Ants of the genus Solenopsis are important invasive pests in the world. Due to their high dispersion capacity, aggressive behavior and because they feed on various sources of vegetable and animal protein, these insects have caused serious damage to public health and agriculture. The control has usually been carried out through the use of organosynthetic insecticides. However, due to the ecological and environmental impacts caused by the use these products, more sustainable alternatives have been sought for their control. Faced with these problems and due to the scarcity of registered products for the control of these insects, we aimed with this study to synthesize and evaluate the anticidal potential of carvacrol derivatives. The lethal and sublethal effects caused by these derivatives were compared with the essential oil of Lippia gracilis (50.7% carvacrol) and with the base molecule — carvacrol. Carvacryl benzoate was the most toxic derivative to Solenopsis sp. with an LD50 of 3.20 g/ mg. This compound was about 2 and 7.6 times, more toxic than carvacrol at the doses needed to kill 50 and 90% of populations, respectively. The workers of Solenopsis sp. showed a rapid reduction in survival when exposed to carvacrol (LT50 = 8.43h) and carvacryl benzoate (LT50 = 8.87h). Insects treated with sublethal doses of the compounds did not show significant effects on self-cleaning, allogrooming and aggregation, with the exception of those treated with L. gracilis essential oil. The oil increased self-cleaning and reduced allogrooming and aggregation. Ants treated with carvacrol and carvacryl benzoate showed greater distance and speed when compared to the control. These compounds decreased meanders and angular velocity. When live workers were exposed to dead individuals at the LD90 of the compounds, carvacryl benzoate was the derivative that most reduced insects survival, due to horizontal transfer. These results show the potential of the carvacrol derivative — carvacryl benzoate — for the alternative management of ants of the genus Solenopsis sp.

Inteins, often referred to as protein introns, are highly mobile genetic elements that invade conserved genes throughout the tree of life. Inteins have been found to invade a wide variety of key genes within actinophages. While in the process of conducting a survey of these inteins in actinophages we discovered that one protein family of methylases contained a putative intein, and two other unique insertion elements. These methylases are known to occur commonly in phages as orphan methylases (possibly as a form of resistance to restriction-modification systems). We found that the methylase family is not conserved within phage clusters and has a disparate distribution across divergent phage groups. We determined that two of the three insertion elements have a patchy distribution within the methylase protein family. We also found that the third insertion element is likely a second homing endonuclease, and that all three elements (the intein, the homing endonuclease, and what we refer to as the ShiLan domain) all have different insertion sites that are conserved in the methylase gene family. Furthermore, we find strong evidence that both the intein and ShiLan domain are partaking in long distance horizontal gene transfer events between divergent methylases in disparate phage hosts within the already dispersed methylase distribution. The reticulate evolutionary history of methylases and their insertion elements reveals high rates of gene transfer and within-gene recombination in actinophages.

Genes coding for enzymes of the denitrification pathway appear randomly distributed among isolates of the ancestral genus Thermus, but only in few strains of the species T. thermophilus the pathway has been studied to a certain detail. Here, we review the enzymes involved in this pathway present in T. thermophilus NAR1, a strain extensively employed as a model for nitrate respiration, on the light of its full sequence recently assembled through a combination of PacBio and Illumina technologies in order to counteract the systematic errors introduced by the former technique. The genome of this strain is divided in four replicons, a chromosome of 2,021,843 pb, two megaplasmids of 370,865 and 77,135 bp and a small plasmid of 9,799 pb. Nitrate respiration is encoded in the largest megaplasmid, pTTHNP4, within a region that includes operons for O2 and nitrate sensory systems, a nitrate reductase, nitrate and nitrite transporters and a nitrate specific NADH dehydrogenase, in addition to multiple insertion sequences (IS), suggesting its mobility-prone nature. Despite nitrite is the final product of nitrate respiration in this strain, the megaplasmid encodes two putative nitrite reductases of the cd1 and Cu-containing types, apparently inactivated by IS. No nitric oxide reductase genes have been found within this region, although the NorR sensory gene, needed for its expression, is found near the inactive nitrite respiration system. These data clearly support that partial denitrification in this strain is the consequence of recent deletions and IS insertions in genes involved in nitrite respiration. Based on these data, the capability of this strain to transfer or acquire denitrification clusters by horizontal gene transfer is discussed.

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.

Antimicrobial resistance (AMR) is one of the greatest public health challenges we are currently facing. To develop effective interventions against this, it is essential to understand the processes behind the spread of AMR. These are partly dependent on the dynamics of horizontal transfer of resistance genes between bacteria, which can occur by conjugation (direct contact), transformation (uptake from the environment) or transduction (mediated by bacteriophages). Mathematical modelling is a powerful tool to investigate the dynamics of AMR, however its application to study the horizontal transfer of AMR genes is currently unclear. In this systematic review, we searched for mathematical modelling studies which focused on horizontal transfer of AMR genes. We compared their aims and methods using a list of predetermined criteria, and utilized our results to assess the current state of this research field. Of the 43 studies we identified, most focused on the transfer of single genes by conjugation in Escherichia coli in culture, and its impact on the bacterial evolutionary dynamics. Our findings highlight the existence of an important research gap on the dynamics of transformation and transduction, and the overall public health implications of horizontal transfer of AMR genes. To further develop this field and improve our ability to control AMR, it is essential that we clarify the structural complexity required to study the dynamics of horizontal gene transfer, which will require cooperation between microbiologists and modellers.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted, and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x10^4 – 2x10^7 CFU per ml and has a sensitivity of S = 1.655x10^5 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10^-9 grams (g), which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

Rotational microwave jet spectroscopy studies of the monoterpenol α-fenchol have so far failed to identify its second expected torsional conformer, despite computational predictions that it is only very slightly higher in energy than the most stable conformer. Vibrational FTIR and Raman jet spectroscopy investigations reveal unusually complex OH and OD stretching spectra compared to other alcohols. Via modelling of the torsional states, observed spectral splittings are explained by delocalization of the hydroxy hydrogen atom through quantum tunneling between the two non-equivalent but accidentally near-degenerate conformers separated by a low and narrow barrier. The energy differences between the torsional states are determined to be only 16(1) and 7(1) cm$^{−1}hc$ for the protiated and deuterated alcohol, respectively, which further shrink to 9(1) and 3(1) cm$^{−1}hc$ upon OH or OD stretch excitation. Comparisons are made with the more strongly asymmetric monoterpenols borneol and isopinocampheol as well as with the symmetric, rapidly tunneling propargyl alcohol. Assigned are also for α-fenchol the third – in contrast localized – torsional conformer and the most stable dimer, as well as for propargyl alcohol the two most stable dimers.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for 2 years. We simulated the area rate constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors, such as the area rate constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p<0.01, n=38). The NO₃⁻-N load was significantly correlated with the removal rate (R²=0.9566, p<0.01), but the NH^₄+-N load was not correlated with the removal rate (R²=0.0187, p>0.01). The area rate constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27.01±26.49 and 16.63±10.58 m∙yr⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.0042 and 0.9604, respectively. The area rate constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p>0.01). The NO₃⁻-N area rate constant was correlated with the corresponding load (R²=0.9625, p<0.01). The NH₄⁺-N area rate was correlated with DO (R²=0.6922, p<0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig's law of the minimum.

Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

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.

Water-soluble and cytocompatible polymers were investigated to enhance a transporting efficiency of biomolecules into cells in vitro. The polymers composed of 2-methacryloyloxyethyl phosphorylcholine (MPC) unit, a hydrophobic monomer unit, and a cationic monomer unit bearing an amino group were synthesized for complexation with model biomolecules, siRNA. The cationic MPC polymer was shown to interact with both siRNA and the cell membrane and was successively transported siRNA into cells. When introducing 20 − 50 mol% hydrophobic units into the cationic MPC polymer, transport of siRNA into cells. The MPC units (10 − 20 mol%) in the cationic MPC polymer were able to impart cytocompatibility, while maintaining interaction with siRNA and the cell membrane. The level of gene suppression of the siRNA/MPC polymer complex was evaluated in vitro and it was as the same level as that of a conventional siRNA transfection reagent, whereas its cytotoxicity was significantly lower. We concluded that these cytocompatible MPC polymers may be promising complexation reagent for introducing biomolecules into cells, with the potential to contribute to future fields of biotechnology, such as in vitro evaluation of gene functionality, and the production of engineered cells with biological functions.

