An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Controlled deposition of CoCrFeNiMo0.2 high entropy alloy (HEA) micro-particles was achieved using laser induced forward transfer (LIFT). Ultra-short laser pulses, 230 fs of 515 nm wavelength, were tightly focused into ∼ 2.4 μm focal spots on the ∼50 nm thick plasma-sputtered films of CoCrFeNiMo0.2. The HTA films were transferred onto glass substrates by magnetron sputtering in vacuum (10−8 atm) from the thermal spray coated substrates. The absorption coefficient of CoCrFeNiMo0.2 α ≈ 6 × 105 cm−1 was determined at 600 nm wavelength. The real and imaginary parts of refractive index (n + iκ) of HEA were determined from reflectance and transmittance using nano-films.

Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactor (MBR). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of membrane fouling. To solve this problem, this paper proposes a compressible diagnosis model (CDM) based on transfer entropy to facilitate the fault diagnosis of the root cause for membrane fouling. Firstly, a framework of CDM between membrane fouling and causal variables is built based on a feature extraction algorithm and mechanism analysis. The framework can identify fault transfer scenarios following the changes in operating conditions. Secondly, the fault transfer topology of CDM based on transfer entropy is constructed to describe the causal relationship between variables dynamically. Thirdly, an information compressible strategy is designed to simplify the fault transfer topology. This strategy can eliminate the repetitious affiliation relationship, which contributes to the root causal variables speedily and accurately. Finally, the effectiveness of the proposed CDM is verified by the measured data from an actual MBR.

The heat and matter transfer during glucose catabolism in living systems and their relation with entropy production are a challenging subject of the classical thermodynamics applied to biology. In this respect, an analogy between mechanics and thermodynamics has been performed via the definition of the entropy density acceleration expressed by the time derivative of the rate of entropy density and related to heat and matter transfer in minimum living systems. Cells are regarded as open thermodynamic systems that exchange heat and matter resulting from irreversible processes with the intercellular environment. Prigogine’s minimum energy dissipation principle is reformulated using the notion of entropy density acceleration applied to glucose catabolism. It is shown that, for out-of-equilibrium states, the calculated entropy density acceleration is finite and negative and approaches as a function of time a zero value at global thermodynamic equilibrium for heat and matter transfer independently of the cell type and the metabolic pathway. These results could be important for a deeper understanding of entropy generation and its correlation with heat transfer in cell biology with special regard to glucose catabolism representing the prototype of irreversible reactions and a crucial metabolic pathway in stem cells and cancer stem cells.

Modulation of the amplitude of high-frequency cortical field activity locked to changes in phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristics. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data, and in both cases we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis.

This work probes the combined effects of magnetic field and viscous dissipation on heat field and examine the second law analysis (entropy generation) in an electrically conducting fluid under the effect of wall mass transfer over continuous stretched non-isothermal surface with variable viscosity. The viscosity of the fluid is assumed to be an inverse linear function of temperature. The governing equation for the problem are changed to dimensionless ordinary differential equations by using similarity transformation and solved numerically by using Rung Kutta and Shooting technique. Velocity, concentration and temperature distribution are obtained and used to compute the entropy generation and the Bejan number in the flow field. The effect of variable viscosity, Schmidt number, Hartman and Reynolds number on the velocity, concentration, temperature, entropy generation and Bejan number are studied and discussed.

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on businesses and individuals. Machine learning approaches to automate ransomware detection have shown promise but grapple with challenges like limited training data. This study introduces a novel deep learning model for few-shot ransomware classification. The model employs entropy features derived directly from malware binaries coupled with a twin neural network architecture utilizing transfer learning. Tests on over 1000 samples across 11 families demonstrate a weighted F1-score of 85.8\%, surpassing existing methods. The approach mitigates biases in limited training data and preserves intricacies lost in image-based features. It exhibits precise classification capabilities even with sparse samples of new ransomware variants. The research highlights the potential of entropy-driven deep learning to equip defenses against emerging zero-day ransomware strains.

This study presents a comprehensive investigation of entropy generation during natural convection in a porous medium with Casson fluid. The governing equations, including the momentum, energy, and entropy balance equations, are solved numerically using finite element method. Through the analysis, various parameters affecting entropy generation are investigated, such as the Casson fluid parameter, Radiation, Prandtl number, and Rayleigh number. The results indicate that the Casson fluid parameter significantly influences the flow and heat transfer characteristics, while the Darcy number and Rayleigh number control the intensity of natural convection. Moreover, the Prandtl number determines the relative significance of heat transfer compared to viscous effects.

Recent advancements in the study of the protein complex photosystem II have clarified the sequence of events leading to the formation of oxygen during the S3->S4->S0 tran-sition, wherein the inorganic Mn4Ca(µ-O)6(OHx)4 cluster finishes photo-catalyzing the water splitting reaction (Greife et al, Nature 2023, 617, 623-628; Bhowmick et al, Nature 2023, 617, 629-636) . During this final step a tyrosine radical (TyrZ), stable for a couple of milliseconds, oxidizes a cluster bound oxygen while the hydrogen bonding patterns of nearby waters shift a proton away. A treatment of this redox reaction within the context of accepted transition state theories predicts rate constants that are significantly higher than experimentally recovered values (10^12s^-1 versus 10^3s^-1). In an effort to understand this disparity, temperature dependent experiments have revealed large entropic con-tributions to the rates with only a moderate energy of activation. We suggest that the entropic source may be related to the observed proton rearrangements, and further possible near isoenergetic variations in the nearby extended h-bonding network de-laying the realization of an ‘ideal’ transition state. In the following we explore this re-lation in the context of Eyring’s transition state theory and Marcus’ electron transfer theory, and evaluate their compatibility with the experimental evidence.

A theory for the micelle formation of nonionic head-tail amphiphiles (detergents) in aqueous solutions is derived based on the traditional molecular thermodynamic modeling approach and a variant of Flory-Huggins theory that goes beyond lattice models. The theory is used to analyze experimental values for the critical micelle concentration of n-alkyl-ß-D-maltosides within a mass action model. To correlate those parts of the micellization free energy, that depend on the transfer of hydrophobic molecule parts into the aqueous phase, with molecular surfaces, known data for the solubility of alkanes in water are reanalyzed. The correct surface tension to be used in connection with the solvent excluded surface of the alky tail is 30 mN/m. This value is smaller than the measured surface tension of a macroscopic alkane-water interface, because the transfer free energy contains a contribution from the incorporation of the alkane or alkyl chain into water representing the change in free volume of the aqueous phase. Flory-Huggins theory works well, if one takes into account the difference in liberation free energy between micelles and monomers, which can be described in terms of the aggregation number as well as the thermal deBroglie wavelength and the free volume of the detergent monomer.

Fermenter is a vessel that maintains optimum environment for the development of significant microorganism used in large scale fermentation process and the commercial production of products like Alcoholic beverages, Enzymes, Antibiotics, Organic acids etc. The fermenter aims to produce biological product like vaccines and hormones, it is necessary to monitor and control the different parameters like external and internal mass transfer, heat transfer, fluid velocity, shear stress, agitation speed, aeration rate, cooling rate or heating intensity, and the feeding rate, nutrients, base or acid valve. Fermentation in the fermenter are accomplished in several configuration and these simple configurations are batch, fed-batch and continuous fermentation process. Fermentation process is carried out in small or large size fermenter depending on product quantity. The selection of the suitable process depends on the fermentation kinetics, type of microorganism used and process economic aspects. Improved modelling tools, reactor operation and reactor design in bioreactor is because of mass transfer behavior and it is important for reaction rate maximizing, throughput rates optimization and cost minimizing. The fermenter design, fermentation process, types of the fermenter that are used in industries and heat and mass transfer in fermenter is discussed.

The heritage of ancient buildings is an important part of the world's history and culture, which has an extremely rich historical-cultural value and artistic research value. Beijing has a large number of palace ancient buildings, and because of the age of their construction, many of them have problems of varying degrees of peeling and mold on the inner surfaces of the envelope. To solve the problems of the damp and moldy interior of palace buildings, a mathematical model of indoor heat and moisture transfer was established based on a wooden palace ancient building in Beijing. Through the indoor mold distribution validation model, the effects of outdoor humidity, soil moisture, wall humidity, and other factors on the indoor heat and moisture transfer of ancient buildings were simulated and analyzed by using the control variables method. The results showed that the molds were distributed at the indoor corners and floors, and the simulation of indoor humidity match the measured humidity. Thus, the simulation results were consistent with the actual situation. The variable trend of the relative humidity of the indoor environment with the outdoor humidity is inconsistent from plane to plane, i.e. it increases or remains constant with the increase of the outdoor humidity. The indoor ambient relative humidity increased with increasing the wall humidity. And the indoor average temperature is 23.3 ℃ and indoor relative humidity ranged between 90.9 % to 92.44 %. Soil moisture and wall humidity were the main factors affecting the indoor environmental relative humidity.

Background: The fascial system provides an environment that enables all body systems to operate in an integrated manner and is capable of modifying its tensional state in response to the stress applied to it. Recent in vitro, animal and cadaveric studies have shown that “myofascial force transfer” (MFT) has the potential to play a major role in musculoskeletal function and dysfunction.Objective: Human evidence for the existence of invivo MFT is scarce. This scoping review attempts to gather and analyse the available evidence of the in-vivo human MFT studies in order to sustain and facilitate further research and evidence based practice in this field.Methods: A search of most major databases was conducted with relevant keywords that yielded 238 articles as of August 2020. A qualitative analysis of the studies was conducted after rating it with Oxford’s Center for Evidence –based Medicine (CEBM) scale.Result: Nineteen studies ranging from randomized controlled trials to case studies covering 540 patients were included in this review. The analysed studies were highly heterogeneous and of lower methodological quality meddling with the quantitative analysis. Ten studies are confirming a ‘most likely’ existence of MFT, eight studies confirming it as ‘likely’ and one study couldn’t confirm any MFT existence in this review.Conclusion: Findings from in vivo human studies supports the animal and cadaveric studies claiming the existence of MFT which need to be corroborated by the future high quality studies. Forthcoming studies on MFT may give answers and solutions to many of the human musculoskeletal mysteries or dysfunctions.

