Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Potential of Non-Extractable Fractions of Dried Persimmon (Diospyros kaki Thunb.) in Streptozotocin-Induced Diabetic Rats

Oxidative stress causes the progression of diabetes and its complications; thus, maintaining the balance between reactive oxygen species produced by hyperglycemia and the antioxidant defense system is important. We herein examined the antioxidant potential of non-extractable fractions of dried persimmon (NEP) against oxidative stress in diabetic rats. Rats with streptozotocin-induced type 1 diabetes (50 mg/kg body weight) were administered NEP for 9 weeks. Antioxidant enzyme activities and concentration of antioxidants in liver tissues were analyzed with a microplate reader. Extensor digitorum longus (EDL) and soleus muscle fibers were stained with succinate dehydrogenase and muscle fiber sizes were measured. The administration of NEP increased the body weight of diabetes rats. Regarding antioxidant activities, the oxygen radical absorbance capacity and superoxide dismutase activity in liver tissues significantly increased. In addition, increases in glutathione peroxidase activity in liver tissues and reductions in the cross-sectional area of EDL muscle fibers were significantly suppressed. In these results, NEP improved the antioxidant defense system in the liver tissues of diabetic rats, in addition to attenuating of muscle fibers atrophy against oxidative damage induced by hyperglycemia.
Earth’s ancient dynamo with silicon precipitation

This is a non-peer reviewed, pre-print manuscript submitted to also Abstract 20 Earth’s core has produced a global magnetic field for the last 4 Gyrs, 21 presently sustained by inner core growth. Models of the core with high 22 thermal conductivity suggest potentially insufficient power available for 23 the geodynamo prior to inner core formation ∼ 1 Ga. Precipitation of 24 SiO 2 from the liquid core might offer an alternative power source for the 25 magnetic field before inner core growth, however, no estimates of parti- 26 tion coefficient exist for conditions of the early core. We present the first 27 determination of the silicon partition coefficient at core-mantle boundary 28 conditions and use these results to build a thermodynamic model that 29 is integrated into a model of coupled core-mantle thermal evolution. We 30 show that models including precipitation of silicon can satisfy constraints 31 of inner core size, mantle convective heat flux, mantle temperature and 32 a persistent ancient geodynamo whilst those excluding fail. Successful 33 power from precipitation favours an oxygen poor initial core composition. 34 We fit this thermodynamic model to experimental values of
Multi-Probe Sensor for Water Content Analysis of Liquid Biofuels

|A multi-probe sensor for water content analysis, in liquid biofuels, by using re(cid:13)ection and transmission measurements in microwave frequencies range, is proposed in this letter. As preliminary step and for a better understanding, the measurements were carried out with ethanol/water mixtures, which mimic bioethanol applications, at room temperature. In order to study water/alcohol mixtures, each of them was characterized using classical techniques like open ended coaxial probe or re(cid:13)ection/transmission coaxial line, before being tested in multi-probe sensors. At the end, the multi-probe sensor aims to be implemented in-line production in order to perform diagnosis of water in liquid biofuels.
Development of Intrinsic Formulation of W.-Z. Chien of the Geometrically Nonlinear Theory of Thin Elastic Shells

Summary Wei-zhang Chien (1944) derived the equilibrium equations and the compatibility conditions of the nonlinear theory of thin, isotropic elastic shells entirely in terms of the surface stress and strain measures associated with the shell base surface. This allowed Him to divide the complex boundary value problem (BVP) of nonlinear shell analysis into two disjoint and supposedly simpler steps: I) finding the surface stress and strain measures, and II) establishing displacements from already known surface strain measures. In the present paper some achievements of this formulation obtained during the last 66 years are reviewed, with special account of the results obtained by the author. In the first part, using the error of the constitutive equations, we remind some consistent intrinsic BVPs proposed in the literature. These are, in particular: 1) the intrinsic BVP in terms of the surface strain measures, 2) the refined intrinsic BVP in terms of the surface stress resultants and the surface bendings, 3) the almost inextensional bending BVP, 4) the almost membrane BVP, and 5) the intrinsic bending BVP reduced to two PDE for the stress and deformation functions. The alternative set of the refined intrinsic shell equations formulated in the rotated surface base is also presented. All discussed BVPs contain the corresponding intrinsic natural and kinematic boundary conditions. In the second part, recent achievements on determination of position of the deformed shell base surface from the surface strains and bendings are reviewed. Three methods of finding such a position are presented: a) by direct solving some vector ODE, b) through establishing the surface deformation gradient, and c) applying the right polar decomposition to the deformation gradient and then solving some ODE for the rotation tensor field. Finally, we briefly discuss some problems related to the intrinsic formulation of thin shell theory. These are: i) determination of position of a surface from components of its two fundamental forms, ii) bifurcation buckling of the axially compressed circular cylinder, iii) determination of position of the deformed base surface from its surface strains and the height function, and iv) basic assumptions of the special class of flexible shells.
Sliding Mode Observer Design for decentralized multi-phase ﬂow estimation