The most important plastic resins used for wire coating are Polyvinyl Chloride (PVC), Nylon, Polysulfone and Low-high density polyethylene (LDPE / HDPE). In this article,the coating process is performed using elastic-viscous fluid as a coating material for wire coating in a pressure type coating die. The elastic-viscous fluid is electrically conducted in the presence of an applied magnetic field. The governing non-linear equations are modeled and then solved analytically by utilizing an Adomian decomposition method (ADM). The convergence of the series solution is established. The results are also verified by Optimal Homotopy Asymptotic Method (OHAM). The effect of different emerging parameters such as non-Newtonian parameters α and β, magnetic parameter M and the Brinkman number Br on solutions (velocity and temperature profiles) are discussed through several graphs. Additionally, the current result also compares with the published work already available in the literature.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

The oscillating heat pipe (OHP) is a new member in the family of heat pipes, and it has great potential applications in energy conservation. However, the fluid flow and heat transfer in the OHP as well as the fundamental effects of inner diameter on them have not been fully understood, which are essential to the design and optimization of the OHP in real applications. Therefore, by combining the high-speed visualization method and infrared thermal imaging technique, the fluid flow and thermal performance in the OHPs with inner diameters of 1, 2 and 3 mm are presented and analyzed. The results indicate that three fluid flow motions, including small oscillation, bulk oscillation and circulation, coexist or, respectively, exist alone with the increasing heating load under different inner diameters, with three flow patterns occurring in the OHPs, viz. bubbly flow, slug flow and annular flow. These fluid flow motions are closely correlated with the heat and mass transfer performance in the OHPs, which can be reflected by the characteristics of infrared thermal images of condensers. The decrease in the inner diameter increases the frictional flow resistance and capillary instability while restricting the nucleate boiling in OHPs, which leads to a smaller proportion of bubbly flow, a larger proportion of short slug flow, a poorer thermal performance, and easier dry-out of working fluid. In addition, when compared with the 2 mm OHP, the increasing role of gravity induces the thermosyphon effect and weakens the 'bubble pumping' action, which results in a little smaller and bigger thermal resistances of 3 mm OHP under small and bulk oscillation of working fluid, respectively.

Diabetes is one of the metabolic syndromes that demands the great concern affecting millions of people all over the world. Viral infections were also considered as responsible for diabetes. Though, variety of medications for prevention and treatment of diabetes are available, numerous limitations are also prevailing that cannot be neglected. Precise drug-targetability, controlled drug-release, oral bioavailability, minimizing side effects are a few to consider. To address most of these shortcomings of traditional drug-delivery models, nanoformulation has been employed therein a wide range of nanoformulation models are developed. The pharmacology of a drug can be greatly enhanced through nanoformulation models in diabetes treatment in terms of enhanced bioavailability, prolonged and sustained drug-lease, maintain maximum drug concentration in serum, higher intestinal absorption, and protection from enzymatic degradation. In this review work, recently designed nanoformulation models for efficient insulin delivery and the other properties concerning diabetes such as reducing blood glucose level, total cholesterol, triglycerides, lipid profile, wound healing, elevating antioxidant enzymes etc. are discussed. Classification of the nanoformulation models is done based on the nature of the fabricating material employed in nanoformulation. This review may be helpful in designing a more efficient nanoformulation model for diabetes treatment.

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.

Due to environmental concerns, natural refrigerants and their use in refrigeration and air conditioning systems are receiving more attention from manufacturers, end users and the scientific community. The study of heat transfer and pressure drop is essential for accurate design and more energy efficient cycles using natural refrigerants. The aim of this work is to provide an overview of the latest outcomes related to heat transfer and pressure drop correlations for ammonia, propane, isobutane and propylene and to investigate the current state of the art in terms of operating conditions. Available data on the existing correlations of heat transfer coefficient and pressure drop for natural refrigerants have been collected through a systematic search. Whenever possible, validity intervals are given for each correlation and the error is quantified. It is the intention of the authors that this could be a valuable support for researchers and an aid to design, with particular reference to heat pumps. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 135 publications qualified for inclusion in the survey; 34 articles report experimental investigations for not usual geometric conditions. Of the 101 selected papers related to usual geometric conditions, N = 50 deal only with HTC, N = 16 deal only with pressure drop and the remainder (N = 35) analyse both HTC and pressure drop. Among the 85 HTC papers (N = 53) deal with the evaporating condition, N = 30 with condensation and only N = 2 with the heat transfer correlations under both conditions. Most of the 101 articles concern propane and isobutane. The high temperatures are less widely investigated.

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

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties putting them on a new path in medicine. Their conformational rigidity that improves proteolytic stability, leads to rapid penetration into tissues via any possible route of administration and elimination, occurring by renal metabolism, is of great importance for people with a history of liver diseases. In addition, each ring can function independently, making them extremely versatile molecules for further optimization. In this paper we compared the potentiometric and spectroscopic properties studied by UV-Vis, MCD, and EPR, of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH). In particular we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts, with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively, and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2).

With metal oxide semiconductors being a promising candidate for accurate indoor air quality assessments, multiple drawbacks of the gas sensors prevent their widespread use. Examples include poor selectivity, instability over time, and sensor poisoning. Complex calibration methods and advanced operation modes can solve some of those drawbacks. However, this leads to long calibration times, which are unsuitable for mass production. In recent years, multiple attempts to solve calibration transfer have been made with the help of direct standardization, orthogonal signal correction, and many more methods. Besides those, a new promising approach is transfer learning from deep learning. This article will compare different calibration transfer methods, including direct standardization, piecewise direct standardization, transfer learning for deep learning models, and global model building. The machine learning methods to calibrate the initial models for calibration transfer are feature extraction, selection, and regression (established methods) and a custom convolutional neural network TCOCNN. It is shown that transfer learning can outperform the other calibration transfer methods regarding the root mean squared error, especially if the initial model is built with multiple sensors. It was possible to reduce the number of calibration samples by up to 99.3 % (from 10 days to approximately 2 hours) and still achieve an RMSE for acetone of around 18 ppb (15 ppb with extended individual calibration) if six different sensors were used for building the initial model. Furthermore, it was shown that the other calibration transfer methods (direct standardization and piecewise direct standardization) also work reasonably well for both machine learning approaches, primarily when multiple sensors are used for the initial model.

Energy dissipation is one of the most important factors in choosing stepped spillways. However, few studies are about energy dissipation with different horizontal face angles. In this paper, the realizable k-ε turbulence model was used to study the flow field and energy dissipation rates of different stepped spillways with five horizontal face angles in the skimming flow regions. It shows that the free water surfaces and energy dissipation rates change with horizontal face angles and the energy dissipation rates initially decrease and then increase as the horizontal face angle increases. And the horizontal face angles cause the great changes of flow field.

As the structure of horizontal gas wells is more complicated than that of vertical wells, the form of the liquid-carrying in different sections does not well agree. This makes it problematic to apply the widely used liquid-carrying theory of vertical gas wells in horizontal gas wells. Since the theory focused on the critical gas flow rate, it cannot quantify how much liquid it can remove. Simultaneously, it ignores the fact that the liquid-carrying ability of gas flow is limited and the producing liquid has a certain amount of flowing energy. In this study, the gas-liquid flow law of horizontal gas wells and wellhead drainage stability in different tubing depths were firstly studied. Then, the stability of gas drainage for different tubing depths was analyzed and confirmed. Given the disadvantages of the typical theory of critical gas flow, the mathematical model of different tubing depths for gas drainage is established for horizontal gas wells. The innovative model could take the energy of gas flow and liquid flow into account, and quantify the liquid volume which was removed. By verifying the model with the experiments, the result showed that the relative error of the model is generally less than 10%. It shows the research could provide a scientific basis for the analysis and liquid-carrying capacity for horizontal gas wells.