In this work, we experimentally investigate mass and heat transport phenomena in a modular device while converting a solution salinity difference into a temperature difference. Operations occur under fixed total ambient pressure and without mechanical moving parts, thus realizing a fully static conversion. Provided that a constant salinity gradient can be imposed, the proposed device is able to generate a steady cooling capacity. Here, we purposely operate with environmentally benign and easily accessible sodium chloride water solutions only. A numerical model is finally elaborated, validated and used to explore a wider range of possible device configurations and operating conditions.

The central mechanism of biological evolution, variation-selection-inheritance (VSI), is one of the most universal mechanisms known. Much of our understanding of VSI, however, has been dominated by the Neo-Darwinian Modern Synthesis with a rather narrow understanding of what constitutes variation, selection, and inheritance. This unduly narrow understanding of VSI might have been a key cause behind our failure to adequately explain some critical puzzles in biological evolution, from the origin of the first cell to the origin of the eukaryotes, the puzzling biology of metabolism, apoptosis, aging, and cancer in metazoan.I broaden our understanding of VSI, in a spirit that is somewhat similar to several recent contributions and then extend this broadened view of VSI to its natural starting point: the origin of the First Universal Cell Ancestor (FUCA). I advance three principal arguments. First, survival comes before replication. Before the coming of reproducer and replicator, there must be survivors, to paraphrase Szathmary and Maynard Smith (1997). Second, natural selection, especially the non-Darwinian kind, can operate without replication or even metabolism, as long as different molecules, complexes, and vesicles have differential survival rate within a system. Third, merger and acquisition, via breaking-and-re-encapsulation, endocytosis, endosymbiosis, and processes similar to them, had been a far more powerful force of variation and selection in the pre-Darwinian period of evolution that led to LUCA and long before eukaryogenesis. Endosymbiosis therefore had been a far more foundational force than even Lynn Margulis and many of her supporters have appreciated. Our thesis thus goes beyond Woese’s emphasis of horizontal gene transfer (HGT) and actually subsumes HGT with Margulis’ emphasis of endosymbiosis. Combing these three new perspectives with other perspectives and evidence sheds important new light upon the origin of FUCA, the singular water-shedding moment in the evolution of life.

Horizontal gene transfer (HGT) is the transmission of genes between organisms by other means than parental to offspring inheritance. While it is prevalent in prokaryotes, HGT is less frequent in eukaryotes and particularly in metazoan. Here, we propose Alienness, a taxonomy-aware web application that parses BLAST results against public libraries to rapidly identify candidate HGT in any genome of interest. Alienness takes as input the result of a BLAST of a whole proteome of interest against any NCBI protein library. The user defines recipient (e.g. metazoan) and donor (e.g. bacteria, fungi) branches of interest in the NCBI taxonomy. Based on the best BLAST E-values of candidate donor and recipient taxa, Alienness calculates an Alien Index (AI) for each query protein. An AI >0 indicates a better hit to candidate donor than recipient taxa and a possible HGT. Higher AI represent higher gap of E-values between candidate donor and recipient and a more likely HGT. We confirmed the accuracy of Alienness on phylogenetically confirmed HGT of non-metazoan origin in plant-parasitic nematodes. Alienness scans whole proteomes to rapidly identify possible HGT in any species of interest and thus fosters exploration of HGT more easily and largely across the tree of life.

The condensing heat exchanger is commonly applied in various heat exchange systems. It can efficiently recover moisture and heat from the flue gas that contains water vapor. However, the convective-condensation heat transfer process is so complicated that no mature thermal calculation model is available. This study develops a thermal calculation model for the widely employed tubular condensing heat exchanger in industry. To characterize the degree of the heat and mass transfer, this study introduces two parameters, namely the sensible and latent heat transfer efficiencies of fin. The thermal calculations are conducted for the condensing heat exchangers reported in the literature to verify the proposed model by comparing it with the experimental data. The results show that the absolute error of the calculated sensible and latent heat transfer efficiencies is 0.0365 and 0.0268, respectively. Under the working conditions in this study, a maximum difference of 5.2 K has been acquired between the measured and calculated values of the outlet temperature. The relative error of the condensate water flowrate is mostly within ±5.0% and ±10.0% under the bare-tube and finned-tube conditions, respectively, with a maximum deviation of 0.7 and 1.4 kg h-1. This study provides a general model for designing and optimizing various tubular condensing heat exchangers accurately.

Billions of euros are invested every year by professional football clubs for the recruitment of players. This article presents the latest advances in the statistical modelling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain almost 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices seems highly relevant.

Chemical vapor deposition (CVD) is a vital process for depositing thin films of various materials with precise control over thickness, composition, and properties. Understanding the heat and mass transfer mechanisms during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in the heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

This study examines the effects of particle size and heat pipe angle on the thermal effectiveness of a cylindrical screen mesh heat pipe using silver nanoparticles as the test substance. The experiment investigates three different particle sizes (30nm, 50nm, and 80nm) and four different heat pipe angles (0°, 45°, 60°, and 90°) on the heat transmission characteristics of the heat pipe. The results show that the thermal conductivity of the heat pipe increased with an increase in heat pipe angle for all particle sizes, with the highest thermal conductivity attained at a 90° heat pipe angle. Furthermore, the thermal resistance of the heat pipe decreased as the particle size decreased for all heat pipe angles. The thermal conductivity measurements of the particle sizes - 30, 50, and 80 nm - were 250 W/mK, 200 W/mK, and 150 W/mK, respectively. The heat transfer coefficient values for particle sizes 30nm, 50nm, and 80nm were 5500 W/m2K, 4500 W/m2K, and 3500 W/m2K, respectively. The study also found that the heat transfer coefficient increased with increased heat pipe angle for all particle sizes, with the highest heat transfer coefficient obtained at a 90° heat pipe angle.

Ferulic acid has numerous beneficial effects for human health, which are frequently attributed to its antioxidant behavior. In this report many of them are reviewed and 185 new ferulic acid derivatives are computationally designed, using the CADMA-Chem protocol. For the later, the chemical space was sampled and evaluated. To that purpose selection and elimination scores were used, which are built from a set of descriptors accounting for ADME properties, toxicity, and synthetic accessibility. After the first screening, 12 derivatives were selected and further investigated. Their potential role as antioxidants was predicted from reactivity indexes, directed related with the formal hydrogen atom transfer and the single electron transfer mechanisms. The best performing molecules were identified by comparisons with the parent molecule and two references: Trolox and alpha-tocopherol. Their potential as polygenic neuroprotectors was investigated through the interactions with enzymes directed related with the etiologies of Parkinson’s and Alzheimer’s diseases. They are acetylcholinesterase, catechol-O-methyltransferase, and monoamine oxidase B. Based on the obtained results, the most promising candidates (FA-26, FA-118, and FA-138) are proposed as multifunctional antioxidants with potential neuroprotective effects. The findings derived from this investigation are encouraging and might promote further investigations on these molecules.

The optical signals detected on multiple satellite platforms over snow surfaces are determined by the optical properties of snow surface and atmosphere. The solution of both direct and inverse problems of an atmosphere – underlying snow system requires simple relationships between top-of-atmosphere (TOA) reflectance R and microphysical/optical characteristics of both snow and atmosphere. The task of this paper is to present a simple analytical relationship between the value of R as detected on a satellite with atmosphere/snow properties. Such a relationship can be established using a numerical solution of integro - differential radiative transfer equation (RTE) (Liou, 2022). However, this path is quite complicated and time consuming. The analytical solutions of RTE are needed for the solution of various applied atmospheric and snow optics problems (Cachorro et al., 2022; Mei et al., 2020, 2022; Kokhanovsky, 2021). This is the main driver of this work. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1nm or so) and the spectral range 400-1000nm.

Accurate transfer learning of clinical outcomes, e.g., of the effects and side effects of drugs or other interventions, from one cellular context to another (in-vitro versus ex-vivo versus in-vivo, or across tissues), between cell-types, developmental stages, omics modalities or species, is considered tremendously useful. Ultimately, it may avoid most drug development failing in translation, despite large investments in the preclinical stages, which includes animal experiments requiring careful justification. Thus, when transferring a prediction task from a source (model) domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring molecular states or processes common to both source and target that can be learned by the predictor, reflected by latent variables. These latent variables may form a compendium of knowledge that is learned in the source, to enable predictions in the target; usually, there are few, if any, labeled target training samples to learn from. Transductive learning then refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive learning considers cases where the target predictor is performing a different yet related task as compared to the source predictor, making some labeled target data necessary. Often, there is also a need to first map the variables in the input/feature spaces (e.g. of gene names to orthologs) and/or the variables in the output/outcome spaces (e.g. by matching of labels). Transfer across omics modalities also requires that the molecular information flow connecting these modalities is sufficiently conserved. Only one of the methods for transfer learning we reviewed offers an assessment of input data, suggesting that transfer learning is unreliable in certain cases. Moreover, source domains feature their very own particularities, and transfer learning should consider these, e.g., as differences in pharmacokinetics, drug clearance or the microenvironment. In light of these general considerations, we here discuss and juxtapose various recent transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in-vivo) outcomes based on molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

This paper presents an alternative conceptualization and definition of technology in the context of university technology transfer. The ambiguity regarding the conceptualization of technology is apparent in the technology transfer literature. An expanded conceptualization of technology potentially opens new approaches to researching the topic of technology transfer. It may also cause policymakers to think more comprehensively about what it means to successfully transfer technologies derived from federally funded research to the private sector for use that benefits the public interest. This paper integrates constructs and ideas in the related literature to provide a new perspective of technology that can support future scholarly research and public policy formulation about technology transfer in general, and university technology transfer specifically. Although the paper focuses on university technology transfer to the private sector in the United States, the insights it presents are relevant to technology transfer more broadly and applicable in other geopolitical contexts.

The steady-state, coupled heat and mass transfer from a fluid flow to a sphere accompanied by an exothermal catalytic chemical reaction on the surface of the sphere is analysed taking into consideration the effect of thermal radiation. The flow past the sphere is considered steady, laminar and incompressible. The radiative transfer is modeled by P₀ and P₁ approximations. The mathematical model equations were discretized by the finite difference method. The discrete equations were solved by the defect correction – multigrid method. The influence of thermal radiation on the sphere surface temperature, concentration and reaction rate was analysed for three parameter sets of the dimensionless reaction parameters. The numerical results show that only for very small values of the Prater number the effect of thermal radiation on the surface reaction is not significant.

