaction between 18 industrial structure and human capital on green total factor productivity is not obvious. Finally, this paper puts 19 forward some policy suggestions to improve green total factor productivity. 20