Miracle-dz/UniFAR-FTUs huggingface dataset:
Rhizosphere-Associated Microbiome Profile of Agricultural Reclaimed Lands in Egypt

Plants especially in their natural habitat are considered part of a rich ecosystem that includes many various microorganisms in the soil. The current study aimed to identify the bacterial profile of agriculture-related soil samples using the metabarcoding technique to compare and explore relevant rhizosphere bacteria associated with plant cultivations in newly reclaimed land and habitual cultivated ones. The total environmental DNA was extracted from rhizosphere and noncultivated samples derived from three land types in Egypt. The bacterial 16S rDNA was amplified and sequenced by NGS technology to profile each sample. The microbial profile was characterized by statistical and literature-based methods. Among all samples, the most identified phyla were Actinobacteriota (28%), followed by Proteobacteriota (26%), Firmicutes (14%), Acidobacteriota and Chloroflexi (7%), Gemmatimonadota (5%), Bacteriodota and Crenarchaeota (3%), and Myxococcota (2%), in addition to 37 other phyla with <1% counts. A total of 74 OTUs were unique to the plant rhizosphere area and classified as Bacteriodota (5.1%:0.3%), Firmicutes (2.4%:0.1%), and Proteobacteria (3.5%:2%) phyla in agricultural and reclaimed lands, respectively. Moreover, the rhizosphere profile included a large portion of uncultured and unidentified bacterial species, which opened a window to further analysis. Our analysis provides a key knowledge about the rhizosphere microbiome and highlights its possible use to create microbial-based biofertilizers targeting plant performance in contrast to traditional fertilizers and their side effect on the agriculture sector.
Genomic Basis for Individual Differences in Susceptibility to the Neurotoxic Effects of Diesel Exhaust

Air pollution is a known environmental health hazard. A major source of air pollution includes diesel exhaust (DE). Initially, research on DE focused on respiratory morbidities; however, more recently, exposures to DE have been associated with neurological developmental disorders and neurodegeneration. In this study, we investigated the effects of sub-chronic inhalation exposure to DE on neuroinflammatory markers in two inbred mouse strains and both sexes, including whole transcriptome examination of the medial prefrontal cortex. We exposed aged male and female C57BL/6J (B6) and DBA/2J (D2) mice to DE, which was cooled and diluted with HEPA-filtered compressed air for 2 h per day, 5 days a week, for 4 weeks. Control animals were exposed to HEPA-filtered air on the same schedule as DE-exposed animals. The prefrontal cortex was harvested and analyzed for proinflammatory cytokine gene expression (Il1β, Il6, Tnfα) and transcriptome-wide response by RNA-seq. We observed differential cytokine gene expression between strains and sexes in the DE-exposed vs. control-exposed groups for Il1β, Tnfα, and Il6. For RNA-seq, we identified 150 differentially expressed genes between air and DE treatment related to natural killer cell-mediated cytotoxicity per Kyoto Encyclopedia of Genes and Genomes pathways. Overall, our data show differential strain-related effects of DE on neuroinflammation and neurotoxicity and demonstrate that B6 are more susceptible than D2 to gene expression changes due to DE exposures than D2. These results are important because B6 mice are often used as the default mouse model for DE studies and strain-related effects of DE neurotoxicity warrant expanded studies.
REVIO: Range- and Event-Based Visual-Inertial Odometry for Bio-Inspired Sensors

Visual-inertial odometry is critical for Unmanned Aerial Vehicles (UAVs) and robotics. However, there are problems of motion drift and motion blur in sharp brightness changes and fast-motion scenes. It may cause the degradation of image quality, which leads to poor location. Event cameras are bio-inspired vision sensors that offer significant advantages in high-dynamic scenes. Leveraging this property, this paper presents a new range and event-based visual-inertial odometry (REVIO). Firstly, we propose an event-based visual-inertial odometry (EVIO) using sliding window nonlinear optimization. Secondly, REVIO is developed on the basis of EVIO, which fuses events and distances to obtain clear event images and improves the accuracy of position estimation by constructing additional range constraints. Finally, the EVIO and REVIO are tested in three experiments—dataset, handheld and flight—to evaluate the localization performance. The error of REVIO can be reduced by nearly 29% compared with EVIO in the handheld experiment and almost 28% compared with VINS-Mono in the flight experiment, which demonstrates the higher accuracy of REVIO in some fast-motion and high-dynamic scenes.
Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN

Many-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and channel for each node is one of the most critical problems for many-to-many data aggregation in multi-channel WSNs, and this problem can be solved with the new distributed scheduling method without communication conflict outlined in this paper. The many-to-many data aggregation scheduling process is abstracted as a decentralized partially observable Markov decision model in a multi-agent system. In the case of embedding cooperative multi-agent learning technology, sensor nodes with group observability work in a distributed manner. These nodes cooperated and exploit local feedback information to automatically learn the optimal scheduling strategy, then select the best time slot and channel for wireless communication. Simulation results show that the new scheduling method has advantages in performance when comparing with the existing methods.
A multi-period reactive power regulation method based on proximal policy optimization

The fluctuations of renewable energies and loads pose a great challenge to reactive power regulation. Considering the time-varying characteristics of new energies and loads, the multi-time reactive power regulation problem is constructed as a reinforcement learning problem. A constraint-objective partitioning and objective preconditioning approach is proposed to design the reward function, and a proximal policy optimization algorithm is used to solve the reinforcement learning problem and obtain the reactive power regulation policy. A case study is carried out with the improved IEEE39 system, and the results show that the proposed reward function can improve the convergence speed of the agent, and the reactive power regulation strategy solved based on reinforcement learning outperforms the traditional deterministic optimization algorithm in terms of decision effect and decision time.
A Novel Lattice Structure for Enhanced Crush Energy Absorption

Lightweight and stiff lattice structures are good energy absorbers. This study evaluates the energy absorption capacity of a few common lattice structures printed out of PLA using fused deposition modeling and proposes an improved lattice structure. Simple cubic (SC), honeycomb (HC), body-centered cubic (BCC), and novel PeckGy80 (PG80) lattice structures were subjected to compressive tests. The quasi-static load-displacement behavior of lattice specimens was characterized in terms of specific energy absorption and crush load efficiency. The damage mechanisms were then related to energy absorption. Cracks and brittle fractures occurred in all lattice structures during the crush test. Different lattice structures induced different damage mechanisms, significantly affecting their energy absorption. SC lattice structure showed structural separation at a small displacement, rendering it an ineffective energy absorber. BCC and HC lattice structures demonstrated almost identical shear band failure modes. The PG80 lattice structure, although made of brittle PLA, displayed progressive failure from the bottom layer to the upper layers, exhibiting both a high peak load and stable post-yield behaviour. This damage mode enabled the PG80 lattice to be far superior in terms of specific energy absorption to HC, SC, and BCC lattice structures.
The Antiproliferative Activity of High-Dose Somatostatin Analogs in Gastro-Entero-Pancreatic Neuroendocrine Tumors: A Systematic Review and Meta-Analysis

Background: The antiproliferative activity of a high dose of somatostatin analogs (HD-SSA) in treating gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) remains under debate. Methods: A systematic review and proportion meta-analysis were made. The primary endpoint was the efficacy measured as incidence density ratio (IDR) at one year. The secondary endpoints were the disease control rate (DCR) and severe adverse events (SAEs). The heterogeneity (I2), when high (>50%), was interpreted by performing a univariate metaregression analysis, analyzing as covariates: type and design of the study, location (Europe or USA), sample size, grading according to 2017 WHO, the metastatic disease rate, previous therapy including surgery, and quality of the study. Results: A total of 11 studies with 783 patients were included. The IDR was 62 new progressions of 100 patients treated with HD-SSA every one year. The heterogeneity was high. The study’s year, type and design, primary tumor, grading, previous treatments, and quality of the studies did not influence the IDR. The IDR was significantly higher in USA centers and studies with more than 50 patients. The IDR was lower when a high rate of metastatic patients was present in the studies. The DCR was 45%. The heterogeneity was high. The DCR was lower in USA studies and in prospective trials. Conclusion: Given the limited efficacy of HD-SSA in preventing the disease progression in unresectable GEP-NENs after failure of standard dose SSA, the use of this therapeutic approach is advisable in selected cases when other antiproliferative treatments are not feasible.
Maximal-acceleration phase space relativity from Clifford algebras

