Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques

Spatial statistics is a powerful tool for analyzing data that are illustrated as points or positions in a regular or non-regular state space. Techniques that are proposed to investigate the spatial association between neighboring positions are based on the point process analysis. One of the main goals is to simulate real data positions (such as antenna base stations) using the type of point process that most closely matches the data. Spatial patterns could be detailed describing the observed positions and appropriate models were proposed to simulate these patterns. A common model to simulate spatial patterns is the Poisson point process. In this work analyses of the Poisson point process—as well as modified types such as inhibition point process and determinantal Poisson point process—are presented with simulated data close to the true data (i.e., antenna base station positions). Investigation of the spatial variation of the data led us to the spatial association between positions by applying Ripley’s K-functions and L-Function.
Isolation, Characterization and Pharmacological Investigations of a New Phenolic Compound along with Four Others Firstly Reported Phytochemicals from Glycosmis cyanocarpa (Blume) Spreng

Plants are serving the mankind with important bioactive phytochemicals from the very ancient ages to develop novel therapeutics against different disease states. Glycosmis cyanocarpa (Blume) Spreng is a plant from the Rutaceae family and a very less explored species from the Glycosmis genus. Thus, this present study was intended to present the chemical and biological investigation of Glycosmis cyanocarpa (Blume) Spreng. The chemical investigation resulted in the isolation of one new phenolic compound to the best of our knowledge which is (4-(3-hydroxy-2-methylpropyl)-2-methoxyphenol) (1) along with four known compounds that are isolated for the first time from this species- 3-methyl-1H-indole (2), Tri-transpoly-cis prenol-12 (3), Stigmasterol (4) and β-sitosterol (5). Their chemical structures were elucidated based on extensive spectroscopic methods, including 1D and 2D NMR, and comparison with the available literature data. Isolated phytochemicals were further investigated to unveil their antioxidant properties with IC50 values (ranged from 9.97–75.48 µg/mL), cytotoxicity with LC50 values (ranged from 1.02–1.92 µg/mL), and antibacterial properties against some selected Gram (+) ve and Gram (−) ve bacteria. Among the compounds, 3-methyl-1H-indole (2) was found to be the most active against Staphylococcus aureus. Moreover, the phenolic compound (1) and the alkaloid (2) revealed the highest antioxidant (9.97 µg/mL) and cytotoxic activities (1.02 µg/mL), respectively. Thus, the isolation of these bioactive phytochemicals from the plant revealed a new perception in the study arena of drug discovery and the findings may ease the development and discovery of novel therapeutics. Further investigations are still recommended to understand their exact molecular mechanism and toxicological impact.
Phase-Averaging Methods for the Natural Flowfield of a Fluidic Oscillator

The presented study examines various methods for phase averaging the naturally oscillating flowfield of a scaled-up fluidic oscillator. No external trigger is employed to control the oscillation of the flow. Mathematical and signal conditioning approaches for phase averaging the data are categorized and described. The results of these methods are evaluated for their accuracy in capturing the natural flowfield. The respective criteria are based on the minimum fluctuation in oscillation period length, the conservation of velocity amplitudes, and the number of snapshots per phase-averaging window. Although all methods produce reasonable qualitative results, only two methods are identified to provide the desired quantitative accuracy and suitability for the investigated flowfield. The first method is based on conditioning a time-resolved pressure signal from the feedback channels in the oscillator. An autocorrelation applied to the reference signal improves the period identification. The second method employs.
Methods for estimating a conditional distribution function

Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya--Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.
Investigating the cost-effective energy efficiency practices with mitigated rebound: the case of energy intensive industries

The present study aims at finding a practical policy for reducing energy consumption in through simultaneous energy subsidy reduction and energy efficiency improvement. To be a practical solution, the efficiency practices should be chosen from feasible energy efficiency potentials. To mitigate the economy-wide rebound effect, energy price should be increased in accordance with efficiency enhancement. The combination of a top-down dynamic general equilibrium model with a bottom-up efficiency improvement module is used to find the set of efficiency practices that should be realized in each period along with the required increase in energy prices. Choosing efficiency practices depends on their costs and on the available funds. The model is applied for the energy-intensive industries in Iran. The model results show that over less than ten years, the energy efficiency of electrical and natural gas equipment in energy intensive industries of Iran can be increased by 12.7% and 18.1% respectively. The rebound effect starts with values above 80% and then falls below zero percent which indicates the success of the proposed policy in preventing the rebound effect. Results also demonstrate that the implementation of the policy realizes the 4% reduction in CO2 emissions by 2030 which is Iran's unconditional pledge.
Molecular Characterization of MHC Class I Alpha 1 and 2 Domains in Asian Seabass (Lates calcarifer)

