Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis. Driving and walking data, as well as confinement policies were collected from February 2020 to February 2021 for twenty-five European countries and were implemented into Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) time-series models. Results reveal a significant number of models in order to estimate mobility during pandemic almost in every country of the study. School closing was found to be the most important exogenous factor for describing driving or walking, while “Stay at home” orders had not a significant effect on the evolution of people movements. In addition, countries which suffered the most due to the pandemic indicated a strong correlation with the restrictive measures. No time-series models were found to describe the countries which implemented weak confinement policies.
A study of cleaning procedures used for foraminiferal Mg/Ca paleothermometry

The various cleaning steps required for preparation of foraminiferal samples for Mg/Ca (and Sr/Ca) analysis are evaluated for their relative importance and effects on measured elemental ratios. It is shown that the removal of silicate contamination is the most important step for the measurement of Mg/Ca ratios. In an example, bulk sample Mg/Ca decreases from 10.5 to 2.5 mmol mol−1 during clay removal. Oxidation of organic material causes a lowering of sample Mg/Ca in the order of 10% or approximately 1°C when converted to temperature. Use of dilute acid leaching to remove adsorbed contaminants causes partial dissolution of the sample carbonate and a corresponding decrease in Mg/Ca. Reductive treatment also causes dissolution of the sample and a decrease in the Mg/Ca ratio of up to 10–15%. Sample preparation for Sr/Ca analysis does not require the same degree of rigor as is necessary for Mg/Ca work. The “within‐run” reproducibility of the method described here for G. ruber in a core‐top sample from the Arabian Sea was ±1.8% (mean sample ratio was 4.72 mmol mol−1). When converted to temperature, this becomes 28 ± 0.2°C. The equivalent result for Sr/Ca was ±0.5% (mean ratio = 1.44 mmol mol−1).
Controlled Memristic Behavior of Metal-Organic Framework as a Promising Memory Device

Metal-organic frameworks (MOFs) have attracted considerable interests for sensing, electrochemical, and catalytic applications. Most significantly, MOFs with highly accessible sites on their surface have promising potential for applications in high-performance computing architecture. In this paper, Mg-MOF-74 (a MOF built of Mg(II) ions linked by 2,5-dioxido-1,4-benzenedicarboxylate (DOBDC) ligands) and graphene oxide composites (Mg-MOF-74@GO) were first used as an active layer to fabricate ternary memory devices. A comprehensive investigation of the multi-bit data storage performance for Mg-MOF-74@GO composites was discussed and summarized. Moreover, the structure change of Mg-MOF-74@GO after introducing GO was thoroughly studied. The as-fabricated resistive random access memory (RRAM) devices exhibit a ternary memristic behavior with low SET voltage, an RHRS/RIRS/RLRS ratio of 103:102:1, superior retention (>104 s), and reliability performance (>102 cycles). Herein, Mg-MOF-74@GO composite films in constructing memory devices were presented with GO-mediated ternary memristic properties, where the distinct resistance states were controlled to achieve multi-bit data storage. The hydrogen bonding system and defects of GO adsorbed in Mg-MOF-74 are the reason for the ternary memristic behavior. The charge trapping assisted hopping is proposed as the operation mechanism, which is further confirmed by XRD and Raman spectra. The GO-mediated Mg-MOF-74 memory device exhibits potential applications in ultrahigh-density information storage systems and in-memory computing paradigms.
LC Subject Headings of Interest to Judaica Librarians

This column is divided into twelve sections: 1. New and Revised Subject Headings 2. Moving-pictures . Motion pictures 3. Cancelled Subject Headings and Subdivisions 4. Changes: Old vs. New Subject Headings 5. [Topic]-Religious aspects-Judaism 6. [Topic]-Religious aspects ( . not subdivided further) 7. Culinary Subject Headings: An Update 8. Mysticism and the Occult: Jewish aspects 9. Subdivisions to be added to Lists of Free-Floating Subdivisions 10. Subdivisions to be deleted from Lists of Free-Floating Subdivisions 11. Geographic Heading Proposals for Name Authority File 12. Children's Literature Subject Headings used on Annotated (AC) Cards
Mediterranean diet affects the metabolic outcome of metabolic dysfunction-associated fatty liver disease

The prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) is on the rise globally. It is currently one of the most prevalent liver diseases and one of the world’s important public health problems. At present, there is no consensus on a pharmacological treatment for MAFLD. By contrast, lifestyle interventions based on exercise and a balanced diet are considered to be the cornerstone of MAFLD management. Mediterranean diet (MD) have a large content of polyphenols, polyunsaturated fatty acids, oleic acid, carotenoids and fiber, which carry out antioxidant, anti-inflammatory and antibacterial benefits. It has been considered to reduce the incidence rate of cardiovascular disease and type 2 diabetes. The purpose of this narrative review is therefore to summarize and analyze the evidence for the effect of MD on metabolic outcomes in MAFLD patients.
Dependence of the surface climate interannual variability on spatial scale

This paper examines the scale dependence of surface climate interannual variability over six regions of the world using a gridded observed dataset of precipitation and surface air temperature for the period 1961–1990. The scales analysed cover the range of 0.5 to 3.5 degrees in longitude/latitude. The interannual variability of precipitation exhibits a strong sensitivity to spatial scale, especially at the finer scales analysed, while the interannual variability of temperature shows a weak sensitivity to spatial scale. The scale sensitivity of interannual variability is maximum in warm climate regimes, such as over tropical and sub‐tropical regions and in the summer season. These results have important implications for climate predictability (e.g., at seasonal and longer temporal scales) and for the identification of climatic trends and changes. They indicate that the prediction and detection of precipitation changes becomes a substantially more difficult task as the scale of interest is increasingly refined. Conversely, for temperature, since the variability is characterized by broader spatial scales, scale refinement is a less important factor.
Genome-Wide Analysis of Glycerol-3-Phosphate Acyltransferase (GPAT) Family in Perilla frutescens and Functional Characterization of PfGPAT9 Crucial for Biosynthesis of Storage Oils Rich in High-Value Lipids

Glycerol-3-phosphate acyltransferase (GPAT) catalyzes the first step in triacylglycerol (TAG) biosynthesis. However, GPAT members and their functions remain poorly understood in Perilla frutescens, a special edible-medicinal plant with its seed oil rich in polyunsaturated fatty acids (mostly α-linolenic acid, ALA). Here, 14 PfGPATs were identified from the P. frutescens genome and classified into three distinct groups according to their phylogenetic relationships. These 14 PfGPAT genes were distributed unevenly across 11 chromosomes. PfGPAT members within the same subfamily had highly conserved gene structures and four signature functional domains, despite considerable variations detected in these conserved motifs between groups. RNA-seq and RT-qPCR combined with dynamic analysis of oil and FA profiles during seed development indicated that PfGPAT9 may play a crucial role in the biosynthesis and accumulation of seed oil and PUFAs. Ex vivo enzymatic assay using the yeast expression system evidenced that PfGPAT9 had a strong GPAT enzyme activity crucial for TAG assembly and also a high substrate preference for oleic acid (OA, C18:1) and ALA (C18:3). Heterogeneous expression of PfGPAT9 significantly increased total oil and UFA (mostly C18:1 and C18:3) levels in both the seeds and leaves of the transgenic tobacco plants. Moreover, these transgenic tobacco lines exhibited no significant negative effect on other agronomic traits, including plant growth and seed germination rate, as well as other morphological and developmental properties. Collectively, our findings provide important insights into understanding PfGPAT functions, demonstrating that PfGPAT9 is the desirable target in metabolic engineering for increasing storage oil enriched with valuable FA profiles in oilseed crops.
Wideband Reflectionless Antipodal Tapered Slot Filtenna With High Out-of-Band Absorption Ratio

An antipodal tapered slot antenna (ATSA) with wideband reflectionless filtering characteristic is proposed in this work. The ATSA incorporates a wideband absorptive bandpass filtering network, which consists of a parallel coupled line (CL), a stepped impedance open stub (SIOS), and a resistively terminated T-shaped bandstop filter. This network is designed using the frequency complementary diplexing concept, where in-band input energy is selected by the CL and SIOS and radiated by the ATSA; out-of-band energy is dissipated on the resistor through the bandstop filter. The SIOS also enhances out-of-band absorption and suppression by introducing additional gain nulls. A prototype was fabricated and measured, yielding a 3 dB gain bandwidth from 3.2 to 6.8 GHz and a wide impedance bandwidth from dc to 11.8 GHz with an out-of-band absorptive ratio greater than 80%. In addition, the realized gain exhibits a sharp bandpass radiation characteristic with measured out-of-band suppression higher than 25 dB in the lower stopband and 16 dB in the upper stopband.
Depression and Suicidal Ideation in Patients with Mental Illness in South Korea: The Mediating Effect of Alcohol Drinking