This paper studies the evaporative heat transfer characteristics of R744 at low temperatures in a horizontal smooth tube as a cascade refrigeration system (CRS) among hybrid cascade refrigeration systems (HCRSs). There is a lack of research on the low-temperature evaporative heat transfer characteristics of R744 under the operating conditions of evaporators used in actual CRSs used in supermarkets. Therefore, this study aims to provide basic data on the evaporative heat transfer characteristics of R744 in the evaporators of refrigerators used in supermarkets. The tube used in the evaporation experiment conducted herein was a smooth horizontal copper tube with an inner diameter and length of 11.46 mm and 8000 mm, respectively. The experimental pa-rameters were as follows: heat fluxes of 12–21.5 kW/m2, mass fluxes of 75–225 kg/(m2·s), and saturation temperatures of −50–−30 °C. The main results are summarized as follows. (1) When designing the R744 evaporator, the mass and heat fluxes must be maximized within the operating conditions, and the saturation temperature must be designed to be as low as possible. (2) The evaporative heat transfer coefficient of R744 can be predicted well by using the correlation formula of Chen at the evaporation temperature of −40 °C in the CRS.

Field implementations of inductive power transfer (IPT) face many practical challenges that dramatically reduce power transmission effectiveness. Coil misalignment is a persistent issue in IPT systems, especially in applications involving vehicles or nonvisible coils. Furthermore, magnetic field symmetry convolutes the decomposition of misalignment measurements in multiple dimensions and frequently impedes the efficacy of traditional position correction strategies. This paper presents an automated methodology to sense misalignments and align IPT coils using robotic actuators and sequential Monte Carlo methods. The misalignment of a Class EF inverter-driven IPT system was modeled by tracking changes as its coils move apart laterally and distally. These models were integrated with particle filters to estimate the location of a hidden coil in 3D, given a sequence of sensor measurements. During laboratory tests on a Cartesian robot, these algorithms aligned the IPT system within 1 cm (0.025 coil diameters) of peak lateral alignment. On average, the alignment algorithms required less than four sensor measurements for localization. After laboratory testing, this approach was implemented with an agricultural sensor platform at the Utah Agricultural Experiment Station in Kaysville, Utah. In this implementation, a buried sensor platform was successfully charged using an aboveground, vehicle-mounted transmitter.

In this work, we develop a software suite for studies of atmosphere – underlying SNOW- spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for local optical properties of atmosphere and snow. The model developed is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 linked to warming event, and a 4 times larger grain size at Aviator Glacier compared to Dome C.

Fertilized zygotes normally display 2 pronuclei (PN) but abnormal fertilization patterns (0, 1 or >2 PN) are daily observed in IVF labs. Multiple PN zygotes (>2) are generally discarded due to an increased risk of aneuploidy. However, the decision to transfer or not 1PN-derived embryos remains controversial. The aims of our study were to analyze the neonatal outcomes of fresh or frozen-thawed embryos derived from 1PN zygotes, and to evaluate the influence of the fertilization method. Data were retrospectively collected from cycles performed between January 2018 and December 2022. Fresh cycles were analyzed for the comparative fate of 1PN zygotes (n=1234) following conventional in vitro fertilization (cIVF; n=648) or intracytoplasmic sperm injection (ICSI; n=586), as well as the results of the 64 transfers of 1PN derived embryos (pregnancy rate (PR) and neonatal outcomes). This pregnancy follow-up was also applied to 167 transfers of frozen-thawed 1PN derived embryos. In fresh cycles, 46% of the 1PN zygotes in cIVF group gave rise to embryos of sufficient quality to be transferred or frozen (day 3 or 5/6). This rate decreased to 33% in the fresh ICSI cycles. Blastulation rate was also significantly higher in cIVF group (44%) in comparison to ICSI group (20%). The fresh embryo transfers (32 per group) allowed 7 pregnancies in the cIVF group (PR=21.9%) as compared to 4 pregnancies in the ICSI group (PR=12.5%). In the cIVF group, 4 deliveries of healthy newborns were achieved and only one in the ICSI group. In frozen/thawed cycles, 36 pregnancies were obtained out of the 167 transfers. A non-significative difference was observed between embryos derived from cIVF cycles (PR=26%) and ICSI cycles (PR=16%) with respectively 18 and 8 healthy babies born. In conclusion, we observed better outcomes for 1PN zygotes in cIVF cycles in comparison to ICSI cycles. Our center policy to transfer good quality 1PN-derived embryos allowed the birth of 31 healthy babies.

Ce³⁺ and Dy³⁺ ions doped Ca₂Al₂SiO₇ (CAS) phosphors were prepared by the solid-state reaction at 12800C for 1h. X-ray diffraction patterns confirmed a tetragonal crystalline structure. The luminescent spectra of CAS: Ce³⁺, Dy³⁺ phosphors consist a board band with peaking at 420 nm corresponding to the luminescence of Ce³⁺ ions and narrow lines of Dy³⁺ ions. The energy transfer from Ce³⁺ ion to Dy³⁺ ion is presented and discussed

This research aimed, within the scope of the anthropotechnological approach, analyze the technology transfer, performed via the offset policy in the field of public health, called the Radiotherapy Expansion Plan, from the Health Ministry. The objective of this policy is to create and improve accredited organizations, concerning the oncological treatment, specifically in the insertion of radiotherapeutic equipment. This process is divided into two stages: the insertion of the radiotherapy equipment, and the compensations provided for in the commercial agreement. To meet this purpose, the research started from understanding the theoretical and methodological approaches of the fields of study of anthropotechnology, technology transfer and offset policy. In this sense, there was used the methodological strategy of the case study, supported by applied research, with a qualitative and exploratory approach. External and internal environments of a specific situation were analyzed, located in the State of Paraná, which received the radiotherapy equipment. It was verified that the initiatives of insertion of radiotherapeutic equipment in the context of the Expansion Plan have undergone numerous confrontations, inserted in the contextual and organizational particularities that affect its development and effectiveness. There are challenges that require responses from a set of organizations involved, in order to implement the trade agreement established by the offset policy, highlighting the first stage as a process of technology transfer. Thus, the situation located in the State of Paraná consistently consolidated the insertion of the radiotherapy equipment. It allowed its disclosure as a reference situation, and based on the dimensions and indicators analysis provided by anthropotechnology, made possible the comprehensionof the technology transfer involved in the process.

To achieve the required mechanical properties in the heat treatment of steel, it is necessary to have an adequate cooling rate and to achieve the desired final temperature of the product. This should be achieved with one cooling unit for different product sizes. In order to provide high variability of the cooling system, different types of nozzles are used in modern cooling systems. Designers often use simplified, inaccurate correlations to predict the heat transfer coefficient, resulting in oversizing of the designed cooling or failure to provide the required cooling regime. This typically results in longer commissioning times and higher manufacturing costs of the new cooling system. Accurate information about the required cooling regime and the heat transfer coefficient of the designed cooling is critical. This paper presents a design approach based on laboratory measurements. Firstly, the way to find or validate the required cooling regime is presented. The paper then focuses on nozzle selection and presents laboratory measurements that provide accurate heat transfer coefficients as a function of position and surface temperature for different cooling configurations. Numerical simulations using the measured heat transfer coefficients allow the optimum design to be found for different product sizes.

Successful work performance is an important part of everyday functioning. Different factors could contribute to efficiency at the workplace. Two studies aim to establish how different forms of knowledge transfer at the workplace are related to self-rated work performance and whether better work performance is related to mindfulness as an aspect of cognitive functioning. The participants were 742 working Bulgarians in Study 1 and 259 workers in Study 2. Ten scales were used in Study 1, measuring different forms of knowledge transfer and self-rated work performance, and two scales were used in Study 2, measuring self-assessed work performance and mindfulness. The results revealed that higher self-assessment of work performance correlated positively but weakly with the sharing of tacit knowledge at the workplace, sharing knowledge with colleagues in dyads, work climate encouraging mastership, sharing knowledge in teams, knowledge transfer by means of cooperation between different generations, institutionalized transfer of knowledge, knowledge receiving, and estimated effectiveness of participation in supervision as a supervisee. Some forms of knowledge transfer (sharing of tacit knowledge and sharing knowledge with colleagues in dyads) seemed more closely related to self-assessed work performance than others. Self-assessment of work performance did not correlate statistically significantly with performance motivational climate at work. The different forms of knowledge transfer correlated with each other. Better work performance is related to better cognitive functioning, as the second study of 259 workers additionally revealed that higher self-assessment of work performance correlated with better self-awareness measured by the Mindful Attention Awareness Scale, i.e., more attention focused on own thoughts, emotions, and needs, i.e., on what is happening right now. Interconnectedness between the different forms of knowledge transfer indicates that stimulating one kind of knowledge transfer could also stimulate many other forms of knowledge transfer, but this may happen only when organizations support a mastery-motivated climate at work. Established connections between the different forms of knowledge transfer and self-rated work performance, as well as between self-assessed work performance and mindfulness, suggest that good cognitive functioning accompanied good functioning, and both may be facilitated by knowledge transfer at the workplace.

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, the problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the internal of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used dataset.

In combustor systems, thermoacoustic instabilities may occur and must be avoided for reliable op-eration. An acoustic network model can be used to predict the eigenfrequencies of the instabilities and the growth rate by incorporating the combustion dynamics with a Flame Transfer Function (FTF). The FTF defines the interconnection between burner aerodynamics and the rate of combus-tion. In the current study, the method to measure the FTF in a pressurized combustor is explored. A siren unit, mounted in the fuel line, induced a fuel flow excitation of variable amplitude and high maximum frequency. This was done here for pressurized conditions at 1.5 bar and 3 bar and at thermal power of 125 kW and 250 kW. In addition to the experimental investigation, a 1-D acoustic network model approach is used. In the model, thermo viscous damping effects and reflection coefficients are incorporated. The model results compare well with experimental data, indicating the proposed method to determine the FTF is reliable. In the approach, a combination of a FTF with a band stop approach and a network modeling approach was applied. The method provides a good match between experimentally observed behavior and analytical approach and can be used for instability analysis.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

A human brain can communicate with another one’s brain using quantum entanglement. Similar particles can entangle without interaction. Sensory communication between two individuals from far distances is still not known. Former studies primarily have been performed regarding brain neurons' quantum behavior. The test was performed on two individuals far from each other. These individuals were put under the same music on the basis of brain neuroplasticity property. By activating the brain reward system, aligned thinking was time scheduled in these individuals’ brains. On the basis of entanglement, compressed information was sent and received between these two individuals.