The Asian seabass is of importance both as a farmed and wild animal. With the emergence of infectious diseases, there is a need to understand and characterize the immune system. In humans, the highly polymorphic MHC class I (MHC-I) molecules play an important role in antigen presentation for the adaptive immune system. In the present study, we characterized a single MHC-I gene in Asian seabass (Lates calcarifer) by amplifying and sequencing the MHC-I alpha 1 and alpha 2 domains, followed by multi-sequence alignment analyses. The results indicated that the Asian seabass MHC-I α1 and α2 domain sequences showed an overall similarity within Asian seabass and retained the majority of the conserved binding residues of human leukocyte antigen-A2 (HLA-A2). Phylogenetic tree analysis revealed that the sequences belonged to the U lineage. Mapping the conserved binding residue positions on human HLA-A2 and grass carp crystal structure showed a high degree of similarity. In conclusion, the availability of MHC-I α1 and α2 sequences enhances the quality of MHC class I genetic information in Asian seabass, providing new tools to analyze fish immune responses to pathogen infections, and will be applicable in the study of the phylogeny and the evolution of antigen-specific receptors.
High-Dose Mycobacterium tuberculosis H37rv Infection in IL-17A- and IL-17A/F-Deficient Mice

During experimental tuberculosis (TB), interleukin (IL)-17A appears to be involved in the formation of lung granulomas, possibly through the attraction of neutrophils to the sites of infection. However, the protective impact of cytokine appears to depend on the degree of its induction. Hence, robust production of IL-17A in mice infected with the hypervirulent isolate Mycobacterium tuberculosis (Mtb) HN878 mediates protection, while the cytokine is dispensable for protective immune responses against low-dose infection with the less virulent strain H37rv. Here, we show that after experimental infection with high doses of Mtb H37rv, IL-17A-deficient (−/−) mice exhibited high susceptibility to the infection, which was mediated by the strong accumulation of neutrophils in the infected lung tissue. Accordingly, we observed nearly unrestricted bacterial replication within the neutrophils, indicating that they may serve as a survival niche for Mtb. By use of IL-17A/IL-17F-double-deficient mice, we demonstrated that the susceptibility in the absence of IL-17A is mediated by a compensatory expression of IL-17F, which, however, appeared not to be dependent on neutrophils. Together, our results illustrate the compensatory potential of the Th17-secreted cytokines IL-17A and IL-17F in the context of experimental TB and once again emphasize the detrimental effect of excessive neutrophil infiltration in response to Mtb.
Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

Big earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.
Role of temperature on linearity and analog/RF performance merits of a negative capacitance FinFET

Temperature plays a decisive role in semiconductor device performance and reliability analysis. The effect is more severe in a negative capacitance (NC) transistor, as the temperature modulates the ferroelectric polarization, implicitly included by the Landau coefficients (α, β, γ) in Technology Computer Aided Design (TCAD) simulations. In this paper, through TCAD simulations, the role of varying ambient temperature is investigated in the linearity and analog/radio-frequency (RF) merits of NC-FinFET. The varying temperature modulates the carrier mobility, the semiconductor bandgap, and the Landau parameter (α). We analyzed the analog/RF and linearity metrics, such as total gate capacitance (C gg), transconductance (g m), unity gain cut-off frequency (f T), the transconductance-frequency product, gain-bandwidth product, higher-order transconductance (g m2 and g m3), voltage intercept points, third-order power intercept and intermodulation points, and 1 dB CP using well-calibrated TCAD models. Our analysis reveals that these parameters are strongly dependent on temperature and the NC span (defined by using S-curve) shrinks with the rise in temperature. Finally, a source follower and three-stage ring oscillator are designed to test the frequency compatibility of the AC simulation for varying temperatures.
The Influence of Phytosociological Cultivation and Fertilization on Polyphenolic Content of Menthae and Melissae folium and Evaluation of Antioxidant Properties through In Vitro and In Silico Methods