Gravel packing completion method for horizontal wells has the advantages of maintaining high oil production for a long time, maintaining wellbore stability and preventing sand production, so it has become the preferred completion method for horizontal wells. At present, this technology still faces the problems of high sand bed height and poor gravel migration. In order to improve the efficiency of gravel packing in horizontal wells, pulsed gravel packing technology for horizontal wells is proposed for the first time. Based on the mechanism of hydraulic pulse, the Eularian model, RNG K-ε model and CFD model are used to simulate the solid-liquid two-phase flow. By optimizing the parameters such as frequency and amplitude of pulse waveform, the optimal pulse waveform of pulsed gravel packing in horizontal wells is determined. The effects of parameters such as sand-carrying fluid displacement, sand-carrying fluid viscosity, sand-carrying ratio, gravel particle size and string eccentricity on pulsed gravel packing in horizontal wells are studied, and the distribution law of gravel migration velocity and volume fraction in horizontal wells is obtained. According to the results, it can be seen that with the increase of displacement and viscosity of carrier fluid, the volume fraction of fixed bed and moving bed decreases gradually, while that of suspension bed increases gradually. With the increase of sand-carrying ratio, gravel particle size and string eccentricity, the volume fraction of fixed bed and moving bed increases gradually, while that of suspended bed decreases gradually. Comparing the effects of conventional gravel packing and pulsed gravel packing in horizontal wells, it can be concluded that the efficiency of pulsed gravel packing in horizontal wells is higher. The volume fraction of fixed bed and moving bed decreased by 30% and 40% respectively, while the volume fraction of suspended bed increased by 20%. The migration velocity of moving bed and suspended bed increased by 40% and 25% respectively. And the migration ability of gravel improved obviously.

Spent nuclear fuel and high-level radioactive waste can be disposed in deep horizontal drillholes in sedimentary, metamorphic, or igneous rocks. Horizontal drillhole disposal has safety, operational, and economic benefits: The repository is deep in the brine-saturated zone far below aquifers in a reducing environment of formations that can be shown to have been isolated from the surface for millions of years; its depth provides safety against inadvertent intrusion, earth¬quakes, and near-surface perturbations; it can be placed close to the reactors and interim storage facilities, minimizing transportation; disposal costs per ton of waste can be kept substantially lower than for mined repositories by its smaller size, reduced infrastructure needs, and staged imple¬mentation; and, if desired, the waste could be retrieved using “fishing” technology. In the proposed disposal concept, corrosion-resistant canisters containing unmodified fuel assemblies from commercial reactors would be placed end-to-end in up to 50 cm diameter horizontal drill¬holes, a configuration that reduces mechanical stresses and keeps the temperatures below the boiling point of the brine. Other high-level wastes, such as capsules containing 137Cs and 90Sr, can be disposed in small-diameter drillholes. We provide an overview of this novel disposal concept and its technology, discuss some of its safety aspects, and compare it to mined repositories and the deep vertical borehole disposal concept.

Noninvasive brain stimulation technique can be used for modulation of motor behaviors such as reaction time (RT). The main aim of this study was to investigate whether RT during a sequential visual isometric pinch task (SVIPT) can be affected by anodal transcranial direct current stimulation (a-tDCS) over three different stimulation sites such as dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1) or posterior parietal cortex (PPC). We also aimed to assess if stimulation of these cortical sites affect the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: 1) left M1, 2) left DLPFC, 3) left PPC and 4) sham. A-tDCS was applied concurrent with SVIPT in which the participants were precisely controlled their forces to reach seven different target forces from 10 to 40% of maximum voluntary contraction (MVC) presenting on a computer screen with right dominant hand. Four test blocks were randomly performed at baseline and 15 min after the intervention including sequence and random blocks with either hand. Our results showed significant differences between a-tDCS groups on ratio RT in the sequence blocks of both the right trained (P = .041) and left untrained hands (P = .023) for target forces of 15% and 30 % of MVC, respectively. In sequence blocks, M1 showed significant elongation in ratio RT for the target force of 15% and 30 % MVC compared to PPC and Sham groups in the right hand. In sequence blocks with left hand, this negative effect were found between M1-DLPFC and M1-Sham groups. In random blocks, no significant differences were found among four groups at each target forces for the left hand while there were significant differences for 15% (P =. 04) and 40% MVC (P =. 035) in the right hand. Our findings suggest that three different stimulation sites (DLPFC, M1 or PPC) were differentially affected by a single session of a-tDCS at some target forces during SVIPT. The effects of a-tDCS on ratio RT were transferred in the left untrained hand for sequence blocks but not random blocks. Further research is needed to understand fundamental aspects of these main areas of the brain on temporal processing in a precision control task such as SVIPT.

Pakistan flora is enriched with potent bioactive compounds that must be explored while Alzheimer's disease (AD) poses a global challenge driven by neurochemical imbalances. Acetylcholinesterase (AChE) inhibitors traditionally alleviate symptoms, but addressing the root cause remains elusive. This gap has led to the exploration of herbal sources for interventions. In this context, we analyzed fifty 50 medicinal plants for their phytochemical composition and antioxidant and acetylcholinesterase inhibition activities. AChE activity was measured using Ellman’s method. Santalum album exhibited significant AChE inhibition (86.2%), surpassing galantamine hydrobromide (81.8%). Promising inhibition was also seen in Bombax ceiba (77.8%), Acacia karoo (52.9%), and Phaseolus lunatus (50.1%). Detailed analysis revealed Santalum album's abundance in terpenoids, flavonoids, tannins, and phenolics, potentially correlating with its antioxidant and AChE inhibitory effects. Acacia karoo demonstrated elevated levels of total alkaloids, antioxidants, terpenoids, and tannins. Both Acacia karoo and Santalum album show promise for AD intervention due to their AChE inhibitory activities and unique phytochemical attributes, warranting further exploration, particularly for Acacia karoo's novel potential in AChE inhibition, to advance targeted AD therapeutics utilizing natural resources. This study contributes to the search for effective AD strategies from nature's pharmacological resources.

A method was developed to perform shape optimization of a tidal stream turbine hydrofoil using a multi-objective genetic algorithm. A bezier curve parameterized the refrence hydrofoil profoil NACA 63815. Shape optimization of this hydrofoil maximized its lift-to-darg ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. The Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed to perform the shape optimization. A comparative study of two-and three-dimensional optimizations was carried out. The effect of varing the angle of attack on the quality of optimized results was also studied. predictions based on two-dimensional panel method results was also studied. Preditions based on a two-dimensional panel method and on a computational fluid dynamics code were compared to experimental measurments.

Evaporating liquid sessile drop deposited on horizontal surface is an important object of applications (printing technologies, electronics, sensorics, medical diagnostics, hydrophobic coatings, etc.) and theoretical investigations (microfluidics, self-assembly of nanoparticle, crystallization of the solute, etc.). The arsenal of formulas for calculating the slow evaporation of an axisymmetric drop of capillary dimensions deposited on a flat solid surface is reviewed. Such characteristics as vapor density, evaporation flux density, total evaporation rate are considered. Exact solutions obtained in the framework of the Maxwellian model, in which the evaporation process of the drop is limited by vapor diffusion from the drop surface to the surrounding air, are presented. The summary covers both well-known results obtained during the last decades and new results published by us in the last few years, but practically unknown to the wide scientific community. The newest formulas, not yet published in refereed publications, concerning exact solutions for a number of specific contact angles are also presented. In addition, new approximate solutions are presented for the first time (total evaporation rate and mass loss per unit surface area per unit time in the whole range of contact angles ), which can be used in modeling without requiring significant computational resources.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

The aim of this work is to evaluate the impact of proposed modifications to conventional capturing and discharging effluents devices in Subsurface Flow Wetland (SSFW) systems. Main modifications proposed consist on extending the influence of the capture and discharge device in such a way that the SSFW width and height are fully covered. Additionally an SSFW prototype was built as pilot including the proposed modifications and the impact of the innovative device is measured based on the efficiency of the Chemical Oxygen Demand (COD) removal as compared to a traditionally built SSFW. The results show that for the innovative device, the COD removal was 10% higher than for the conventional device.

