Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient data generation for the determination of effective oxygen diffusion coefficients of cementitious materials

To develop new cementitious materials with the goal of reducing the carbon footprint a rapid assessment of information about their resistance against damaging processes is of utmost importance to ensure long‐term durability. The Oxygen diffusion test is an efficient method to evaluate the ingress of gas in concretes and mortars without changing the porous structure of the specimen during testing. However, currently no standardized test method exists. We determined the effective Oxygen diffusion coefficient, De,O2 of mortar discs using a single diffusion cell method. Furthermore, we enhanced the procedure by extrapolating the oxygen saturation curves of the diffusion cells with a simple asymptotic function to identify a minimum required test duration where the extrapolated data still delivers reliable results and hence, shorten the overall required measurement time. In our case, we were able to significantly reduce the effort needed to obtain results that are within the range of the respective standard deviation of individual specimens. Our findings further strengthen the feasibility of time and cost efficient gas diffusion testing methods that could deliver complementary information for evaluating existing and designing new cementitious materials.
Small proteins in Gram-positive bacteria

Abstract Small proteins comprising less than 100 amino acids have been often ignored in bacterial genome annotations. About 10 years ago, focused efforts started to investigate whole peptidomes, which resulted in the discovery of a multitude of small proteins, but only a number of them have been characterized in detail. Generally, small proteins can be either membrane or cytosolic proteins. The latter interact with larger proteins, RNA or even metal ions. Here, we summarize our current knowledge on small proteins from Gram-positive bacteria with a special emphasis on the model organism Bacillus subtilis. Our examples include membrane-bound toxins of type I toxin–antitoxin systems, proteins that block the assembly of higher order structures, regulate sporulation or modulate the RNA degradosome. We do not consider antimicrobial peptides. Furthermore, we present methods for the identification and investigation of small proteins.
Research Progress on Effects of Ginsenoside Rg2 and Rh1 on Nervous System and Related Mechanisms

Neurological-related disorders are diseases that affect the body’s neurons or peripheral nerve tissue, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). The development of neurological disorders can cause serious harm to the quality of life and functioning of the patient. The use of traditional therapeutic agents such as dopamine-promoting drugs, anticholinergic drugs, cholinesterase inhibitors, and NMDA receptor antagonists is often accompanied by a series of side effects such as drug resistance, cardiac arrhythmia, liver function abnormalities, and blurred vision. Therefore, there is an urgent need to find a therapeutic drug with a high safety profile and few side effects. Herbal medicines are rich in active ingredients that are natural macromolecules. Ginsenoside is the main active ingredient of ginseng, which has a variety of pharmacological effects and is considered to have potential value in the treatment of human diseases. Modern pharmacological studies have shown that ginsenosides Rg2 and Rh1 have strong pharmacological activities in the nervous system, with protective effects on nerve cells, improved resistance to neuronal injury, modulation of neural activity, resistance to cerebral ischemia/reperfusion injury, improvement of brain damage after eclampsia hemorrhage, improvement of memory and cognitive deficits, treatment of AD and vascular dementia, alleviation of anxiety, pain, and inhibition of ionic-like behavior. In this article, we searched the pharmacological research literature of Rg2 and Rh1 in the field of neurological diseases, summarized the latest research progress of the two ginsenosides, and reviewed the pharmacological effects and mechanisms of Rg2 and Rh1, which provided a new way of thinking for the research of the active ingredients in ginseng anti-neurological diseases and the development of new drugs.
Multi-Trajectory Prediction of Pedestrian Based on Deep Neural Network Architecture

Multimodal learning input has been used to predict the single trajectory of pedestrians. However, the single-trajectory is not efficient in making the vehicle avoid the sudden direction changes of the pedestrian in the drivable area. Therefore, we proposed a multi-trajectory prediction model with a Deep Neural Network (DNN) using an egocentric perspective. First, we input unimodal learning i.e., the historical trajectory sequences into the temporal encoder module. Second, the vision DNN-based network module was used to process the trajectory predictions through the temporal attention mechanism. Third, the linear projection was used for the decoder module to generate single- and multi-trajectory predictions for collision avoidance of vehicles and pedestrians. Finally, the proposed model outperformed previous state-of-the-art models in the experiments on publicly available Pedestrian Intention Estimation (PIE) and Joint Attention in Autonomous Driving (JAAD) datasets.
Distance Learning and Online Peer Tutoring for Better Digital Education Environments: a post-graduate experience

