The physiological mechanism of membrane potential generation relies on the passage of mobile ions across the plasma membrane, which is selectively permeable to mobile ions. The existence of a membrane and the transport of ions across it are essential for the generation of membrane potential. However, a long-forgotten theory, Association-Induction hypothesis, asserts that the heterogeneous distribution of charges in space, due to ion adsorption, is the true cause of membrane potential generation. Using an artificial living cell model consisting of a viscous aqueous solution, the mechanism of membrane potential generation is restudied in this work. The results indicate that membrane potential can be generated even in the absence of a plasma membrane and that ion adsorption leading to a heterogeneous spatial charge distribution is an essential factor for membrane potential generation. Consequently, the authors invites people to reconsider the Association-Induction hypothesis as a genuine mechanism for generating membrane potential.

Accurate prediction of the membrane potential by membrane theory is possible on the basis that the plasma membrane is selectively permeable to ions and that permeability determines the characteristics of the membrane potential. However, an experimental and artificial cell system with an impermeable membrane serving as a model plasma membrane has a non-zero membrane potential, and this potential generated across the membrane is somehow consistent with the potential characteristics predicted by the membrane theory, despite the impermeability of the membrane to ions. A long-forgotten theory, called the association-induction hypothesis (AIH), has emerged as a more plausible mechanism for generating the membrane potential than the membrane theory to explain this unexpected behavior. The AIH asserts that ion-selective membrane permeability is not necessary for the generation of the membrane potential, which is contrary to the membrane theory. Although such an idea is not easy to accept, the experimental results clearly suggest the correctness of the AIH.

Epistemological naturalism dismisses the notion that epistemology is a basis for the empirical sciences. In particular, it rejects the demand for a general justification of induction. Making inductive generalisations is a basic habit among humans. There is no such thing as a logic of inductive inference. The role of induction in science is heuristic; it is our way of inventing new theoretical predicates and developing theories. We discover new laws by applying inductive thinking; but this is not any kind of inference which can be evaluated as more or less rational.

This short technical note proves some observed novel corollaries of the Collatz Conjecture. For this, a unique ‘Collatz Representation’ of Hailstone numbers has been employed. After that, an extension of this corollary is explained using the fact that only certain differences in the last digit of the ‘Collatz Representation’ gives a whole number difference between numbers. A more general result is obtained. The final proof is for a corollary that some numbers the same number of steps to reach 1 if the Collatz Conjecture is true and all variables are whole numbers.

Faraday's Law of induction is often stated as "a change in magnetic flux causes an EMF"; or, more cautiously, "a change in magnetic flux is associated with an EMF"; It is as well that the more cautious form exists, because the first "causes" form can be shown to be incompatible with the usual expression for EMF. This is not, however, to deny the causality as reasonably inferred from experimental observation ­ it is the equation for Faraday's Law of induction which does not represent the claimed cause-and-effect relationship. Here we investigate a selection of different approaches, trying to see how an explicitly causal mathematical equation, which attempts to encapsulate the "a change in magnetic flux causes ..." idea, might arise.

MicroRNA plays a critical role in regulating gene expression post-transcriptionally. Variations in mature microRNA sequences, known as isomiRs, arise from imprecise cleavage and nucleotide substitution or addition. These isomiRs can target different mRNAs or compete with their canonical counterparts, thereby expanding the scope of miRNA post-transcriptional regulation. Our study investigated the relationship between cis-acting single nucleotide polymorphisms (SNPs) in precursor miRNA regions and isomiR composition, represented by the ratio of a specific 5’-isomiR subtype to all isomiRs identified for a particular mature miRNA. Significant associations between 95 SNP-isomiR pairs were identified. Of note, rs6505162 was significantly associated with both 5’-extension of hsa-miR-423-3p and 5’-trimming of hsa-miR-423-5p. Comparison of breast cancer and normal samples revealed that expression of both isomiRs was significantly higher in tumors than in normal tissues. This study sheds light on the genetic regulation of isomiR maturation and advances our understanding of post-transcriptional regulation by microRNA.

Callus induction of F. moluccana (sengon) was still an obstacle to indirect organogenesis regeneration. The purpose of the study was to determine the callus induction formation from root fragments of F. moluccana plantlets. Primary explants (fragments of roots) were cultivated on MS induction basal media and three concentration combination of PGRs (BAP and NAA): 0.5 ml/l BAP; 0,1 ml/l NAA (single PGR); and combination of 0.5 ml/l BAP + 0.1 ml/l NAA (double PGR). When roots were used as explants, high formation rates of callus (more than 70%) were obtained. Highest formation rates of callus were in NAA added at all clones (12 clones), then BAP added (7 clones) and BAP + NAA added (5 clones). The results indicated that BAP and NAA concentrations used in the media were influence the producing callus and affect the amount of callus produced from roots of sengon. The addition of NAA also gives higher callus proliferation results than the addition of BAP or the addition of a combination of the two hormones. The results indicated that BAP and NAA concentrations used in the media were influence the producing callus and affect the amount of callus produced from roots of sengon.

Shea tree (Vitellaria paradoxa) is an important fruit tree crop because of its oil used for cooking and industrial manufacture of cosmetics. Despite its many benefits, quantitative trait loci linked to the economic traits have not yet been studied. In this study, we performed association mapping on a panel of 374 shea tree accessions using 7,530 single-nucleotide polymorphisms (SNPs) markers for oil yield and seed related traits. Twenty three markers that were significantly (–log10 (p) = 4.87) associated to kernel oil content, kernel length; width and weight were identified. The kernel dry matter oil content and kernel width had the most significant Marker Trait Association (MTA) on chromosomes 1 and 8 respectively. Sixteen candidate genes that condition early induction of flower buds and somatic embryos, seed growth and development, substrate binding, transport, lipid biosynthesis, metabolic processes during seed germination and disease resistance and abiotic stress adaptation were identified. The presence of these genes suggest their role in promoting shea bioactive functions that condition high oil synthesis. This study provides insights into the important marker-linked seed traits with genes controlling them, useful for molecular breeding for improving oil yield in the species.

Biotic and abiotic factors influence the formation of fungal-algal pairings in lichen symbiosis. It is poorly known which factors determine the specific associations, especially when distantly related fungi are considered. Here, we investigated the effect of different drivers on the association patterns of taxonomically diverse lichenized fungi and their trebouxioid symbiotic partners. In 200 samples collected across three biomes, we found 41 species of lichenized fungi, associating with 16 species of trebouxioid green algae, of which 62% were previously unreported. Species identity of the fungal and algal partner had the greatest effect on the outcome of the symbiosis compared with abiotic factors such as climatic variables and geographic distance. Partner specificity was found to be higher in tropical regions than in temperate and arctic regions. Co-phylogenetic analyses indicated congruent phylogenies of trebouxioid algae and associated fungi, rejecting their random associations. Evolutionary mechanisms contributing most to the observed phylogenetic patterns were ‘loss’ and ‘failure to diverge’ of the algal partners. This study broadens our knowledge of fungi-algae symbiotic patterns in view of Trebouxia-associated fungi.

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using Electromagnetic Induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated CMD2 soil apparent electrical conductivity (ECa) measurements were taken and inverted to generate electromagnetic conductivity imaging (EMCI), representing soil electrical conductivity (σ) distribution through time-lapse inversion. This process involved converting EMCI data into salinity cross sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated the accurate prediction ability of soil salinity with R2 value of 0.81. The soil salinity cross sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmer’s income.

JIS SUJ2 is most widely used in bearing steels. The advantages are good hardenability, excellent fatigue, wear resistance and comprehensive mechanical properties. For wear resistance and fatigue resistance of the steel are getting more and more attention, the residual stress state and its distribution on the surface of the heat affect zone are critical factors affecting the fatigue life and wear resistance of the parts. In this study, using the SUJ2 as the material to talk about the surface residual stress and retained austenite distribution. Doing the quenching and tempering treatment to obtain different microstructures, using the induction method to re-quench the case region. After the heat treatment, measuring the residual stress and retained austenite volume on the surface by X-ray diffraction, observing the microstructure and comparing the hardness. The results show that the microstructure after heat treatment contains unsolidified carbides, tempered martensite and retained austenite. The residual stress on the surface is the tensile state. In the re-hardened area after induction quenching, the residual stress is all compressive, and the values are more than -800 MPa. In conclusion, in the same output power condition can be observed that the microstructure of the specimens before induction hardening has a significant impact on the effective case depth. The surface residual stress has changed from a tensile to a compressive state. In the re-hardened area, the maximum of the residual compressive stress increased with the austenitized temperature increase.

The objective of this research-action, teaching and extension project was to support the beginning teacher, who graduated from the Pedagogy courses at UFF-INFES and UFRRJ/IE, raising the difficulties and solutions of this teacher's pedagogical practice, as well as implementing and analyzing strategies of professional induction of the newly graduated professor by the proposed pilot course with a focus on promoting dialogue between university and school. This project was aimed at graduates of Pedagogy courses who started as teachers in the early years of elementary school and early childhood education (with less than 5 years as a teacher). These teachers participated in two pilot courses (one in 2021 and the other in 2022) to help recent graduates in their professional induction through innovative actions that help them to take a leading role in their practices. For this, we seek to investigate: the impact of this program on their professional induction through narrative interviews, accompany them in this process and analyze the difficulties and solutions they found in their pedagogical practice as a result of the help that the project offered them; the role of mentors (teachers running the school) and the University at the beginning of their professional activities. Finally, we concluded that this course worked as a support for beginning teachers, allowing for greater reflection and better professional performance, as well as a way to reduce the shock of reality, demotivation and teacher abandonment.

