Miracle-dz/UniFAR-FTUs huggingface dataset:
From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.
Do audit committee attributes improve integrated reporting quality? Evidence from European Companies listed on STOXX EUROPE 600 INDEX

This paper aims to shed light on the relationship between audit committee attributes and the integrated reporting quality (IRQ). This study uses panel data and multiple regression. The outcomes almost entirely confirm the expectations relating to the impact of audit committee attributes on the IRQ. In particular financial expertise and meetings are shown to have a significant positive impact on IRQ. However, we do not find a significant association between key aspects of the audit committee function, such as audit committee independence and IRQ.
Absolute Configuration of Aliphatic Hydrocarbon Enantiomers Identified by Gas Chromatography: Theorized Application for Isoprenoid Alkanes and the Search of Molecular Biosignatures on Mars

Acyclic saturated hydrocarbon enantiomers were resolved by gas chromatography using a β-cyclodextrin-based chiral stationary phase. The stereospecific synthesis of single enantiomers of 4-methyloctane allowed to assign the absolute stereochemical configuration to the resolved enantiomers. Data show that the (S)-4-methyloctane shows higher chromatographic retention as compared to the (R)-4-methyloctane due to stronger van der Waals interactions with the β-cyclodextrin chiral selector. This introductive research presents future prospects for the separation of stereoisomers of larger branched hydrocarbons. We discuss the importance of chiral hydrocarbons, more precisely the stereochemistry of the isoprenoid alkanes pristane and phytane, as potential biosignatures stable on geological timescales. The origins of pristane and phytane in Earth sediments are presented, and we detail the implications for the search of extinct or extant life on Mars. The data presented here will help to systematically investigate the chirality of hydrocarbon enantiomers in biological and nonbiological samples and in samples to be analyzed by the ESA’s ExoMars rover to trace the chiral precursors of life in 2023.
Efficacy of Wearable Device Gait Training on Parkinson's Disease: A Randomized Controlled Open-label Pilot Study

Objective To investigate the efficacy of home-based gait training using the wearable Stride Management Assist (SMA) exoskeleton in people with moderately advanced Parkinson's disease. Methods This was a single-center, open-label, parallel, randomized controlled trial. We included outpatients with idiopathic Parkinson's disease who were capable of walking independently with or without walk aids and had Hoehn and Yahr stage 2-4 in the ON state. Patients were randomly assigned (1:1 ratio) to receive either SMA gait training (SMA group) or control gait training (control group). All participants underwent gait training for approximately 30 min. These training sessions were conducted 10 times for 3 months. We measured clinical outcomes at baseline and post-intervention. The between-group difference of distance in the three-minute walk test was the primary outcome. Results Of the 15 randomly assigned participants, 12 (five in the SMA group) completed this study. The between-group difference was a mean of 13.7 meters (standard error of the mean: 7.8) in the 3-minute walk test (p=0.109). The distance traversed increased from 141.4 m to 154.7 m in the SMA group (p=0.023), whereas there was no marked change in the control group. In addition, although there was a decrease in the physiological cost index from 0.29 to 0.13 in the SMA group (p=0.046), it remained unchanged in the control group. Conclusion These findings suggest that home-based SMA gait training may increase the exercise endurance in people with moderately advanced Parkinson's disease.
Comparing the Effects of Eight Weeks of Whole Body Vibration Exercise Combined With Rope Skipping at Two Different Intensities on Physical Performance of Older Men: A Randomized Single-Blind Clinical Trial