Since medicinal plants are widely used in treating various diseases, phytoconstituents enrichment strategies are of high interest for plant growers. First of all, we investigated the impact of phytosociological cultivation on polyphenolic content (total flavonoids—TFL, and total polyphenols—TPC) of peppermint (Mentha piperita L.) and lemon balm (Melissa officinalis L.) leaves, using spectrophotometric methods. Secondly, the influence of chemical (NPK) and organic (BIO) fertilization on polyphenolic content and plant material quality was also assessed. Dry extracts were obtained from harvested leaves using hydroethanolic extraction solvents for further qualitative and quantitative assessment of phytoconstituents by FT-ICR MS and UHPLC-MS. Furthermore, the antioxidant activity of leaf extracts was determined in vitro using DPPH, ABTS and FRAP methods. Molecular docking simulations were employed to further evaluate the antioxidant potential of obtained extracts, predicting the interactions of identified phytochemicals with sirtuins. The concentration of polyphenols was higher in the plant material harvested from the phytosociological culture. Moreover, the use of BIO fertilizer led to the biosynthesis of a higher content of polyphenols. Higher amounts of phytochemicals, such as caffeic acid, were determined in extracts obtained from phytosociological crops. The antioxidant activity was dependent on polyphenols concentration, more potent inhibition values being observed for the extracts obtained from the phytosociological batches. Molecular docking studies and MM/PBSA calculations revealed that the obtained extracts have the potential to directly activate sirtuins 1, 5 and 6 through several polyphenolic compounds, such as rosmarinic acid, thus complementing the free radical scavenging activity with the potential stimulation of endogenous antioxidant defense mechanisms. In conclusion, growing medicinal plants in phytosociological cultures treated with biofertilizers can have a positive impact on plant material quality, concentration in active constituents and biological activity.
A Systematic Implementation of Machine Learning Algorithms for Multifaceted Antimicrobial Screening of Lead Compounds

: This study employed machine learning algorithms to identify lead compounds that inhibit 11 the antibiotic targets, DNA gyrase and Dihydrofolate reductase in Escherichia coli , and identified 12 new, multifaceted antimicrobial compounds. This study used three separate datasets: 1) 326 Esche-13 richia coli DNA gyrase inhibitors and 132 non-inhibitors, 2) 346 Escherichia coli Dihydrofolate re-14 ductase inhibitors and 176 non-inhibitors, and 3) 18387 non-specific drug-like chemicals. All da-15 tasets were then processed using ECFP-4 fingerprints and split into train, test, and validation da-16 tasets according to a 70-15-15 train-test-validation split. We explored the potential of six different 17 classification algorithms, all optimized with Bayesian optimization. Our results indicate that the 18 Gradient Boosting Classifier (GBC) performed the best at identifying a compound's efficacy towards 19 DNA gyrase with an accuracy, precision, recall, F1-score, and AUC of 0.91, 0.92, 0.86, 0.88, and 0.933, 20 respectively. The Random Forest Classifier (RFC) performed optimally for identifying a com-21 pound’s effectiveness towards Dihydrofolate reductase with an accuracy, precision, recall, F1 -score, 22 and AUC of 0.86, 0.83, 0.85, 0.84, and 0.944, respectively. As a result, the GBC and RFC were used 23 to search for compounds that inhibited both DNA gyrase and Dihydrofolate reductase. Out of 18387 24 compounds, we identified 5 novel compounds that have a predicted probability greater than 95% 25 to inhibit both DNA gyrase and Dihydrofolate reductase, suggesting a high antimicrobial potential. 26 The models evaluated in this study, particularly the GBC and RFC models, hold tremendous prom-27 ise in computationally screening large libraries of compounds for antimicrobial potential.
Quantum Matching Theory (with new complexity theoretic, combinatorial and topological insights on the nature of the Quantum Entanglement)

Classical matching theory can be defined in terms of matrices with nonnegative entries. The notion of Positive operator, central in Quantum Theory, is a natural generalization of matrices with nonnegative entries. Based on this point of view, we introduce a definition of perfect Quantum (operator) matching . We show that the new notion inherits many "classical" properties, but not all of them . This new notion goes somewhere beyound matroids . For separable bipartite quantum states this new notion coinsides with the full rank property of the intersection of two corresponding geometric matroids . In the classical situation, permanents are naturally associated with perfects matchings. We introduce an analog of permanents for positive operators, called Quantum Permanent and show how this generalization of the permanent is related to the Quantum Entanglement. Besides many other things, Quantum Permanents provide new rational inequalities necessary for the separability of bipartite quantum states . Using Quantum Permanents, we give deterministic poly-time algorithm to solve Hidden Matroids Intersection Problem and indicate some "classical" complexity difficulties associated with the Quantum Entanglement. Finally, we prove that the weak membership problem for the convex set of separable bipartite density matrices is NP-HARD.
Bayesian Joint Channel-and-Data Estimation for Quantized OFDM Over Doubly Selective Channels