The gut microbiota's part in colon cancer has become an exciting and hopeful area of study because it shows complex links that affect how cancer starts, spreads, and responds to treatments. Dysbiosis, which is an imbalance in the community of germs in the gut, has been linked to a higher risk of colon cancer by causing inflammation and making a place where tumours can grow. Researchers have found that some kinds of bugs can either help colon cancer grow or stop it from doing so. This shows how important it is to maintain a healthy gut bacteria. In both preclinical and clinical research, therapeutic treatments that target the microbiome in the gut have shown promise. Probiotics and prebiotics can change the environment around a tumour, change the balance of microorganisms in the gut, and boost immune responses that fight the cancer. Faecal microbiota transfer (FMT) is being looked at as a new way to change the bacteria in the guts of people with colon cancer. By adding microbiota-targeted drugs to standard cancer treatments, it may be possible to make the treatment more effective and lessen side effects. Microbiota-focused treatments for colon cancer are still in their early stages, but they show promise. More research needs to be done to find out how they work and show that they work in the clinic. The link between the bacteria in the gut and colon cancer opens up new ways to help patients and give them better results. This might make a difference in how colon cancer is handled in the future.

Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasites detection remains the golden standard procedure for the diagnosis of parasites cyst or eggs, but this approach is costly time-consuming (30min/sample), highly tedious, and also required specialist. However, computer vision based on deep learning has made great stride in recent time. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore the potential of these techniques in the field of parasitology, specifically for intestinal parasites. Therefore, the goal of this research is to evaluate the performance of proposed state-of-the-art transfer learning architecture for detecting and classifying intestinal parasite eggs from images. We would ensure that patients receive prompt treatment while also relieving experts of extra work if we used such an architecture. Here, in stage first, we applied image pre-processing and augmentation to the dataset, and in stage second, we utilized the YOLOv5 algorithms for detection and classification and then compared their performance based on different parameters. Our algorithms achieved a mean average precision of 97% approximatiely and 8.5 ms detection time per sample for 5,393 intestinal parasite images. Thus, this approach may form a solid theoretical basis for real-time detection and classification in routine clinical examinations while accelerating the process to satisfy increasing demand.

Space shuttle has been a hall mark of American space program since its inception. Despite its temporary shutdown few years ago, with the recent interest in space exploration which includes revitalizing human outpost in microgravity and transportation required to build it, realize other experiments (e.g. in space telescopes, in space manufacturing) and interplanetary voyages, it has regained attention. Its superior design, manufacturing, materials, performance, durability, and efficiency place it among the best, in fact, the only effort by mankind to build a reusable craft horizontally, launch vertically like a rocket and fly back like a plane. Various requirements emerge during its design (thermal, fluid, acoustics, vibration and structural) and design of its main engine (Aerojet Rocketdyne RS 25) which requires considerable attention, heat transfer being most important. This facilitated and necessitated use of various types of heat exchangers such as single coil, heat pipe, built in internal heat exchanger (IHEX), external heat exchanger (EHEX), condensing heat exchanger (CHEX), Interface heat exchangers (InHEX), regenerative heat exchanger (RHEX) and compact heat exchangers (CoHEX), change, manipulate and optimize their configurations in piping and instrumentation diagrams (PIDs). In this short narrative, an effort has been made to summarize them, and their developments over time with a focus on the application, design, manufacturing, materials, and performance (in service and final operation).

Mathematical models across the applied sciences often utilize a standard methodological representation called a state variable formulation more commonly referred to as state space form. Recent research in unmanned underwater vehicle motor turbine blade thermal modeling for fatigue-life is generalized here permitting the proposed novel state space from to be applied to electrodynamics, motion mechanics, and many other disciplines. Proposed here is a very compact form inherently representing time variance, with a convenient presentation of dynamic variables applicable to all proper transfer functions, where all the distinct, real poles, zeros and gain of the transfer function appear as explicit components in the state space. The resulting manifestation simplifies utilization of the state space methods broadly across the applied sciences.

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes to a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure under the above described constraints.

When a microgrid is grid-tied to a distribution system, it can provide surplus power generation to the distribution system, if any abnormality or interruption occurs in the distribution system, the microgrid can operate in standalone mode to isolate the impact of the abnormality or interruption. However, if the microgrid can not collect enough information from the distribution system, it may cause the failure of generation transferring of distribution feeders, or even further influence the stability of the distribution system. In this paper, a strategy for the resilient control of a microgrid is proposed. It can solve the above-mentioned problem, reduce the duration of the outage of loads. This strategy is experimented in the microgrid in the Institute of Nuclear Energy Research (INER), the reliability is also analyzed to evaluate the unavailability of the microgrid in INER, and it is verified that the proposed strategy can reduce the duration of the outage of loads, and hence the reliability of a microgrid can be upgraded.

In the present paper, laminar forced convection nanofluid flows in a uniformly heated horizontal tube were revisited by direct numerical simulations. Single and two-phase models were employed with constant and temperature-dependent properties. Comparisons with experimental data showed that the mixture model performs better than the single-phase model in the all cases studied. Temperature-dependent fluid properties also resulted in a better prediction of the thermal field. A particular attention was paid to the grid arrangement. The two-phase model was used then confidently to investigate the influence of the nanoparticle size on the heat and fluid flow with a particular emphasis on the sedimentation process. Four nanoparticle diameters were considered: 10, 42, 100 and 200 nm for both copper-water and alumina/water nanofluids. For the largest diameter dnp = 200 nm, the Cu nanoparticles were more sedimented by around 80 %, while the Al₂O₃ nanoparticles sedimented only by 2.5 %. Besides, it was found that increasing the Reynolds number improved the heat transfer rate, while it decreased the friction factor allowing the nanoparticles to stay more dispersed in the base fluid. The effect of nanoparticle type on the heat transfer coefficient was also investigated for six different water-based nanofluids. Results showed that the Cu-water nanofluid achieved the highest heat transfer coefficient, followed by C, Al₂O₃, CuO, TiO₂, and SiO₂, respectively. All results were presented and discussed for four different values of the concentration in nanoparticles, namely φ = 0, 0.6, 1 and 1.6%. Empirical correlations for the friction coefficient and the average Nusselt number were also provided summarizing all the presented results.

Phylogenomics has enriched our understanding that the Tree of Life can have network-like or reticulate structures among some taxa and genes. Non-vertical modes of evolution—such as hybridization/introgression and horizontal gene transfer—deviate from a strictly bifurcating tree model, causing non-treelike patterns. Here, we present a brief overview of a phylogenomic workflow for inferring organismal histories and compare methods for detecting reticulate evolution. We discuss how the timing of coalescent events can help disentangle introgression from incomplete lineage sorting and how horizontal gene transfer events can help determine the relative timing of speciation events. In doing so, we identify pitfalls of certain methods and discuss how to extend their utility across the Tree of Life. Workflows, methods, and future directions discussed herein underscore the need to embrace reticulate evolutionary patterns for understanding the timing and rates of evolutionary events, providing a clearer understanding of life’s history.

Social recommendation is almost as the integration of the business platform and social platform, and gradually become a top in recommendation system. Social recommendation algorithm solves the problem of cold start and data sparseness for traditional commodity, while the internal structure of the relationship graph in social relations has not been fully excavated. This paper proposes two models of Micro Relation Transfer Model and Macro Relation Transfer Model of social relations, and applies the social relations transfer models into the social recommendation system. A relationship graph is built from the relationship between customers on the Internet. Micro Relation Transfer Model establishes the transfer activation function by calculating the relationship between the two customers using the similarity of interests set. Micro Relation Transfer Model spreads the relationship of friends by calculating the proportion of common neighbors held by the customer's social relations. In order to effectively control the transmission range and effect of social relations graph, we introduce pruning algorithm based on Monte Carlo Decision Tree convergence algorithm. The experimental results show that SRRTC algorithm enhances the success rate and stability significantly.

Continuing the investigation already started (See Farcaş, A.-A.; Bende, A. PCCP 2024, 26, 14937), the light absorption and charge-transfer properties of the dopamine (DA), dopamine zwitterion (called as dopamine-semi-quinone or DsQ) and dopamine-o-quinone (DoQ) adsorbed on the graphene nanoparticle surface have been investigated using the ground state and linear-response time-dependent density functional theories, considering the B97X-D3BJ/def2-TZVPP level of theory. In terms of the strength of molecular adsorption on the surface, the DsQ form has 50 % higher binding energy than that for the DA or DoQ cases (-20.24 kcal/mol vs. -30.41 kcal/mol). The results obtained for electronically excited states and UV-Vis absorption spectra show that the photochemical behavior of DsQ is more similar to DA than that observed for DoQ. Of the three systems analyzed, the DsQ-based complex showed the most active CT phenomenon, both in terms of the number of CT-like states and the amount of charge transferred. Of the first thirty electronically excited states computed for the DsQ case, eleven are purely CT-type and nine are mixed CT and localized (or Frenkel) excitations. By varying the adsorption distance between the molecule and the surface vertically, the amount of charge transfer obtained for DA decreases significantly as the distance increases, for DoQ it remains stable, while for DsQ there are states for which little change is observed, while for others there is a significant change. Furthermore, the mechanistic compilation of the electron orbital diagrams of the individual components cannot describe in detail the nature of the excitations inside the complex.

Our world is increasingly challenged by managing the impacts of natural disasters, particularly floods, which are frequent, dangerous, and costly. Traditional flood mapping methods, reliant on Optical satellites (MSI), struggle under cloud cover which is typical during such events. Synthetic Aperture Radar (SAR) offers a promising alternative with its cloud-penetrating capability, though its use has been limited due to complexity and data labeling challenges. This project aims to develop a SAR-based flood segmentation model that can rapidly and accurately map floods globally, with high adaptability to different flood types and regions. By utilizing deep learning and a novel transfer-learning technique that combines the strengths of Optical/MSI and SAR data, the model seeks to bypass the challenges of manual labeling and improve mapping accuracy. Initial results show the model's effective generalization across various flood events, with superior performance indicated by an Intersection over Union (IoU) of 0.72, outperforming existing methods and demonstrating promising capabilities in precise flood mapping.