This study aimed to investigate the mediating effect of alcohol drinking on the relationship between depression and suicidal ideation inpatients with mental illness. A survey was conducted among 163 individuals with various major mental disorders using a self-reported questionnaire. Data were collected from July to September 2020. Subsequently, the data were analyzed using a t-test, one-way analysis of variance, Pearson’s correlation coefficients, hierarchical multiple linear regression, and a Sobel test. Significant relationships were found between alcohol depression and drinking (r = 0.26, p < 0.001), depression and suicidal ideation (r = 0.63, p < 0.001), and alcohol use and suicidal ideation (r = 0.36, p < 0.001). In addition, alcohol drinking was found to partially mediate the association between depression and suicidal ideation (Z = 3.63, p < 0.001). These results indicated that patients with mental illness who are concerned about drinking alcohol might be able to reduce suicidal thoughts by consulting with a healthcare professional or taking informed actions with the support of a counselor or support group.
Impact of aerosols and clouds on the troposphere and stratosphere radiation field with application to twilight photochemistry at 20 km

A series of coordinated investigations of polar stratospheric chemistry, radiation, and dynamics has resolved and generated many questions regarding ozone depletion in the Earth's atmosphere. Photodissociation plays a key role, and the radiation field models utilized have often been singled out as possible sources of uncertainty. Mean intensities (source functions) are compared for three independent models: discrete-ordinate, integral equation, and Monte Carlo simulation. It was found that the results differ by <10% for clear, cloudy, and aerosol conditions over an altitude range of 0–50 km, wavelength interval of 175–800 nm and a wide range of solar zenith angles. It is shown that in the presence of Pinatubolike aerosol concentrations, twilight photodissociation rates within an aerosol layer centered at 20 km can be reduced by more than an order of magnitude, whereas for solar zenith angles <60°, photodissociation rate changes are a few percent or less. Assessment of the effects of the aerosols on twilight stratospheric chemistry within the aerosol layer shows that radiation field perturbations on the homogeneous photochemistry are small and that heterogeneous processes are the major source of the altered photochemistry.
Fault Estimation and H∞ Fuzzy Active Fault-Tolerant Control Design for Ship Steering Autopilot Subject to Actuator and Sensor Faults

This article investigates the problem of fault estimation and <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> fuzzy active fault-tolerant control design for ship steering autopilot (SSA) system with simultaneous partial states that are not easily measured, actuator (steering machine), and sensor (compass) faults. First, the Takagi–Sugeno (T–S) fuzzy model is employed to describe the nonlinear dynamics of SSA system, and the feasibility of the designed model is analyzed with the collected navigation data. Then, an auxiliary <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> fuzzy observer (AHFO) is introduced for asymptotic estimation of unknown states and faults. Using the observation information provided by the AHFO, an AHFO-based active fault-tolerant controller (AHFO-based AFTC) is designed for stabilizing the SSA system with steering and compass faults to achieve satisfactory control requirements, including stability and tracking performance. Finally, an example of ship course fault-tolerant control is conducted to verify the effectiveness of this method.
Semi-Supervised Transfer Learning Framework for Aging-Aware Library Characterization

Transistor aging significantly affects digital circuit performance, the development of aging-aware standard cell libraries for reliability analysis is essential. Machine learning-based methods have shown promise in efficient library characterization. However, existing methods neither investigate the unlabeled samples nor consider the similarity between timing arcs within the standard cell. In this brief, a semi-supervised transfer learning framework is proposed, which leverages unlabeled samples and correlated timing arcs to enhance the efficiency of aging-aware library characterization. Experimental results demonstrate a 22% reduction in training data compared to the state-of-the-art method while achieving improved prediction accuracy. The proposed framework is evaluated on a RISC-V processor design, demonstrating an average relative error of 1.04% and $100\times $ speedup in aging-aware static timing analysis compared to full simulation-based library generation. The proposed framework is generic and can be applied to any underlying model and other characterization library prediction scenarios.
Hybrid CNN–Transformer Network for Electricity Theft Detection in Smart Grids

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.
Conduction Heating of Formations With Limited Permeability By Condensing Gases

A mathematical model that represents the conduction heating of a rock formation of limited permeability is formulated. Heat is introduced by the injection of a hot condensing gas into horizontal fractures through the formation. The fractures are assumed to be equally spaced; the flow of fluid is linear. The model consists of 3 coupled partial differential equations and 2 functional relationships. Temperature histories for both fractures and formation are obtained by numerical procedures. Application of the model in prediction in situ heating of oil shale is illustrated with 4 sample cases. Influences of shale thermal diffusivity, fluid pressure in the fracture, and fracture spacing upon heating rate are demonstrated. In all the cases considered, several years were required to heat all the formation to 700$F with 1,000$F steam. Less than 32% of the injected heat was utilized for heating the shale.
Analysis of Stochastic Hydrologic Systems

Hydrologic phenomena are in reality stochastic in nature; that is, their behavior changes with the time in accordance with the law of probability as well as with the sequential relationship between the occurrences of the phenomenon. To analyze the hydrologic phenomenon, a mathematical model of the stochastic hydrologic system to simulate the phenomenon must be formulated. In this study, a watershed is treated as the stochastic hydrologic system whose components of precipitation, runoff, storage, and evapotranspiration are simulated as stochastic processes by time series models to be determined by correlograms and spectral analysis. The hydrologie system model is then formulated on the basis of the principle of conservation of mass and composed of the component stochastic processes. To demonstrate the practical application of the method of analysis so developed, the upper Sangamon River basin above Monticello in east central Illinois is used as the sample watershed. The watershed system model so formulated can be employed to generate stochastic streamflows for practical use in the analysis of water resources systems. This application is of particular value in the economic planning of water supply and irrigation projects, which is concerned with the long-range water yield of the watershed.
Thermal‐contraction‐crack networks as evidence for late‐Pleistocene permafrost in Inner Mongolia, China

Numerous wedges on the Ordos Plateau show typical characteristics of periglacial sand wedges that enable them to be distinguished from desiccation cracks in clayey illuviation soil horizons. The sand wedges are organized in two generations of polygonal networks. The older generation is a large‐scale network with a diameter of 8 to 9 m and wedge depths up to more than 2 m. The younger generation has a mean diameter of 3 to 4 m and is formed within the large networks. It consists of shallow (0.6 to 1 m deep) but relatively wide wedges. In contrast to the typical sand wedges that form within continuous permafrost, the shallow wedges formed probably as ‘ground wedges’ by seasonal freezing. Ice wedges and cryoturbations developed only rarely, and exclusively in the most humid areas. Thermal‐contraction cracking occurred mainly between 26 and 20 ka BP, indicating mean annual temperatures at least 13°C lower than present. Before and after that period mean annual temperatures were at least 7 to 8.5° lower than today. The presence of these periglacial phenomena show that the southern limit of continuous permafrost in Inner Mongolia occurred south of 38°N during the Last Glacial Maximum. Copyright © 2004 John Wiley & Sons, Ltd.
Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Forecasting oil production is crucially important in oilfield management. Currently, multifeature-based modeling methods are widely used, but such modeling methods are not universally applicable due to the different actual conditions of oilfields in different places. In this paper, a time series forecasting method based on an integrated learning model is proposed, which combines the advantages of linearity and nonlinearity and is only concerned with the internal characteristics of the production curve itself, without considering other factors. The method includes processing the production history data using singular spectrum analysis, training the autoregressive integrated moving average model and Prophet, training the wavelet neural network, and forecasting oil production. The method is validated using historical production data from the J oilfield in China from 2011 to 2021, and compared with single models, Arps model, and mainstream time series forecasting models. The results show that in the early prediction, the difference in prediction error between the integrated learning model and other models is not obvious, but in the late prediction, the integrated model still predicts stably and the other models compared with it will show more obvious fluctuations. Therefore, the model in this article can make stable and accurate predictions.
Visible imaging spectro-polarimeter

A computed tomography imaging channeled spectropolarimeter (CTICS) is a combination of a computed tomography imaging spectrometer (CTIS) and a channeled spectropolarimeter (CHSP). The CTICS instrument can simultaneously obtain image spatial and spectral information as well as polarization Stokes vectors at each resolution element in a single focal plane array (FPA) integration time with no moving parts. An instrument has been designed and built for the visible wavelength region at the University of Arizona. Performance testing is underway. In this work, we present initial results from data acquired during testing of the CTICS instrument.
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.