This paper investigates the design of orthogonal frequency division multiplexing (OFDM) receiver, which operates over time-frequency doubly selective (DS) channels and uses a low-resolution analog-to-digital converter (ADC) to quantize the received signal. The time-varying multipaths in DS channel and the nonlinear quantization distortion caused by low-resolution ADC, destroy the orthogonality among OFDM subcarriers, incurring severe inter-carrier interference (ICI). Therefore, joint channel-and-data estimation is preferred, since in this way one can effectively tackle the ICI and make the best of the quantized signal in each OFDM symbol. In this paper, we elaborately develop the joint estimation scheme by casting the problem into the Bayesian inference framework. The proposed scheme not only takes into account of the nonlinear quantization in a probabilistic manner, but also exploits the channel sparsity enabled by the basis expansion model (BEM) approximation to DS channels. Furthermore, to benchmark the proposed scheme, we derive the asymptotic theoretical bounds in the large-system regime. Simulation results demonstrate that the proposed scheme can achieve the performance close to the theoretical bounds, thereby verifying the effectiveness of the proposed scheme.
A Random Access Protocol for Crowded Massive MIMO Systems Based on a Bayesian Classifier

The strongest user collision resolution (SUCRe) protocol is an efficient grant-based random access (RA) solution to provide connectivity for large numbers of user equipments (UEs), leveraging massive MIMO propagation features, typically available in fifth-generation and beyond networks. In this letter, we propose to replace the retransmission rule of SUCRe protocol by a Bayesian classifier for identifying the strongest user, aiming to resolve the collisions in a decentralized way, at the UEs’ side. As an offline training stage, we first conduct a statistical learning procedure to obtain the density estimations of the UEs’ decision variables in the SUCRe protocol, both in the cases of the UE being the strongest contender or not. Then, following the maximum a posteriori decision criterion, we determine how the UE can decide if it is the strongest contender (retransmitting the chosen RA pilot) or not (staying idle and trying again later). The numerical results show that our proposed method achieves significant connectivity performance improvements compared with other protocols, without requiring any additional complexity or overhead.
Exposure to high thermal conditions for a long time induces apoptosis and decreases total RNA concentration in peripheral blood mononuclear cells among Indian Zebu–Jersey crossbreds

Background and Aim: Global warming has grave consequences on livestock production systems and profound negative effects on animal production. This study aimed to carry out an in vitro thermal stress stimulation (TSS) of bovine peripheral blood mononuclear cells (PBMCs) using different thermal assault conditions (TACs), including normal to extreme temperatures and varying durations of thermal exposure (DTE) to understand how PBMCs of Indian Zebu–Jersey crossbreds respond to various levels and durations of heat shock. Materials and Methods: Ten milliliters of blood were collected from 70 Indian Zebu–Jersey crossbreds under aseptic conditions and were sampled for isolating PBMCs. Peripheral blood mononuclear cells were divided into seven groups, each comprising 10 PBMC samples isolated from 10 different animals. Aliquots of 500 μL of PBMCs were stressed by exposure to different TACs (37, 40, and 45°C) for DTEs of 3 or 6 h. Subsequently, the cells were harvested. The control unstressed samples (500 μL aliquots of PBMCs) were exposed to no TAC (0°C) and zero DTE (0 h). Total RNA from all the treatment groups of PBMCs were isolated and quantitated. Results: We found a very strong association between TACs and RNA levels. In addition, PBMCs viability was negatively affected by heat shock. This led to an exponential reduction in PBMC count as TACs toughened. Only 3.59 × 105 ± 0.34 cells/mL were viable after exposure to 45°C for a 6 h DTE. This cell viability was lower than that measured in controls subjected to no stress and zero time DTE (2.56 × 107 ± 0.22 cells/mL). We also observed a reduction in the concentration of RNA isolated from thermally stressed PBMCs. Conclusion: In vitro TSS of PBMCs provided biological information on the response of cellular systems to heat shock after exposure to TACs. This will help to mitigate and manage the effects of thermal stress in bovine species. The association between the reduction in PBMC count after in vitro TSS and the expression of heat shock protein 70 gene will be investigated in the future to further understand how Indian Zebu–Jersey crossbreds respond to in vitro thermal conditions. This will be used to determine the in vivo response of Indian Jersey crossbreds to different environmental thermal conditions and will further enable the in vivo understanding of thermotolerance potentials of bovine species for better adaptation, survival, and production performance.