Climate change and the increasing frequency and severity of drought events pose significant challenges for sustainable agriculture worldwide. Soil microorganisms, both beneficial and pathogenic, play a crucial role in mediating plant-environment interactions and shaping the overall functioning of agroecosystems. This review synthesizes the current knowledge on the contrasting adaptive mechanisms utilized by different groups of plant-soil microorganisms focusing on beneficial and pathogenic bacterial and fungal communities in response to drought and desiccation stresses. The review examines the common survival strategies employed by microbes specifically rhizobacteria and arbuscular mycorrhizal fungi, such as the production of osmoprotectants, altered gene expression, and biofilm formation. It also highlights the distinct adaptive mechanisms of pathogenic versus mutualistic microbes, with pathogens tending to prioritize virulence factors and suppress plant growth, while beneficial microbes enhance plant growth and stress tolerance. Genetic exchange such as horizontal gene transfer (HGT) is identified as a key adaptive mechanism, allowing both pathogenic and non-pathogenic microbes to acquire beneficial traits like stress tolerance and virulence factors. Environmental stressors like drought can promote increased genetic exchange and the spread of pathogenic traits within the soil microbiome. The complex interplay between drought-adapted microbes and their interactions with plants is discussed, emphasizing the need for a deeper understanding of soil microbiome dynamics under climate change. This knowledge can be utilized in sustainable agricultural practices to mitigate the impacts of drought on plant health and productivity. This review provides insights into the divergent survival strategies of soil microorganisms in response to drought and desiccation, for managing the resilience of agroecosystems to climate change.

We sequenced and assembled genomes for 17 isolates of Staphylococcus cohnii isolated from osteomyelitis lesions in young broilers from two separate experiments where we induced lameness using a hybrid wire-litter flooring system. Whole genome comparisons using three different methods support a close relationship of genomes from both S. cohnii and Staphylococcus urealyticus. The data support three different lineages, we designated Lineage 1, Lineage 2 and Lineage 3, uniting these two species within an evolving complex. We present evidence for horizontal transfer between lineages of genomic regions from 50-440 kbp. The transfer of a 186 kbp region from Lineage 1 to Lineage 2 appears to have generated Lineage 3. Human-associated isolates appear to be limited to Lineages 2 and 3 but Lineage 2 appears to contain a higher number of human pathogenic isolates. The chicken isolates from our lameness trials included genomically diverse isolates from both Lineage 1 and 2, and isolates from both lineages were obtained from osteomyelitis lesions of individual birds. Our results expand the diversity of Staphylococci associated with osteomyelitis in poultry and suggest a high diversity in the microbiome of day-old chicks. Our data also support a reevaluation and unification of the taxonomic classifications of S. cohnii and S. urealyticus.

ABSTRACT The work is focused on determining the energy transfer from EV’s to grid for different operating conditions. The study has been simulated on a scaled model and later validated through experimental tests on batteries of 20 kWh, 40 kWh, 60 kWh and 80 kWh for EV’s energy rates from 130 Wh/km to 180 Wh/km. Tests have been developed to evaluate the energy transfer from driving distance between 0 and 50 km. Results have proved there is a linear dependence between energy transfer and energy rate. Experimental and simulated values have been correlated showing a 99.6% accuracy. The ratio of energy transfer to battery energy capacity depends not only on this latter parameter, but also on the energy rate of the electric vehicle. Experimental tests have shown a variation from a minimum transfer ratio of 54.1% for the highest electric vehicle energy rate and lowest battery energy capacity and power transfer to grid rate to a maximum of 88.6% for the highest power transfer to grid rate and battery energy capacity and lowest electric vehicle energy rate.

Radical reactions are powerful in the synthesis of diverse molecular scaffolds bearing functional groups. In pervious review articles, we have presented 1,2-difunctionalizations, remote 1,3-, 1,4-, 1,5-, 1,6- and 1,7-difunctionalizations, and addition followed by cyclization reactions. Presents in this paper are radical cyclization followed by the second functionalization reactions. The second functionalization could be realized by atom transfer reactions, radical or transition metal-assisted coupling reactions, and reactions with neutral molecules, cationic and anionic species.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

Regeneratively cooled scramjet has already been successfully verified as propulsion device for hypersonic vehicle. As a result, dynamic process of flow and heat transfer in cooling channel is commonly occurred which may bring hazard and control difficulties for scramjet. To study transient flow and heat transfer process in cooling channel, a dynamic numerical model is established. Dynamic characteristics of flow and heat transfer under different conditions are calculated and discussed, which includes change of inlet fuel mass flow, heat flux and pressure working conditions. Results show that stable time of cooling channel outlet fuel temperature is related to the change rate of inlet mass flow and heat flux. The stable time of outlet fuel temperature under heat flux decreasing working conditions are about 12.5 s. The initial response of working pressure fluctuation will lead to fuel temperature fluctuation, which has little effect on wall temperature.

Contactless charging is evolving fast in many domains and applications such as the electrical vehicles (EV). The principle consists of transferring energy between two systems without having any electrical connection between them. This power transfer can be realized by emitting a magnetic field for Inductive Power Transfer (IPT). In the automotive domain, the application of contactless charging to supply power to the vehicle while driving is one of the solutions envisaged to solve the limited autonomy range of EV and reduced its cost but the system must comply with existing standards (e.g., allocated frequency range and radiated electromagnetic emissions), as summarized in this paper.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

In this study, we report a novel synthesis of hydrophobic silica aerogel powder composites, functionalized and binary-doped with [Tb(phen)₂](NO₃)₃ and [Eu(phen)₂](NO₃)₃ nanocrystals, employing a two-step sol-gel methodology. The investigation delves into the structural elucidation, optical properties and thermal conductivity of these functionalized Tb(III)-Eu(III) composites. Our analysis encompasses diffuse reflectance spectra, along with excitation and luminescence spectra, highlighting the quantum yields of composites with varying chemical composition. Remarkably, these samples exhibit a strong luminescence, with distinct hues of red or green color upon the specific doping type and level. The detailed examination of excitation spectra and quantum yields unequivocally establishes robust energy transfer mechanisms from the 1,10-phenanthroline molecule to the lanthanide ions. Notably, our study uncovers a Tb³⁺→Eu³⁺ energy transfer phenomenon within the binary functionalized samples, providing compelling evidence for a structural formation process occurring within the mesoporous framework of the aerogel powders.

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

This paper proposes a simulation technique for investigating the battery thermal management system based direct refrigerant cooling (BTMS-DRC). It employs finite element method for a combined-conduction-convection heat transfer to predict the module temperature and to prove the temperature uniformity. The refrigerant side cooling is based on the two-phase flow evaporation which is represented by the convection heat transfer (flow evaporation) under a certain refrigerant saturation temperature. The battery heat generation is modeled as the constant heat flux. The main module is modeled as the conduction heat transfer. The real BTMS-DRC is constructed for experimentation with the test bench which is based on the dual-evaporators vapour compression refrigeration system. The simulated result is validated with the experimental results to ensure the correction of the modelling. The simulation also investigates the impact of the heat generation, convection heat transfer coefficient, refrigerant saturation temperature, and thermal conductivity on the module temperature and temperature uniformity. It is found that the simulated results agree well with the experimental results. The errors are around 2.9 - 7.2% throughout the specified working conditions. Overall, the proposed technique can be used as a design tool for further developing the BTMS-DRC.

The inherent dependency of deep learning models to labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling.

Gastrodia pubilabiata is a nonphotosynthetic and mycoheterotrophic orchid belonging to subfamily Epidendroideae. Compared to other typical angiosperm species, the plastome of G. pubilabiata is dramatically reduced in size to be only 30,698 base pairs (bp). This reduction has led to the loss of most photosynthesis-related genes and some housekeeping genes in the plastome, which now only contains 19 protein coding genes, three tRNAs, and three rRNAs. This study decoded the entire mitogenome of G. pubilabiata, which consisted of 44 contigs with a total length of 867,349 bp. Its mitogenome contained 38 protein coding genes, nine tRNAs, and three rRNAs. To determine possible gene transfer events between the plastome and the mitogenome, individual BLASTN searches were conducted, using all available orchid plastome sequences and flowering plant mitogenome sequences. Plastid rRNA fragments were found at a high frequency in the mitogenome. Seven plastid protein coding gene frangments (ndhC, ndhJ, ndhK, psaA, psbF, rpoB, and rps4) were also identified in the mitogenome of G. pubilabiata. Phylogenetic trees using these seven plastid protein coding gene fragments suggested that horizontal gene transfer (HGT) from plastome to mitogenome occurred before losses of photosynthesis related genes, leading to the lineage of G. pubilabiata. Compared to species phylogeny of the lineage of orchid, it was estimated that HGT might have occurred approximately 30 million years ago.

Chickpea is one of the most widely consumed pulses globally because of its high protein content. The morphological features of chickpea seed, such as colour, texture, are observable and play a major role in classifying different chickpea varieties. This process is often carried out by human experts, and is time-consuming, inaccurate, and expensive. The objective of the study was to design an automated chickpea classifier using an RGB colour image-based model by considering the morphological features of chickpea seed. As part of the data acquisition process, five hundred and fifty images were collected per variety for four varieties of chickpea (CDC-Alma, CDC-Consul, CDC-Cory, and CDC-Orion) using an industrial RGB camera and a mobile phone camera. Three CNN-based models such as NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 were evaluated using a transfer learning-based approach. The classification accuracy was 97%, 99%, and 98% for NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 models, respectively. The MobileNetV3 model was used for further deployment on an Android mobile and Raspberry Pi 4 devices based on its higher accuracy and light-weight architecture. The classification accuracy for the four chickpea varieties was 100% while the MobileNetV3 model was deployed on both Android mobile and Raspberry Pi 4 platforms.

Demand for development of the convergence industry, research studies on electrical conductivity, thermal characteristics, semiconductors, motors, and batteries using special materials have come to the fore. Meanwhile, molybdenum (Mo) exhibits relatively small inorganic qualities, and the thermal conductivity rate is applied to various fields. In this study, in-depth characteristics were considered regarding the concentration of thermal characteristics, IR car terminal characteristics, and IR ve-hicles. This study calculated each phase temperature of the molybdenum sputtered specimens in the steady state according to the heat transfer theory. When the molybdenum-sputtered fabric’s metal layer pointed to the outside air, the heat transfer rate (Q) was high at 5748.3W, In contrast, if the molybdenum sputtered film’s metal layer of the pointed toward the heat source, the heat transfer rate (Q) was low at 187.1W. As a result of measuring the IR transmittance, the infrared transmit-tance of the molybdenum sputtering-treated sample was significantly reduced compared to the untreated sample. In the case of untreated samples, the transmittance ranged from 92.7 to 42.0%. When only the cross part was treated with molybdenum sputtering and the molybdenum surface was directed toward the IR irradiator, the IR transmittance was 66.8~0.7%. It is believed that the molybdenum sputtering polyamide samples produced in this study can be applied to multifunc-tional military wear, biosignal detection sensors, semiconductor products, batteries, etc., by utilizing excellent electrical properties, stealth functions, IR blocking properties, and lightness for infrared thermal imaging detectors.