A modified free-wake vortex ring model is proposed to compute the dynamics of a floating horizontal-axis wind turbine. The model is divided into two parts. The near wake model uses a blade bound vortex model and trailed vortex model, which is developed based on vortex filament method. By contrast, the far wake model is based on the vortex ring method. The proposed model is a good compromise between accuracy and computational cost, for example when compared with more complex vortex methods. The present model is used to assess the influence of floating platform motions on the performance of a horizontal-axis wind turbine rotor. The results are validated on the 5MW NREL rotor and compared with other aerodynamic models for the same rotor subjected to different platform motions. It was found that the results from the proposed method are more reliable than the results from BEM theory especially at small angles of attack in the region of low wind speeds, on the one hand, and high wind speeds with blade pitch motions, on the other hand. And also the proposed method is less time consuming and has similar accuracy when comparing with more advanced vortex based methods.

(1) Background: This manuscript introduces the "Lines and Dots" (LAD) technique, a novel method for administering Botulinum Toxin Type A (BoNT-A) in treating horizontal forehead lines. This technique emphasizes precision in targeting nerve endplates, considering individual anatomical variations, and optimizing treatment efficacy. (2) Methods: The LAD technique focus on using the frontalis muscle contraction dynamics to identify the specific distribution of the supraorbital and supratrochlear nerves It involves creating a grid of predetermined lines and points on the forehead that align with nerve paths and motor endplate zones. (3) Results: By targeting injections along these precise lines and points, the LAD technique ensures more effective utilization of BoNT-A. This approach not only enhances the toxin's effect but also allows for a reduction in the overall dosage. The strategic placement of injections results in a more natural, symmetrical, and sustained therapeutic effect, ensuring an improved aesthetic outcome. (4) Conclusions: The LAD technique represents a significant advancement in the realm of cosmetic injectables. It offers a tailored, patient-specific approach, maximizing the effectiveness of BoNT-A injections for treating forehead lines while minimizing risks and enhancing patient satisfaction. This method underscores the importance of a deep anatomical understanding and precision in cosmetic procedures.

A novel drive current model covering the effects of source/drain voltage (VDS) and gate voltage (VGS) and incorporating drift and diffusion current on the surface channel at the nano-node level, especially beyond 28nm node is presented. The effect of the diffusion current added is more satisfactory to describe the behavior of the drive current in nano-node MOSFETs, fabricated with the atomic-layer-deposition (ALD) technology. This breakthrough in model establishment can expose the long and short channel devices together. Introducing the variables of VDS and VGS, the mixed current model more effectively and meaningfully demonstrates the drive current of MOSFETs under the operation of horizontal, vertical, or mixed electrical field. In comparison between the simulation and experimental consequences, the electrical performance is impressive. The error between both is less than 1%, better than the empirical adjustment to issue a set of drive current models.

In this research, a novel method to investigation the transient heat transfer coefficient in a channel is suggested experimentally, in which the water flow, itself, is considered both just liquid phase and liquid-vapor phase. The experiments were designed to predict the temporal and spatial resolution of Nusselt number. The inverse technique method is non-intrusive, in which time history of temperature is measured, using some thermocouples within the wall to provide input data for the inverse algorithm. The conjugate gradient method is used mostly as an inverse method. The temporal and spatial changes of heat flux, Nusselt number, vapor quality, convection number, and boiling number have all been estimated, showing that the estimated local Nusselt numbers of flow for without and with phase change are close to those predicted from the correlations of Churchill and Ozoe (1973) and Kandlikar (1990), respectively. This study suggests that the extended inverse technique can be successfully utilized to calculate the local time-dependent heat transfer coefficient of boiling flow.

Steel fibres provides ductility to concrete structures. This in turn gives possibility to replace or reduce conventional reinforcement in structural elements. In this study the focus is on structural walls and the fibres as potential replacement for horizontal reinforcement in areas where vertical re-bars are needed. An experimental study was conducted, in which prismatic specimens with longitudinal re-bars were subjected to centric loading. Ten samples with 12 specimens in each were tested. The parameters considered were: fibre content, concrete cover for the longitudinal bars, and presence of stirrups. Self-compacting concrete with 30 and 60 kg/m3 steel fibres was used. The influence of fibres on maximum load, stiffness, and ductility of the specimens is evaluated. The results show that fibres eliminated brittle collapse and spalling of concrete at failure. Samples with fibres tend to have reduced maximum force and stiffness but increased ductility. The specimens with fibre content of 60 kg/m3 showed more ductile behaviour than the ones with minimum amount of conventional stirrups. The study suggests that combination of steel fibres and conventional re-bars can lead to less qualitative compactness of the self-compacting concrete, which in turn may reduce load bearing capacity and stiffness of the structure.

Drilling vibrations has been identified as a key performance limiter that prevents successful energy transfer from the surface to the bit. The resultant irregular bit rotation speed causes drilling challenges like poor hole quality and reduction in rate of penetration to name a few. Amongst other vibration suppression techniques, active control of vibrations is widely used and recommended. Active control system utilizes a closed loop feedback control to continuously adapt to downhole vibrations detected at the surface. The system requires regular tuning and upgrades which is done experimentally before field deployment. The paper presents a PID based experimental tuning of horizontal and vertical strings for irregular RPM. In doing so, the paper highlights the need of having horizontal experimental setups for state-of-the-art PID tuning for downhole RPM. The settling time of irregular RPM for horizontal setup is found to be lower than the vertical configuration due to multiple contact points and increased friction coefficient.

In this study, a matched-mode autoregressive source depth estimation method (MMAR) based on autoregressive (AR) wavenumber estimation is proposed for a moving source in shallow water waveguides. The signal original frequency and the environmental parameters, namely, the sound speed profile and bottom properties are known as a prior knowledge. The mode wavenumbers are estimated by the AR modal wavenumber spectrum. On the basis of the mode wavenumber estimation, the mode amplitudes can be estimated by the wavenumber spectrum that is obtained by generalized Hankel transform. The source depth estimation is determined by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with other methods of moving source depth estimation, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The selection of horizontal line array depth is illustrated by simulation and normal mode theory in details.

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.

A recent study by the Wall Street Journal reveals that the hydrofractured horizontal wells in shales have been producing less than forecasted by the industry with the empirical hyperbolic decline curve analysis (DCA). As an alternative to DCA, we introduce a simple, fast and accurate method of estimating ultimate recovery (EUR) in oil shales. We adopt a physics-based scaling approach to analyze oil rates and ultimate recovery from 14,888 active horizontal oil wells in the Bakken shale. To predict EUR, we collapse production records from individual horizontal shale oil wells onto two segments of a master curve: (1) We find that cumulative oil production from 4,845 wells is still growing linearly with the square root of time; and (2) 6,401 wells are already in exponential decline after approximately seven years on production. In addition, 2,363 wells have discontinuous production records, because of refracturing or changes in downhole flowing pressure, and are matched with a linear combination of scaling curves superposed in time. The remaining 1,279 new wells with less than 12 months on production have too few production records to allow for robust matches. These wells are scaled with the slopes of other comparable wells in the square-root-of-time flow regime. In the end, we predict that total ultimate recovery from all existing horizontal wells in Bakken will be some 4.5 billion barrels of oil. We also find that wells completed in the Middle Bakken formation, in general, produce more oil than those completed in the Upper Three Forks formation. The newly completed longer wells with larger hydrofractures have higher initial production rates, but they decline faster and have EURs similar to the cheaper old wells. There is little correlation among EUR, lateral length, and the number and size of hydrofractures. Therefore, technology may not help much in boosting production of new wells completed in the poor immature areas along the edges of the Williston Basin. Operators and policy-makers may use our findings to optimize the possible futures of the Bakken shale and other plays. More importantly, petroleum industry may adopt our physics-based method as an alternative to the overly-optimistic hyperbolic DCA that yields an "illusory picture" of shale oil resources.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

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.