Developing new ways to estimate probabilities can be valuable for science, statistics, and engineering. By considering the information content of different output patterns, recent work invoking algorithmic information theory has shown that a priori probability predictions based on pattern complexities can be made in a broad class of input-output maps. These algorithmic probability predictions do not depend on a detailed knowledge of how output patterns were produced, or historical statistical data. Although quantitatively fairly accurate, a main weakness of these predictions is that they are given as an upper bound on the probability of a pattern, but many low complexity, low probability patterns occur, for which the upper bound has little predictive value. Here we study this low complexity, low probability phenomenon by looking at example maps, namely a finite state transducer, natural time series data, RNA molecule structures, and polynomial curves. Some mechanisms causing low complexity, low probability behaviour are identified, and we argue this behaviour should be assumed as a default in the real world algorithmic probability studies. Additionally, we examine some applications of algorithmic probability and discuss some implications of low complexity, low probability patterns for several research areas including simplicity in physics and biology, a priori probability predictions, Solomonoff induction and Occam's razor, machine learning, and password guessing.

The phenolics concentrations of rapeseed are important because they are antinutritional com-pound or directly positive related to antioxidant actives. In this study, single nucleotide poly-morphisms (SNPs) identified from genotyping-by-sequencing (GBS), and genome-wide associa-tion study of phenolic contents were investigated in 95 rapeseed mutant lines derived from gamma rays and their original cultivar. A total of 3,196,318 SNPs were detected, identifying a set of 70,208 union SNPs used to perform association studies. Six phenolics and glucosinolate (pro-goitrin) were identified, sinapine was the major phenolic compound (91.2–99.2%) in all geno-types. The association study was conducted for six phenolic compounds, progoitrin, total sinap-ine content (TSC), total phenolic content (TPC) and seed yield. The association study identified 241 SNPs that were significantly associated with six phenolic compounds, progoitrin, TSC and TPC. Based on the detected SNP markers, BnaA06g31740D, a gene associated progoitrin, BnaC-nng39930D, a gene associated trans-sinapine 1, BnaA07g31720D, a gene associated trans-sinapine 2, BnaC03g31950D and BnaA06g09180D, a gene associated TSC, and BnaAnng27700D, a gene associ-ated TPC, were selected as candidate genes. The SNP makers associated major phenolic com-pounds identified and could be used as targets for breeding programs aimed at improving anti-oxidant contents and reducing antinutritional components.

Objective. The aim of this study was to determine the incidence and prevalence of psoriatic arthropathy and to point out the problem of their occurrence in a sample of patients diagnosed with psoriasis. Methods. 109 patients with diagnosed psoriasis and psoriatic arthropathy, took part in the survey. The average age of patients was 48,85±10,11. We analyzed the incidence and prevalence of the psoriatic arthropathy occurrence for the patients with psoriasis. Results. Psoriatic arthropathy registered with patients wer those with arthritic changes on: sacroiliac joints with 24 (22,02%) patients, 19 (79,17%) male and 5 (20,83%) female (p<0,001); spine with 11 (10,09%) patients, 6 (54,56%) male and 5 (45,45%) female (p=0,578); peripheral joints with 74 (67,89%) patients, 33 (44,59%) male and 41 (55,41%) female (p<0,01). When comparing the symptoms, most of the patients (63,30%) skin changes were represented before arthritis, for 27,52% patients arthritis preceded the skin changes, and for 9,17% patients the changes were simultaneous, Conclusion. Psoriatic arthropathy occurs more often in median age (31 to 50 years old) and it is more common for men than women. For most patients psoriatic skin changes preceded joints affection, with the most common ones being polyarthritis.

Breastmilk is the only recommended source of nutrition for infants below six-months of age. However, a significant proportion of children are either on supplemental breastfeeding(SBF) or weaned due to the early introduction of solid/semi-solid/soft food and liquids(SSF) before 6 months of age. There is good evidence that Exclusive Breastfeeding(EBF) in infants below six-months of age protects them from preventable illnesses, including malnutrition. The relationship between infant feeding practices and coexisting forms of malnutrition(CFM) has not yet been explored. This study examined the association of different feeding indicators(continuation of breastfeeding, predominant feeding, and SSF) and feeding practices(EBF, SBF, and complete weaning) with CFM in infants aged below six-months of age in Pakistan. National and regional datasets of Pakistan from the last ten years were retrieved from the Demographic Health Surveys(DHS) and UNICEF data repositories. In Pakistan, 34.5%(n=6131) of infants have some form of malnutrition. Among malnourished infants, 44.7%(~15.4% of the total sample) had a CFM. Continuation of breastfeeding was observed in more than 85% of infants, but less than a quarter were on EBF, and the rest were either SBF(65.4%) or weaned infants(13.7%). Compared to EBF, complete weaning increased the odds of coexistence of underweight with wasting and underweight with both wasting and stunting by 1.96(1.12-3.47) and 2.25(1.16-4.36), respectively. Overall, breastfed children had lower odds of various forms of CFM (compared to non-breastfed), except for the coexistence of stunting with overweight/obesity. Continuation of any breastfeeding protects infants in Pakistan from various types of CFM during the first six months of life.

Different soil nutrients affect plant metabolites accumulation characteristics. The main soil nutrients and their correlation with Pepino metabolites were investigated in this study to evaluate differences between greenhouses on the Loess Plateau in northwest China. A total of 269 Pepino metabolites in the fruits were identified using a UPLC-QTOF-MS approach from plants grown in three major Pepino growing regions. Their differential distribution characteristics were analyzed. 99 metabolites differed among the Pepino fruits in the three regions. The main classes of the differentially accumulated metabolites were ranked as Amino acids and derivatives, Nucleotides and derivatives, Organic acids, Alkaloids, Vitamins, Saccharides and Alcohols, Phenolic acids, Lipids, and others. Environmental factor analysis indicated that soil nutrients were the primary differentiating factor. Five soil nutrient indicators: TN（total nitrogen）, TP（total phosphorus）, AP（available phosphorus）, AK（available potassium）, and OM（organic matter）, exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and Mantel test indicated that TNand OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in Pepino metabolites variability. This study clarified the metabolite variability and the relationship between Pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino's nutritional value and cultivation management.

The specificity and potency of venom components gives them a unique advantage in development of various pharmaceutical drugs. Though venom is a cocktail of proteins rarely is the synergy and association between various venom components studied. Understanding the relationship between various components is critical in medical research. Using meta-analysis, we found underlying patterns and associations in the appearance of the toxin families. For Crotalus, Dis has the most associations with the following toxins: PDE; BPP; CRL; CRiSP; LAAO; SVMP P-I & LAAO; SVMP P-III and LAAO. In Sistrurus venom CTL and NGF had most associations. These associations can be used to predict presence of proteins in novel venom and to understand synergies between venom components for enhanced bioactivity. Using this approach, the need to revisit classification of proteins as major components or minor components is highlighted. The revised classification of venom components needs to be based on ubiquity, bioactivity, number of associations and synergies. The revised classification will help in increased research on venom components such as NGF which have high medical importance.

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as reference panel to detect associations with the phenotype at the gene level and were gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as reference panel to conduct tissue specific TWAS on cigarettes per day (CPD) over 13 brain tissues in two large cohorts: UK Biobank (UKBB; N=142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N=143,210), and then meta-analyzed the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues, partially homogeneous genes associated with CPD in cortex, cerebellum and hippocampus tissues, and lastly the tissue-specific genes associated with CPD in only few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

Induction heating (IH) technology is widely recognized and utilized in residential applications due to its high efficiency and safe operating characteristics. Resonant inverter circuits are widely used in IH systems because of their high efficiency and ability to perform soft-switching. Among the various resonant inverters used in IH systems, the single-switch quasi-resonant (SSQR) inverter topology is typically preferred for low-cost and low-output-power applications. Despite its cost advantage, the SSQR topology has a relatively narrow soft-switching range, which can be unstable depending on the electrical parameters of the load and the resonant converter circuit. Accurately determining the capacitance value of the resonant capacitor and the inductance value of the induction coil, which are the key circuit elements of the SSQR induction cooker, is crucial for designing a reliable, efficient, and durable cooking system. In other words, there exists a critical relationship between the resonant converter circuit parameters, load characteristics, and safe operating conditions. Additionally, when considering closed-loop control methods used for power control and safety, selecting appropriate resonant circuit elements becomes vital in ensuring both reliable and efficient operation. This paper focuses on a novel and simplified design method for the SSQR inverter utilized in household appliances. The proposed method and its advantages in terms of the safe operating area of the switch are theoretically investigated and verified through simulations and prototype circuits.

Electromagnetic induction (EMI) and electrical resistivity tomography (ERT) are geophysical techniques measuring soil electrical conductivity, providing insights into properties correlated with it, to depths of several meters. EMI measures apparent electrical conductivity (ECa, dS m−1) without physical contact, while ERT acquires apparent electrical resistivity (ERa, ohm m) using electrodes. Both involve mathematical inversion to obtain models of spatial distribution for soil electrical conductivity (σ, mS m−1) and electrical resistivity (⍴, ohm m), respectively, where ⍴ is the reciprocal of σ. Soil salinity can be assessed from σ over large areas by a calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (ECe, dS m−1), used as a proxy for soil salinity. This research aims to compare the prediction abilities of the faster EMI to the more reliable ERT for estimating σ and predicting soil salinity. The study conducted surveys and sampling at four locations with distinct salinity levels in Portugal, analysing the agreement between the techniques and obtained 2D vertical soil salinity maps. In our case study, the agreement between EMI and ERT models was fairly good in three locations, with σ varying between 50 and 500 mS m−1. However, this was not the case at location 4, where σ exceeded 1000 mS m−1 and EMI significantly underestimated σ, when compared to ERT. As for soil salinity prediction, both techniques generally provided satisfactory and comparable regional-level predictions of ECe, and the observed underestimation in EMI models did not affect significantly the overall estimation of soil salinity. Consequently, EMI demonstrated an acceptable level of accuracy in comparison to ERT in our case studies, supporting the confidence in utilizing this faster and more practical technique for measuring soil salinity over large areas.