Robust ﬂow measurement in multi-phase systems has extensive applications in understanding, design, and operation of complex environmental, energy and industrial processes. The nonlinearity and spatiotemporal variability of the interactions between diﬀerent ﬂow phases makes the multi-phase ﬂow measurement a challenging task. Two Sliding Mode Observer (SMO) schemes are proposed in this study for the state estimation of a decentralized multi-phase ﬂow measurement system. The developed observers are shown to be theoretically valid and numerically applicable for a real-life case study data. The multi-phase ﬂow system considered in this paper can be described as two interconnected sub-systems including ﬂuid and gas sub-systems, and two scenarios are considered in the design of the observers. The ﬁrst scenario considers the interconnections as bounded disturbance (SMOD), while the second scenario considers the interconnections as an uncertainty (SMOU). Hence, the Sliding Mode Observers are adopted to mitigate the eﬀects of disturbance in the system and uncertainties in the parameters. Numerical simulations are conducted using MATLAB and dynamic HYSYS simultaneously, using the data obtained from ﬁeld-based multi-phase ﬂow measurements. The results demonstrate the appropriateness and robustness of the proposed Sliding Mode Observer (SMO) for estimation of the multi-phase ﬂuid speciﬁcations including the density, velocity, and the volume phases fraction in each subsystem. The analysis of the results highlights that the proposed model is computationally eﬃcient with fast transient response, accurate tracking capability of the real process data, and very low steady-state error. This study shows that choosing an appropriate Lyapunov-Krasovsky function results in the asymptotic stability of the decentralized system and improves the performance of the proposed observers. Uncertainty analysis is conducted on the velocity estimation results obtained from the Sliding Observers. Overall, SMOU method shown better performance with RMSE of 0.24%, while RMSE of 0.46% was achieved for the SMOD. Comparison of the numerical results with the ﬁeld-based ﬂow measurement, as a benchmark, shows that although uncertainty in SMOU is approximately half of the uncertainty in SMOD, state estimation for both schemes was achieved in a ﬁnite time with high order of precision. It was shown that both observers developed in this study are well capable of estimating the multi-phase ﬂow variables and states.
The First Sale Doctrine and the Digital Challenge to Public Libraries

Libraries deliver the majority of US book consumption, and library costs are kept low by the ﬁrst sale doctrine’s cap on prices that libraries pay for print but not electronic books. Growth in library ebook prices may imperil libraries. Using data on over 8,000 library systems for 2013-2019, we compare the welfare impacts of higher print vs ebook prices. We document causal impacts of holdings on circulation, and we develop a structural model of patron demand and library holdings showing that increased print book prices would have almost ten times the negative patron welfare impact of higher ebook prices. ∗ We thank seminar participants at NUS Singapore, Brandeis University, JLU Gießen, Indiana University, Boston University, the Rotman School at Toronto, Stanford University, the University of East Anglia, the 2022 IIOC, the 2022 Munich Summer Institute, the 2022 ZEW ICT Conference, Nova Business School, and the 2022 NBER Summer Institute. We also thank Devesh Raval, Avi Collis, Marcus Biermann, and Shane Greenstein for providing insightful discussant comments.
Employment and TFP Impact of Technologies in the Developing World: Domestic versus Imported Expertise

This study assesses the impact of technology import, input import, foreign ownership of the company and domestic innovation on productivity and employment. For this purpose, we analyze enterprises-level survey data for developing countries across the world. In terms of labour productivity, we noted that it varies inversely with all the three foreign sources. However, the combined effect of foreign technology and imported input on labour productivity is positive which may be indicative of the complementary relationship between the two. Turning to TFP the results are positive: foreign technology, imported inputs - whether measured in terms of dummy or the percentage of inputs imported from abroad - and the status of foreign ownership in the company all three raise the TFP. On employment the impact of foreign technology, imported inputs and foreign ownership is positive. Domestic innovation or research and development expenditure also results in higher levels of employment. Hence, foreign technology and better quality imported inputs can be beneficial for the firms as new opportunities may come up for expansion in activities though the joint effect on employment turns out to be negative.
Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine

Abstract. Riverbank erosion occurs along many of the Earth's river systems, affecting riverine populations by destroying agricultural land and housing. In this study, we detected past events of riverbank erosion along the Jamuna River in Bangladesh using time series of Sentinel-1 satellite radar imagery, ground-range-detected (GRD) data with a 12 d revisit cycle, available in the Google Earth Engine (GEE). Eroded land is detected by performing a land cover classification and by detecting land cover changes from vegetated areas before the monsoon to sand or water after the monsoon. Further, settlements are detected as persistent scatterers and classified as eroded if they are located on eroded land. We found that with Sentinel-1 data, erosion locations can be determined already 1 month after the end of the monsoon and hence potentially earlier than using optical satellite images which depend on cloud-free daylight conditions. Further, we developed an interactive GEE-based online tool allowing the user to explore where riverbank erosion has destroyed land and settlements along the Jamuna in five monsoon seasons (2015–2019). The source code of our implementation is publicly available, providing the opportunity to reproduce the results, to adapt the algorithm and to transfer our results to assess riverbank erosion in other geographical settings.
SHOULDER ARTHROPATHY SECONDARY TO SYRINGOMYELIA – NEW X-RAY CLASSIFICATION OF SHOULDER DEGENERATION

Introduction Shoulder neuroarthropathy is a rare joint degeneration mostly related to syringomyelia. An X-ray is a basic tool to stage the advancement of shoulder destruction in shoulder neuroarthropathy. Aim We aimed to create and verify the reliability of our own radiographic classification of shoulder degeneration (NGH) and to correlate it with clinical features. Material and methods The study included 39 cases (45 shoulders with neuroarthropathy secondary to syringomyelia) collected from a systematic literature review and our own pooled series of 10 cases. We found 34 papers, all being case reports or case series. Inclusion criteria were an X-ray in two projections and the availability of clinical data. Our NGH classification was as follows: for glenoid (G) and head (H), three stages were distinguished: G0/H0, no X-ray changes; G1/H1, partial degeneration; and G2/H2, total degeneration. Results The statistical analysis showed almost perfect agreement between the evaluators for the humeral head and glenoid in both measurements and almost perfect compatibility. The majority of patients had total head degeneration without correlation with a range of motion. The glenoid condition was various and had no statistical influence on shoulder function. Both the G and H stages had an impact on the incidence of swelling and weakness. Conclusions These findings indicate that our NGH classification is highly reliable for staging shoulder degeneration related to syringomyelia. The classification seemed to correlate with the clinical condition partly. Under this system, patients with stages G0 and G1 can still qualify for reverse shoulder arthroplasty.
Uniformitarian Prediction of Early‐Pleistocene Atmospheric CO2

Despite recent empirical and modeling advances, atmospheric CO2 concentrations during the early Pleistocene remain uncertain. Using a recently‐developed Bayesian paleoclimate model, an ensemble of seven different CO2 records is inferred conditional on reconstructions of past sea level. Five ensemble members give a consensus prediction that CO2 concentrations averaged 241 ppm (238–245 ppm 95% CI) between 2 and 0.8 Ma. Uncertainty estimates account for contributions from orbital forcing, age uncertainties, and other factors. Our consensus prediction aligns well with a compilation of previously published δ11B‐based CO2 reconstructions after calibration to late‐Pleistocene ice‐core CO2 values, as well as with 60 early‐Pleistocene CO2 measurements from the Allan Hills in East Antarctica. Our consensus prediction can be definitively tested by obtaining continuous ice‐core atmospheric CO2 records that extend into the early Pleistocene.
Comparison of machine learning techniques for reservoir outflow forecasting

Abstract. Reservoirs play a key role in many human societies due to their capability to manage water resources. In addition to their role in water supply and hydropower production, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function, since extreme events have increased in the last few decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood damage will be key in the coming decades. Having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. When historical data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyzes the efficiency of different machine learning techniques to predict reservoir outflow, namely multivariate linear regression (MLR) and three artificial neural networks: multilayer perceptron (MLP), nonlinear autoregressive exogenous (NARX) and long short-term memory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques analyzed, the NARX approach was the option that provided the best estimations on average.
Task Factorization in Curriculum Learning