Quantum cryptography is the field of cryptography that explores the quantum properties of matter. Its aim is to develop primitives beyond the reach of classical cryptography or to improve on existing classical implementations. Although much of the work in this field has been dedicated to quantum key distribution (QKD), some important steps were made towards the study and development of quantum oblivious transfer (QOT). It is possible to draw a comparison between the application structure of both QKD and QOT primitives. Just as QKD protocols allow quantum-safe communication, QOT protocols allow quantum-safe computation. However, the conditions under which QOT is actually quantum-safe have been subject to a great amount of scrutiny and study. In this review article, we survey the work developed around the concept of oblivious transfer in the area of theoretical quantum cryptography, with an emphasis on some proposed protocols and their security requirements. We review the impossibility results that daunt this primitive and discuss several quantum security models under which it is possible to prove QOT security.

Hydrogenases are iron-sulfur enzymes that catalyze proton reduction and H₂ oxidation with outstanding efficiency. They are considered blueprints for the design of transition metal complexes, e.g. as heterogenous catalysts in the context of H₂ production from water. Moreover, hydrogenases are biological model systems for metal hydride chemistry and proton-coupled electron transfer. Depending on the composition of the active site cofactor, [NiFe]-hydrogenases are distinguished from [FeFe]-hydrogenases. The former binds a hetero bimetallic nickel/iron site, embedded in the protein by four cysteine ligands. The later, by contrast, carries a homo bimetallic iron/iron site attached to the protein by only a single cysteine. Carbon monoxide and cyanide ligands (CO/CN) at the active site facilitated detailed investigations of hydrogenase catalysis by infrared spectroscopy, owing to strong signals and redox-dependent frequency shifts. However, the details of proton transfer have not been addressed experimentally.We found that specific redox state transitions in [NiFe]- and [FeFe]-hydrogenase can be triggered by visible light to record extremely sensitive ‘light-minus-dark’ infrared difference spectra monitoring key amino acid residues as shown in the ToC figure. As these transitions are coupled to protonation changes, our data allowed investigating dynamic hydrogen-bonding changes that go well beyond the resolution of protein crystallography. In [NiFe]-hydrogenase, photolysis of the bridging hydride ligand in the ‘Ni-C’ state was followed by rapid accumulation of the ‘Ni-SI_a’ state and/or ‘Ni-L’ state. Infrared difference spectra in various isotopic media clearly indicated the formation of a protonated cysteine residue as well as hydrogen-bonding changes involving the COOH group of a glutamic acid residue and a ‘dangling water’ molecule. These findings are in excellent agreement with crystallographic analyses of [NiFe]-hydrogenase in the Ni-R state and allowed devising a molecular precise model of catalytic proton transfer. In [FeFe]-hydrogenase, an external redox dye was used to accumulate the ‘Hred’ state over the oxidized resting state ‘Hox’. Infrared difference spectra of wild-type enzyme and numerous amino acid variants indicated hydrogen-bonding changes involving the COOH groups of two glutamic acid residues. Moreover, we noted the deprotonation of an arginine residue. Crystallographic analyses of [FeFe]-hydrogenase in the Hox state failed to explain the rapid proton transfer due to a ‘breach’ in the succession of residues. To this end, our findings facilitated a molecular precise model of ‘discontinued’ proton transfer.The comparison of catalytic proton transfer in bimetallic hydrogenases emphasizes the role of the outer coordination sphere. We suggest that the stable protonation of a nickel-ligating cysteine in [NiFe]-hydrogenase has a crucial influence on the preferred direction of proton flow and catalysis (i.e., H₂ oxidation). On the contrary, proton transfer in [FeFe]-hydrogenase involves an adjacent cysteine as a relay group that promotes both proton release and proton uptake. We presume that this causes the notable bidirectionality of [FeFe]-hydrogenase. These observations must guide the design of biomimetic compounds for the production or consumption of H₂.

This research explores the application of Support Vector Machine (SVM) and Digital Surface Models (DSM) derived from Unmanned Aerial Vehicle (UAV) data for a vertical-horizontal classification of mangrove ecosystems in Tongke-tongke and Samataring Village. The study focuses on integrating high-resolution DSM subtracted from Digital Terrain Models (DTM) to classify mangrove vegetation into sixteen distinct vertical-horizontal classes based on vegetation type and elevation. The analysis revealed that older mangroves, particularly those within the 25-35 meters elevation range, dominate the landscape, accounting for 43.40% of the total classified area. This dominance suggests a mature and ecologically stable mangrove ecosystem, which is crucial for biodiversity conservation and carbon storage. The study demonstrates the effectiveness of SVM and UAV technologies in providing detailed and accurate ecological assessments of mangrove forests. The classification results contribute valuable insights into the structural diversity and health of mangrove ecosystems, facilitating targeted conservation and management strategies. Additionally, the research highlights the utility of advanced remote sensing and machine learning techniques in enhancing the precision of environmental monitoring and the assessment of carbon sequestration capabilities within mangrove ecosystems. The findings underscore the potential of UAV-based remote sensing for ecological studies, offering a replicable method for similar assessments in other regions, thereby supporting global environmental management and climate action initiatives.

A linear stability analysis of the parallel uniform flow in a horizontal channel with open upper boundary is carried out. The lower boundary is considered as an impermeable isothermal wall, while the open upper boundary is subject to a uniform heat flux and it is exposed to an external horizontal fluid stream driving the flow. An eigenvalue problem is obtained for the two-dimensional transverse modes of perturbation. The study of the analytical dispersion relation leads to the conditions for the onset of convective instability as well as to the determination of the parametric threshold for the transition to absolute instability. The results are generalised to the case of three-dimensional perturbations.

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.

Recently, the use of drones/unmanned aerial vehicles (UAVs) has notably increased due to their broad commercial spread and low cost. The wide diffusion of drones increases the hazards of their misuse in illegitimate actions such as drug smuggling and terrorism. Thus, the surveillance and automated detection of drones are crucial for safeguarding restricted regions or special zones from illegal drone interventions. One of the most challenging issues in drone detection in surveillance videos is the apparent similarity of drones and birds against complex backgrounds. In this work, an automated image-based drone-detection system utilizing an advanced deep-learning-based object-detection method known as you only look once (YOLOv5) is introduced for protecting restricted regions or special zones from unlawful drone interventions. Due to the lack of sufficient data, transfer learning was utilized to pretrain the object-detection method to increase the performance. The experiments showed outstanding results, and an average precision of 94.7% was accomplished.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

Since the turn of the 21^st Century Chian has enacted a range of policies to improve its water ecosystem. Some of these measures include (but are not limited to) the creation of Seven River Conservancy Commissions to engage in river basin management and the development (combination and recombination) of series of ministries and departments to address environmental pollution and degradation. The most recent attempt to address the nations polluted waterways and lakes occurred in December 2016, when the General Office of the Chinese Central Committee and the State Council issued The Options on Full Implementation of the River Chief System (RCS) Across the Country. The intent behind Options was not only to fully implemented he river chief system but to bring the nations drinking water supply quality to no worse than category III. Based on the Options Erik Solheim, UN Under Secretary General and Executive Director of the UN Environment Programme, commented ‘I'm convinced that what I have seen in Pujiang County and Anji County will be the future of China, even the future of the world’ (cited in Xianqiang 2021, 435; emphasis added). It is to this observation that the reminder of this article will be directed.

The horizontal flatbed electrophoresis system has been used for the electrophoresis of protein samples and is often preferred for a two-dimensional polyacrylamide gel electrophoresis. In currently available equipment, cathode and anode electrodes were placed on top of a gel at each end. An electric field entered from the top of a gel, and its strength was relatively weaker in the bottom part of the gel. Such configuration caused migrating protein bands to lie down forward in the migrating direction. New electrodes, i.e., the clamp-shaped and the double-deck electrodes, were developed to simultaneously apply an electric field from the top and bottom of a gel. Electrophoretic separation with these electrodes made protein bands more focused and improved protein band resolutions. When the FIGE technique was tested together with new electrodes, protein bands were aligned nearly vertically and showed the best electrophoretic resolutions.