We present a new physical model that links the maximum speed of light with the minimal Planck scale into a maximal-acceleration Relativity principle in the spacetime tangent bundle and in phase spaces (cotangent bundle). Crucial in order to establish this link is the use of Clifford algebras in phase spaces. The maximal proper-acceleration bound is a = c^2/ \Lambda in full agreement with the old predictions of Caianiello, the Finslerian geometry point of view of Brandt and more recent results in the literature. We present the reasons why an Extended Scale Relativity based on Clifford spaces is physically more appealing than those based on kappa-deformed Poincare algebras and the inhomogeneous quantum groups operating in quantum Minkowski spacetimes. The main reason being that the Planck scale should not be taken as a deformation parameter to construct quantum algebras but should exist already as the minimum scale in Clifford spaces.
Analyzing ML-Based IDS over Real-Traffic

The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users. Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets.
Legal Regulations of Restrictions of Air Pollution Made by Mobile Positive Pressure Fans—The Case Study for Europe: A Review

This paper presents a review of mobile positive pressure ventilation fans for use in rescue operations available on the European market in correlation with the approval regulations of the drive units. The authors determined the approval limits for harmful compound emissions by their power units, as applicable within the European Union. The purpose of this paper is to estimate the emissions of harmful compounds from the exhausts of rescue fan power units. It was demonstrated that electric fans are not in direct competition with internal combustion fans because they are provided in different power ranges, and 74% of the electric fan market falls between 1.1 kW and 2.2 kW, whereas fans powered by combustion engines are offered in a much higher power range from 0.7 kW to 92 kW, and the most popular power range offered by manufacturers is from 3.4 kW to 6.3 kW (57% of the market). On the basis of the limits derived from approval tests it can be demonstrated that 90% of engines on the European market which are used in fans (spark ignition (SI) engine up to 19 kW) are allowed carbon monoxide (CO) emissions of 610 g/kWh and hydrocarbon and nitrogen oxide emissions (HC + NOx) of 8 g/kWh.
Decision self-information based on parameterized fuzzy β neighborhood and its application in three-way multi-attribute group decision-making

As a special kind of entropy, decision self-information effectively considers the uncertainty information of both the lower and upper approximations. However, it is limited to rough binary relations, which limits its application to complex problems. In addition, parameterized fuzzy β covering, as an extension of the covering-based rough set model, can effectively characterize the similarity between samples. We combine decision self-information with a parameterized fuzzy β neighborhood to propose decision self-information in fuzzy environments, and we study its important properties. On this basis, a three-way multi-attribute group decision-making algorithm is established, and a practical problem is solved. The effectiveness of the proposed method is verified by experimental analysis.
Development and implementation of a sustainable COVID-19 training package for healthcare workers: Experience from a teaching hospital of North India

Background: With the impending threat of future COVID-19 waves, it is imperative that teaching hospitals develop, implement, and evaluate a systematic training program to render HCW elastic in delivering COVID-19 related services. We present our experience in developing, implementing, and evaluating a sustainable and scalable COVID-19 patient management training package for healthcare workers. Materials and Methods: A mixed-methods study design was used. Rapid assessment to understand the need of the trainees and identify the available resources was done followed by planning of the training module and its implementation. The program was evaluated for effectiveness and sustainability. Data analysis was done using descriptive statistics and qualitative data generated from open-ended questions in the feedback forms and the discussions were analyzed using rapid content analysis. Results: A total of 66.8% of the doctors and 18.9% of the nurses were trained by online synchronous mode while 55.0% of the nursing officers and 47.1% of the nursing orderlies and paramedical staff were trained in onsite skill development sessions. Need assessment identified that healthcare workers were ill-prepared to use medical devices such as Bipap machines, ventilators, and oxygen delivery devices. The participants mentioned that the multidisciplinary approach and video-based demonstrations facilitated their online learning while the incremental learning approach, easy-to-understand terminology and hands-on experience facilitated their onsite skill development sessions. Conclusion: The COVID-19 training package developed was multidisciplinary, effective, sustainable, and scalable in a resource-limited setting. We suggest that this model can be adapted by healthcare organizations to develop and implement such training packages for their healthcare workers.
HUBBLE SPACE TELESCOPE PROPER MOTION (HSTPROMO) CATALOGS OF GALACTIC GLOBULAR CLUSTERS. III. DYNAMICAL DISTANCES AND MASS-TO-LIGHT RATIOs