This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with 4 poles split winding with 250W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

The objective of this research is to mitigate vibrations in induction motors. To achieve this goal, a discontinuous pulse width modulation (PWM) control strategy based on carrier wave modulation is proposed for multilevel inverters. This study provides justification for the reduction of machine vibrations compared to existing control techniques documented in the technical literature. Additionally, the proposed technique offers the advantage of attenuating the Total Harmonic Distortion of the multilevel inverter's output voltage while simultaneously achieving a higher RMS value for the same DC level. By modifying a parameter of the carrier wave, the control strategy allows for variations in the electrical spectrum while avoiding natural mechanical resonance frequencies, thereby reducing motor vibrations. Laboratory results demonstrating the application of different modulation strategies in a multilevel inverter for an induction motor and a comparison with the presented strategy are provided.

This paper has the objective of implementing the radial rotor position control of a three-phase bearingless induction machine with split winding and optimized drive structure, using the ADRC technique. Because it is a multivariable, nonlinear, time-varying system with coupled variables, it is necessary to use advanced control strategies in order for the system to operate efficiently and with good dynamic performance. The ADRC controller considers the total disturbance composed of unmodeled dynamics, nonlinearities, uncertainties, and load variations, as a new system state, to be estimated in real-time through an extended state observer. In this mode, disturbances are compensated in real-time, eliminating regime errors and with good response to disturbances in general.

The existence of the hole on a plate affects the calculation of eddy current problems. Consequently, the accuracy for the prediction of the material properties decreases if the effect of the hole is not taken into account. In this paper, a novel analytical method based on the modified integration range is proposed which can address the presence of the hole. Due to the presence of the hole, the conventional Dodd-Deeds analytical solution cannot be used to calculate the inductance change. Therefore, a revised upper integration limit is introduced to replace the original limit -- ∞ when using the co-axially air-core electromagnetic sensor. With the presence of the hole, the magnitude of the received signal reduces, and the peak frequency feature changes. The analytical method is validated by measured and numerical simulation results. It is found that the upper limit is related to the radius of the open hole. With the new technique, the thickness of sample plates with holes can be estimated based on the peak frequency feature.

Flower bud formation and flowering in chrysanthemum occur under short day conditions (SD), but the molecular basis for the switch to reproductive growth is less well understood than in model plants. Here, a spontaneous mutant able to flower under long days is described. In an attempt to reveal the pathway(s) involved in the formation of flower buds under contrasting daylengths, transcriptome sequencing was carried out in plants grown both under SD and under long day conditions (LD). A number of differentially transcribed genes involved in the various known flowering pathways were identified. Both circadian clock genes and CmFTL3 were up-regulated under SD, thereby inducing floral bud formation and flowering. The gibberellin (GA) signalling pathway-related genes GA20ox and GID1 were up-regulated in the mutant under LD, while the catabolic gene GA2ox and GAI was down-regulated, thereby inducing the transcription of CmFTL1, SOC1 and LFY. The GA content of the leaf was higher in the mutant than in the wild type under LD. When treated with GA, the mutant flowered earlier under both SD and LD, but there was no other detectable phenotype difference between the two lines. The indication was that the photoperiod pathway majorly regulates flower bud formation and flowering time in chrysanthemum under SD. GA signalling pathway only plays a subsidiary role for flowering. However, the GA signalling pathway predominated for flowering under LD.

B4GALNT2 is a vital candidate gene that affects the growth traits litter size in and growth traits of sheep, but whether it has the same function in goats remains to be further investigated. In this study, we utilized 348 Nanjiang Yellow goats as experimental subjects, conducted a whole exon, and the conserved the non-coding region SNP scanning of the B4GALNT2 gene. Our results revealed the presence of a synonymous mutation, rs672215506, within the exon of the B4GALNT2 gene in the Nanjiang Yellow goat population. This mutation was found to be associated with reduced mRNA stability of the B4GALNT2 gene. The results of SNPs detection of conserved non-coding region of B4GALNT2 gene showed that there were five potential regulatory SNPs in the Nanjiang Yellow goat population, except for rs66095343, the ~500 bp fragments of four SNPs (rs649127714, rs649573228, rs652899012 and rs639183528) could significantly increase luciferase activity both in goat skeletal muscle cells and 293T cells . Genetic diversity indexes indicated low or intermediate levels for all six SNPs analyzed, each conforming to Hardy-Weinberg equilibrium regarding genotypic frequencies. Association analysis indicated that rs660965343, rs649127714 and rs649573228 were significantly correlated with growth traits in the later stage of growth and development of Nanjiang Yellow goat. The haplotype combinations of H2H3 and H2H2 had higher body weight and greater body size; moreover, H2H2 haplotype combinations were significantly correlated with the litter size of Nanjiang Yellow goat. The results of our study demonstrate the potential utility of the B4GALNT2 gene as a functional genetic marker in breeding programs for Nanjiang Yellow goats.

This study aimed to examine the association of blood triglyceride levels with fasting blood glucose levels and type 2 diabetes (T2D) prevalence in Chinese adults with normal triglyceride levels (<1.7 mmol/L), using linear regression and binary logistic regression, respectively. This cross-sectional study included 16,706 Chinese adults, among which 1,067 had T2D. Triglycerides were positively associated with fasting plasma glucose after multivariate adjustment (β=0.034, P<0.001). One natural log unit increase in blood triglycerides was associated with a 61% higher multivari-ate-adjusted risk of T2D (odds ratio [OR], 1.61; 95% confidence interval [CI], 1.19-2.17; P=0.002). The positive associations remained when participants who were treated with lipid-lowering or an-ti-diabetic drugs were excluded. The optimal cut-off of triglycerides for T2D was 1.09 mmol/L (obtained using Receiver Operating Characteristic curve analysis). Participants with triglycerides of ≥1.09 mmol/L had a 28% higher risk of T2D (OR, 1.28; 95% CI, 1.07-1.53; P=0.006) compared to those with triglycerides below the cut-off. In conclusion, this study showed that, in people with normal triglyceride levels, triglycerides at the upper normal range correlated with increased T2D diagnosis risk, with an optimal cut-off of 1.09 mmol/L. Therefore, adults with high “normal” triglyceride levels (1.09-1.69 mmol/L) may need to be closely monitored for the development of T2D.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

The purpose of this paper is to develop a phytosociological, ecological, cytogenetic, eco-protective and economic study of the vegetation of the wetland ecosystems in the Vlădeasa Massif built by the phytocoenoses of the association Carici echinatae-Sphagnetum (Balázs 1942) Soó 1955. In order to attain the aim and objectives put forward we carried out 10 phytocoenological surveys (surveys) in the phytocoenoses of the ecosystems during the optimal vegetation periods related to the summer-serotinal season in the timeframe 2020-2021. The taxonomically inventoried species were included in an association table by the criteria of their belonging to the basic coenotaxa of the association, alliance, order, vegetation class. The outcomes of the phytocoenosis research of the vegetation of the wetland ecosystems were processed, analysed, interpreted based on tables, histograms, diagrams, regarding the numerical and percentage weight of the species in the ecological categories of bioforms, phytogeographic elements (geoelements) and cytogenetic elements. The ecological behaviour of the species confined in ecosystems was also analysed by their relationship with ecological factors, edaphic soil moisture, air temperature and chemical reaction of the soil. The current state of ecosystems, potential threats, sustainable conservation of biodiversity, dynamics of phytocenoses, economic and scientific relevance were also subjected to research. The results obtained in the surveyed territory were analysed and compared numerically and as percentage with the data provided by two reference scientific works belonging to authors who independently carried out research with a similar topic in two different geographical regions of the Western Carpathians.

Typical modern information systems are required to process copious data. Conventional manual approaches can no longer effectively analyze such massive amounts of data, and thus humans resort to smart techniques and tools to complement human effort. Currently, network security events occur frequently, and generate abundant log and alert files. Processing such vast quantities of data particularly requires smart techniques. This study reviewed several crucial developments of existent data mining algorithms, including those that compile alerts generated by heterogeneous IDSs into scenarios and employ various HMMs to detect complex network attacks. Moreover, sequential pattern mining algorithms were examined to develop multi-step intrusion detection. These studies can focus on applying these algorithms in practical settings to effectively reduce the occurrence of false alerts. This article researched the application of data mining algorithms in network security. The academic community has recently generated numerous studies on this topic.

Boron carbide (B4C) powders with defined stoichiometry, high crystallinity, minimal impurity content, and a fine particle size are imperative for realizing the exceptional properties of this compound in advanced high-technology applications. Nevertheless, achieving the desired stoi-chiometry and particle size using traditional synthesis methods, which rely on prolonged, high-temperature processes, can be challenging. The primary objective of this study is to syn-thesize fine B4C powders characterized by high crystallinity and a sub-micron particle size, em-ploying a fast, and energy-efficient method. B4C powders are synthesized from elemental boron and carbon in a high-frequency induction heating furnace using the Electromagnetic Induction Synthesis (EMIS) method. The rapid heating rate achieved through contactless heating promotes the ignition and propagation of the exothermic chemical reaction between boron and carbon. Additionally, electromagnetic effects accelerate atomic diffusion, allowing the reaction to be completed in an exceptionally short timeframe. The grain size and crystallinity of B4C can be finely tuned by adjusting various process parameters, including the post-ignition holding temperature and the duration of heating. As a result, fine B4C powders can be synthesized in under 10 minutes. Moreover, these synthesized B4C powders exhibit high resistance to oxidation when exposed to air.

Background and Aim: Tacrolimus is highly effective in intractable ulcerative colitis, but maintenance may be problematic. This study examined 52-week maintenance and adverse events. Methods: This retrospective study of patients who used tacrolimus from April 2009 to December 2021 (N = 136) examined remission induction rates and background factors in the remission (n = 108) and non-remission groups (n = 28); 52-week maintenance; drug-specific maintenance (azathioprine [n = 58] vs tacrolimus [n = 12] and vs biological product [Bio, n = 8]); and adverse events. Results: The mean observation period was 1230175 days. Remission occurred in 72.7%. Significant differences were observed in sex and endoscopic score (before treatment and 2 weeks after tacrolimus trough levels). Maintenance rate at 52 weeks was 61.6%; significant differences were observed in sex, hospitalization duration, and prednisolone during hospitalization (P < 0.05). Drug-specific 52-week maintenance was 67% with azathioprine, 54% with tacrolimus, and 37% with Bio. No tacrolimus-related serious adverse events occurred that led to discontinuation. Discussion: Remission was more common in women with improved endoscopic scores after tacrolimus trough levels and patients with improved endoscopic scores after tacrolimus therapy. Remission maintenance was more common in women with a long hospitalization at remission and high total steroid dose. Results suggest that treatment with adequate prednisolone use during the period with more inflammation may improve subsequent maintenance. Conclusions: In intractable ulcerative colitis, tacrolimus can induce remission even in patients who cannot use azathioprine for maintenance. Remission can be maintained with azathioprine, tacrolimus, or a Bio.