As development progresses, when the bottom hole flowing pressure or formation pressure is less than the saturation pressure of crude oil in the reservoir, oil and gas two-phase seepage occurs in the reservoir. Due to the characteristics of oil and gas two-phase seepage, after the oil and gas two-phase seepage occurs in the reservoir, the well production will be reduced, or even greatly reduced. Therefore, how to predict the horizontal well capacity better in this case is an important problem that needs to be solved urgently. In this paper, the method and process of establishing the transient calculation model of two-phase flow in horizontal wells are introduced in detail from three aspects: fluid physical properties, reservoir oil-gas two-phase seepage, and the coupling model of Inflow Performance and Flow in Wellbore. The model is more reliable through the verification of production data from five wells in two oilfields.

Abstract: Coal bed methane (CBM) reservoirs are complex systems whose properties differ from those of conventional reservoirs. Coal seams are dual-porosity systems that comprise the porosities of the matrix and cleat system. Gas in the coal seams can be stored as free gas in the cleat system and as adsorbed gas in the porous medium. The flow mechanisms of the natural gas through the formation include desorption, diffusion, and Darcy’s flow regimes. The permeability of CBM reservoirs is more sensitive to pressure variations than conventional gas reservoirs. To study the flow behavior of CBM reservoirs it is mandatory to use a model that considers their unique characteristics. The objective of this study was to propose a physical and mathematical model of production performance for horizontal wells in CBM reservoirs whose permeability is dependent on pressure. A solution for the model was obtained by applying Pedrosa´s transformation, perturbation theory, Laplace transformation, the point source method, and Sthefest´s algorithm. The solution to this problem was validated with previous work thoroughly. The type curves of the model were built and the pressure transient behavior of the model was analyzed and discussed. The effects of several parameters on pressure behavior were also discussed.

Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

During last decades, production and consumption of small ruminant milk has been increased. As a result of it, sheep and goat farming has been developing and scientists are focused on these animal researches both clinical and feeding strategies. By the evolutionary challenges and adaptations, colostrum has a crucial role of immune complementation for litter. As a result of these challenges and adaptations neonatal life is especially more important in ruminants because of it affects their whole life and future of livestock. Passive immune transfer is the main mechanism that explained by biological evolution between dam and lamb and also it is effected by factors up to dam and up to the litter. Today importance of passive immune transfer is well known for the future of livestock economy and animal welfare. In the literature, researchers are focused on correlation between colostrum quality (especially immunoglobulin amounts) and blood serum levels of newborns. Aims of present review are to discuss datas of recent studies, point out different effecting factors in colostrum quality and passive immune transfer, enlighten and give new ideas to researchers.

Transformers are increasing replacing older generation of deep neural networks due to their success in a wide range of application. The dominant approach of using transformers is to pre-train them on a large training dataset and then fine-tune them on a downstream task. However, as transformers becoming larger, the fine-tuning approach is become an infeasible approach for transfer learning. In this short survey, we list a few recent methods that makes using transformers based on transfer learning more efficient.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

Three-dimensional transfer functions (3D TFs) are generally assumed to fully describe the transfer behavior of optical topography measuring instruments such as coherence scanning interferometers in the spatial frequency domain. Therefore, 3D TFs are supposed to be independent of the surface under investigation resulting in a clear separation of surface properties and transfer characteristics. In this paper we show that the 3D TF of an interference microscope differs depending on whether the object is specularly reflecting or consists of point scatterers. In addition to the 3D TF of a point scatterer, we will derive an analytical expression for the 3D TF corresponding to specular surfaces and demonstrate this as being most relevant in practical applications of coherence scanning interferometry (CSI). We additionally study the effects of temporal coherence and disclose, that in conventional CSI temporal coherence effects dominate. However, narrowband light sources are advantageous if high spatial frequency components of weak phase objects are to be resolved, whereas for low-frequency phase objects of higher amplitude the temporal coherence is less affecting. Finally, we present an approach that explains the different transfer characteristics of coherence peak and phase detection in CSI signal analysis.

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.

This study conducted experiments to optimize the electro-dewatering of sludge using a dynamic model calculation method and the Wild Horse optimizer. Specifically, we explored two factors that might influence dewatering: the composition of the electrode materials and the strength of the electrical field. Compared to other electrodes commonly used in electro-dewatering—such as RuO2/Ir2O3-Ti, graphite, Ir2O3/Ta2O5-Ti, and Ti—the Electro-kinetic Geosynthetics (EKG) electrode exhibited superior performance. This was determined by considering both energy consumption and model establishment. Our findings also identified electric-field strength as a crucial factor determining the final moisture content. As the electrical intensity increased, the dewatering effect improved, aligning with our model calculations. However, we observed a steep increase in energy consumption per kilogram of filtrate when the electric-field strength exceeded a certain threshold. To optimize the energy consumption of the dehydration process, we constructed a multi-factor model. The simulation results from this model confirmed that electric-field intensity is the primary factor affecting dehydration. It was established that 10 V/cm is a critical threshold for the process.

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.

In this paper, we revisit a modified version of the classical Whitehead's theory of gravity in which all possible bilinear forms are considered to define the corresponding metric. Although, this is a linear theory that fails to give accurate results for the most sophisticated predictions of general relativity, such as gravity waves, it can still provide a convenient framework to analyze some new phenomena in the Solar System. In particular, recent development in the accurate tracking of spacecraft and the ephemerides of planetary positions have revealed certain anomalies in relation with our standard paradigm for celestial mechanics. Among them the so-called flyby anomaly and the anomalous increase of the astronomical unit play a prominent role. In the first case the total energy of the spacecraft changes during the flyby and a secular variation of the semi-major axis of the planetary orbits is found in the second anomaly. For this to happen it seems that a net energy and angular momentum transfer is taken place among the orbiting and the central body. We evaluate the total transfer per revolution for a planet orbiting the Sun in order to predict the astronomical unit anomaly in the context of Whitehead's theory. This could lead to a more deeply founded hypothesis for an extended gravity model.

: Two-dimensional instability of a horizontal layer of boiling liquid with a finite height is experimentally studied. In this layer, “vapor columns” rose at the corners of a square rectangular grid. The symmetry of “vapor column” location on the heating surface is considered. The model considers the approach to the boiling crisis in terms of both developed nucleate boiling and transitional boiling (the Zuber problem). When dealing with developed nucleate boiling, the layer of boiling liquid is considered in calculations as an isotropic homogeneous system (foam). It is shown how the conditions on the heating surface (capillary-porous coating) affect external hydrodynamics of the liquid layer and, ultimately, the value of the critical heat flux. The calculation ratio obtained by approaching the boiling crisis with regard to developed nucleate boiling takes into account the dependence of the critical heat flux on the void fraction of the boiling liquid layer. A new solution to the boiling crisis problem is proposed when approaching the crisis from the point of transitional boiling (the Zuber problem). This new solution eliminates some shortcomings of the classical problem (in particular, the void fraction of the layer corresponds to the experiments).

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.

In this paper, a further mathematical analysis of some previous results of the author’s ongoing investigation regarding Prandtl’s equations is accomplished. In particular, the objective of the present work is the performance of an approximate explicit solution to Prandtl’s system of equations for a laminar, isothermal, incompressible and steady boundary layer flow of a Newtonian fluid, over an infinite horizontal flat plate.