We present dynamical distance estimates for 15 Galactic globular clusters (GCs) and use these to check the consistency of dynamical and photometric distance estimates. For most of the clusters, this is the first dynamical distance estimate ever determined. We extract proper-motion (PM) dispersion profiles using cleaned samples of bright stars from the Hubble Space Telescope PM catalogs recently presented in Bellini et al. and compile a set of line of sight (LOS) velocity-dispersion profiles from a variety of literature sources. Distances are then estimated by fitting spherical, non-rotating, isotropic, constant mass-to-light ratio (M/L) dynamical models to the PM and LOS dispersion profiles together. We compare our dynamical distance estimates with literature photometric estimates from the Harris GC catalog and find that the mean fractional difference between the two types is consistent with zero at just −1.9 ± 1.7%. This indicates that there are no significant biases in either estimation method and provides an important validation of the stellar-evolution theory that underlies photometric distance estimates. The analysis also estimates dynamical M/Ls for our clusters; on average, the dynamically inferred M/Ls agree with existing stellar-population-based M/Ls that assume a Chabrier initial mass function (IMF) to within −8.8 ± 6.4%, implying that such an IMF is consistent with our data. Our results are also consistent with a Kroupa IMF, but strongly rule out a Salpeter IMF. We detect no correlation between our M/L offsets from literature values and our distance offsets from literature values, strongly indicating that our methods are reliable and our results are robust.
Synthesis of Nanoparticles Loading Indenopyrazole Derivatives and Evaluation of Biological Features

Objective: In this study, it was aimed to prepare nanoparticle formulations using chitosan, a cationic natural polymer, and tripoly phosphate, and to perform mechanical characterization and in vitro cell culture studies. In addition, the cytotoxic effects of nanoparticles containing indenopyrazol derivatives against human glioma cells (C6) and human cervical cancer cells (HeLa) were investigated. Methods: Within the scope of the study, nanoparticles containing indenopyrazole derivative were prepared and characterization of particle size, zeta potential and morphological properties were performed. XTT cytotoxicity test was applied to evaluate the antiproliferative activities of nanoparticles containing these components. Results: Particle size, zeta potential and morphological properties of nanoparticles were observed to be suitable for application. In vitro cell culture studies showed that nanoparticles containing indenopyrazol derivatives showed better cytotoxic effects in both cell lines. Conclusion: The results showed that the mechanical properties of nanoparticles containing indenopyrazol derivatives are suitable and can be applied in anticancer activity studies.
Pyramiding of gn1a, gs3, and ipa1 Exhibits Complementary and Additive Effects on Rice Yield

Pyramiding of quantitative trait loci (QTLs) is a powerful approach in breeding super-high-yield varieties. However, the performance of QTLs in improving rice yield varies with specific genetic backgrounds. In a previous study, we employed the CRISPR/Cas9 system to target three yield-related genes, gn1a, gs3, and ipa1 in japonica ‘Zhonghua 11’, mutants of which featured large panicle, big grain, few sterile tillers, and thicker culm, respectively. In this paper, four pyramided lines, including gn1a-gs3, gn1a-ipa1, gs3-ipa1, and gn1a-gs3-ipa1, were further generated by conventional cross-breeding to be tested. Agronomic traits analysis showed that: (1) the stacking lines carried large panicles with an increased spikelet number in the main panicle or panicle; (2) the grain weight of the stacking lines, especially gs3-ipa1 and gn1a-gs3-ipa1, were heavier than those in single mutants; (3) both gn1a-gs3 and gs3-ipa1 produced more grain yield per plant than single mutant lines; (4) pyramided lines were higher than single mutants and transcriptome analysis found improved expression levels of genes related to lipid, amino acid, and carbohydrate transport and metabolism in lines pyramiding three mutant alleles, possibly as a result of complementary and additive effects. Accordingly, the alteration of gene-expression patterns relating to hormone signaling, plant growth, and seed size control was characterized in pyramided lines. The present study not only investigates the effects of pyramiding genes, but also may provide an efficient strategy for breeding super-high-yield rice by reducing the time cost of developing pyramided lines.
Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite‐dimensional parameter spaces.