Recent studies have revealed that several cancer cell types can upregulate inducible nitric oxide synthase (iNOS) and iNOS-derived nitric oxide (NO) after a moderate photodynamic challenge sensitized by 5-aminolevulinic acid (ALA)-induced protoporphyrin-IX. The NO signaled for cell resistance to photokilling as well as greater growth and migration/invasion aggressiveness of surviving cells. On this basis, it was predicted that diffusible NO from PDT-targeted cells in a tumor might enhance growth, migration, and invasiveness of non- or poorly PDT-targeted bystander cells. This was tested using a novel approach in which ALA-PDT targeted cancer cells on a culture dish were initially segregated from non-targeted bystander cells of the same type via impermeable silicone-rimmed rings. Several hours after LED irradiation, the rings were removed, and both cell populations analyzed in the dark for various responses. After a moderate extent of targeted cell killing (~25%), bystander proliferation and migration were evaluated, and both were found to be significantly enhanced. Enhancement corelated with iNOS/NO upregulation in surviving PDT-targeted cancer cells in the following cell type order: PC3 > MDA-MB-231 >U87 > BLM. If occurring in an actual PDT-challenged tumor, such bystander effects might compromise treatment efficacy by stimulating tumor growth and/or metastatic dissemination. Possible mitigation of these negative effects by using pharmacologic inhibitors of iNOS expression or activity as PDT adjuvants will be discussed.

In this paper, MgAl Layered double hydroxides (LDH) were synthesized by co-precipitation method using a colloid mill and characterized by XRD, IR and SEM. The LDHs was repeatedly tested and compared with different bearing lifetime tester in lithium base grease, and it was found that the environmental-friendly LDHs had greater performance than the traditional antioxidant, and it was the development direction of a new generation of environmental-friendly antioxidants in the future. By adding LDHs into large electric shovel greats (GRK-A) in open-pit coal mine, it can be seen by PDSC evaluation that the service lifetime of grease is extended by 20% while the overall performance of grease is not affected. With the increase of LDHs addition, the grease sample gets the greater activation energy, the stronger thermal oxidation and decomposition resistance. Comparing the energy storage modulus and flow transition index at different temperatures, it can be seen that adding the right amount of LDHs needs close attention for the system oxidation resistance and viscoelasticity. For the electric shovel grease system, the best oxidation resistance and rheological properties can be achieved by adding 2% of LDHs. The rheological viscosity-temperature curves show that the grease samples with different ratios of solid LDHs have better low-temperature properties than the mine grease.

The potential of biological consortia designed for laccase production and dye treatment is discussed in this review. The poor yields in laccase production and low efficiency in dye decolorization of monoculture-based treatments has encouraged the use of designed biological consortia. A consortium is a system where the growth of two or more organisms, chosen to improve a particular bioprocess, is induced in the same medium. Chemical and natural mediators are being replaced by consortia for the production of laccases because, in addition to being less toxic, they induce new enzyme isoforms and lead to high laccase yields. On the other hand, consortia act synergistically in the decolorization of azo dyes through the enzymes they produce, so overall degradation is improved. Designed consortia are an attractive alternative still in development that could provide new biotechnological tools for the treatment of textile effluents.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.

The melanoma differentiation-associated protein 5 (MDA5; encoded by the IFIH1 gene) mediates the activation of the interferon pathway in response to viral infection. This protein is also upregulated in autoimmune diseases and psoriasis skin lesions. IFIH1 gene variants that increased the MDA5 activity have been associated with increased risk for immune mediated diseases, including psoriasis. Our aim was to determine the association between three IFIH1 variants (rs35337543, intron8 +1G>C; rs35744605, Glu627Stop; and rs1990760, Ala946Thr) and the main clinical findings in a cohort of Spanish patients with psoriatic disease (N=572; 77% early-onset). Early-onset psoriasis (EOPs) had a significant higher frequency of severe disease and Cw6+. Carriers of the 946Thr variant were more common in EOPs (p<0.001), and the effect was more pronounced among Cw6-negatives. This variant was also associated with an increased risk of psoriatic arthritis (PsA) independently of other factors (OR=1.62, 95%CI=1.11-2.37). The rs3533754 and rs35744605 have been reported as risk factors for viral infection and protective for autoimmune diseases, but we did not find significant differences between the two onset age or PsA groups. However, due to the reduced frequency of the two variants (<0.02) the size of our cohort was too low to conclude a significant effect. In conclusion, the common IFIH1 rs1990760 T allele that has been linked to increased gene expression was significantly more frequent in EOPs patients. This variant was also an independent risk factor for PsA in our cohort. This risk allele was in linkage disequilibrium with other variants previously associated with the risk of psoriasis and PsA. Our study reinforces the widely reported role of IFIH1 gene variants on psoriatic disease and other immune mediated diseases.

The most important factor that determines beef tenderness is its proteolytic activity and the balance between calpain1 protease activity and calpastatin inhibition is especially important, while contributions could arise from calpain2 and possibly calpain3. These processes are however affected by the meat aging process itself. To determine whether genotypes in the calpaincalpastatin system can enhance tenderness throughout a 20 day aging period, South African purebred beef bulls (n=166) were genotyped using the Illumina BovineHD SNP BeadChip, through genebased association analysis targeting the cast, capn3, capn2 and capn1 genes. The WarnerBratzler shear force (WBSF) and myofibril fragment length (MFL) of Longissimus thoracis et lumborum (LTL) steaks were evaluated between d 3 d 20 of aging, with protease enzyme activity in the first 20 h postmortem. Although several of the 134 SNP associated with tenderness, only seven SNP in the cast, capn2 and capn1 genes sustained genetic associations, additive to agingassociated increases in tenderness for at least three of the four aging periods. While most genomic associations were relatively stable over time, some genotypes within SNP responded differently to aging, resulting in altered genomic effects over time. The level of aging at which genomic associations are performed is an important factor that determines whether SNP affect tenderness phenotypes.

The objective of this study were to conduct association mapping for drought tolerance at the seedling stage and yield-related traits. 60 cowpea accessions were used in the study. Single-nucleotide polymorphisms (SNPs) discovered through genotyping by sequencing (GBS) were used for genotyping. Association mapping was conducted using single-marker regression (SMR) in Q Gene, and general linear model (GLM) and mixed linear model (MLM) built in TASSEL. The population of the cowpea accessions were analysed using STRUCTURE 2.3.4 and the peak of delta K in the greenhouse showed seven population types, whereas the peak of delta K in the glasshouse indicated the presence of six population types. One SNP marker, 14083649|F|0-9 was associated with NP with a p value <0.001. Fifty SNP markers were associated with PWT at p <0.001. Four SNP markers, 14074781|F|0-16, 100047392|F|0-36, 14083801|F|0-28 and 100051488|F|0-49 were associated with AVSPD at p <0.001. SNP markers, 14074781|F|0-16, 14083801|F|0-28 and 100051488|F|0-49 were associated with PL at P <0.001. Five SNP markers, 100047392|F|0-36, 14083801|F|0-28, 100072738|F|0-34, 14076881|F|0-49 and 14076881|F|0-49 were associated with PWDTH at p <0.001. The 65 SNP markers identified can be used in cowpea molecular breeding to select for AVSPD, NP, PL, PWDTH, PWT, and RR through marker assisted selection (MAS).

Exome sequencing has been commonly used in rare diseases by selecting multiplex families or singletons with an extreme phenotype (EP) to search for rare variants in coding regions. The EP strategy covers both extreme ends of a disease spectrum and it has been also used to investigate the contribution of rare variants to heritability in complex clinical traits. We have conducted a systematic review to find evidence supporting the use of EP strategies to search for rare variants in genetic studies of complex diseases, to highlight the contribution of rare variation to the genetic structure of multiallelic conditions. After performing the quality assessment of the retrieved records, we selected 19 genetic studies considering EP to demonstrate genetic association. All the studies successfully identified several rare variants, de novo mutations and many novel candidate genes were also identified by selecting an EP. There is enough evidence to support that the EP approach in patients with an early onset of the disease can contribute to the identification of rare variants in candidate genes or pathways involved in complex diseases. EP patients may contribute to a better understanding of the underlying genetic architecture of common heterogeneous disorders such as tinnitus or age-related hearing loss.

Estimation of the reality can easily be flawed, hence, in order to result in accurate and useful estimates the process has to be protected from bias and confounding and should follow other methodological milestones inherent to different types of empirical observations. Candidate-gene association studies are a specific form of observations that have been rather extensively applied in psychiatry yielding valuable information on various aspects – when methodologically adequate and used in appropriate settings. However, certain flaws that may occur in such studies might not be bluntly obvious, at least not at first glance, and may pass unnoticed by researchers and reviewers. This case study uses two recent published candidate-gene association reports suggesting involvement of cannabinoid receptor type 1 and of heat shock protein single nucleotide polymorphisms in development of neurocognitive performance and psychopathology in a cohort of adult first episode psychosis patients to point-out the types of flaws inevitably resulting in inaccurate and useless estimates.