The ability to predict the likelihood of a live birth after single fresh embryo transfer is important for treatment planning and managing patient expectation, particularly in their first in vitro fertilization (IVF) cycle. Cryopreservation of supernumerary embryos is often regarded as an important prognostic variable and a surrogate marker of success for several reasons. While previous large studies have examined the association between the number of oocytes retrieved and cleavage-stage embryos available, and the odds of a live birth following a fresh embryo transfer, the relationship between the number of supernumerary blastocysts cryopreserved following a fresh embryo transfer has not been rigorously studied. We performed a retrospective analysis of data collected between 2006 and 2018 for all first time IVF patients with a fresh autologous day 5 single blastocyst transfer. The relationship between the likelihood of a live birth and number of supernumerary blastocysts cryopreserved was assessed according to patient age group. In patients aged <35 years and 35-39 years old, the likelihood of a live birth increased linearly from 0.33 (95%CI:0.31–0.34) to 0.80 (95%CI:0.74–0.86; P<.0001) and 0.30 (95%CI:0.28–0.32) to 0.82 (95%CI:0.73–0.91;P<.0001) between 1-6 blastocysts cryopreserved and then non-linearly to 0.95 (95% CI 0.92–0.97; P<.0001) and 0.96 (95%CI:0.93–0.99; P<.0001) if 10 or more blastocysts were cryopreserved, respectively. When aged 40 years and above, the likelihood of a live birth increased linearly from 0.26 (95%CI:0.19–0.32) to 0.83 (95%CI:0.68–0.97; P<.0001) between 1-4 blastocysts cryopreserved and then non-linearly to 0.99 (95%CI:0.98–0.99; P<.0001) if 10 or more blastocysts were cryopreserved. The present study demonstrated a non-linear relationship between the number of supernumerary blastocysts cryopreserved and the likelihood of a live birth after single blastocyst transfer in the first autologous fresh IVF/ICSI cycle across different age groups.

Mass transfer is often the rate determining step for solid-liquid chemical reactions. Decrease of the concentration boundary layer thickness is essential to intensify the chemical reaction. Because the concentration boundary layer exists in the velocity boundary layer, force imposition in the concentration boundary layer by superimposing an electrical current and a magnetic field was proposed. Through, flow can be directly excited in the concentration boundary layer. The previous result indicates that by superimposing a DC current and a gradient magnetic field, the development of the concentration boundary layer was suppressed, because of a macro-scale flow excitation in the whole vessel. And by superimposing the gradient magnetic field with a modulate current, the development of the concentration boundary layer was further suppressed. This is because of the macro-scale flow enhancement and the excitation of a micro-scale flow near the solid-liquid interface. However, the mechanism for the micro-scale flow excitation has not been clarified. To clarify this, a uniform magnetic field was superimposed with the DC current or the modulate current. By this means, only the micro-scale flow was excited near the anode surface. The results found that the non-unform electromagnetic force distribution is the main reason for the micro-scale flow excitation.

Gas-liquid mass transfer is a significant issue in most bioprocesses. More importantly, gas-liquid mass transfer limitation requires further attention during syngas fermentation (SNF). The gas-liquid mass transfer of gaseous substrates (CO, CO2, and H2) into the fermentation broth is a rate-limiting step in SNF that leads to low productivity and poor economic feasibility. Enhancing this process during SNF can result in high efficiency, better production of ethanol, as well as lower energy consumption. While pressure and power input are important factors for improving reactor design, adding magnetic nanoparticles (MNPs) in the liquid phase is critical to achieving an enhanced gas-liquid mass transfer. The present study reviewed recent advances in the application of MNPs for an improved gas-liquid mass transfer during syngas fermentation. A brief overview of SNF and the effects of MNPs on SNF process are outlined. In addition, the hydrodynamic effect at the gas-liquid boundary is also seen as a mechanism in which nanoparticles increase mass transfer, and the mechanism is elucidated in detail.

The threat of fine particulate matter concentration (PM2.5) is increasing globally, Tackling this issue requires an accurate understanding of its trends and drivers. The article investigates the PM2.5 characteristics of 285 prefecture-level cities in China from 2000-2018 based on multiscale geographically weighted regression(MGWR), and the results show that（1）previous studies based on classical MGWR models may be somewhat unstable, while MGWR can reflect the scale of influence of different variables on the dependent variable, and its regression results are more reliable.（2）PM2.5 is very sensitive to carbon emission(CE) factors, and there is a high degree of spatial heterogeneity, and the influence scale of location is the smallest among all variables, close to the municipal scale.（3）In 2000, the constant term all, IS, OFT, CE, and LT positively affect PM2.5, while GDP (jurisdiction) and UR negatively affect PM2.5; in 2010, the constant term all, GDP (jurisdiction), IS, OFT and LT positively affect PM2.5, while UR and CE negatively affect PM2.5; in 2018 the constant term all, IS, OFT and CE factors positively affect PM2.5, and GDP (jurisdiction), UR and LT negatively affect PM2.5.

The study of the influence of the nanoparticle volume fraction and aspect ratio of microchannels on the fluid flow and heat transfer characteristics of nanofluids in microchannels is important in the optimal design of heat dissipation systems with high heat flux. In this work, the computational fluid dynamics method was adopted to simulate the flow and heat transfer characteristics of two types of water–Al2O3 nanofluids with two different volume fractions and five types of microchannel heat sinks with different aspect ratios. Results showed that increasing the nanoparticle volume fraction reduced the average temperature of the liquid–solid heat transfer surface and thereby improved the heat transfer capacity of the nanofluids. Meanwhile, the increase of the nanoparticle volume fraction led to a considerable increase in the pumping power of the system. Changing the aspect ratio of the microchannel effectively improved the heat transfer capacity of the heat sink. Moreover, increasing the aspect ratio effectively reduced the average temperature of the heating surface of the heat sink without significantly increasing the flow resistance loss. When the aspect ratio exceeded 30, the heat transfer coefficient did not increase with the increase of the aspect ratio. The results of this work may offer guiding significance for the optimal design of high heat flux microchannel heat sinks.

Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

By introducing a suitable renormalization process the charge carrier and phonon dynamics of a double-stranded helical DNA molecule is expressed in terms of an effective Hamitonian describing a linear chain, where the renormalized transfer integrals explicitly depend on the relative orientations of the Watson-Crick base pairs, and the renormalized on-site energies are related to the electronic parameters of consecutive base pairs along the helix axis, as well as to the low-frequency phonons dispersion relation. The existence of synchronized collective oscillations enhancing the π-π orbital overlapping among different base pairs is disclosed from the study of the obtained analytical dynamical equations. The role of these phonon-correlated, long-range oscillation effects on the charge transfer properties of double standed DNA homopolymers is discussed in terms of the resulting band structure.

Generic object detection is one of the most important and flourishing branches of computer vision and has real-life applications in our day to day life. With the exponential development of deep learning-based techniques for object detection, the performance has enhanced considerably over the last 2 decades. However, due to the data-hungry nature of deep models, they don't perform well on tasks which have very limited labeled dataset available. To handle this problem, we proposed a transfer learning-based deep learning approach for detecting multiple pigs in the indoor farm setting. The approach is based on YOLO-v2 and the initial parameters are used as the optimal starting values for train-ing the network. Compared to the original YOLO-v2, we transformed the detector to detect only one class of objects i.e. pigs and the back-ground. For training the network, the farm-specific data is annotated with the bounding boxes enclosing pigs in the top view. Experiments are performed on a different configuration of the pen in the farm and convincing results have been achieved while using a few hundred annotated frames for fine-tuning the network.

The paper concludes the first research on mouth-based Emotion Recognition (ER), adopting a Transfer Learning (TL) approach. Transfer Learning results paramount for mouth-based emotion ER, because a few data sets are available, and most of them include emotional expressions simulated by actors, instead of adopting a real-world categorization. Using TL we can use fewer training data than training a whole network from scratch, thus more efficiently fine-tuning the network with emotional data and improving the convolutional neural network accuracy in the desired domain. The proposed approach aims at improving the Emotion Recognition dynamically, taking into account not only new scenarios but also modified situations with respect to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in an healthcare management environment, or portable applications supporting disabled users having difficulties in seeing or recognizing facial emotions. This work takes advantage from previous preliminary works on mouth-based emotion recognition using CNN deep-learning, and has the further benefit of testing and comparing a set of networks on large data sets for face-based emotion recognition well known in literature. The final result is not directly comparable with works on full-face ER, but valorizes the significance of mouth in emotion recognition, obtaining consistent performances on the visual emotion recognition domain.

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using Land use transfer flow, and Land use transfer matrix. At the same time, based on the remote sensing Ecological index (RSEI) model, the ecological environment quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of ecological environment quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on Spatial autocorrelation and Geographic detector. The results show that from 2013 to 2023, the decrease of forest land (16.23km²) and the increase of construction land (13.25km²) were the main land use transfer in Longnan City. The ecological environment index of Longnan City in 2013, 2018 and 2023 was 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environment quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environment quality, while traffic land, construction land and other land have a negative impact on ecological environment quality. The response of ecological environment quality to different land use transfer modes is related to the change of the overall ecological environment quality. The interaction between LUCC and other factors had a great impact on the evolution of ecological environment quality in Longnan City.

Since the photocatalytic reaction is a surface reaction, enhancing the gas movement around photocatalyst would promote photocatalytic CO₂ reduction performance. A new approach to enhance the gas movement around the photocatalyst by the natural thermosiphon movement of gasses around photocatalyst using black body material was proposed and confirmed experimentally, but the heat transfer mechanism on the phenomena was not clarified yet. The purpose of this study is to clarify the heat transfer mechanism of it. This study calculated the temperature of mixed gases of CO₂ and NH₃ around P₄O₁₀/TiO₂ photocatalyst by the heat transfer formula. It is revealed that there was no difference between the increase temperature (T_g) from the temperature at the beginning of the CO₂ reduction experiment (T_ini) and the temperature of mixed gases of CO₂ and NH₃ measured by thermocouple in the experiment (T_e) under the illumination condition with a visible light (VIS) + an infrared light (IR) and IR only. The heat transfer model proposed by this study has predicted T_g well under the illumination condition with VIS + IR and IR only well. On the other hand, it is revealed that the difference between T_g and T_e was as large as 10 ℃ under the illumination condition with an ultra violet light (UV) + VIS + IR.