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.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

Abstract: The aim of this study was to evaluate the effects of solids accumulation on greenhouse gas (GHG) emissions, substrate, plant growth and performance of a horizontal flow (HF) treatment wetland (TW) planted with Phragmites australis (Cav.) Trin. ex Steud. sub sp. australis. The study was carried out in an eight-year-old full-scale HF-TW located in the Mediterranean region (Sicily, Italy). To collect data inside the HF unit, nine observation points (besides the inlet and the outlet) along three 8.5 m long transects (T1, T2, and T3) were identified. The first transect (close to the inlet zone) showed hydraulic conductivity (Ks) reduction of about one order of magnitude higher than the other two. Results highlighted GHG emissions increasing during the summer when temperature and solar radiation were higher than in the rest of the year. Carbon dioxide (CO2) emis-sions decreased from T1 to T3, with maximum monthly values in T1 (21.4 g CO2 m-2 d-1) about double with respect to T2 (12.6 g CO2 m-2 d-1) and T3 (10.7 g CO2 m-2 d-1) observed in July. The CO2 seasonal trend was similar to that of P. Australis growth. Theoretical me-thane (CH4) emissions followed the trend of volatile solids (VS), which was about 3.5 and 4 times in T1 to T2 and T3. The highest CH4 emissions in T1 were probably due to anaerobic bacteria (methanogens) that proliferated in the waterlogged, anoxic part of TW. The pore-clogging affected the chemical oxygen demand (COD) removal efficiency which de-creasing from T1 to T3 for the observation period. Notwithstanding this behaviour, the final effluent quality was very satisfactory with the average value of COD removal efficiency above 90%.

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.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

In this review, photoinduced electron transfer processes in specifically designed assembled ar-chitectures have been investigated on the basis of recent results reported from our laboratories. A convenient and useful way to study these kinds of systems has been described in order to under-stand the rules that drive to a light induced charge separation and its subsequent decay to the ground state, also with the aim of offering a tutorial for young researchers. Assembled systems of covalent or supramolecular nature have been presented and some functional multicomponent systems for the conversion of light energy into chemical energy have been discussed.

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.

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.

We aimed to investigate the clinical results following poor-quality embryo transfer and the parameters to foresee the prognosis. In this study, 2123 cycles that had day 3 and day 5 single-fresh embryo with a poor-quality embryo transfers and a good-quality embryo transfers were compared. The cycles according to transfer day were evaluated by conducting a subgroup analysis. The correlation between all the obtained demographic characteristics, controlled ovarian stimulation parameters and cycle results were analysed. Clinical pregnancy was established in 53 patients that underwent transfer in the poor-quality embryo group (14.9%). Of these patients, 36 had live birth (live birth rate per clinical pregnancy 67.9%). In cleavage-stage embryos, live birth rates per clinical pregnancy were higher in poor-quality blastocyst transfer. When analysing the factors affecting live births in poor-quality embryo group, as the total gonadotropin dose increases, the probability of live birth decreases, as in the probability of hCG positivity. In conclusion; although the probability of pregnant is low, when clinical pregnancy is established, there is a high chance of having a live birth after poor-quality embryo transfers. This could be regarded as an acceptable option in cycles when only poor-quality embryos were available.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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.

Skin cancer is an uncommon but serious malignancy. Dermoscopic images examination and biopsy are required for cancer detection. Deep learning (DL) is extremely effective in learning characteristics and predicting malignancies. However, DL requires a large number of images to train. Image augmentation and transferring learning were employed to overcome the lack of images issue. In this study we divided images into two categories: benign and malignant. To train and test our models, we used the public ISIC 2020 database. Melanoma is classified as malignant in the ISIC 2020 dataset. Along with categorization, the dataset was studied to demonstrate variation. The performance of three top pretrained models was then benchmarked in terms of training and validation accuracy. Three optimizers were employed to optimize the loss: RMSProp, SGD, and ADAM. Using ResNet, VGG16, and MobileNetV2, we obtained training accuracy of 98.73%, 99.12%, and 99.76%, respectively. Using these three pretrained models, we attained a validation accuracy of 98.39%.

The organo-catalyzed enantioselective benzylation reaction of α-trifluoromethoxy indanones afforded α-benzyl-α-trifluoromethoxy indanones with a tetrasubstituted stereogenic carbon center in excellent yield with moderate enantioselectivity (up to 57% ee). Cinchona alkaloid-based chiral phase transfer catalysts were found to be effective for this transformation, and both enantiomers of α-benzyl-α-trifluoromethoxy indanones were accessed, depended on the use of cinchonidine and cinchonine-derived catalyst. The method was extended to the enantioselective allylation reaction of α-trifluoromethoxy indanones to give the allylation products in moderate yield with good enantioselectivity (up to 76% ee).

Transfer factor is the name given to material derived from activated lymphocytes that is probably composed of a complex of a peptide and a short segment of RNA and which has the reported ability to transfer specific T cell immunity to uncommitted lymphocytes. Many independent groups around the world reported isolating transfer factors between 1955 and 1990 and demonstrating their ability to transfer passive immunity from one animal or individual to another, often within 24 hours of inoculation. Such activity is potentially revolutionary both in making T cell vaccines readily manufacturable and also because the existence of transfer factors would undermine the basic assumptions of the clonal selection theory, which currently dominates immunological theory. Unfortunately, lack of the microanalytical and synthetic techniques required to properly identify transfer factors, combined with safety factors associated with it derivation from blood sources susceptible to HIV and prion infections, put an end to transfer factor research after 1990. This paper reviews the evidence supporting transfer factor activity and suggests that this potentially revolutionary concept be resurrected and subjected to renewed scrutiny in light of CRISPR-Cas mechanisms and because of its potential to make possible T cell vaccination and provide a novel basis for understanding immunological function.

The growth of state transfers to equalize regional development disparities affects the stability of the state's financial system. The research aims at determining how the mechanism of horizontal alignment of fiscal imbalances between local budgets affects the stability of financial system of Ukraine. To assess the asymmetry of the alignment formula, we use a quasi-experimental strategy of analyzing potential scenarios, we calculate base grants (for recipient municipalities) and reverse grants (for donor municipalities) for 1439 municipalities in Ukraine. Then we conduct counterfactual experiments, where we change the calculation subject, the number of budgets in calculation and the calculation period. Under different calculation scenarios, we determine whether the alignment mechanism has positive or negative affect on the stability of the public finance system based on the criterion of non-accumulation of budgetary debt. We establish that the current system of horizontal fiscal alignment in Ukraine overstates the fiscal capacity of communities, artificially inflating the indicators of the performance of the imbalance alignment system reform and distorting the overall picture of public finance stability. The alignment system, designed to mitigate unevenness in socio-economic community development, actually exacerbates it. The mechanism for calculating community fiscal capacity, which underlies horizontal financial alignment, requires an improved approach to determining the calculation subject and further reform.

In recent years, the number of open preservation surgeries for treatment of laryngeal Cancer has increased. The purpose of this surgery is to avoid the highly mutilating intervention of a total laryngectomy (TL) and to maintain laryngeal function while ensuring oncological radicality. The aim of this study was to assess the oncologic results of OPHL I and II and to identify prognostic factors that could impact patient survival and local failure rates. This study was conducted on 182 patients with laryngeal squamous cell carcinoma treated with OPHLs between 2005 and 2015. The survival rates of a group of patients treated with TL between 2004 and 2014 were taken into con-sideration to compare survival outcomes. The DSS in pT2 and pT3 tumors in relation to the type of surgery showed no statistically significant difference between OPHLs and TL (p=0.54 and p=0.63 respectively). The 5-years T-recurrence free survival showed that pT4 tumors, perineural/vascular positive invasion and OPHL IIb + ARY treated tumors were associated with a higher risk of tumor recurrence (p<0.0001, p=0.0005 and p=0.016 respectively). OPHLs represent an excellent alterna-tive to TL considering the characteristics of the tumor and the general conditions of the patient.