Phase change refers to the process of maturation and transition from the juvenile to the adult stage. In response to this shift, certain species like chestnut lose the ability to form adventitious roots, thereby hindering the successful micropropagation of adult plants. While auxin is the main hormone involved in adventitious root formation, other hormones, such as ethylene, are also thought to play a role in its induction and development. In this study, experiments were carried out to determine the effects of ethylene on the induction and growth of adventitious roots. The analysis was performed in two types of chestnut microshoots derived from the same tree, a juvenile-like line with high rooting ability derived from basal shoots (P2BS) and a line derived from crown branches (P2CR) with low rooting responses. By means of the application of compounds to modify ethylene content or inhibit its signalling, the potential involvement of this hormone in the in-duction of adventitious roots was analyzed. Our results show that ethylene can modify the rooting competence of mature shoots, while the response in juvenile material was barely affected. To further characterize the molecular reasons underlying this maturation-derived shift in behaviour, specific gene expression analyses were developed. Findings suggest that several mechanisms, including ethylene signalling, auxin transport and epigenetic modifications, relate to the modu-lation of the rooting ability of mature chestnut microshoots and their recalcitrant behaviour.

This article presents the impact analysis of space harmonics (SH) presence on a single-sided three-phase linear induction motor, along with a comprehensive parametric investigation. The presence of SH often reduces the LIM performances. The electromagnetic phenomena are governed by Maxwell's equations associated with equivalent circuit modelling. The chosen mathematical model uses 2D formulation with magnetic vector potential A. The model implementation is performed with finite element method on Gmsh-GetDP free platform. The electromagnetic thrust is calculated in current excitation case by a numerical model (FEM) with and without term generating SH in order to highlight the effect of SH. The adaptation to voltage supply operation is obtained by equivalent electric circuits (EEC), through calculation of the operational impedance. The main objective of the study consists in a comparative exploration of SH presence using two numerical models (single and multi-harmonics), and an assessment of parameters effect on space harmonic presence and various machine characteristics such as thrust, efficiency, and power factor. The motor’s characteristics (ie: thrust, efficiency and power factor) strongly depend on parameters such as the pole pair number and conductivity. Improving the operation and maximizing the performances of such a machine for a given specification requires the use of optimization algorithms.

The evaluation of the possibility of obtaining dihaploids by the method of anther culture in vitro to accelerate selection for resistance to prolonged flooding of seeded rice with water was carried out. The object of research is rice hybrids F2 of the Rice Breeding and Seed Production Laboratory of the “Donskoy” ASC, obtained as a result of interbreeding the best economically valuable varieties with samples bearing genes of resistance to prolonged flooding with water. Basic nutrient medium with an optimal composition of nutrition elements and growth hormones stimulating callo- and morphogenesis were used. For the induction of callus formation, 12604 anthers were planted, according to 26 hybrid combinations represented by 68 plants, as a result, 716 neoplasms were obtained, including 586 non–morphogenic callus, 130 regenerating plants. Cultivation of anthers revealed large genotypic differences in the samples. According to the responsiveness to neoplasms, 60% of the panicles showed a positive result, the rest did not give callus. The most responsive to the formation of callus were hybrid combinations: 5009/2 – 84 pcs., 5010/2 – 94 pcs., 4565/3 – 85 pcs., 4641/2 – 69 pcs. They also showed the ability to morphogenesis. Androgenic plants were obtained by 14 hybrid combinations, their share was 1.03% of the total number of inoculated anthers. 30 green regenerative lines from four rice hybrids were obtained, differing in visual morphological assessment: 5009/2 - 5 pcs., 5010/2 – 5 pcs., 4565/3 – 2 pcs., 4641/2 – 18 pcs. The isolated lines are characterized by good responsiveness in the culture of anthers in vitro, carry genes for resistance to prolonged flooding, and can be used in rice breeding programs using DG technologies.

In 1948, Skinner described the behavior of pigeons under response-independent schedules as “superstitious,” and proposed that the responses were reinforced by contiguous, adventitious food deliveries. Subsequently, response-independent schedules have been of interest to both basic and applied researchers, first to understand the mechanisms involved, and later, as “noncontingent reinforcement” (NCR) to reduce undesirable behavior. However, the potential superstitious effects produced by these schedules have been challenged, with some researchers arguing that antecedent variables play a significant role. This paper examines the evidence for adventitious reinforcement from both laboratory and applied research, the results of which suggest that antecedent, non-operant functions may be important in fully understanding the effects of NCR. We propose an applied-basic research synthesis, in which attention to potential non-operant functions could provide a more complete understanding of response-independent schedules. We conclude with a summary of the applied implications of the non-operant functions of NCR schedules.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

The golden ratio, also known as Phi (ϕ ≈ 1.618034), attracted the interest of mathematicians, artists, and intellectuals for many centuries, probably from when it was discovered in human anthropometry. Even in recent times, researchers found the presence of golden ratio in Renaissance paintings and aesthetic preferences. The reasons behind the fascination with the golden ratio remain unclear, but it has been suggested that stimuli containing this proportion are often perceived as beautiful. However, evidence is conflicting, and the literature struggles to establish the existence of individual preferences for golden ratio. To gain new insights into the nature of these preferences, one hundred participants completed an implicit association task, with either golden ratio or random stimuli presented with positive or negative words. Participants initially categorized the stimuli based on their assigned categories. Then, we assessed their explicit preferences by asking to rate the stimuli in terms of pleasantness and by completing a line bisection task and the Ultimate Game. The results revealed the typical effects observed in implicit association tasks, with improved response times and accuracy when golden ratio stimuli were associated with positive word categories. In contrast, explicit ratings yielded mixed results. We discuss our findings in relation to previous studies that have explored this issue, highlighting the ongoing debate surrounding preferences for the golden ratio.

Japanese apricot (Prunus mume) is a culturally and economically important fruit tree in East Asia. ‘Nanko’, the primary cultivar of the Japanese apricot well known in Japan, usually suffers from scab, a disease caused by Venturia carpophila. Scab results in the development of black spots on the fruit surface, which considerably reduces its commercial value. Despite being an economic concern for Japanese apricot, there have been few reports on the phenotypic variation in scab resistance/susceptibility, the underlying genetic factors, and the development of DNA markers. Therefore, in this study, we performed a comprehensive trait analysis for scab resistance and susceptibility for four years and conducted a genome-wide association study (GWAS) to identify the associated loci. The results showed that significant GWAS peaks were detected in 2017 and 2018. Promising candidate gene encoding domains associated with disease resistance were found at the nine single-nucleotide polymorphisms (SNPs) identified in 2017 and 2018. These SNPs were thought to be associated with scab susceptibility of ‘Nanko’ lineages. These findings shed light on the mechanisms of scab resistance in P. mume and will assist future breeding programs to improve scab resistance in ‘Nanko’ lineages.

Advancement in biotechnology and genomics research have promoted access to DNA markers and their use in breeding programs. Genome-wide association study (GWAS), Genomic selection (GS) and Marker-Assisted Selection (MAS) are some of the applications of DNA markers in plant breeding. Researchers have suggested combining these individual applications for better selection accuracies. This study examines the potential advantages of incorporating GWAS-results into MAS and GS as well as the validity of the different methods for combining these approaches. From this study, it was concluded that number of QTNs have greater effects on prediction accuracies compared to heritability estimates. Also, the increase in prediction accuracy from the invalid method of incorporating GWAS results into GS and MAS model is similar to results recorded with using the valid approach. However, greater difference may be observed in another scenario which can lead to spurious results when used to make breeding decisions.

Fracture is a global public health disease. Bone health and fracture risk have become the focus of public and scientific attention. Observational studies have reported that tea consumption is associated with fracture risk, but the results are inconsistent. The present study was conducted to evaluate whether tea consumption was causally associated with the risk of bone fracture through two-sample Mendelian Randomization (MR) analysis. We included a large genome-wide association study (GWAS) associated with tea consumption of 447,485 individuals and analyzed the effects of genetic instruments on fractures using fracture cases from the UK Biobank dataset (n=361,194). Inverse variance weighted (IVW) indicated no causal effects of tea consumption on fractures of the skull and face, shoulder and upper arm, hand and wrist, femur, calf, and ankle (odds ratio=1.000, P=0.881; OR=1.000, P=0.857; OR=1.002, P=0.339; OR=0.997, P=0.054; OR=0.998, P=0.569, respectively). Consistent results were also found in MR-Egger, weighted median, and weighted mode. Our research provided evidence that tea consumption is unlikely to affect the incidence of fractures.

Plant breeding conventionally depends on genetic variability available in a species to improve a particular trait in the crop. However, epigenetic diversity may provide an additional tier of variation. The recent advent of epigenome technologies has elucidated the role of epigenetic variation in shaping phenotype. Further, the development of epigenetic recombinant inbred lines (epi-RILs) in the model species such as Arabidopsis has enabled accurate genetic analysis of epigenetic variation. Subsequently, mapping of epigenetic quantitative trait loci (epiQTL) allowed association between epialleles and phenotypic traits. Thus, quantitative epigenetics provides ample opportunities to dissect the role of epigenetic variation in trait regulation, which can be eventually utilized in crop improvement programs. Moreover, locus-specific manipulation of DNA methylation by epigenome-editing tools such as clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) can facilitate epigenetic based molecular breeding of important crop plants.

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits in a population. To date, a total of 267 QTL for 29 quantitative traits have been reported in 13 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, scaffold sequences, or pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but were mainly based on simple sequence repeat (SSR) or single nucleotide polymorphism (SNP) markers. This article provides methods with software tools and database files to uniquely map SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules. Using these methods, 195 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters. Mapping of QTL from different studies to the same reference enables comparisons and facilitates genome-wide QTL analysis, candidate gene scanning, and breeding applications.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

We present here a five hours experimentation of a didactical path about the electromagnetic induction addressed to students of the last year of an Italian scientific high school and oriented to better understand the physical origin of the induced electromotive force. The expression of the induced electromotive force as the sum of the term linked to the time variation of the magnetic field and of the motional one has been obtained in a detailed way, still suitable for presentation at high school. Many examples have been proposed to the students in order to clarify the conceptual physical knots. The students’ responses to a 6-questions multiple choice questionnaire have been analyzed. It emerged that our approach is concretely feasible although we find the well-known difficulties in calculating flux and circulation of a vector field. Furthermore, it emerged that an integral approach to the problem masks the understanding of the nature of the forces acting locally on the charges. Hence our proposal of a “redefinition” of the induced electric field in terms of the magnetic vector potential is also presented.