This paper presents the results of qualitative research conducted on two post-graduate courses Museum Education and Advanced Studies in Museum Education (jointly University of Modena and Reggio Emilia and University of RomaTre) in the academic year 2021/2022. Students were involved in focus groups in order to enquire how digital transformation in education brought about by the COVID-19 pandemic can foster distance learning for better futures and help shape digital education environments that are accessible and equitable. Results show that the combination of technologies for distance learning and online peer tutoring can play a pivotal role in helping students develop their potential and build a sense of belonging, thus promoting their well-being.
Optimization of Materials Delivery in Mobile Cabin Hospitals Based on Agent Simulation

Facing the spread of COVID-19, China has built numerous mobile cabin hospitals. Due to the tight construction period, the construction process has faced problems such as site congestion, low efficiency in material delivery, presence of potential safety hazards, etc. In this article, an agent-based simulation model is constructed by abstracting the manpower and vehicle resources in the construction process of mobile cabin hospital into agents. The optimization objective function is constructed by taking the total transportation time and resource vacancy rate as indicators, and is solved by genetic algorithm, which effectively optimizes the resource allocation of the project and improves the efficiency of material transportation. The practical application in Shanghai Jinshan Mobile Cabin Hospital and other projects show that the model has the characteristics of simple modeling and fast convergence speed. Therefore, the proposed method can provide intuitive decision-making means for materials transportation in the construction process of the mobile cabin hospital, and has reference significance for similar projects.
Adult Attachment and Fear of Missing Out: Does the Mindful Attitude Matter?

Fear of missing out (FoMO) involves the desire or urge to stay continuously connected to and kept up-to-date with a social reference group. The present study explored the relationships between adult attachment and FoMO and the potential mediating effect of a mindful attitude. The present study was carried out on 192 participants (meanage = 23.24 years; SDage = 4.33 years), of whom 151 (78.6%) were female and the remaining 41 (21.4%) were male. The participants completed the Adult Attachment Scale—Revised (AAS-R), which evaluates Close, Depend, and Anxiety attachment, the Cognitive and Affective Mindfulness Scale—Revised (CAMS-R) and the Fear of Missing Out Scale (FoMOs). The results indicated that the three forms of adult attachment (Close, Depend, and Anxiety) were associated with FoMO through the indirect effect of mindful attitudes. This study yielded relevant theoretical and practical implications regarding the critical role of a mindful attitude as a protective factor against FoMO. Limitations and future research directions were also discussed.
Experimental demonstration of coupled differential oscillator networks for versatile applications

Oscillatory neural networks (ONNs) exhibit a high potential for energy-efficient computing. In ONNs, neurons are implemented with oscillators and synapses with resistive and/or capacitive coupling between pairs of oscillators. Computing is carried out on the basis of the rich, complex, non-linear synchronization dynamics of a system of coupled oscillators. The exploited synchronization phenomena in ONNs are an example of fully parallel collective computing. A fast system’s convergence to stable states, which correspond to the desired processed information, enables an energy-efficient solution if small area and low-power oscillators are used, specifically when they are built on the basis of the hysteresis exhibited by phase-transition materials such as VO2. In recent years, there have been numerous studies on ONNs using VO2. Most of them report simulation results. Although in some cases experimental results are also shown, they do not implement the design techniques that other works on electrical simulations report that allow to improve the behavior of the ONNs. Experimental validation of these approaches is necessary. Therefore, in this study, we describe an ONN realized in a commercial CMOS technology in which the oscillators are built using a circuit that we have developed to emulate the VO2 device. The purpose is to be able to study in-depth the synchronization dynamics of relaxation oscillators similar to those that can be performed with VO2 devices. The fabricated circuit is very flexible. It allows programming the synapses to implement different ONNs, calibrating the frequency of the oscillators, or controlling their initialization. It uses differential oscillators and resistive synapses, equivalent to the use of memristors. In this article, the designed and fabricated circuits are described in detail, and experimental results are shown. Specifically, its satisfactory operation as an associative memory is demonstrated. The experiments carried out allow us to conclude that the ONN must be operated according to the type of computational task to be solved, and guidelines are extracted in this regard.
Application of Computational Methods for Comparative Music Analysis