Objectives: Whole-Body Vibration (WBV) exercise seems to be an effective alternative to improve physical performance in the elderly. This study aims to compare the effects of eight weeks of WBV exercise combined with rope skipping at two different intensities on physical performance of older men. Methods & Materials: This is a randomized single-blind clinical trial. Participants were 30 older men (Mean±SD age= 65.83±4.16 years; Mean±SD height= 169.26±3.90 cm; Mean±SD weight= 77.04±4.62 kg; Body Mass Index= 26.86±0.69 kg/m2) living in Mashhad, Iran in 2019, who were selected by purposive and convenience sampling methods and randomly divided into three groups: high intensity (HI; n=12), low intensity (LI; n=12) and control (n=10). The WBV exercise was performed at a frequency of 25-40 Hz and amplitude of 3 mm. Rope skipping was performed based on the Borg scale at 13-14 level intensities with 30-35 jumps per minute. Physical performance indicators, body mass index, and fat percentage were measured before and after eight weeks of intervention. Repeated measures analysis of variance was used for data analysis in SPSS v. 20 software. The significance level was set at P≤0.05. Results: In HI and LI groups, body fat percentage (-4.50%, -5.99%), lower body flexibility (105%, 102%), upper limb muscle strength (42.43%, 42.93%), handgrip strength (43.38%, 39.45%), dynamic leg strength (36.47%, 26.43%), lower limb muscular endurance (27.20%, 26.10%), cardiorespiratory function (10.27%, 10.90%), and dynamic balance (-32.60%, -24.10%) showed a significant improvement compared to the control group (P<0.05). There was no significant difference in body mass index between exercise and control groups (P>0.05), and no significant difference between the two exercise groups in any of the study variables (P>0.05) Conclusion: It seems that low intensities of the WBV exercise + rope skipping is effective in improving body composition and physical performance in older men. The selection of intensity level depends on the physical condition of the elderly.
Balancing the scales in China’s smart courts: driving case standardisation through AI

ABSTRACT This paper positions the use of new technologies in China’s smart courts as a continuation of the professionalisation of China’s judiciary. This series of reforms is the backbone of China’s rule of law society reforms, as set out in the 14th five-year plan, and has been adopted as a method to standardise judgements within a court system headed by elite justices, yet underpinned by a legacy of under-qualified judges with significant practical experience. While the smart court systems have demonstrated significant savings in terms of time and costs for litigants, smart courts are no panacea for betterment of rule of law. The quality of judicial big data, potential black box nature and bias of the algorithms used, coupled with the potential for over-reliance or under-reliance on the software, all present significant challenges to the safe and successful deployment of legal tech in China’s smart courts. Reaching the lofty goals set out for its use requires a consistent and mindful approach to the betterment of both the technology itself and the training of those using it, so that pitfalls and faults in its employ can be both ameliorated and flagged by the users themselves: the judges.
Coronavirus Fusion and Entry are Targets for New Therapies

The current treatments for coronavirus infections consist of monoclonal antibodies to prevent spike protein attachment and antiviral agents to block RNA-dependent RNA polymerase interrupting viral replication. Fusion and entry of coronaviruses present new opportunities for therapy and are explained and summarized in this report. Additional effective treatments for current and future coronavirus infections will be of great benefit globally. Abstract The current therapies for coronavirus infections are directed at only two targets. Monoclonal antibodies prevent virus attachment to the host cell membrane and antiviral agents attack RNA-dependent RNA polymerase to interrupt replication of the virus. The fusion and entry stage of coronavirus infection can proceed through an endocomal route of a direct plasma membrane route. Molecular virology has provided detailed knowledge of these processes and each step presents an opportunity for therapeutic intervention. Currently available drugs have been repurposed and new compounds have been identified as potential coronavirus treatment options which target fusion and entry of these viruses.
SATAM: A SAT Attack Resistant Active Metering Against IC Overbuilding

In the horizontal semiconductor business model, a foundry can tamper or overbuild the integrated circuits (ICs) while the IC owner knows nothing about it. Active IC metering scheme has been proposed to solve this problem by enabling IC owners to uniquely identify each manufactured chip. We propose a new active metering scheme, SATAM. In the scheme, a new cell of switchable scannable flip-flop (WFF) is introduced to be inserted in the non-critical paths or replace some original scan cells. Without a correct key on these WFFs, the synchronization status of the original design is violated and hence the circuit logic is locked (obfuscated). The scan design is also obfuscated by the introduction of WFFs, which helps the locking scheme to resist the typical SAT attack and other existing attacks on logic locking. To enable each fabricated chip to have a unique key to unlock, a via-based physical unclonable function (PUF) is adopted due to its perfect reliability. A new post-processing method is proposed to improve its uniqueness and randomness. Also, we propose to modify the original finite state machine (FSM) so that the PUF response can only be retrieved by the chip designer in a secure way. The experimental results show that the locking scheme based on WFFs can well resist the SAT attack and other existing attacks on logic obfuscation. The PUF design can achieve satisfactory uniqueness and randomness while incurring lower overhead. The FSM-based retrieval scheme can secure the PUF response by slightly modifying the original FSM. The overhead due to the overall metering method is smaller than most of the existing metering methods while it can resist typical attacks.
Research on visual-inertial localization with planar two-dimensional code markers