The authors of the paper have made an attempt to detect the fibre content and fibre spacing in a steel fibre reinforced concrete (SFRC) industrial floor. Two non-destructive testing (NDT) methods: an electromagnetic induction technique and a radar-based technique were applied. The first method allowed to detect the spacing in subsequent layers located in the thickness of the slab. The result of the second method was a 3D visualization of the detected fibre in the volume of concrete slab. The conducted tests showed aptitude and limitations of the applied methods in estimating fibre volume and spacing. The two techniques also allowed to locate the areas with relatively low fibre concentration which are very likely to be characterized by low mechanical properties.

Two new Mn(II) and Zn(II) metal-organic compounds of 1,10-phenanthroline and methyl benzoates viz. [Mn(phen)2Cl2]2-ClBzH (1) and [Zn(4-MeBz)2(2-AmPy)2] (2) (where, 4-MeBz = 4-methylbenzoate, 2-AmPy = 2-aminopyridine, phen = 1,10-phenanthroline, 2-ClBzH = 2-chlorobenzoic acid) have been synthesized and characterized using elemental analysis, TGA, spectroscopic (FTIR, electronic) and single crystal X-ray diffraction techniques. Crystal structure analysis of the compounds reveals the presence of various non-covalent interactions which provides stability to the crystal structures. Crystal structure analysis of the compound 1 reveals the formation of supramolecular dimer of 2-ClBzH enclathrated within the hexameric host cavity formed by the neighboring monomeric units. Compound 2 is a mononuclear compound of Zn(II) where flexible binding topologies of 4-CH3Bz is observed with the metal centre. Moreover various non-covalent interactions such as lp(O)-π, lp(Cl)-π, C‒H∙∙∙Cl, π-stacking interactions as well as N‒H∙∙∙O, C‒H∙∙∙O and C‒H∙∙∙π hydrogen bonding interactions are found to be involved in plateauing the molecular self-association of the compounds. The remarkable enclathration of the H-bonded 2-ClBzH dimer into a supramolecular cavity formed by two [Mn(phen)2Cl2] complexes has been further studied theoretically using density functional theory (DFT) calculations, non-covalent interaction (NCI) plot index and quantum theory of atoms in molecules (QTAIM) computational tools. Synergistic effects have been also analysed using molecular electrostatic potential (MEP) surface analysis.

Background and Objectives Neurofibromatosis 1 (NF1 MIM # 162200), also known as von Recklinghausen disease, is among the most common autosomal dominantly transmitted disorders characterized mainly by neurocutaneous manifestations such as café au lait spots, intertriginous freckling, various types of neurofibromas (cutaneous neurofibromas or plexiform tumors), neurological manifestations, and bone abnormalities. It occurs with an incidence of 1:3000–1:4000 cases. Cone-rod dystrophies belong to the large group of retinal dystrophies, a highly heterogeneous group of disorders clinically manifested mainly by damage to cone and rod cells and have a broad spectrum of clinical manifestations. Many genes are involved in their etiology, with more than 100 currently identified, including the CRX gene responsible for the autosomal dominant form of cone-rod dystrophy (ad CORD). Materials and Methods: The authors present an intriguing case of a patient diagnosed with Neurofibromatosis type 1 (NF1) as an infant and ocular involvement (myopia and astigmatism onset at age 15), which yielded a surprising molecular testing result. Results: Molecular DNA analysis identified a rare occurrence of two mutational variants, including a pathogenic variant in the NF1 gene, c.3871-2A, and a likely pathogenic variant in the CRX gene c.119G>A. Conclusions: The authors present this case to highlight the surprises offered by molecular testing in a condition where the diagnosis is usually based on clinical criteria, thus providing patients with competent genetic advice.

Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene Ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, associated with the significant SNP chr6.S_155668107 of axis tissue, encodes a cysteine proteinase inhibitor, suggesting its potential role in alleviating Hg toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.

Background: Genome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD. Methods: To address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG. Results: Significant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models. Conclusion: This is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.

Polymyositis is a rare condition with an unknown etiology occurring more frequently in adult women. There is a lack of evidence on the coexistence of PM and CMV infection in a patient with Hashimoto Thyroiditis hypothyroidism. However, the increasing incidence of CMV infection and autoimmune diseases overlapping points out a relationship, while the association direction remains unclear. Case outline: A 32-year-old woman recently treated for HT hypothyroidism was admitted to the hospital two weeks after being treated for common flu by the family doctor, complaining about a worsening condition with muscle pain, weakness, frequent falls, and fatigue. The first tests showed a normalized thyroid function, with elevated values of troponin and serum creatinine kinase (KC). The immunological tests revealed the presence of a high titer of CMV IgG antibodies and raised levels of CMV DNA. Pelvis MRI images demonstrated markedly elevated signals on the STIR sequences in the pelvis, thighs, and calves, indicating active and severe multifocal myositis. The diagnosis of PM was confirmed with the muscle biopsy on day 7 of hospitalization. The patient showed significant improvements within two weeks after the medical therapy and physiotherapy.

Plant breeding aims to develop elite crop varieties appropriate for various environments with higher quality and quantity of production. Researchers use quantitative trait loci (QTL) mapping and association studies to identify regions in the genome responsible for the variation of the quantitative traits of interest. However, mapped regions do not always translate to functional proteins, which makes it challenging to identify genes associated with traits of interest. The biological functions of proteins are strongly dependent on their 3D structure. Alternatively, if proteins can be directly linked with the phenotypes, the effect of mutations on phenotypic changes can be assessed. Innovation of deep learning models in biology opens new avenues of exploration. AlphaFold is an AI system that predicts the 3D structure of a protein from its amino acid sequence with near experimental accuracy and was used in this study. Point mutations with a significant influence on the 3D structure of a protein can capture the effect on phenotypes through association study, and this provides insights into the regions that are of functional importance. In the current study, 534 plants were selected based on plant vigor, and 154 missense variants that change amino acid sequences, including 5 significant hits from previous study, were included. The changes in protein 3D structure were assessed by association with the phenotype. The analysis identified five significant associations, four of which were also identified in previous study of SNPs GWAS, however, a new fifth association was also identified which was annotated as disease resistance gene in Medicago truncatula. This study helps to associate SNPs that could be missed by GWAS due to stringent Bonferroni corrected p-values by providing a more robust filter for SNPs using features from predicted protein 3D structures.

Ningxiang pig is renowned breed for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters (L.LD, a.LD, b.LD), and pH at two postmortem intervals (45 minutes and 24 hours) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30 ~ 0.80) for carcass traits and from low to high (0.11 ~ 0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the HMGA1 gene emerged as a pleiotropic gene correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs.

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

Mixed reality technology is being increasingly used in cultural heritage attractions to enhance visitors’ experience. However, how the characteristics of mixed reality affect satisfaction and brand loyalty has not been explored in previous research. The purpose of this study is to identify factors affecting satisfaction with mixed reality experiences at cultural and artistic visitor attractions and their influence on brand loyalty, which is connected with management performance. We propose a theoretical model based on brand equity theory in the context of mixed reality experience. Survey data were gathered from 251 respondents visiting a cultural and artistic visitor attraction in Seoul, Korea using a stratified sampling method. PLS-SEM was employed for the data analysis. The results suggest that the characteristics of mixed reality (interactivity, vividness) not only influence the affective aspects (perceived immersion, perceived enjoyment) of visitors’ experience, but also positively affect brand awareness, brand association, and brand loyalty.

Malignant mesothelioma is a rare proliferative cancer that develops in the thin layer of tissues surrounding the lungs. Malignant mesothelioma is associated with an extremely poor prognosis and the majority of patients do not show symptoms. The epidemiology of mesothelioma is important for the identification of disease. The primary aim of this study is to explore the risk factors associated with mesothelioma. The dataset consists of healthy and mesothelioma patients but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease’s factors; asbestos exposure, duration of asbestos exposure, duration of symptoms, erythrocyte sedimentation rate and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with mesothelioma may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with mesothelioma which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

An analysis of workplace accidents in the mining sector has been done using the database from the Spanish administration between the period 2005-2015 and applying data mining techniques. Data has been processed by means of the software Weka. Two scenarios were chosen regarding the accidents database, surface and underground mining. The most important variables involved in occupation accidents and their association rules have been determined. These rules are formed by several predictor variables that cause an accident, defining its characteristics and context. This study exposes the 20 most important association rules of the sector, either surface or underground mining, based on statistical confidence levels of each rule obtained by Weka. The outcomes display the most typical immediate causes with the percentage of accident basis of each association rule. The most typical immediate cause is body movement with physical effort or overexertion and type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident change in both scenarios. Data mining techniques have been proved as a very powerful tool to find out the root of the accidents, apply corrective measures and verify their effectiveness, either for public or private companies.

In the last decade Electromagnetic Induction (EMI) measurements have been increasingly used for investigating the soil salinization caused by the use of brackish or saline water as irrigation source. EMI measurements proved to be a powerful tool for providing spatial information of the investigated soil because of the strict correlation between the output geophysical parameter, i.e. the electrical conductivity, to soil moisture and salinity. In addition, their non-invasive nature and their capability to collect a high number of data over broad areas and in relatively short time, makes these measurements attractive for monitoring flow and transport dynamics, otherwise undetectable with conventional measurements. In an experimental field, EMI measurements were collected during the growth season of the tomato, irrigated with three different irrigation strategies. The data were collected over three months in a time-lapse mode in order to visualize changes in electrical conductivity to be associated with soil salinity. A rigorous time-lapse inversion procedure has been set for modeling the soil salinization induced by brackish irrigation water.