This paper presents the results of a study aimed at understanding how technology maturity level influences the incidence of university technology transfer to the private sector. The study examined the topic from the perspective of private sector organizations. It used data from a random sample of patent applications filed with the United States Patent and Trademark Office and a theoretically guided sampling of multiple cases of private sector organizations that contemplated obtaining and assimilating technologies created at universities in the United States. The patent application data were analyzed using nonparametric statistical techniques and the case data were analyzed using qualitative comparative analysis (QCA). The findings of the study suggest that the typical maturity level of technologies created at U.S. universities is a TRL-5 or lower on as scale adapted from the NASA technology readiness level (TRL) scale. A technology maturity level of TRL-6 or higher is likely an insufficient but necessary part of at least one unnecessary but sufficient configuration of conditions that tends to result in the occurrence of university technology transfer. However, under certain circumstances, a technology maturity level of at least TRL-6 could be a sufficient but unnecessary condition for the occurrence of university technology transfer. These findings have several important implications. First, they provide support for the notion that university technology transfer is subject to causal complexity. Moreover, it may be possible to increase the incidence of university technology transfer in the United States by implementing public policy and practices that explicitly take technology maturity level into consideration.

Modules composed of a resistance gene flanked by Xer site-specific recombination sites, the vast majority of which were found in Acinetobacter baumannii, are thought to behave as elements that facilitate horizontal dissemination. The A. baumannii xerC and xerD genes were cloned, and the recombinant clones used to complement the cognate Escherichia coli mutants. The complemented strains supported resolution of plasmid dimers, and, as is the case with E. coli and Klebsiella pneumoniae plasmids, the activity was enhanced when cells were growing in low osmolarity growth medium. Binding experiments showed that partially purified A. baumannii XerC and XerD proteins (XerC_Ab and XerD_Ab) bound synthetic Xer site-specific recombination sites, some of them with a nucleotide sequence deduced from existing A. baumannii plasmids. Incubation with suicide substrates resulted in covalent attachment of DNA to a recombinase, probably XerC_Ab, indicating that the first step in the recombination reaction took place. The results described show that XerC_Ab and XerD_Ab are functional proteins and support the hypothesis that they participate in horizontal dissemination of resistant genes among bacteria.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

Generic text embeddings have demonstrated considerable success across a multitude of applications. However, these embeddings are typically derived by modeling the co-occurrence patterns within text-only corpora, which can limit their ability to generalize effectively across diverse contexts. In this study, we investigate methodologies that incorporate visual information into textual representations to overcome these limitations. Through extensive ablation studies, we introduce a novel and straightforward architecture named VisualText Fusion Network (VTFN). This architecture not only surpasses existing multimodal approaches on a range of well-established benchmark datasets but also achieves state-of-the-art performance on image-related textual datasets while utilizing significantly less training data. Our findings underscore the potential of integrating visual modalities to substantially enhance the robustness and applicability of text embeddings, paving the way for more nuanced and contextually rich semantic representations.

The parallel connection of multiple flexible rings in raceways can significantly improve the electrical conductivity of rolling conductive devices. Multiple flexible rings are generally separated by idlers to form ring-idler rolling pairs. In the present work, alumina (Al2O3), polytetrafluoroethylene (PTFE), polyether ether ketone (PEEK), and polyimide (PI) were used as idler materials. Rolling tests for four types of ring-idler contact pairs were carried out in the air at a rotating speed of 10 rpm and a current of 60 A. It was noticed that the voltage drop was the lowest when Al2O3 was incorporated as the idler material. The energy spectrum analysis indicated material transfer between the organic idlers and the flexible ring. Especially on the flexible ring surface, the transferred organic thin film covered the gold-plated layer of the flexible ring and affected the electrical contact of the flexible ring. The transfer of Al2O3 was not obvious, and the mass fraction of Au on the flexible ring surface after the rolling test was 93.1%. The above results could help in selecting the best idler material for high-power conductive rotary joints.

China has enacted a range of policies to improve its water ecosystem since the middle of the 20th Century. Some of these measures include the creation of Seven River Conservancy Commissions, the development (combination and recombination) of series of ministries and departments to address environmental pollution and degradation, and recently the implementation of the river chief system. To understand if the river chief system can transfer to other political jurisdictions the authors gathered data using key word searches of the Web of Science and Google Scholar, official government sites, media sites and key international organizations. Based on this the authors review the development of the river chief system in the context of broader ecological reforms, place the rise of the river chief in the context of potential lessons for future improvement, and speculate on the possibility that China could transfer the river chief to countries facing similar challenges in their water governance regimes.

Aging has been increasingly recognized as a global health burden. Current aging research suggests that preventative strategies may increase both lifespan and healthspan. Many natural compounds or natural products have demonstrated promising antiaging benefits and gained popularity in aging research. The present study evaluated two compositions comprised of nicotinamide mononucleotide, quercetin, alpha ketoglutarate, white button mushroom extract (standardized to spermidine), Transfer Factor (extracts from cow colostrum and chicken egg yolk) with or without milk thistle fruit extract (standardized to silybin) and apigenin for their antiaging effect in Caenorhabditis elegans. The active ingredients included in the two compositions were screened for their senolytic effects on cell viability of irradiated human umbilical vein endothelial cells. To further explore their potential molecular mechanisms of action, the two compositions were also tested for their binding activity to human sirtuin enzymes, and TF on natural killer cell activities in human peripheral blood mononuclear cells from both young and old donors. Both compositions significantly increased the lifespan and healthspan of C. elegans. Both compositions demonstrated significant dose-dependent inhibitory effect on several sirtuins. When treated with Transfer Factor, both younger and older human peripheral blood mononuclear cells bolstered natural killer cell activity. The present study suggested that these two compositions significantly improved both lifespan and healthspan of Caenorhabditis elegans likely by modulating sirtuin activity and rejuvenating senescent immune cells.

Companion cropping involves growing two or more species together, benefiting the conservation of natural resources and improving fertilization and nutrient cycling by balancing chemical and organic sources. Studies, as in the case of tree tomato and Hass avocado, have shown a significant increase in yield compared to monocultures, highlighting the viability of this practice. In addition to their environmental benefit, companion crops offer economic advantages to producers by obtaining multiple products in a single harvest, strengthening food security and the economy of rural families. This study evaluated three levels of fertilization and interactions between fruit trees at different altitudes, observing differential behavior in the variables evaluated. The combination of cape gooseberry and blackberry showed significantly positive results, with more leaves and fewer pests, demonstrating the benefits of companion plants. A trend towards the combined use of chemical and organic fertilizers was observed, a potential strategy to reduce costs and improve crop growth. The results were shared with producers interested in implementing companion cropping, encouraging the adoption of this more sustainable and profitable agricultural practice. Companion crop agriculture represents a viable and promising strategy for a more efficient and sustainable agriculture, with both environmental and economic benefits.

Time-of-Flight (ToF) cameras are subject to high levels of noise and errors due to Multi-Path-Interference (MPI). To correct these errors, algorithms and neuronal networks require training data. However, the limited availability of real data has led to the use of physically simulated data, which often involves simplifications and computational constraints. The simulation of such sensors is an essential building block for hardware design and application development. Therefore, the simulation data must capture the major sensor characteristics. This work presents a learning-based approach that leverages high-quality laser scan data to generate realistic ToF camera data. The proposed method employs MCW-Net (Multi-Level Connection and Wide Regional Non-Local Block Network) for domain transfer, transforming laser scan data into the ToF camera domain. Different training variations are explored using a real-world dataset. Additionally, a noise model is introduced to compensate for the lack of noise in the initial step. The effectiveness of the method is evaluated on reference scenes to quantitatively compare to physically simulated data.

A novel approach is developed to improve the convergence of Physics-Informed Neural Networks (PINNs), aiming to employ them as real-time computational models within the framework of the digital twin for manufacturing processes. This method entails the weighting of physical equations, boundary conditions, and initial conditions to ensure their comparable magnitudes, with power being the chosen quantity in this study. The approach is applied to thermal problems, which are crucial for predicting manufacturing part defects. Different configurations, including complex boundary conditions and complex physics, were tested to assess the model’s robustness. The W-PINN demonstrates good predictions and strong stability compared to the classical PINN.

In this paper, we present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (wav2vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained thanks to forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages.

Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.

This paper proposes a position recognition method for the receiver coil by the secondary coil current. Based on the practical value of the current of the receiver coil, the position recognition of the receiver coil in the WPT system is realized. This paper proposes a 3×3 array multi-transmitter coil grouping and control logic. The mathematical model of mutual inductance between the receiver and transmitter coil at different positions on the X-Y plane is established. A method for identifying the position of the receiver coil according to the current of the receiver coil is proposed. Compared with the traditional position recognition method of the receiver coil, this method does not need to add a detection coil and position sensor and can realize the position recognition of the receiver coil on the 2D plane.

Registration of the flow pattern fine structure and the matter distribution of a free falling liquid drop in a target fluid at rest in the impact mode of coalescence, when the kinetic energy of the drop exceeds its available potential surface energy (APSE), was carried out by photo and video recording. The main attention was paid to the study of the flow structure at the initial stage of the cavity formation. To carry out color registration, the flow pattern was illuminated by several matrix LED and fiber optic sources of constant light. The planning of experiments and interpretation of the results were based on the properties of the complete solutions of the fundamental equations of fluid mechanics system, including the transfer and conversion of energy processes. Complete solutions of the system of equations describe large-scale flow components that are waves or vortices as well as thin jets (ligaments, filaments, fibers, trickles). In experiments, the jets are accelerated by the converted available potential surface energy (APSE) when the free surfaces of merging fluids were eliminated. The experiments were performed with the coalescence of water, solutions of alizarin ink, potassium permanganate, copper sulphate or iron sulphate drops in deep water. In all cases, at the initial contact, the drop begins to lose its continuity and breaks up into a thin veil and jets, the velocity of which exceeds the drop contact velocity. Small droplets, the size of which grows with time, are thrown into the air from spikes at the jet tops. On the surface of the liquid, the fine jets leave colored traces that form linear and reticular structures. Part of the jets penetrating through the bottom and wall of the cavity forms an intermediate covering layer. The forming layer jets are separated by interfaces of the target fluid. The processes of molecular diffusion equalize the density differences and form an intermediate layer with sharp boundaries in the target fluid. All noted structural features of the flow are also visualized when a fresh water drop isothermally spreads in the same tap water. The fast-changing and finely structured diffuse boundary of merging fluids, which at the initial stage has a complex and irregular shape, is gradually smoothed out by molecular diffusion processes. The similar flow patterns were observed in all performed experiments, however the geometric features of the flow depend on the individual thermodynamic and kinetic parameters of the contacting fluids.