Music performance analysis can thrive from computational methods of music information retrieval. Besides extracting and analyzing symbolic music data, performance analysis also focuses on retrieving performance parameters from digital audio recordings. On the other hand, the aim of the comparative performance analysis is often qualitative and stands on our perception and musical principles. In this paper, we utilize feature extraction strategies and comparative analysis, leveraging computational methods while focusing on the goals of musicology. We aim to provide insight into music performance data for subsequent case studies. As the main contribution of this paper, we present a specific combination of extraction methods for performance music analysis on the application level. Furthermore, we demonstrate an early version of open-source software that deploys the proposed strategy in a user-friendly web-based environment.
Agricultural Performance in Uttar Pradesh: A Historical Account

In this paper, we attempt to identify and analyze the issues and problems associated with the agriculture sector of India’s most populous state, Uttar Pradesh (U.P.). We begin with the pre-Green Revolution period, from the early 1960s and examine the growth of agricultural outputs springing from the Green Revolution in U.P. and those in relation to Punjab and Haryana, India’s most successful states as far as the agriculture sector is concerned. Additionally, we study of the growth of agricultural inputs in U.P overtime to consider intrastate variations in patterns of agricultural development between western and eastern U.P. In order for U.P. to be able to attain and sustain higher levels of growth in its agriculture and allied sectors, the following areas will require much higher public investments and the state government’s attention: increased focus on irrigation; increased expenditure in agricultural research and development; capacity expansion in U.P.’s agricultural universities; diversification of crops; revamping of the agricultural extension system to assist farmers in adopting new technologies; building up rural infrastructure, and promotion of agro-based industries. Nirupam Bajpai is a Senior Development Advisor and Director of the South Asia Program at the Center on Globalization and Sustainable Development (CGSD), Columbia University. Nicole Volavka is a Research Coordinator with the South Asia Program at CGSD, Columbia University.
Deep Dive on Checkers Endgame Data

For games such as checkers and chess, large endgame databases/tablebases have been constructed to capture the perfect win/loss/draw value for positions near the end of the game. Such databases/tablebases can be used to enhance game-playing performance. However, this approach quickly runs into computational and storage resource limitations. An enticing alternative is to learn from such data and apply the learned evaluation to even larger data sets through transfer learning. This paper reports on research that uses deep learning to a) correctly learn a high percentage of checkers endgame positions; b) learn patterns that can be used for transfer learning; c) demonstrates that learning from a small sample of a large data set is an efficient way to compute a neural net evaluation that achieves most of the benefits; and d) shows that dynamically choosing between neural network prediction and using it in a one-ply search yields about 96% prediction accuracy.
Safety, tolerability, and immunogenicity of a SARS-CoV-2 recombinant spike protein vaccine: a randomised, double-blind, placebo-controlled, phase 1-2 clinical trial (ABDALA Study).

Aim: To evaluate the safety and immunogenicity of a SARS-CoV-2 recombinant spike protein vaccine (Abdala), administered intramuscularly in different strengths and vaccination schedules. Method: A phase 1-2, randomized, double-blind, placebo-controlled trial was done. Subjects were randomly distributed in 3 groups: placebo, 25 and 50 g RBD. The product was applied intramuscularly, 0.5 mL in the deltoid region. During the first phase, two immunization schedules were studied: short (0-14-28 days) and long (0-28-56 days). In phase 2, only the short scheme was evaluated. The main endpoints were: safety and proportion of subjects with seroconversion of anti-RBD IgG antibodies to SARS-CoV-2. Blood samples were collected in several points according to the corresponding vaccination schedule to determine the level of RBD-specific IgG antibodies (seroconversion rates and geometric mean of the titers), the percentage of inhibition of RBD-ACE-2 binding and levels of neutralizing antibodies. Results: The product was well tolerated. Severe adverse events were not reported. Adverse reactions were minimal, mostly mild and local (from the injection site), resolved in the first 24-48 hours without medication. In phase 1, at day 56 (28 days after the third dose of the short vaccination schedule, 0-14-28 days) seroconversion of anti-RBD IgG was seen in 95.2 % of the participants (20/21) for the 50 g group and 81 % of the participants (17/21) for the 25 g group, and none in the placebo group (0/22); whereas neutralizing antibodies to SARS-CoV-2 were seen in 80 % of the participants (8/10) for the 50 g group and 94.7% of the participants (18/19) for the 25 g group. For the long schedule, at day 70 (14 days after the third dose) seroconversion of anti-RBD IgG was seen in 100% of the participants (21/21) for the 50 g group and 94.7% of the participants (18/19) for the 25 g group, and none in the placebo group (0/22); whereas neutralizing antibodies to SARS-CoV-2 were seen in 95 % of the participants (19/20) for the 50 g group and 93.8% of the participants (15/16) for the 25 g group In phase 2, at day 56 seroconversion of anti-RBD IgG was seen in 89.2% of the participants (214/240) for the 50 g group, 77.7% of the participants (185/238) for the 25 g group, and 4.6% in the placebo group (11/239); whereas neutralizing antibodies to SARS-CoV-2 were seen in 97.3% of the participants (146/150) for the 50 g group and 95.1% of the participants (58/61) for the 25 g group. Conclusion: Abdala vaccine against SARS-CoV-2 was safe, well tolerated and induced humoral immune responses against SARS-CoV-2 among adults from 19 to 80 years of age.
The Potential of the zingiberaceae family as spice plants medicinal effectiveness