Keywords: Wheat; Speed Breeding; Maize Pollen Induction; Chromosome Elimination; Doubled Haploid (DH)

Castanea sativa Mill., an indigenous species of the mountainous Gabala region of Azerbaijan, where its local variety is now facing the danger of extinction. The preservation of this variety of European chestnut requires the development of effective strategies for reliable in vitro regeneration systems as an alternative to traditional methods, which has become the main objective of this study. In solving this problem, the generally accepted technique of micro-multiplication of axillary shoots was mainly used. First, a phased sterilization of was carried out using liquid soap, Previkur fungicide and mercury (II) chloride. DKW (Driver and Kuniyuki Walnut) nutrient medium was used for germination of explants, into which growth stimulants BAP (Benzilamunapurine), IBA (Indole - 3 butyric acid), IAA (Indole Acetic acid), NAA (Naphthalene Acetic Acid) and GA3 (Gibberelic acid) were introduced in various combinations and quantities. The test of the above-mentioned sterilization model revealed significant shortcomings in terms of the acceptability of the results obtained (16-77%). It was also found that the germination of explants takes 14 days and it is better to conduct it in a DKW environment containing hormones BAP (0.6 mg), IBA (0.1 mg) and GA3 (0.1 mg) / 1 L DKW. A mixture of BAP (0.1mg) + IBA (0.35 mg) is more suitable for the reproduction of grown explants + GA3 (0.2 mg) / 1 l DKW (the result is 3 new micro-plants for each explant), and for good root formation (it takes 30 days) – a mixture of IBA 1.0 mg + NAA 0.5 mg + IAA 0.5 mg / l L DKW. After the shoots have acquired a certain length (at least 1.5 cm), it is required to transfer them for 22 days to a DKW medium containing IBA (1 mg), IAA (0.5 mg) and NAA(0.5 mg) / l L DKW so that the root splitting process begins and ends.

: In the automotive industry, high-strength plates have been increasingly used to reduce the vehicle body weight due to stringent regulations on fuel economy and safety. Such plates achieve tensile strengths as high as 1.5 GPa owing to the hot-stamping process. However, these plates suffer from scale generation and surface decarburization because of the surface oxidation of the material. Recently, studies on materials subjected to high-frequency induction heating have reported strength improvement owing to the coil shape and cooling method. This study presents the surface temperature changes, mechanical properties, and microstructures of boron steels subjected to longitudinal heating, which uses eddy currents generated from high-frequency induction heating. The surface temperature data were analyzed under different high-frequency induction heating powers (15, 18, 21, 24, 27, and 30 kW) and distances from the specimen (6, 9, and 15 mm). The mechanical properties and microstructures were also analyzed in different high-frequency induction heating coil directions. The correlations between the high-frequency induction heating power and the distance from the specimen with the maximum tensile strength were determined.

The manuscript presents a unified methodology for the engineering design of resonant inverters used for power sources to power various technologies based on induction heating. The methodology is compiled using the generalized consideration of electromagnetic processes in a series RLC circuit and the quasi-boundary method for the analysis of resonant inverters with and without reverse diodes operating in soft and hard commutation modes. On this basis, the transmission functions of the inverters are derived, and the matching between the parameters of the inverter and the load through complicated output circuits is also considered. The basic ratios for determining the circuit elements and their current and voltage loading are presented. The methodologies are verified using several computational examples, simulations and measurements from induction heating devices. The simplified design approach presented is useful for its application in power electronics training and also in the design of various devices using the principle of induction heating. Another important result is the achievement of formalization and algorithmization of the design process, which is a consequence of the application of a unified approach both in the analysis and in the design of a whole class of power electronic devices.

Cyanobacteria are microorganisms able to adapt to a wide variety of environmental conditions and abiotic stresses. They produce a very large number of metabolites that can participate in the adaptation of cyanobacteria to a large range of resources such as light, temperature, or nutrient. The metabolites variation is one way to understand the physiological status and adaptation of cells. In this study, we aim to understand how the diversity and the dynamics of the whole metabolome is dependent of the growth phases and under control of abiotic factors (e.g. light intensity and temperature). The cyanobacteria Aliinostoc sp. PMC 882.14 was selected for its large number of biosynthetic gene clusters. Metabolomes were analyzed by using mass spectrometry (qTOF-MS/MS) combined with untargeted analysis to investigate the metabolite dynamics. Significant variations were characterized between exponential and stationary phases, whatever the culture conditions (“control”, “higher light”, or “higher temperature”). ”Higher light” and “higher temperature” favored the synthesis of metabolites belonging to the same molecular families. Among highly regulated metabolites, we observe the presence of mycosporine-like amino acids (MAAs), and various variants of somamides, microginins, and microviridins. Through Aliinostoc sp. PMC 882.14, this study shows the importance of knowing the physiological state of cyanobacteria for comparative global metabolomics and questions the regulation processes involve into metabolite families production. Our results also open up new perspectives in the context of the production of targeted bioactive metabolites.

The magnetic polarization tensor has a promising capability of determining the geometry and material properties of metallic samples. In this paper, a novel computation method is proposed to estimate the magnetic polarization tensors for the metallic samples using the boundary element method. In this method, the metallic sample is placed in a uniformly distributed magnetic field. Based on assumptions that the excitation frequency and/or the conductivity of the sample is very high, the metallic sample is regarded as a perfect electrical conductor (PEC). Therefore, the scattered field at a certain distance can be simulated. By utilising the boundary element method, the magnetic polarization tensor can be derived from the simulated scattered field. The theoretical calculation is presented and simulations and experiments have been carried out to validate the proposed method. The results from the simulation are matched with the analytical solution for the case of sphere samples. Moreover, there is a good agreement between the simulation results and the experimental results for the copper cylindrical samples.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost‐effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naïve integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with Fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Control of Tropical Theileriosis, a tick-borne disease with a strong impact on cattle breeding, can be facilitated by using marker-assisted selection in breeding programs. Genome-wide association studies (GWAS) using high-density arrays are extremely important for the ongoing process of identifying genomic variants associated with resistance to Theileria annulata infection. In this work, Single Nucleotide Polymorphisms (SNPs) were analyzed in the Portuguese autochthonous cattle breeds Alentejana and Mertolenga. In total, 14 SNPs suggestive of significance (p ≤ 10-4) were identified for Alentejana cattle and 20 SNPs for Mertolenga cattle. The genomic regions around these SNPs were further investigated for annotated genes and Quantitative trait loci (QTLs) previously described by other authors. Regarding the Alentejana breed, the MAP3K1, CMTM7, SSFA2, and ATG13 genes are located near suggestive SNPs and appear as candidate genes for resistance to Tropical Theileriosis, considering its action the immune response and resistance to other diseases. On the other hand, in the Mertolenga breed, the UOX gene is also a candidate gene due to its apparent link to the pathogenesis of the disease. These results may represent a first step towards the possibility of including genetic markers for resistance to Tropical Theileriosis in current breed selection programs.

We report a detailed investigation of vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy and electronic absorption measurements. The experimental data were analyzed in view of density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes are resolved in the acoustic spectra, which are assigned to conformational changes between GSH conformers, self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

Atopic dermatitis (AD) has been extensively investigated for genetic associations utilizing both candidate gene approaches and genome-wide scans. Here, we comprehensively evaluated the available literature to determine the association of candidate genes in AD to gain additional insight in the etiopathogenesis of the disease. We systematically screened all studies that explored the association between polymorphisms and AD risks in cases of European and Asian ancestry and synthesized the available evidence through random-effects meta-analysis. We identified 97 studies that met our inclusion/exclusion criteria that examined 17 candidate loci in Europeans and 14 candidate genes in Asians. We confirmed the significant associations between FLG variants in both European and Asian populations and AD risk, while additional synthesis of available data revealed novel loci mapped to IL18 and TGFB1 genes in Europeans and IL12RB1 and MIF in Asians, that have not yet been identified by genome-wide association studies. Our findings provide comprehensive evidence for AD risk loci in cases of both European and Asian ancestries, validating previous associations as well as revealing novel loci that could imply previously unexplored biological pathways.

The surface defects of shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise the operation safety. To effectively detect the multiple surface defects, this research employs a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting the inspection of the shield tunnel on a metro line section, various surface defects are identified by the TIT, including water leakage defect, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, the association rules between different defects are calculated via an improved Apriori algorithm. Results show that: i) there are significant differences in different association rules of various surface defects of the shield tunnel; ii) the average confidence of the association rule “dislocation &amp; spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; iii) the weakest rule appears at “water leakage → spalling”, with the average confidence of 13%. The association analysis can be used in predicting the critical defects influencing the structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for the shield subway tunnel.

The gelation time tg necessary for a solution of functional (associating) molecules to reach its gel point after a temperature jump, or a sudden concentration change, is theoretically calculated as a function of the concentration and temperature for the molecules carrying the number f of functional groups. Quite generally, tg is given by the product of the relaxation time tR and a thermodynamic factor Q. For pairwise cross-linking, and cross-linking with a fixed multiplicity k, precise values of tg are derived. For cross-linking with variable multiplicity, kinetic equation of stepwise association is derived for the reactivity pk of the functional groups, on the basis of which tg and tR are estimated. The thermodynamic factor Q is shown to generate a singularity of logarithmic divergence as the temperature (concentration) approaches the equilibrium gel point, while the relaxation time tR changes continuously across it. The retardation effect on the gelation time due to the reversibility of the cross-linking is explicitly calculated for some specific models of cross-linking. For a micellar cross-linking covering a wide range of the multiplicity, as seen in hydrophobically-modified water-soluble polymers, tR is shown to obey a formula similar to the Aniansson-Wall law.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in male and female patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33×10^-8 calculated with the Yates-corrected Chi-square test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWAS of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network. This study indicates that a number of genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The results also suggest the genetic basis of sex-specific psychiatric comorbidities of AD.