The shortage of precooling equipment in litchi producing regions could lead to a high loss rate, poor quality of litchis. It is urgent to develop a portable precooling device for litchi producing regions. In this study, a novel spray hydrocooler with thermal energy storage (TES) were designed, fabricated, and tested. A simple mathematical model of TES capacity, ice coil thermal resistance and refrigeration system was employed to determine hydrocooler parameters. Then designed the structure of the spray hydrocooler. Maximum charging test was implemented with full TES capacity and litchi spray hydrocooling experiments were carried out at different charging times, spray flow rate, and litchi load with one-third TES capacity. Results showed that: (1) the spray hydrocooler allows for the rapid and effective precooling of litchis; (2) the hydrocooler can precool 299 kg litchis with one-third TES storage, meet the precooling requirements; (3) the effective TES capacity achieved 1.25×108 J at the maximum TES capacity of the hydrocooler, while the energy efficiency ratio (EER) is 2; (4) the precooling capacity was maximum and the average power consumption was minimum when the litchi load was 23 kg and the spray flow rate was 30 L min-1. Longer charging time is the most important factor in increasing precooling capacity and reducing average power consumption.

This study aimed to assess prescription transfer and medicines collection through Wasfaty, an electronic prescription service recently introduced in Saudi Arabia. A link to a cross-sectional online questionnaire was sent to all students and staff at the University of Jeddah, targeting beneficiaries who received e-prescriptions at the University Medical Centre (n = 2067). The questionnaire comprised 20 items under the following sections; demographics, patient perceptions and satisfaction with the Wasfaty service, and the availability of medicines. Of the 217 questionnaires received, the majority were filled by females (n = 125, 57.6%). Most were satisfied with the initial registration process of Wasfaty (n = 183, 84.1%). However, almost one-third of the participants reported that they could not find the prescribed medicines (n = 64, 29.7%), and most of them had to look for another pharmacy to obtain their treatment (n = 138, 63.9%). Respondents voiced their displeasure owing to the lack of access to certain pharmaceuticals, including anti-hypertensives and antidiabetics. This pilot study identified some challenges relating to the use of the Wasfaty service. Further attention to these challenges is required from the service providers, and a large-scale national study is warranted.

This study utilised an ensemble of pre-trained networks and digital mammograms to develop a supplementary diagnostic tool for radiologists. Digital mammograms and their associated information were collected from the department of radiology and pathology, Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and explored in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks based on PR-AUC, precision, and F1 score. The final ensemble model had a mean precision, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated a balanced performance across mammographic density. In conclusion, this study exhibited the good performance of the ensemble transfer learning on digital mammograms for the purpose of breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus, reducing their workloads.

The underlying assumption of the discourse about technology transfer is that government intervention is legitimate. Little scholarship has examined whether this assumption is valid or not and on what basis. Legitimacy is an important construct in the context of public policy. Government intervention can only be sustained if the public views such action as legitimate. The creation and transfer of technologies to the private sector is an area where there is significant government intervention. This paper reconceptualizes political legitimacy in the context of technology transfer policy. The analysis illuminates several concerns and challenges regarding the traditional approach to understanding whether specific government interventions in technology transfer are legitimate. It subsequently applies social constructionism and the notion of morality tales to describe an alternative conceptualization of political legitimacy that integrates aspects of other frameworks. In doing so, it reimagines political legitimacy as less of an unattainable normative principle of limited practical value to policymakers and more of a descriptively understood social phenomenon that policymakers can apply while formulating not only technology transfer policy, but other kinds of public policy as well. The paper demonstrates that there is a broader basis for claims of political legitimacy for government intervention in technology transfer, there is likely a more expansive range of technology transfer problems with which the government can rightly concern itself as well as possible solutions that policymakers can justifiably consider for addressing those problems, and that the political consequences of potential overreach in technology transfer policy are likely minimal.

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

Segmentation is an important step in medical imaging. In particular, machine learning, especially deep learning, has been widely used to efficiently improve and speed up the segmentation process in clinical practice. Despite the acceptable segmentation results of multi-stage models, little attention was paid to the use of deep learning algorithms for brain image segmentation, which could be due to the lack of training data. Therefore, in this paper, we propose a Generative Adversarial Network (GAN) model that performs transfer learning to segment MRI brain images.Our model enables the generation of more labeled brain images from existing labeled and unlabeled images. Our segmentation targets brain tissue images, including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). We evaluate the performance of our GAN model using a commonly used evaluation metric, which is Dice Coefficient (DC). Our experimental results reveal that our proposed model significantly improves segmentation results compared to the standard GAN model. We observe that our model is 2.1–10.83 minutes faster than stat-of-the-art-models.

Horizontal transfer of transposons (HTT) is an important source of genomic evolution in eukaryotes. The HTT dynamics is well characterized in eukaryotes including insects however, but there is a huge gap of knowledge about HTT regarding many eukaryotes’ species. In this study we analyzed the events of the HTT between Rhus gall aphids (Hemiptera) and other insects. We analyzed the Mariner-like transposable elements (MLEs) belongs to Rhus gall aphids for the possible HT events. The MLEs have patchy distribution and have high similarity over the entire length of element with insects MLEs from different orders. We selected representative sequences from the Rhus gall MLEs and identified five events of HT between MLEs of Rhus gall aphids and other insects from five different orders. We also found multiple HTT events among the MLEs of insects from the five orders which demonstrate that these Mariner elements have been involved in recurrent HT between these six order of insects. Our current study closed the knowledge gap of HTT and reported the events between Rhus gall aphids and other insects for the first time. We believe that this study about HTT events will help to understand the evolution and spread of transposable elements in the genomes of Rhus gall aphids.

COVID-19 has a severe risk of spreading rapidly, the quick identification of which is essential. In this regard, chest radiology images have proven to be a practical screening approach for COVID-19 affected patients. This study proposes a deep learning-based approach using Densenet-121 to detect COVID-19 patients effectively. We have trained and tested our model on the COVIDx dataset and performed both 2-class and 3-class classification, achieving 96.49% and 93.71% accuracy, respectively. By successfully utilizing transfer learning, we achieve comparable performance to the state-of-the-art method while using 15x fewer model parameters. Moreover, we performed an interpretability analysis using Grad-CAM to highlight the most significant image regions at test time. Finally, we developed a website that takes chest radiology images as input and detects the presence of COVID-19 or pneumonia and a heatmap highlighting the infected regions. Source code for reproducing results and model weights are available.

There is a crucial need for advancement in the online educational system due to the unexpected, forced migration of classroom activities to a fully remote format, due to the coronavirus pandemic. Not only this, but online education is the future, and its infrastructure needs to be improved for an effective teaching-learning process. One of the major concerns with the current video call-based online classroom system is student engagement analysis. Teachers are often concerned about whether the students can perceive the teachings in a novel format. Such analysis was involuntarily done in the offline mode, however, is difficult in an online environment. This research presents an autonomous system for analyzing the students' engagement in the class by detecting the emotions exhibited by the students. This is done by capturing the video feed of the students and passing the detected faces to an emotion detection mode. The emotion detection model in the proposed architecture was designed by finetuning VGG16 pre-trained image classifier model. Lastly, the average student engagement index is calculated. We received considerable performance setting reliability of the use of the proposed system in real-time giving a future scope to this research.

Semantically meaningless strings that are associated with affective attributes (US) can become emotionally valenced CS. Jurchiș et al (2020) recently demonstrated CS-US associations may influence evaluations towards previously-unseen letter strings if the latter share grammar construction rules with CS. We replicated those authors' findings in a modified extension (Experiment 1; N₁ = 108), where happy/angry faces (US) were differentially associated with letter strings (CS) constructed using familiar (English) or non-familiar (Phoenician) alphabets. CS-US sequences were sandwiched by evaluations of strings that never appeared as CS, but shared grammar construction rules. However, post-hoc tests indicated valence effects were restricted to participants classified as 'high awareness' or those who had been exposed to longer stimulus durations, suggesting resource-intense deliberations were central during evaluations. Qualitative awareness checks additionally showcased many participants had attributed valences to recurring elements across conditioned and evaluated exemplars. These limitations were collectively addressed in Experiment 2 (N₂ = 140), where participants viewed Phoenician (/English) CS during conditioning but viewed English (/Phoenician) strings during evaluations, meaning no strings nor elements recurred between phases. We found credible valence effects across English and Phoenician strings, with the latter observed across all awareness categories. Because participants were unable to consciously specify any evaluative strategies while evaluating Phoenician strings, we speculate grammar construction rules (organizing relations) may have been non-consciously acquired during conditioning.

Artificial Intelligence (AI) has seen increased application and widespread adoption over the past decade despite, at times, offering a limited understanding of its inner working. AI algorithms are, in large part, built on weights, and these weights are calculated as a result of large matrix multiplications. Computationally intensive processes are typically harder to interpret. Explainable Artificial Intelligence (XAI) aims to solve this black box approach through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

Using machine learning or deep learning to solve the problem of garbage recognition and classification is an important application in computer vision, but due to the incomplete establishment of garbage datasets and the poor performance of complex network models on smart terminal devices, the existing garbage classification models The effect is not good.This paper presents a waste classification and identification method base on transfer learning and lightweight neural network. By migrating the lightweight neural network MobileNetV2 and rebuild it, The reconstructed network is used for feature extraction, and the extracted features are introduced into the SVM to realize the identification of 6 types of garbage. The model was trained and verified by using 2527 pieces of garbage labeled data in the TrashNet dataset, which ultimately resulted in a classification accuracy of 98.4% of the method, which proves that the method can effectively improve the classification accuracy and time and overcome the problem of weak data and less labeling. The over-fitting phenomenon encountered by small data sets in deep learning makes the model robust.