Nowadays, visual-inertial localization method is becoming a hotspot of research, but this localization method is based on odometry, which will produce cumulative error. So it needs absolute position information to help to correct. In order to solve the above problems, this paper proposes a method which adding two-dimensional code marker to assist localization based on visual-inertial localization system. The aim of this method is to eliminate the cumulative errors generated by the localization method of odometry and improving the positioning accuracy. The principle of visual-inertial localization and two-dimensional code marker localization are introduced, and the localization method is verified by experiments. Finally, the experimental results are analyzed.
Prediction model of crash severity in imbalanced dataset using data leveling methods and metaheuristic optimization algorithms

Abstract Road accident is one of the important problems in the world which caused large number of deaths. In a road crash dataset, the fatal crash samples, often constitute very small proportion in comparison with non-fatal crash samples. Accurate prediction of fatal crashes, as a minority class, is one of the important challenges in such imbalanced sample distribution in the most of machine learning algorithms. This study introduced data leveling methods based on two metaheuristic optimization algorithms (biogeography-based optimization and invasive weed optimization) to obtain more balanced data. Then, three machine learning algorithms including decision tree, support vector machine (SVM) and k-nearest neighbor were applied for obtained balanced dataset. Performances of the prepared models were evaluated by improving the accuracy of the models in detecting the fatal crashes. It is found that data leveling methods of imbalanced dataset with metaheuristic algorithms improve the performance of crash prediction models in detecting fatal crashes especially in SVM algorithm up to 100% compared to previous studies. Also, results of sensitivity analysis on the developed model represented that head-on crashes, curved roads, and large type vehicles can increase the probability of fatal crashes up to 27.2%, 29%, and 36.8% at high posted speed limit, respectively. Also, two-vehicle crashes are much more likely to be involved in fatal crashes than single-vehicle crashes.
Smart FFT measurement for Reconfigurable Sensor using a Wideband digital receiver

The detection of multiple signals with no proper understanding proves to be a challenge for current UWB advanced radar collectors. It is recommended that the new smart FFT (SFFT) based collector operate at a transfer speed of 1,200 MHz. Different undersampling rates consider a bunch of big data (32,768) from the 2.56 GSPS 12-digit ADC to distinguish more signals and more precisely. The accuracy of frequency identification is achieved by 1 / fifth of the frequency target (2.56 GHz/4096) using the SFFT method. The receiver of SFFT achieves an identification rate of 90 per cent for signals as low as −19 dB SNR at a false caution rate10-7. It offers a dynamic range of 30 to 55 dB with a repeating contrast somewhere in the range of one and five containers to recognize a few signals (up to 5 signals) with varying associating speeds. On the Tesla K40c GPU, the SFFT algorithm makes some 175 μs of run memory.
X-ray non-destructive inspection of automobile subframe based on machine learning

Aiming at the low efficiency of manual detection of X-ray image defects in the production process of casting gearboxes, combined with the machine learning theory in computer vision recognition, a defect detection algorithm based on feature engineering and machine learning is proposed. Through image preprocessing and part X-ray image feature extraction, different feature models are established, and the algorithm is evaluated through different machine learning classifiers. Car subframe products are used for experimental verification. In general, the combination of directional gradient histogram (HOG) and Naive Bayes (GNB) can achieve the best results, with an accuracy rate of 83%. Compared with manual detection, this algorithm effectively improves the classification speed, and the classification accuracy has also been greatly improved, which proves the effectiveness of the proposed algorithm.
Technical Note - The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays

In the paper “The Multinomial Logit Model with Sequential Offerings: Algorithmic Frameworks for Product Recommendation Displays,” we consider a sequential assortment problem that has applications ranging from appointment scheduling in hospitals, restaurants, and fitness centers to product recommendation in e-commerce settings. Our main contribution comes in the form of a strongly polynomial-time approximation scheme for the most general form of the problem. We also conduct an extensive case study in which we fit our sequential model to historical search data from Expedia’s hotel booking platform. We observe substantial gains in fitting accuracy when our model is benchmarked against other well-known choice models designed for the setting at hand.
Sequential Delivery of Novel Triple Drug Combination via Crosslinked Alginate/Lactoferrin Nanohybrids for Enhanced Breast Cancer Treatment