In northern Thailand, the problem of small particulate matter happens every year, with the pri-mary source being agricultural weed burning and wildfire. The tourism industry is strongly impacted and has been the spotlight for the past few years. Thus, the study aims to investigate the effect of small particulate matter on tourism and related SMEs in Chiang Mai, Thailand. The data was collected from 286 entrepreneurs in the tourism and related SMEs sectors. The data was analyzed using data mining and association rule techniques. The study revealed that small particulate matter has a considerable impact on customer factors, especially the number of customers has decreased. Operational factors and prod-uct/service factors are also affected by the dust in the form of adjustments to keep the business running and the protection of the health of employees and customers. Certainly, financial factors are affected by the small particulate matter situation, both lower revenues and higher costs.

Background: Alcohol consumption is associated with the development of cardiovascular diseases, cancer, and liver disease. The biological mechanisms are still largely unclear. Here, we aimed to use an agnostic approach to identify phenotypes mediating the effect of alcohol on various diseases. Methods: We performed an agnostic association analysis between alcohol consumption (red, and white wine, beer/cider, fortified wine, and spirits) with over 7,800 phenotypes from the UK biobank comprising 223,728 participants. We performed Mendelian randomisation analysis to infer causality. We additionally performed a Phenome-wide association analysis and a mediation analysis between alcohol consumption as exposure, traits in causal relationship with alcohol consumption as mediators, and various diseases as outcome. Results: Of 45 traits in association with alcohol consumption, 20 were in causal relationship with alcohol consumption. Gamma glutamyltransferase (GGT; β=9.44; CI,5.94-12.93; Pfdr=9.04×10-7), mean sphered cell volume (β=0.189; CI,0.11-0.27; Pfdr=1.00×10-4), mean corpuscular volume (β=0.271; CI,0.19-0.35; Pfdr=7.09×10-10) and mean corpuscular haemoglobin (β=0.278; CI,0.19-0.36; Pfdr=1.60×10-6) showed the strongest causal relationships. We also identified GGT and physical activity as mediators causing liver cirrhosis and alcohol dependence. Conclusion: Our study provides evidence of causality between alcohol consumption and 20 traits and a mediation effect for physical activity on health consequences of alcohol consumption.

The GAI‐RGA ‐ and ‐SCR (GRAS) proteins belong to the plant-specific transcription factor gene family and involved in several developmental processes, phytohormone and phytochrome signaling, symbiosis, stress responses etc. GRAS proteins have a conserved GRAS domain at C-terminal and hypervariable N-terminal. The C-terminal conserved domain directly affects the function of the GRAS proteins. For instance, in Arabidopsis, mutations in this domain in Slender rice 1 (SLR1) and Repressor of GA (RGA) proteins cause significant phenotypic changes. GRAS proteins have been reported in more than 30 plant species and till now it has been divided into 17 subfamilies. This review highlighted GRAS protein's importance during several biological processes in plants, structural features of GRAS proteins, their expansion and diversification in the plants, GRAS-interacting proteins complexes and their role in biological processes. We also summarized available recent research that utilized CRISPR-Cas9 technology to manipulate GRAS genes in a plant for different traits. Further, the exploitation of GRAS genes in crop improvement programs has also been discussed

The effects of cognate synonymy in L2 word learning are explored. Participants learned the names of well-known concrete concepts in a new fictional language following a picture-word association paradigm. Half of the concepts (set A) had two possible translations in the new language (i.e., both words were synonyms): one was a cognate in participants’ L1 and the other one was not. The other half of the concepts (set B) had only one possible translation in the new language, a non-cognate word. After learning the new words, participants’ memory was tested in a picture-word matching task and a translation recognition task. In line with previous findings, our results clearly indicate that cognates are much easier to learn, as we found that the cognate translation was remembered much better than both its non-cognate synonym and the non-cognate from set B. Our results also seem to suggest that non-cognates without cognate synonyms (set B) are better learned than non-cognates with cognate synonyms (set A). This suggests that, at early stages of L2 acquisition, learning a cognate would produce a poorer acquisition of its non-cognate synonym, as compared to a solely learned non-cognate. These results are discussed under the light of different theories and models of bilingual mental lexicon.

Copulas are useful tools for modeling the dependence structure between two or more variables. Copulas are becoming a quite flexible tool in modeling dependence among the components of a multivariate vector, in particular to predict losses in insurance and finance. In this article, we study the dependence structure of some well-known real life insurance data (with two components mainly) and subsequently identify the best bivariate copula to model such a scenario via VineCopula package in R. Associated structural properties of these bivariate copulas are also discussed.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

Pepino mosaic virus (PepMV) causes significant economic losses in tomato crops worldwide. Since its first detection infecting tomato in 1999, aggressive PepMV variants have emerged. This study aimed to characterize two aggressive PepMV isolates, PepMV-H30 and PepMV-KLP2. Both isolates were identified in South-Eastern Spain infecting tomato plants, which showed severe symptoms, including bright yellow mosaics. Full-length infectious clones were generated, and phylogenetic relationships were inferred using their nucleotide sequences and another 35 full-length sequences from isolates representing the five PepMV known strains. Our analysis revealed that PepMV-H30 and PepMV-KLP2 belong to the EU and CH2 strains, respectively. Amino acid sequence comparisons between these and mild isolates identified 8 and 15 amino acid substitutions for PepMV-H30 and PepMV-KLP2, respectively, potentially involved in severe symptom induction. None of the substitutions identified in PepMV-H30 had previously been described as symptom determinant. The E236K substitution, originally present in the PepMV-H30 CP, was introduced into a mild PepMV-EU isolate, resulting in a virus that causes symptoms similar to those induced by the parental PepMV-H30. In silico analyses revealed that this residue is located at the C-terminus of the CP and is solvent-accessible, suggesting its potential involvement in CP-host proteins interactions. We also examined the subcellular localization of PepGFPm2E236K in comparison to that of PepGFPm2, focusing on chloroplast affection, but no differences were observed in the GFP subcellular distribution between the two viruses. Due to the easily visible symptoms that PepMV-H30 and PepMV-KLP2 induce, these isolates represent valuable tools in programs destined to breeding resistance to PepMV in tomato.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

The high Nb-containing TiAl-based alloy ingot beyond laboratory scale with a composition of Ti-46Al-8Nb (at. %) was prepared by a vacuum induction melting process based on BaZrO3 refractory. An ingot without macroscopic casting defects (such as pipe shrinkage and center line porosity) was finally obtained, and the chemical composition, solidification path, microstructure and tensile properties of the ingots were investigated. The results show that the deviations of Al and Nb content along a 430 mm long central part of the ingot are approximately \mathrm{\pm}0.39 at. % and \mathrm{\pm}0.14 at. %, and the oxygen content in the ingot can be controlled at around 1000 ppm. During the solidification process, the alloy suffered from peritectic reaction and formed columnar grains with anisotropy. In addition to Al segregation and Nb segregation, β-phase particles associated with γ phase at the triple junction of the colonies were observed. Moreover, the mechanical properties of the ingot in the transverse direction are significantly better than those in the longitudinal direction, with a tensile strength of up to high as 700 MPa and a corresponding fracture elongation of 1.1 %.

The effect of Zr addition on the melting temperature of the CoCrFeMnNi High Entropy Alloy (HEA), known as the “Cantor’s Alloy”, is investigated, together with its microstructure, mechan-ical properties and thermo-mechanical recrystallization process. The base and Zr-modified al-loys are obtained by vacuum induction melting of mechanically pre-alloyed powders followed by recrystallization. The alloys were characterized by X-ray diffraction, scanning and transmis-sion electron microscopy, thermal analyses, mechanical spectroscopy and indentation measures. The main advantages of Zr addition are: 1) a fast vacuum induction melting (VIM) process; 2) the lower melting temperature, due to Zr eutectics formation with all the Cantor’s alloy elements; 3) the good chemical alloy homogeneity; 4) the mechanical properties improvement of recrystallized grains with a coherent structure. The crystallographic lattice of both alloys resulted to be FCC. Results demonstrate that the Zr-modified HEA presents a higher recrystallization temperature and smaller grain size after recrystallization with respect to the Cantor’s alloy, with precipita-tion of a coherent second phase which enhance the alloy hardness and strength, while maintaining a good tensile ductility.

Background and objectives: Induction of labor (IOL) is an event that occurs in up to one-quarter of pregnancies; less is known about the outcomes and safety of IOL in obese pregnant woman; no data is available on misoprostol vaginal insert (MVI) IOL in high-risk pregnancy obese women. Objectives: (1) to evaluate the rate of successful IOL with 200 μg MVI in obese (Body Mass Index - BMI over 30 kg/m2) high-risk pregnant women: late-term pregnancy, hypertension or diabetes, compared to obese non-high-risk ones; (2) to evaluate the safety profile of MVI in high-risk pregnancy obese patients. Study design: We conducted a cross-sectional study in "Filantropia" Clinical Hospital, Bucharest, Romania, from June 2017 to September 2019 (28 months). From a total of 11,096 registered live births, IOL was performed in 206 obese patients; 74 obese high-risk pregnant patients matched the inclusion criteria; of these, 33.8% pregnancies (n=25) were late-term (41 – 41+6 weeks), 43.2% (n=32) had associated pathologies (hypertension and diabetes); labor induction was guided using a standardized protocol. We evaluated the maternal and gestational age, parity, fetal tachysystole, hyper-stimulation, initial cervical status, time from induction to delivery, drug side effects, mode of delivery, and neonatal outcomes. Results: (a) The overall successful labor induction rate, evaluated by the vaginal delivery rate, was 71.6% (n=53), spontaneously or instrumentally assisted; 28.4% (n=21) births were unsuccessful MVI IOL, converted into caesareans. (b) No significant differences were found regarding the maternal outcomes; in terms of perinatal outcomes of safety, four cases of high-risk pregnancies vaginally delivered were associated with neonatal intensive care unit (NICU) admissions and a one-minute Apgar score under seven (5.4%). Most cases with adverse effects of misoprostol have been managed conservatively, except for three emergency C-section cases. Conclusions: Misoprostol vaginal insert is a safe choice in IOL in obese high-risk pregnancies with good maternal and perinatal outcomes.