This research was motivated by the absence of previous research that examined the potential of the Zingiberaceae family as a spice plant's medicinal effectiveness in the Dayak Linoh tribe in Belonsat Village. This can result in a loss of knowledge regarding that, so it is necessary to have a device that is used to document in writing the potential of the Zingiberaceae family as spice plants' medicinal effectiveness in the Dayak Linoh tribe at Belonsat Village. This research aims to 1) identify types of medicinal spice plants from the Zingiberaceae family in the Dayak Linoh tribe, 2) identify parts of medicinal spice plants from the Zingiberaceae family in the Linoh Dayak tribe, 3) identify how to use medicinal spice plants from the Zingiberaceae family in the Dayak Linoh tribe. This research uses a qualitative approach, the research methods used include surveys and interviews. The results of the research found 8 types of medicinal spice plants from the Zingiberacea family. The part of the plant that is most widely used is the rhizome, then there is 1 type of plant that uses flowers and young stems. Then, there are 11 methods used to use spice plants as medicinal ingredients, then there are 12 types of diseases that are believed to be able to be treated using types of spice plants from the Zingiberaceae family.
Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping

Abstract The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.
Tropical transition: Tropical cyclone formation from extratropical disturbances [presentation]

Tropical transition (TT) refers to the dynamic and thermodynamic transformation of disturbances of subtropical or extratropical origin into tropical cyclones. This is often synonymous with a transition from a cold-core disturbance to a warm-core disturbance. In this paper, we discuss two pathways of TT, one involving initially strong extratropical precursors (often baroclinic cyclones) and the other involving perturbations that mainly organize convection but do not grow from baroclinic energy conversion. The two classes of precursors are referred to as strong and weak extratropical (SE and WE), respectively. The synoptic-scale composite structure for four SE cases (Florence, Michael, Erin and Karen, Fig. 1a) shows a pronounced, localized trough in the upper troposphere to the west of the surface low just prior to TT. The lowertropospheric temperature pattern exhibits a locally enhanced equatorward temperature gradient in the vicinity of the surface low, with drastically reduced equatorward gradients further north (Fig. 1b). This mini-composite strongly resembles the 24-case composite shown by Bracken and Bosart (2000), although it is larger in amplitude. It also strongly resembles the anticyclonic wave breaking scenario (LC1) presented in Thorncroft et al. (1993). Figure 1. (a) 250 hPa height anomaly and (b) 850 hPa temperature anomaly for 4-case composite p to tropical cyclogeneis. The position of the composite surface low center is indicated with an ‘L’. Composite maps were constructed using software from the Climate Diagnostics Center of the rior
Comparison of the blood immune repertoire with clinical features in chronic lymphocytic leukemia patients treated with chemoimmunotherapy or ibrutinib

Chronic lymphocytic leukemia (CLL) is characterized by the accumulation of CD19+ CD5+ clonal B lymphocytes in the blood, bone marrow, and peripheral lymphoid organs. Treatment options for patients range from historical chemoimmunotherapy (CIT) to small molecule inhibitors targeting pro-survival pathways in leukemic B cells, such as the Bruton’s tyrosine kinase inhibitor ibrutinib (IBR). Using biobanked blood samples obtained pre-therapy and at standard response evaluation timepoints, we performed an in-depth evaluation of the blood innate and adaptive immune compartments between pentostatin-based CIT and IBR and looked for correlations with clinical sequelae. CD4+ conventional T cells and CD8+ cytotoxic T cells responded similarly to CIT and IBR, although exhaustion status differed. Both treatments dramatically increased the prevalence and functional status of monocyte, dendritic cell, and natural killer cell subsets. As expected, both regimens reduced clonal B cell levels however, we observed no substantial recovery of normal B cells. Although improvements in most immune subsets were observed with CIT and IBR at response evaluation, both patient groups remained susceptible to infections and secondary malignancies during the study.