While breast cancer remains a global health concern, the elaboration of rationally designed drug combinations coupled with advanced biocompatible delivery systems offers new promising treatment venues. Herein, we repurposed rosuvastatin (RST) based on its selective tumor apoptotic effect and combined it with the antimetabolite pemetrexed (PMT) and the tumor-sensitizing polyphenol honokiol (HK). This synergistic three-drug combination was incorporated into protein polysaccharide nanohybrids fabricated by utilizing sodium alginate (ALG) and lactoferrin (LF), inspired by the stealth property of the former and the cancer cell targeting capability of the latter. ALG was conjugated to PMT and then coupled with LF which was conjugated to RST, forming core shell nanohybrids into which HK was physically loaded, followed by cross linking using genipin. The crosslinked HK-loaded PMT–ALG/LF–RST nanohybrids exhibited a fair drug loading of 7.86, 5.24 and 6.11% for RST, PMT and HK, respectively. It demonstrated an eight-fold decrease in the IC50 compared to the free drug combination, in addition to showing an enhanced cellular uptake by MCF-7 cells. The in vivo antitumor efficacy in a breast cancer-bearing mouse model confirmed the superiority of the triple cocktail-loaded nanohybrids. Conclusively, our rationally designed triple drug-loaded protein/polysaccharide nanohybrids offer a promising, biocompatible approach for an effective breast tumor suppression.
The effect of black rot on grape berry composition

Guignardia bidwellii, indigenous to North America, is a significant pathogen of grapes long known in Hungary, infecting only the growing green parts of the vine (leaves, petioles, shoots, and bunches). In the absence of adequate plant protection and extreme weather conditions such as a predominantly humid, warm year, black rot of grapes can be expected. The pathogen can cause high yield losses due to grape rot and reduce wine quality if the infection is severe. The evolution of certain biogenic amine compounds were investigated under the influence of grape black rot. The results obtained showed that they were present in low concentrations from an oenological point of view. Polyphenol composition was consistent with the literature, blackening affected mainly the concentration of catechin. Black rot fungus does not produce β-glucosidase enzyme. In terms of resveratrol content, black rot has no particular effect. However, like Botrytis cinerea, it produces glycerol and, proportionally, gluconic acid in lower concentrations. It can be concluded that black rot of grapes does not cause health problems when introduced into wine processing.
A Study of Bullying on Social Media among Senior Secondary School Students With Reference to Gender and Type of School

Bullying on various social media platforms is a new societal issue that has gotten a lot of attention from academics. It has been recognized as a serious health issue among adolescents. Cyber bullying can take many different forms. It doesn't always imply breaking into someone's accounts or pretending to be someone else. It also includes making disparaging remarks about someone or circulating rumors in order to slander them. Because everyone is engrossed in social media, it is quite easy for anyone to abuse their access. The present study is an attempt to analyze various aspects of bullying on social media among senior secondary school students. The study has also taken into consideration the type of school (government and private) and gender variables which was carried out on the sample of 305 senior secondary school adolescents of Aligarh district. The study has revealed mixed results which have been interpreted with some suggestive measures.
Novel Magnetic Resonance Imaging Tools for the Diagnosis of Degenerative Disc Disease: A Narrative Review

Low back pain (LBP) is one of the leading causes of disability worldwide, with a significant socioeconomic burden on healthcare systems. It is mainly caused by degenerative disc disease (DDD), a progressive, chronic, and age-related process. With its capacity to accurately characterize intervertebral disc (IVD) and spinal morphology, magnetic resonance imaging (MRI) has been established as one of the most valuable tools in diagnosing DDD. However, existing technology cannot detect subtle changes in IVD tissue composition and cell metabolism. In this review, we summarized the state of the art regarding innovative quantitative MRI modalities that have shown the capacity to discriminate and quantify changes in matrix composition and integrity, as well as biomechanical changes in the early stages of DDD. Validation and implementation of this new technology in the clinical setting will allow for an early diagnosis of DDD and ideally guide conservative and regenerative treatments that may prevent the progression of the degenerative process rather than intervene at the latest stages of the disease.
Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Smartphones can be used to collect granular behavioral data unobtrusively, over long time periods, in real-world settings. To detect aberrant behaviors in large volumes of passively collected smartphone data, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic in our method was a weighted average, with more weight on the between-individual component when the amount of data available for the individual was limited and more weight on the within-individual component when the data were adequate. The algorithm took only an O(1) runtime in each update, and the required memory usage was fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity, and specificity, was consistently better than or equal to the offline method that it was built upon, depending on the sample size of the individual data. Future applications of our method include early detection of surgical complications during recovery and the possible prevention of the relapse of patients with serious mental illness.