A common challenge for learning when applied to a complex “target” task is that learning that task all at once can be too difficult due to inefficient exploration given a sparse reward signal. Curriculum Learning addresses this challenge by sequencing training tasks for a learner to facilitate gradual learning. One of the crucial steps in finding a suitable curriculum learning approach is to understand the dimensions along which the domain can be factorized. In this paper, we identify different types of factorizations common in the literature of curriculum learning for reinforcement learning tasks: factorizations that involve the agent, the environment, or the mission. For each factorization category, we identify the relevant algorithms and techniques that leverage that factorization and present several case studies to showcase how leveraging an appropriate factorization can boost learning using a simple curriculum.
Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding

Abstract. Heavy-rainfall events in mountainous areas trigger destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are commonly mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights into the processes behind the triggered landsliding require information on both the spatial extent and the timing of landslides. Here we demonstrate that Sentinel-1 synthetic aperture radar amplitude time series can be used to constrain landslide timing to within a few days and present four techniques to accomplish this based on time series of (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability in amplitude between pixels within the landslide, and (iii) geometric shadows and (iv) geometric bright spots cast within the landslide. We test these techniques on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that a method combining them allows 20 %–30 % of landslides to be timed with an accuracy of 80 %. Application of this method could provide an insight into landslide timings throughout events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis.
Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro

Abstract. As temperatures decrease in autumn, vegetation of temperate and boreal ecosystems increases its tolerance to freezing. This process, known as hardening, results in a set of physiological changes at the molecular level that initiate modifications of cell membrane composition and the synthesis of anti-freeze proteins. Together with the freezing of extracellular water, anti-freeze proteins reduce plant water potentials and xylem conductivity. To represent the responses of vegetation to climate change, land surface schemes increasingly employ “hydrodynamic” models that represent the explicit fluxes of water from soil and through plants. The functioning of such schemes under frozen soil conditions, however, is poorly understood. Nonetheless, hydraulic processes are of major importance in the dynamics of these systems, which can suffer from, e.g., winter “frost drought” events. In this study, we implement a scheme that represents hardening into CLM5.0-FATES-Hydro. FATES-Hydro is a plant hydrodynamics module in FATES, a cohort model of vegetation physiology, growth, and dynamics hosted in CLM5.0. We find that, in frozen systems, it is necessary to introduce reductions in plant water loss associated with hardening to prevent winter desiccation. This work makes it possible to use CLM5.0-FATES-Hydro to model realistic impacts from frost droughts on vegetation growth and photosynthesis, leading to more reliable projections of how northern ecosystems respond to climate change.
Long-Short Term Memory Network-Based Monitoring Data Anomaly Detection of a Long-Span Suspension Bridge

Structural health monitoring (SHM) systems have been widely applied in long-span bridges and a large amount of SHM data is continually collected. The harsh environment of sensors installed at structures causes multiple types of anomalies such as outlier, minor, missing, trend, drift, and break in the SHM data, which seriously hinders the further analysis of SHM data. In order to achieve anomaly detection from a large amount of SHM data, this paper proposes a long-short term memory (LSTM) network-based anomaly detection method. Firstly, the proposed method reduces the workload for preparing training sets. Secondly, the purpose of real-time anomaly detection can be met. Thirdly, the problem of high alarm rate can be avoided by utilizing double thresholds. To validate the effectiveness of the proposed method, a case study of finite element model simulation is firstly introduced, which illustrates the detailed implementation process. Finally, acceleration data from the SHM system of a long-span suspension bridge located in Jiangyin, China is employed. The results show that the proposed method can detect anomaly with high accuracy and identify abnormal accidents such as a ship collision quickly.
Subordinate ravens (Corvus corax) increase alarm calling when temporarily separated from group members

evaluations of only individual variations in or while impossible to differences effects. In this study, we directly compared common raven anti-predator behaviour both alone and in a social group. We trained 15 ravens to recognize and respond to a novel experimental and then tested them in both and isolation settings across 1.5
Learnable Model Veriﬁcation through Reinforcement Learning

Formal Veriﬁcation of Cyber-Physical Systems provides safety justiﬁcation for the modeling of the environment. However, an accurate model is usually hard to acquire due to the exquisite nature of physics in the real world. With the aid of model-based reinforcement learning, this paper proposes an approach that updates model parameters and provides formal veriﬁcation via differential dynamic logic (d L ). We present the learning process for a series of common reinforcement learning tasks in Cyber-Physical Systems and show that the safety of the model is preserved by proving the parameterized model.