After the ban on tributyltin-based antifouling paints, DCOIT (4,5-Dichloro-2-octylisothiazol-3(2H)-one) became one of the most used antifouling biocides. DCOIT is a pseudo-persistent contaminant in port areas and is toxic to non-target species, but its ability to bioaccumulate and be transferred along the trophic chain remains unknown. This study aimed to preliminarily investigate the bioaccumulation, trophic transfer, and biomagnification of DCOIT and SiNC-DCOIT (DCOIT immobilized in silica mesoporous nanocapsules) from the microalga Tetraselmis chuii to the mussel Mytilus galloprovincialis during exposure experiments considering 24 h of uptake followed by 72 h depuration. Mussels rapidly internalized and metabolized both DCOIT and SiNC-DCOIT. The predator-prey biomagnification factors (BMFTL) suggested that both forms of the biocide can be trophically transferred, with higher concentrations of DCOIT and SiNC-DCOIT in mussels than in microalgae (BMFTL &gt; 1). However, the bioaccumulation and bioconcentration factors indicated a low bioaccumulation capability in the short term, suggesting that the depuration rate overcomes the uptake. Our findings contribute to the development of environmental risk assessments for DCOIT and SiNC-DCOIT, but do not exclude the possibility of DCOIT bioaccumulation in the long-term in areas with constant input of DCOIT. Further studies are needed to understand these processes under continuous exposure scenarios to DCOIT.

New analytical solutions of the heat conduction equation are presented in cylindrical and spherical coordinates. Then these solutions are reproduced with high accuracy by recent explicit and unconditionally stable finite difference methods. After this, real experimental data from the literature regarding a heated cylinder are reproduced by the explicit numerical methods as well as by Finite Element Methods (FEM) ANSYS workbench. Convection and nonlinear radiation are also considered on the boundary of the cylinder, and it is shown that the explicit methods are much more accurate than the commercial software.

The aim of this paper is to contribute to the body of knowledge about Project-Based Learning (PBL) methodology in higher education by describing and analysing interrelations between competencies, and their contribution to knowledge transfer (KT) and students’ rating of the project. The sample consisted of 464 students from the Universities of Huelva (N=347; 74.8%) and Murcia (N= 117; 25.2%), enrolled in the second year of a degree in either Infant or Primary Education. Data was collected through a self-administered questionnaire comprising a total of 53 items measuring General, Specific and Transversal competencies, as well as students’ rating of the project. Competencies were selected from the course programmes for the degrees in Infant and Primary Education. Preliminary results showed that competencies were moderately to highly acquired after PBL, and that students reported notable KT as well as a positive assessment of the project. KT showed a high degree of association with students’ ratings and was established as a key factor in learning and learner satisfaction in higher education.

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.

The United Nations Sustainable Development Goals call for the eradication of poverty, and China has proposed the Rural Revitalization Strategy based on the achievements of its poverty alleviation efforts in 2020. As a vital component of this strategy, the impact of land transfer on farmers' income growth has become a hot topic in both theoretical and practical sectors. This paper utilizes data from the CFPS database for the years 2010-2018 to construct OLS regression models and a Heckman two-stage model to analyze the effects of land transfers on the incomes of different types and sources among farmers. The empirical results show: First, land transfers do promote income growth among farmers, but the effect is primarily positive for those transferring out land, with negligible impact on those acquiring land. Second, the impact of land transfers varies between different types of income for transferees and transferors. Wage income contributes up to 88.26% to the income growth of transferors, a significantly higher rate than the decrease in business income; however, the increase in business income for transferees does not offset the decline in wage income, leading to no significant change in overall income. Further sensitivity analysis using the Heckman two-stage model confirms the robustness of these findings. The conclusions of this study provide theoretical and empirical evidence to optimize land transfer policies, enhance participation in land transfers, and ultimately achieve the mission of rural revitalization.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

Chatbots are computer programs that use artificial intelligence to imitate human conversations. Recent advancements in deep learning have shown interest in utilizing language transformers, which do not rely on predefined rules and responses like traditional chatbots. This study provides a comprehensive review of previous research on chatbots that employ deep learning and transfer learning models. Specifically, it examines the current trends in using language transformers with transfer learning techniques to evaluate the ability of Arabic chatbots to understand conversation context and demonstrate natural behavior. The proposed methods explore the use of AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator) transformers, with different variants of these transformers and semantic embedding models. Two datasets were used for evaluation: one with 398 questions and corresponding documents, and another with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions, using confidence and similarity metrics. The experimental results showed that the AraElectra-SQuAD model achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD model consistently outperformed other models, displaying remarkable performance, high confidence, and similarity scores, as well as robustness, highlighting its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the AraElectra-SQuAD model can be further enhanced and applied in various tasks such as chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users. By combining the power of transformer architecture with fine-tuning on SQuAD-like large data, this trend demonstrates its ability to provide accurate and contextually relevant answers to questions in Arabic.

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

Electroactive microorganisms have received a lot of interest in the field of environmental engineering and biotechnology because of their extracellular electron transfer (EET) capability of energy recovery and environmental remediation. However, the low efficiency of EET remains a barrier to its practical implementation. This review presents an introduction to EET, followed by addressing strategies for enhancing EET through different approaches including enhancing EET by mediator materials, external electricity, photo, and magnetism as well as integrated strategies, that is, the combination of multiple means to promote extracellular electron transport and improve the performance of microbial electrochemical systems. Finally, the challenges, perspectives, and new directions for EET enhancement will be discussed.

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.

In the past few years, the possibility to transfer power wirelessly has experienced growing interest from the research community. Since the wireless channel is subject to a large number of random phenomena, a crucial aspect is the statistical characterization of the energy that can be harvested by a given device. For this characterization to be reliable, a powerful model of the propagation channel is necessary. The recently proposed Generalized-K model has proven to be very useful, as it encompasses the effects of path-loss, shadowing and fast fading for a broad set of wireless scenarios, and it is analytically tractable. Accordingly, the purpose of this paper is to characterize, from a statistical point of view, the energy harvested by a static device from an unmodulated carrier signal generated by a dedicated source, assuming that the wireless channel obeys the Generalized-K propagation model. Specifically, using simulation-validated analytical methods, this paper provides exact closed-form expressions for the average and variance of the energy harvested over an arbitrary time period. The derived formulation can be used to determine a power transfer plan that allows multiple or even massive numbers of low-power devices to operate continuously, as expected from future network scenarios such as IoT or 5G/6G.

In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

Fungal endophytes have been found in all plants surveyed to date, yet for many fungi the function of endophytism is still unknown. The Foraging Ascomycete Hypothesis (FAH) proposes that saprotrophic fungi utilize an endophytic stage in leaves to modify dispersal. Under this hypothesis, leaves can provide food and water during time of environmental scarcity and they can transport the fungi to other substrates upon dehiscence. If the FAH is accurate, then some endophytes should have the ability to colonize saprobic substrates directly from a leaf-endophyte stage, though this has been little studied. To assess this ability, twelve surface-sterilized leaves of a tropical tree (Nectandra lineatifolia Mez) were placed directly on wood and incubated for six weeks. Fungi from the wood were subsequently cultured and identified by ITS sequences or morphology. 477 fungal isolates comprising 26 OTUs were cultured from the wood, the majority of which belong to saprotrophic genera (70.8% of OTUs, 82.3% of isolates). The mean OTU richness per leaf was 5.67. The term viaphyte (literally, “by way of plant”) is introduced and defined as fungi that colonize living leaves as endophytes and use the leaves to transfer to another substrate, such as wood, when the leaves dehisce. These results strengthen the Foraging Ascomycete Hypothesis and expose the possibility that viaphytism plays a significant role in the dispersal of fungal saprotrophs.

Reemergence of deep Neural Networks (CNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is possible because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be a lot more challenging and for various reasons using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper,we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data is easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other learning competing approaches.

In this paper, based on the symmetrical reliability theory, a research method for the safety factor of overall overturning stability of continuous beam bridge under carbon fiber reinforced polymers (CFRP) during cantilever and horizontal rotation construction is proposed. Under the premise that the given target reliability index is known, the method solves the overall overturning stability safety factor of continuous beam bridge under carbon fiber reinforced polymers during cantilever and horizontal rotation construction by using the symmetrical reliability analysis theory and considering the randomness of structural parameters. This method is used to calculate the safety factor and the design reinforcement aera of CRFP of overturning stability and the design reinforcement aera of CRFP of a continuous beam bridge, and the parameters are analyzed. The calculation results show that the randomness of parameters will have a great impact on the overall safety factor and the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge, and ignoring the influence of randomness of parameters will lead to overestimation of the overall safety factor and underestimation of the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge.