The unsteady flow around the monopile under the influence of wave and vibration is studied by numerical method. Based on VOF model and SST Turbulence model, combined with Intermittent Transition model and dynamic mesh method, wave generation and pile vibration in numerical flume are simulated. The flow field around vibrating monopile in a calm water flume and a wave flume is simulated and the simulation results are compared. The calculation results show that the monopile vibration has a great disturbance to the unsteady flow around the pile, and the disturbance degree intensifies with the increase of the vibration amplitude of the monopile: Under the action of inertia, the vibration of the monopile leads to the reattachment of the shedding vortices; Under the influence of the reattachment vortices, the asymmetry of the vorticity on the side wall of the monopile intensifies; The intensification of the disturbance causes the reduction of the phase lead angle of the bed shear stress on the side of the single pile, and an additional high bed shear stress appears on the side of the monopile. In addition, the vortex structure formed alternately around the monopile during the vibration process leads to the periodic change of the high bed shear stress.

This short paper demonstrates that the galactic spiral equations developed from ROTASE model can be used to morphologically simulate the phyllotactic spiral-ring patterns of plants. It is also noticed during this project that although the numbers in the Fibonacci sequence dominate the phyllotactic numbers, there are still significant phyllotactic numbers are not in the Fibonacci sequence, however, most of those numbers are well fit into a Fibonacci sibling sequence starting with numbers 0 and 2. Many plants show the phyllotactic number pairs of 10-16, 16-26 and 26-42 from the Fibonacci sibling sequence, which is the second dominated sequence in the phyllotaxis.

Several red seaweeds have shown to inhibit enteric CH₄ production; however, adaptation of fermentation parameters to their presence is not well understood. The objective of this study was to examine the effect of three red seaweeds (Asparargopsis taxiformis, Mazzaella japonica, Palmaria mollis) on in vitro fermentation, CH₄ production, and adaptation using the rumen simulation technique (RUSITEC). The experiment was conducted as a completely randomized design with four treatments, duplicated in two identical RUSITEC apparatus equipped with eight fermenter vessels each. The four treatments included the control (barley straw and barley silage) and the three red seaweeds added to the control diet at 2% diet DM. The experimental period was divided into four phases including a baseline phase (d 0-7; no seaweed included), adaptation phase (d 8-11; seaweed included in treatment vessels), intermediate phase (d 12-16) and a stable phase (d 17-21). The digestibility of organic matter (P = 0.04) and neutral detergent fibre (P = 0.05) was decreased by A. taxiformis during the adaptation phase, but returned to control levels in the stable phase. A. taxiformis supplementation resulted in a decrease (P < 0.001) in molar proportions of acetate, propionate and total volatile fatty acid (VFA) production, with an increase in molar proportions of butyrate, caproate, and valerate; the other seaweeds had no effect (P > 0.05) on molar proportions or production of individual VFA. A. taxiformis was the only seaweed to suppress CH₄ production (P < 0.001), with the suppressive effect increasing (P < 0.001) across phases. Similarly, A. taxiformis increased (P < 0.001) the production of hydrogen (H₂, %, mL/d) across the adaptation, intermediate and stable phases, with the intermediate and stable phases having greater H₂ production than the adaptation phase. In conclusion, M. japonica and P. mollis did not impact rumen fermentation or inhibit CH₄ production within the RUSITEC. In contrast, we conclude that A. taxiformis is an effective CH₄ inhibitor and its introduction to the ruminal environment requires a period of adaptation; however, the large magnitude of CH₄ suppression by A. taxiformis inhibits VFA synthesis, which may restrict production performance in vivo.

The amplified Arctic warming is one of several factors influencing atmospheric dynamics. In this work, we consider a series of numerical experiments to identify the direct role of the Arctic sea ice reduction process in forming climatic trends in the northern hemisphere. Aimed at this, we used two more or less independent mechanisms of ice reduction. The first is traditionally associated with increasing the concentration of carbon dioxide in the atmosphere from the historic level of 360 ppm to 450 ppm and 600 ppm. This growth increases air temperature and decreases the ice volume. The second mechanism is associated with a reduction in the reflectivity of ice and snow. We assume that comparing the results of these two experiments allows us to judge the direct role of ice reduction. The most prominent consequences of ice reduction, as a result, were the weakening of temperature gradient at the tropopause level in mid-latitudes, the slower zonal wind at 50-60∘N, intensification of wave activity in Europe, Western America, and Chukotka, and its weakening in the south of Siberia and Kazakhstan. We also consider how climate change may alter regimes such as blocking and stationary Rossby waves. The study used the INM-CM48 climate system model .

In this work, a small-strain phase-field model is presented, which is able to predict crack propagation in systems with anisotropic brittle and ductile constituents. To model the anisotropic brittle crack propagation, an anisotropic critical energy release rate is used. The brittle constituents behave linear-elastically, in a transversely isotropic manner. Ductile crack growth is realised by a special crack degradation function, depending on the accumulated plastic strain, which is calculated by following the J2-plasticity theory. The mechanical jump conditions are applied in solid-solid phase transition regions. The influence of the relevant model parameters on a crack, propagating through a planar brittle-ductile interface, and furthermore a crack developing in a domain with a single anisotropic brittle ellipsoid, embedded in a ductile matrix, is investigated. We demonstrate that important properties, concerning the mechanical behaviour of grey cast iron, such as the favoured growth of cracks along the graphite lamellae and the tension-compression load asymmetry of the stress-strain response, are covered by the model. The behaviour is analysed on basis of a simulation domain consisting of three differently oriented elliptical inclusions, embedded in a ductile matrix, which is subjected to tensile and compressive load. The used material parameters correspond to graphite lamellae and pearlite.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

(1)Background: To perform a simulation-based analysis of 2019 COVID-19 outbreak and how would the electronic commerce indursty help China’s economy resilient. (2)Methods: As the epidemic continues, it is possible to use Computable General Equilibrium model to simulate the economic consequences and analyse the role of electronic commerce industy in COVID-19 outbreak. (3)Results: Estimates and models produced at the time of the outbreak suggested that COVID-19 could have a catastrophic effect on China’s economy. National statistics were examined for anomalies that corresponded to the timing of COVID-19 outbreak and, where possible, the size of any gain or loss found estimated. Our analysis suggests that the electronic commerce industry could help China’s economy to recover by stimulating consumption, improving technological level and expanding investment. (4)Conclusions: This exercise holds important lessons for estimating the electronic commerce’s role of similar events – such as pandemic influenza – and measures to recover the economy. We suggest that electronic commerce industry should be paid more attention in economic development, especially in epidemic time. The implications of our findings are discussed in the light of a prospective epidemic.

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder. One of the important therapeutic approaches of AD is the inhibition of β‐site APP cleaving enzyme‐1 (BACE1). This enzyme plays a central role in the synthesis of the pathogenic β-amyloid peptides (Aβ) in Alzheimer's disease. A group of potent BACE1 inhibitors with known x-ray structures (PDB ID 5i3X, 5i3Y, 5iE1, 5i3V, 5i3W, 4LC7, 3TPP) were studied by molecular dynamics simulation and binding energy calculation employing MM_GB(PB)SA. The calculated binding energies gave Kd values 0.139 µM, 1.39 nM, 4.39 mM, 24.3 nM, 1.39 mM, 29.13 mM and 193.07 nM, respectively. These inhibitors showed potent inhibitory activities in enzymatic and cell assays. The Kd values were compared with experimental values, the structures were discussed in view of the energy contributions to binding. Drug likeness of these inhibitors is also discussed. Accommodation of ligands in the catalytic site of BACE1 is discussed depending on the type of fragment involved in each structure. Molecular dynamics (MD) simulations and energy studies were used to explore the recognition of the selected BACE1 inhibitors by Asp 32, Asp228 and the hydrophobic flap. The results show that selective BACE1 inhibition may be due to the formation of strong electrostatic interactions with Asp32 and Asp228 and a large number of hydrogen bonds, π-π and Van der Waals interactions with the amino acid residues located inside the catalytic cavity. Interactions with the ligands show a similar binding mode with BACE1. These results help to rationalize the design of selective BACE1 inhibitors.

Nowadays, inherently safer designs are considered as key priorities to prevent or mitigate serious incidents with devastating consequences. The need for process safety assessment during early design phases has motivated the development of several contributions related to computer-aided assessment methodologies in order to measure the inherent safety of chemical processes. In this work, the large-scale production of chitosan from shrimp wastes was evaluated from process safety point of view using the numerical descriptive inherent safety technique (NuDIST).To this end, simulation of the chitosan production was performed using Aspen Plus ® to obtain extended mass and energy balances. The assessment of all the chemicals involved within the process was carried out for the following safety parameters: explosivity (EXP), flammability (FL) and toxicity (TOX). The safety assessment of the process included the parameters of temperature (T), pressure (P) and heat of reaction (HR). The maximum chemical safety score was estimated in 171.01 with ethanol as main contributor to the parameter of explosivity and flammability. The score associated with operating data was calculated in 209.30 and heat of reaction reported to be the most affecting parameter. The NuDIST score was estimated in 380.30. This NuDIST value revealed the low hazards associated with the handling of substances such as shrimp wastes, chitosan and water, as well as the non-extreme temperature and pressure conditions. In general, the large-scale production of chitosan from shrimp shells showed to be an inherently safe alternative of waste valorization.

In the case of a severe accident, natural convection plays an important role in the atmosphere mixing of nuclear reactor containments. In the previous study, to simulate the natural convection in the accident scenario within a nuclear reactor containment, the steady thermal boundary conditions (BCs) were prescribed on either cooled or heated wall. The present study, therefore, aims at the transient 3D numerical simulations of natural convection of air around a cylindrical containment with unsteady thermal BCs at the vessel wall. For that purpose, the experiment series was done in the CIGMA facility at Japan Atomic Energy Agency (JAEA). The upper vessel or both the upper vessel and the middle jacket was cooled by subcooled water, while the lower vessel was thermally insulated. A 3D model was simulated with OpenFOAM®, applying the Unsteady Reynolds-averaged Navier–Stokes equations (URANS) model. Different turbulence models were studied, such as the standard k-ε, standard k-ω, k-ω Shear Stress Transport (SST) and, low-Reynolds-k-ε Launder-Sharma. The results of the four turbulence models were compared versus the results of experimental data. The k-ω SST showed a better prediction compare to other turbulence models. Also, the accuracy of the predicted temperature and pressure were improved when the heat conduction on the internal structure, i.e., flat bar, was considered in the simulation. Otherwise, the predictions on both temperature and pressure were underestimated compared with the experimental results. Hence, the conjugate heat transfer in the internal structure inside the containment vessel must be modeled accurately.

The SARS-CoV-2 was confirmed to cause the regional outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China. The 3C-like protease (3CL^pro), an essential enzyme for viral replication, is a valid target to compacts SARS-CoV and MERS-CoV. In this research, an integrated library consisting of 1000 compounds from Asinex Focused Covalent (AFCL) library and 16 FDA-approved protease inhibitors were screened against SARS-CoV-2 3CL^pro. Top compounds with significant docking scores and making stable interactions with catalytic dyad residues were obtained. The screening results in identification of compound 621 from AFCL library as well as Paritaprevir and Simeprevir from FDA-approved protease inhibitors as potential inhibitors of SARS-CoV-2 3CL^pro. The mechanism and dynamic stability of binding between the identified compounds and SARS-CoV-2 3CL^pro were characterized using 50 nanoseconds (ns) molecular dynamic (MD) simulation approach. The identified compounds are potential inhibitors worthy of further development as SARS-CoV-2 3CLpro inhibitors/drugs. Importantly, the identified FDA-approved therapeutics could be ready for clinical trials to treat infected patients and help to curb the COVID-19.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

Aurora kinase A (AURKA) is a normal cell proliferation-inducing enzyme encoded by AURKA gene, with over-expression observed in different types of malignancies. Hence, the goal is to find potential inhibitors against AURKA. In this study, molecular docking, Standard Precision and Extra Precision methods were employed. After the docking study, the ligands showed an extremely low binding score which suggested very high binding affinity of the ligands. Furthermore, Quantum polarized ligand docking (QPLD) was performed to predict the binding status of the molecules. Based on the binding affinity, the top four compounds were chosen for further analysis. The docked complexes were further analyzed in explicit water conditions using 100 ns molecular dynamics simulations and binding free energy calculation. Then, density functional theory (DFT) calculation was used to calculate the molecular properties of the molecules. Finally, systems biology experiments validated the molecular docking and molecular dynamics simulation studies and indicated that quercetin, kaempferol, luteolin and rutin could inhibit the AURKA. The results show that, these four molecules have high binding affinity to the AURKA and significant interactions (LEU139, GLU211and ALA213) were also identified with the hinge region of Aurora kinase A. Thus, LEU139, GLU211, and ALA213 were identified as the crucial protein mechanisms.

Considering the need to reduce greenhouse gas emissions, onshore wind energy is certain to play a major role in future energy systems. This topic has received significant attention from the research community, producing many estimations of Europe's onshore wind potential for capacity and generation. Despite this focus, previous estimates have relied on distribution assumptions and simulation schemes that summarily under predict both the amount of available future wind capacity as well as its performance. Foremost in this regard is the common use of contemporary, or at least near-future, turbine designs which are not fitting for a far-future context. To fulfill this role, an improved, transparent, and fully reproducible work flow is presented for determining European onshore wind potential. Within a scenario of turbine cost and design in 2050, 13.5 TWof capacity is found to be available, allowing for 34.4 PWh of generation. By sorting the explicitly-placed potential generation locations by their expected generation cost, national relations between turbine cost and performance versus a desired capacity are exposed. In this way, it is shown that all countries possess some potential for onshore wind energy generation below 4 €_ct kWh^-1. and, furthermore, that it is unlikely for these costs to exceed 6 €_ct kWh^-1.

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

The VisionCube is a 2-unit CubeSat developed in-house, of which the primary mission is aimed at detecting the occurrence of transient luminous events in the upper atmosphere and obtaining corresponding images from the low Earth orbit. An on-board TLE observation system of the VisionCube cubesat is designed and developed by incorporating a photon-sensitive MaPMT and a CMOS image sensor. Also, a distinctive TLE observation software which enables detecting the TLEs and capturing images in a timely manner is devised. By taking into account the limited resources of a small CubeSat in size and power, the on-board observation system is developed employing a system-on-chip design architecture by which both hardware and software can be integrated seamlessly. The purpose of this study is to investigate the functionality of the hardware and the validity of the software algorithm to show the on-board system will operate properly with no human intervention during the space operation. To this end, a ground simulation facility is constructed to emulate the space environment of the TLE occurrence using a set of UV LEDs inside a darkbox. Based on the analysis of the spectral and temporal properties of the TLEs, the randomly generated UV LED pulses are opted for verification scenarios for the TLE observation system. The validation results show that the hardware and the software algorithm of the on-board observation system can effectively detect the TLEs and obtain the images during the on-orbit operation.

A method for solving the turbulence equations embedded in the sigma ocean general ocean circulation model is proposed. Like the general circulation model, the turbulence equations are solved using the splitting method by physical processes. The turbulence equations are split into two main stages describing transport-diffusion and generation-dissipation processes. Parameterization of turbulence in the framework of equations allows, at the generation-dissipation stage, to use both numerical and analytical solutions and to ensure high efficiency of the algorithm. The results of large-scale ocean dynamics simulation taking into account the parameterization of vertical turbulent exchange are considered. Numerical experiments were carried out using k-omega turbulence model embedded to the Institute of Numerical Mathematics Ocean general circulation Model (INMOM). Both the circulation and turbulence models are solved using the splitting method with respect to physical processes. The coupled model is used to simulate the hydrophysical fields of the North Atlantic and Arctic Oceans for 1948--2009. The model has a horizontal resolution of 0.25 degree and 40 sigma-levels along the vertical. The sensitivity of the solution to the changes in mixing parameterization is studied. Experiments demonstrate that taking into account the climatic annual mean buoyancy frequency improves the reproduction of large-scale ocean characteristics. There is a positive effect of Prandtl number variations for reproducing the upper mixed layer depth. The experiments also demonstrate the computational effectiveness of the proposed approach in solving the turbulence equations.

Grid energy storage system for PV Applications is connected with three different power sources i.e. PV Array, Battery and the Grid. It is advisable to have Isolation between these three different sources to provide safety for the equipment. The configuration proposed in this paper provides the complete isolation between the three sources. A Power Balancing Control (PBC) for this configuration is proposed to operate the system in three different modes of operation. Control of a dual active bridge (DAB) based battery charger which provides a galvanic isolation between batteries and other sources is explained briefly. Various modes of operation of a Grid energy storage system are also presented in this paper. Hardware-In-Loop (HIL) Simulation is carried out to check the performance of the system and the PBC algorithm. Power circuit (comprises of inverter, dual active bridge based battery charger, grid, PV cell, batteries, contactors and switches) is simulated and the controller hardware and user interface panel are connected as HIL with the simulated power circuit through Real Time Digital Simulator (RTDS). HIL simulation results are presented to explain the control operation, steady state performance in different modes of operation and the dynamic response of the system.

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

Hydraulic fracturing in shale gas reservoirs has usually resulted in complex fracture network. The results of micro-seismic monitoring showed that the nature and degree of fracture complexity must be clearly understood to optimize stimulation design and completion strategy. This is often called stimulated reservoir volume (SRV). In the oil & gas industry, stimulated reservoir volume has made the shale gas exploitation and development so successful, so it is a main technique in shale gas development. The successful exploitation and development of shale gas reservoir has mainly relied on some combined technologies such as horizontal drilling, multi-stage completions, innovative fracturing, and fracture mapping to engineer economic completions. Hydraulic fracturing with large volumes of proppant and fracturing fluids will not only create high conductivity primary fractures but also stimulate adjacent natural fractures. Fracture network forming around every hydraulic fracture yields a stimulated reservoir volume. A model of horizontal wells which was based on a shale gas reservoir after volume fracturing in China was established to analyze the effect of related parameters on the production of multi-fractured horizontal wells in this paper. The adsorbed gas in the shale gas reservoir is simulated by dissolved gas in the immobile oil. The key to simulate SRV is to accurately represent the hydraulic fractures and the induced complex natural fracture system. However, current numerical simulation methods, such as dual porosity modeling, discrete modeling, have the following limitations: 1) time-consuming to set up hydraulic and natural fracture system; 2) large computation time required. In this paper, the shape of the stimulated formation is described by an expanding ellipsoid. Simplified stimulated zones with higher permeability were used to model the hydraulic fracture and the induced complex natural fracture system. In other words, each primary fracture has an enhanced zone, namely SRV zone. This method saves much developing fine-grid time and computing time. Compared with the simulation results of fine-grid reference model, it has shown that this simplified model greatly decreases simulation time and provides accurate results. In order to analyze the impacts of related parameters on production, a series of simulation scenarios and corresponding production performance were designed. Optimal design and analyses of fracturing parameters and the formation parameters have been calculated in this model. Simulation results showed that the number of primary fractures, half length, SRV half-width and drop-down have great effects on the post-fracturing production. Formation anisotropies also control the production performance while the conductivity of the primary fractures and SRV permeability do not have much impact on production performance. The complexity of stimulated reservoir volume has strong effect on gas well productivity. Fracture number mainly affects the early time production performance. The increase of SRV width cannot enlarge the drainage area of the multi-fractured horizontal wells, but it can improve the recovery in its own drainage region. Permeability anisotropies have much effect on production rate, especially the late time production rate. The results prove that horizontal well with volume fracturing plays an irreplaceable role in the development of ultra-low permeability shale gas reservoir.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Nowadays and in the web era, there is a huge amount of information available due to automatically and easily generated websites. This information has many shared opinions from people toward different entities. Over the last decade there was a significant huge interest to determine the sentiment of any type of text especially on twitter. Twitter provides unstructured text that has many misspelling words, slang words and shortened forms. More complexity and challenges have come to the sentiment analysis researchers in the context of twitter. In this paper we conducted a literature review of the recent research of Twitter sentiment analysis on different languages. After that, we investigated challenges related to twitter sentiment analysis of Arabic language. Then we found a new solution for Sentiment analysis of Arabic twitter. So, we designed the main component of the proposed system and presented its algorithm. Finally, we performed many experiments under two kinds of datasets. The system achieves highest accuracy and shows many important results..

This study presents a comprehensive approach to calculating the free energy of hydration of methane using Molecular Dynamics simulations. We employed the Free Energy Perturbation (FEP) method to determine the Gibbs free energy associated with transferring a methane molecule into water. Simulations were conducted in a cubic simulation box containing one methane molecule and 1000 TIP4P water molecules. The forward and backward routes were both analyzed, providing consistent free energy differences, which validates the robustness of our methodology. The Gibbs free energy profiles demonstrated characteristic energy barriers and stabilization points, indicative of the interaction dynamics in the solvation process. The results demonstrate the robustness of the FEP method implemented in LAMMPS, yielding free energy in close agreement with experimental data and previous computational studies. The calculated free energy differences for methane hydration exhibited deviations of 13% and 8% for the forward and backward routes, respectively, compared to experimental values. These findings underscore the accuracy and efficiency of using LAMMPS for free energy calculations, contributing to the broader understanding of solvation thermodynamics. Additionally, radial distribution functions were analyzed to provide insights into the structural arrangement of water molecules around methane, revealing strong interaction between methane's carbon atom and water molecules.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Camping offers invaluable experiences in nature exploration, but the expense of acquiring camping gear can deter potential enthusiasts. To mitigate this, camping gear rental services emerge as a practical solution, promoting accessibility and reducing financial barriers. However, existing rental systems often encounter issues such as limited payment options, inconvenient pickup/return processes, and opaque management. In response, this paper introduces "Rent-n-Play," a web-based camping gear rental platform built on Laravel framework. It enables cashless transactions, offers pickup/delivery services, and employs a real-time admin system for transparent management. User feedback guided platform enhancements, including review systems and responsive support. The "Rent-n-Play" platform enhances user experience, fosters trust, and promotes sustainable camping practices, thus contributing to a more accessible and enjoyable camping culture.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

Target recognition and fine-grained ship classification in remote sensing face the challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of region aware instantiation and attention mechanism. Implement adversarial learning through image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the do-main encoder and domain discriminator modules are responsible for extracting feature representations of foreground and background, and further aligning ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on 8 typical fine-grained classification algorithms, and the accuracy of fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

In this work, an improved collocation method based on the Bernoulli polynomials is presented to solve the Volterra integral equation (VIE) of the second kind. The main idea of the proposed method is to improve the results of the classic Bernoulli collocation method (BCM) by dividing the interval into some sub-intervals and considering the collocation points on each of them. Here, the zeros of the shifted Chebyshev polynomials (SCPs) are considered as collocation points. Then, BCM is applied step by step from the first sub-interval to the last one. By this process, a system of algebraic equations is attained for each sub-interval that could be easily solved. Convergence of the scheme is analyzed. For the purpose of demonstrating the validity, applicability, and efficiency of the suggested scheme several numerical examples are provided. Numerical results illustrate that the accuracy of the improved Bernoulli collocation method (IBCM) is more than BCM.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

We overview the main equations of the Rayleigh-Ritz variational method and discuss their connection with the problem of simultaneous diagonalization of two Hermitian matrices.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

This study proposes an innovative pharmacovigilance approach, VIGI-4, leveraging ChatGPT Teams Pro version. Our method involves aggregating data from all phase 4 studies published in February 2024 to identify adverse reactions. These results are categorized by disease state alphabetically, demonstrating the method's capacity to generate structured and easily navigable outputs. The results section is tailored to exemplify the valuable, actionable insights attainable through our workflow. This research emphasizes the game-changing potential of integrating advanced technologies such as ChatGPT into pharmacovigilance, signaling a new era in safeguarding drug safety.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

Chromosome analysis (CA) and chromosomal microarray analysis (CMA) have been successfully used to diagnose genetic disorders. However, many conditions remain undiagnosed due to limitations in resolution (CA) and detection of only unbalanced events (CMA). Optical genome mapping (OGM) has the potential to address these limitations by capturing both structural variants (SVs) resulting in copy number changes and balanced rearrangements with high resolution. In this study, we investigated OGM’s concordance using 87 SVs previously identified by CA, CMA, or Southern blot. Overall, OGM was 98% concordant with only three discordant cases: (1) uncalled translocation with one breakpoint in a centromere; (2) uncalled duplication with breakpoints in the pseudoautosomal region 1; and (3) uncalled mosaic triplication originating from a marker chromosome. OGM provided diagnosis for three previously unsolved cases: (1) disruption of the SON gene due to a balanced reciprocal translocation; (2) disruption of NBEA gene due to an inverted insertion; (3) disruption of the TSC2 gene due to a mosaic deletion. We show that OGM is a valid method for detection of many types of SVs in a single assay and is highly concordant with legacy cytogenomic methods; however, it has limited SV detection capabilities in centromeric and pseudoautosomal regions.

The increasing focus on the development of positioning techniques reflects the growing interest in applications and services based on indoor positioning. Many applications necessitate precise indoor positioning or tracking of individuals and assets, leading to rapid growth in products based on these technologies in certain market sectors. Ultrasonic systems have already proven effective in achieving the desired positioning accuracy and refresh rates. The typical signal used in ultrasonic positioning systems for estimating the range between the target and reference points, specifically the linear chirp, can undergo shape aberration due to the effects of acoustic diffraction when the aperture exceeds a certain limit. The extent of the aberration is influenced by the shape and size of the transducer, as well as the angle at which the transducer is observed by the receiver. This aberration also impacts the shape of the cross-correlation, causing it to lose its characteristic easily detectable single global peak. In such instances, cross-correlation techniques yield results with a significantly higher error than anticipated. In this study, an alternative technique to global peak detection is proposed, leveraging the inherent symmetry observed in the shape of the aberrated cross-correlation. The numerical simulations, performed using the academic acoustic simulation software Field II, conducted using a typical ultrasonic chirp and ultrasonic emitter, compare the classical and the proposed range techniques in a standard office room. The results demonstrate that the proposed technique enables accurate range estimation even in the presence of severe cross-correlation shape aberrations. This allows the use of emitting transducers with a much larger aperture than that allowed by the classical cross-correlation technique. Consequently, it becomes possible to have greater acoustic power available, leading to improved Signal-to-Noise Ratio (SNR).

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET. Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR, used to select the optimal project. This paper, considered as case studie, describes a method, i.e. application of a new principles and an innovative way to solve a problem for developing countries.

Due to the particular structure of the beveloid gear, it can’t be hobbed directly by an ordinary gear hobbing machine. The existing processing method is complex and high-cost. Therefore, the mass production and industrialization of beveloid gears are limited. To improve the machining efficiency and accuracy of processing beveloid gears, we proposed a hobbing method via the modification of ordinary hobbing machines. At first, we completed the derivation and calculation of the relevant processing parameters of the beveloid gear based on the study of the structural characteristics of the beveloid gear and the principle of hobbing machining. Then, we proposed and designed a beveloid gear hobbing method, and the modification of the ordinary hobbing machine was completed by using a hanging wheel mechanism in synchronous belt type. Finally, we completed the actual hobbing of the beveloid gear, and the feasibility of the proposed method was verified. After that, we analyzed the machining error of the trial-produced beveloid gear, the results showed that the accuracy of the trial-produced beveloid gear met the 6-level standard, which also verified the accuracy of the proposed method.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

With a software-defined network (SDN), the control plane is separated from the data plane, allowing the control of network flows to be easy and dynamic at the same time. While the controller, which is equipped with various control applications, is capable of analyzing a large amount of data, it may become overburdened when it is faced with a large volume of traffic. The intrusion detection system (IDS) is an example of control applications that can be used to detect intrusions. It is recommended to deploy instances of the IDS across the data plane. This will improve the processing power and detection rate and as a result reduce the load on the controller at the same time. While there is no doubt that an IDS would be beneficial for improving detection rates, installing one at each switch in the data plane would be costly. To address this issue, this paper proposes the deployment of IDS chains across the data plane. The controller directs incoming traffic via alternative paths, including the IDS chain, which increases the detection rate while avoiding overloading. In order to improve transmission efficiency, our study proposes a method of balancing the distribution of flows and assigning them to specific IDS chains. This approach minimizes the cost of flow grouping through the use of a new incoming traffic grouping technique. Our method is tested and evaluated through the use of a test bed and trace-based simulation, and has been proven to effectively reduce delays and hop counts across various traffic scenarios.

The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.

Since the come up in the 70´s, various decentralized control methodologies have been developed to deal with the challenge of controlling complex and/or spatially distributed systems with multiple inputs and multiple outputs (MIMO), e.g., chemical plants, power systems, water systems, etc. In general, the use of distributed information and control structures requires to synthesize control laws in a constrained (decentralized) information structure. The article presents another version of the unified frequency domain robust decentralized controller design method [1]. The proposed design procedure is appropriate for uncertain dynamic MIMO systems given as a set of transfer function matrices. Its main framework is provided by the Equivalent Subsystems Method [1, 24] guaranteeing fulfillment of the necessary and sufficient stability condition of the overall closed-loop system if individual closed-loop equivalent subsystems are stable. Generating sets of equivalent subsystems for all transfer function matrices describing the uncertain MIMO plant allows to use the QFT method [19] to independently design local robust SISO controllers that constitute the resulting decentralized controller implemented on true subsystems. The developed design procedure is verified and illustrated on a case study on robust decentralized level controller design for a quadruple tank process [2, 24].

This paper presents a numerical method for solving a two-dimensional subdiffusion equation with a Caputo fractional derivative. The problem considered assumes symmetry in both the equation’s solution domain and the boundary conditions, allowing for a reduction of the two-dimensional equation to a one-dimensional one. The proposed method is an extension of the fractional Crank-Nicolson method, based on the discretization of the equivalent integral-differential equation. To validate the method, the obtained results were compared with a solution obtained through Laplace transform. The analytical solution in the image of the Laplace transform was inverted using the Gaver-Wynn-Rho algorithm implemented in the specialized mathematical computing environment, Wolfram Mathematica. The results clearly show the mutual convergence of the solutions obtained by the two methods.

Multi-source image registration often suffered from great radiation and geometric differences. Specifically, gray scale and texture from similar landforms in different source images often show significantly different visual features. And these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.

In this paper, a new multi parameter iterative algorithm is proposed to address the PageRank problem based on the multi-splitting iteration method described by Gu et al . The proposed method in each iteration needs to solve two linear subsystems by splitting the coefficient matrix, therefore, we consider inner and outer iteration to find the approximate solutions of these linear subsystems. It can be shown that the iterative sequence generated by the multi parameter iterative algorithm finally converges to the PageRank vector when the parameters satisfy the certain conditions. Numerical experiments show that the proposed algorithm has better convergence and numerical stability than the existing algorithms.

The environmental pollution by microplastics (MPs) has become a growing concern, and measures are being taken in many countries. Long-term and extensive MPs investigations involving not only professional researchers but also the citizenry are needed to understand the pollution situation and to confirm the decreasing trend of MPs pollution as a result of pollution control measures. In this study, the author evaluated the accuracy of a simple method of investigating MPs on sandy beaches that can be conducted even by high school students. In a land survey using such simple tools as a tape measure and cardboard, 70% of the deviations were within approximately 20 cm when multiple surveys of approximately 20 m distance were performed. Even without heavy liquid, 89% of MPs could be recovered using only seawater. An investigation of MPs content by sampling 0.5 cm of the surface layer of sand could explain more than half of the MPs content when the sand was sampled to a depth of approximately 50 cm below the surface layer. A method in which the recovered MPs are not visually sorted but floating matter after boiling is considered as MPs is acceptable. If there was no concern about pumice contamination, the overestimation was within approximately 1.5 times. Simple laboratory equipment such as buckets, sieves, seawater, hot plates, dryers, and electronic balances could achieve lower limits of quantification of MPs of 13 mg-MPs/m2-sand and 2 mg-MPs/kg-sand.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

Engine Overheating is a condition where the engine temperature has increased above the working temperature. This analysis aims to analyze the damage, determine the factors causing the damage, identify corrective steps and prevent engine overheating. The inspection procedure is carried out by checking the cooling system's components, cylinder head, and cylinder block. Further analysis using a fishbone diagram to analyze the causes of engine overheating troubleshooting. Damage analysis results Engine overheating occurs because the thermostat is damaged, namely, the thermostat valve which cannot open when the temperature reaches the standard temperature (71oC), and also due to damage to the cylinder gasket which causes bubbles due to compression leaks and mixing of radiator coolant and oil. Repair steps are taken in the form of replacing damaged components with new ones. Preventive measures are taken by not using the unit on an over-running basis, checking coolant levels, carrying out daily checks properly and correctly, and carrying out preventive maintenance regularly.

The article proposes an amendment to the relativistic continuum mechanics which introduces the relationship between density tensors and the curvature of spacetime. The resulting formulation of a symmetric stress-energy tensor for a system with an electromagnetic field, leads to the solution of Einstein Field Equations indicating a relationship between the electromagnetic field tensor and the metric tensor. In this EFE solution, the cosmological constant is related to the invariant of the electromagnetic field tensor, and additional pulls appear, dependent on the vacuum energy contained in the system. In flat Minkowski spacetime, the vanishing four-divergence of the proposed stress-energy tensor expresses relativistic Cauchy's momentum equation, leading to the emergence of force densities which can be developed and parameterized to obtain known interactions. Transformation equations were also obtained between spacetime with fields and forces, and a curved spacetime reproducing the motion resulting from the fields under consideration, which allows for the extension of the solution with new fields.

Recently, the Hypervolume Newton method (HVN) has been proposed as fast and precise indicator-based method for solving unconstrained bi-objective optimization problems with objective functions that are at least twice continuously differentiable. The HVN is defined on the space of (vectorized) fixed cardinality sets of decision space vectors for a given multi-objective optimization problem (MOP) and seeks to maximize the hypervolume indicator adopting the Newton-Raphson method for deterministic numerical optimization. To extend its scope to non-convex optimization problems the HVN method was hybridized with a multi-objective evolutionary algorithm (MOEA), which resulted in a competitive solver for continuous unconstrained bi-objective optimization problems. In this paper, we extend the HVN to constrained MOPs with in principle any number of objectives. We demonstrate the applicability of the extended HVN on a set of challenging benchmark problems and show that the new method can be readily be applied to solve equality constraints with a high precision problems, and to some extend also inequalities. We finally use HVN as local search engine within a MOEA and show the benefit of this hybrid method on several benchmark problems.

Classical numerical methods for solving ordinary differential equations often produce less accurate results when applied to problems with oscillatory or periodic behaviour. To adapt them for such problems, they are usually modified using the exponential fitting technique. This adaptation allows for the construction of new methods from their classical counterparts. The new methods are usually more accurate, efficient and suitable for handling the oscillatory or periodic behaviour of the problem. In this work, we construct a two-parameter exponentially-fitted Taylor method suitable for solving oscillatory or periodic problems that possess two frequencies. The construction algorithm is based on a proposed six-step flowchart discussed by authors in related literature. Two standard test problems were used to illustrate the accuracy and performance of the proposed method.

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in 3 dimensions: emphysema, ground glass opacity and consolidation. This method was evaluated on a 60 patient lot and the results show a clear quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

During the smelting process of AOD furnace, the unbalanced reaction of material will lead to the occurrence of splashing. It will not only damage the smelting equipment, but also seriously injure the personnel. In this study, first, the information of liquid level, audio information, and vibration information are detected by multiple sensors respectively. Then, the fused information is used to forecast the splashing. Finally, the multitasking fuzzy controller is used to suppress splashing. The results show that the method of forecasting and suppressing splashing can accurately forecast and achieve rapid suppression. Thus, the efficiency of smelting can be improved.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

With this article we would like to clarify the often-disregarded fact by virtue of which the European Missionaries in Asia acted as catalysts of a kind of nuanced acculturation named Accommodatio (adaptation). To a great extent they became harbingers of Culture and Science more than Faith itself to the dismay of many, including the Roman Church. Such cultural and scientific transference was actually two-pronged, for simultaneously they presented in Europe unique findings related to Language, e.g. the Chinese Characters (considered to be the sole natural language), Geography, Cosmology and even Governance. We would try to prove that such procedure contributed positively to the modern scientific notions of sustainability and to provide the kind of accoutrements that model the modern world as we know it.

Background: the devastating outbreak of COVID-19 poses serious challenges for the diagnostics laboratories, which are often facing global shortage of reagents and equipment. With the aim of increasing the diagnostic throughput for SARS-CoV-2 molecular test, the purpose of this study was to validate an additional RNA extraction method respect to those already recommended by WHO and the US Centers for Disease Control and Prevention (CDC). Methods: a new protocol for RNA extraction from nasopharyngeal swab was set up, adapting the Qiagen RNeasy 96 plate and validated on a set of 100 clinical samples analyzed in parallel by Roche-Magnapure method (already recommended by CDC guidelines). Results: the internal control and target genes analysis showed a good agreement between the two extraction methods indicating that the two methods can be considered equivalent and that the RNeasy-adapted method can be applied for the SARS-CoV-2 diagnostics. The addition of this new extraction method resulted in a throughput increase for SARS-CoV-2 molecular test of about 2000 samples/month during the initial months of the pandemic emergency in which the lack of reagents for the extraction led to an insufficient sample processing throughput of the analysis of the swabs.

Radio communication system in an underground mine is one of the very essential systems for the underground mine. It is required that the radio communication system must be reliable from the starting to construct the underground mine to the closure of the mine. However, underground mine radio communication systems reliability is required to be tested on an active radio communication system in a real environment. In the study was suggested a new research methodology that is studied reliability using dynamic system modeling on Vensim software instead of traditional method to study the reliability of radio communication systems calculating large-scale differential equations. In other words, we suggest a new research methodology. The Motorola Dimetra (TETRA) radio communication system's availability readiness information was used to simulate the reliability of the underground mine radio communication system probability of reliability using Vensim software for system dynamic modeling.Also, the factors that affect the reliability of underground mining radio communication systems was studied. The study was determined factors that affect the underground mine radio communication system from the following risks. The study was in the examples of the Oyu Tolgoi underground mine. The factors that affect the reliable operation of the underground mine radio communication system were determined using the failure statistics of TETRA radio communication system in the Oyu Tolgoi mine in 2015-2018.

Manual measurement of rock discontinuities is time-consuming and subjective according to the experience of the surveyor. This work proposes a three-dimensional laser scanning-based method for the semi-automatic identification of rock discontinuities. Multisite cloud scanning is performed with real-time kinematic (RTK)-assisted orientation to estimate the rock fracturing degree; then, discontinuity orientations are extracted with the man–machine interactive method or automatic method. The proposed method was applied to actual examples to illustrate its accuracy at identifying rock discontinuities. The sensitivity of the identification accuracy to different parameters was investigated.

Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

BACKGROUND: To present a new method of urethral pressures examination, and to evaluate diagnostic capabilities of three dimensional profilometry , as an alternative to classical urethral profile (UPP) .Using four channel catheters and dedicated software global urethral pressures image is obtained. The method eliminates the main limitation of classical urethral profilometry where the catheter orientation determines the pressures picture limited to only one point in the uretral circumference while we observed even 50% differences in pressure measures depending on the point of measurement. METHODS: This is a preliminary study containing method presentation and analisys of the use in varied clinical cases of either healthy patients and patients with LUTS. The article includes technique and equipment decription, full evaluation of selected cases, including three dimensional urethral pressures distribution graphics. RESULTS AND CONCLUSIONS:Three dimensional profilometry compared to classical technique is comparable regarding the time, cost, technical difficulty and patient discomfort. At the same time we obtain much more data on the urethral pressures and its distribution.The results are easy for interpretation due to 3D movable graphics created automatically through the dedicated software.

Conventionally, partial differential equations (PDE) problems are solved numerically through discretization process by using finite difference approximations. The algebraic systems generated by this process are then finalized by using an iterative method. Recently, scientists invented a short cut approach, without discretization process, to solve the PDE problems, namely by using machine learning (ML). This is potential to make scientific machine learning as a new sub-field of research. Thus, given the interest in developing ML for solving PDEs, it makes an abundance of an easy-to-use methods that allows researchers to quickly set up and solve problems. In this review paper, we discussed at least three methods for solving high dimensional of PDEs, namely PyDEns, NeuroDiffEq, and Nangs, which are all based on artificial neural networks (ANNs). ANN is one of the methods under ML which proven to be a universal estimator function. Comparison of numerical results presented in solving the classical PDEs such as heat, wave, and Poisson equations, to look at the accuracy and efficiency of the methods. The results showed that the NeuroDiffEq and Nangs algorithms performed better to solve higher dimensional of PDEs than the PyDEns.

Qualitative and quantitative values estimation is a crucial aspect of social data science and plays a vital role in social and psychological research. Numerous methods of personal values measurement such as Schwartz Value Survey (SVS) and its re-modified versions have been proposed and invented, but many are challenged with one or more limitations. In this article, an alternative approach in a new paradigm of values measurement called Kabiru’s Value Survey (KVS) was proposed to measure the relative degree of importance or significance attached to particular value-actions of an individual(s) in a given population over a period. The methodological innovations in KVS unlike other existing methods of human value measurement include the use of habitually reported practices associated with a particular value-action in place of judging the series of Schwartz hypothetical statements, the use of multiple dependent variables rather than a single coded hypothetical statement. In terms of data analysis, a new approach of multivariate compression is introduced which merges the chain of multiple dependent variables into one coded scale. Psychometric result interpretation is also another innovative aspect of KVS methodology. Finally, the proposed methodology however solved some of the criticized limitations of the other methods of human value measurement.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

It has remained a hard nut for years to segment sonar images, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds to be calculated. To realize this goal, it follows the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, segment the region into the background region, buffer region and target regions. After filtering, reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts. Finally, remove the remaining noise from images by means of morphological image processing. The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar image quickly and effectively, with no problem of incomplete image target shape. Thus, the stable and feasible method is testified.

In Electronic Nicotine Delivery System (ENDS), coil resistance is an important factor in the generation of heat energy used to change e-liquid into vapor. An accurate and unbiased method for testing coil resistance is vital for understanding its effect on emissions and reporting results that are comparable across different types and brands of ENDS and measured in different laboratories. This study proposes a robust, accurate and unbiased method for measuring coil resistance. An apparatus is used which mimics the geometric configuration and assembly of ENDS pods and power control units. The method is demonstrated on two commonly used ENDS devices, the ALTO by Vuse and JUUL. Analysis shows that the proposed method is stable and reliable. The two-wire configuration introduced a positive measurement bias of 0.086 [ohm], which is a significant error for sub-ohm coil designs. The four-wire configuration is far less prone to bias error. We observed a significant difference in coil resistance of 0.593 [ohm] (p<0.001) between the two products tested. The mean resistance and standard deviation of the pod coil assemblies was shown to be 1.031 (0.067) [ohm] for ALTO and 1.624 (0.033) [ohm] for JUUL. The variation in coil resistance between products and within products can have significant impacts on aerosol emissions.

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

Visual Evoked Potentials (VEPs) are used in clinical applications in ophthalmology, neurology and extensively in brain computer interface (BCI) research. BCI literature covers steady state VEP (SSVEP) and code modulated VEP (c-VEP) BCIs along with sophisticated methods to improve information transfer rates (ITR). There is a gap of knowledge regarding the VEP adaptation dynamics, physiological generation mechanisms and relation with BCI performance. A simple dual display VEP switch was developed to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50 and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits Quasi-Steady-State VEPs (QSS-VEPs) that are utilized for simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs and it is assumed that it shares the similar generators of the SSVEPs. Eight subjects were recorded and the performance of the overall system was analyzed by means of Receiver Operating Characteristic (ROC) curves, accuracy plots and ITRs. In summary QSS-VEPs performed better than transient VEPs. It was found that in general 32rps stimulation had the highest ROC area, accuracy and ITRs in general. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated. Moreover, QSS-VEPs were found to lead to higher accuracy by the template matching compared to SSVEPs at 10rps and 32rps.

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

This manuscript discusses a novel method to map pressure results from one 3D surface shell mesh onto another. This technique is especially important when transferring results from one numerical analysis to another. This method works independent of the actual pressures, and only focuses on ensuring the surface areas consistently match. By utilizing this approach, the cumulative forces consistently match for all input pressures. This method is demonstrated to work for pressure profiles with precipitous changes in pressures, and with small quadrangular source elements being applied to a mix of large quadrangular and triangular target elements, and the forces at all pressure profiles match remarkably.

We apply utilized the extended form of the auxiliary equation method to obtain extensively reliable exact travelling wave solutions of perturbed Gerdjikov–Ivanov equation (GIE) that is widely used as a model in the field theory of quanta and non-linear optics. The method is based on a simple first order second degree ODE. The new form of the approach gives more solutions to the governing equation efficiently.

We construct a real square system related to a given over-determined real system. We prove that the simple real zeros of the over-determined system are the simple real zeros of the related square system and the real zeros of the two systems are one-to-one correspondence with the constraint that the value of the sum of squares of the polynomials in the over-determined system at the real zeros is identically zero. After certifying the simple real zeros of the related square system with the interval methods, we assert that the certified zero is a local minimum of the sum of squares of the input polynomials. If the value of the sum of the squares of the input polynomials at the certified zero is equal to zero, it is a zero of the input system. As an application, we also consider the heuristic verification of the isolated zeros of polynomial systems and their multiplicity structures. Notice that a complex system with complex zeros can be transformed into a real system with real zeros.

It is demonstrated in this study that PhabrOmeter creates genuine wrinkle on fabrics comparable to that in AATCC specifications, and measures fabric wrinkle recovery values fast with repeatable and reliable results with high resolution, over wide range of fabric types. It thus provides an appealing alternative to the existing test methods that are known to be slow, insensitive and unreliable. In addition, with enormous varieties of fabrics, we used a new criterion termed fabric linear density λ so that fabrics can in general be divided into relatively homogeneous groups for further study. The calculation and application of this parameter are showed in this report.

This article reports a novel and efficient method to synthesize graphene by thermal decomposition process. In this method, silicon carbide (SiC) thin films grown on Si(100) wafers with an AlN buffer layer were used as substrates. A CO₂ laser beam heating without vacuum or controlled atmosphere was applied for SiC thermal decomposition. The physical, chemical, morphological, and electrical properties of the laser-produced graphene were investigated for different laser energy densities. The results demonstrate that graphene was produced in form of small islands with quality, density and properties depending on the applied laser energy density. Furthermore, the produced graphene exhibits a sheet resistance characteristic similar to graphene grown on mono-crystalline SiC wafer, which indicates its potential for electronic device applications.

The eddy current loss should be optimized to be as less as possible for the stability of permanent magnet in high speed permanent magnet synchronous motor (HSPMSM) rotor and ensure the high efficiency and low temperature of the motor. This paper analyzes the eddy current distribution in rotor, with consideration of the conflict of the thickness of sleeve and diameter of the rotor, calculating the eddy current loss (ECL) and the thermal distribution via Separation of variables method for solving Maxwell's equations with analytical hieratical model of ECL constructed. The optimization result of ECL of the HSPMSM whose power and rated speed is 30kw 48000r/min can be got by multi-objective optimization method, combined weighting coefficient method and traversal algorithm based on chaotic local search particle swarm optimization (CLSPSO), utilizing ECL analytical model and other analytical constraints. Related experiment and measurement has been implemented with new approach of loss separation.

Introduction: This empirical study investigates the impact of two distinct mathematical problem-solving methods – the Algebraic Formula Method and the Newton Sum Method – on enhancing ChatGPT's competence in effectively solving quadratic root functions. The integration of Artificial Intelligence (AI) into mathematical problem-solving has paved the way for innovative approaches. In this study, we delve into the Algebraic Formula Method and the Newton Sum Method, essential techniques for solving quadratic root functions. We aim to showcase the profound influence of these methods on ChatGPT's capacity to excel in solving quadratic equations. Evidence Through concrete evidence, we demonstrate ChatGPT's adept utilization of the Newton Sum Method for quadratic root function calculations. While ChatGPT can compute quadratic root functions of the form α^15 + β^15 using this method, its proficiency in using algebraic formula methods typically extends only up to α^4 + β^4. This marked discrepancy underscores the pivotal role that different methods play in amplifying the AI system's mathematical capabilities Result The results of this study provide concrete evidence of ChatGPT's superior utilization of the Newton Sum Method for calculating quadratic root functions. The model adeptly computes expressions of the form α^15 + β^15 using this method, while its proficiency using algebraic formula methods is generally limited to α^4 + β^4. This striking discrepancy underscores the transformative impact that different methods can have on elevating the AI system's mathematical prowess. Conclusion :Pushing Boundaries: Pioneering Novel Maths Approaches for Overcoming Limitations in AIThis study serves as an illuminating testament to the significance of pioneering innovative methodologies, rules, theorems, or formulas to surmount the current limitations in AI systems like ChatGPT. These innovative pursuits hold the key to unlocking the untapped potential that lies within, propelling AI systems to greater heights of proficiency. In essence, they offer a strategic pathway towards expanding the capabilities of AI and pushing the boundaries of what can be achieved. Discussion The outcomes derived from this study underscore the significant influence wielded by the method selection in augmenting the mathematical competencies of ChatGPT. Particularly noteworthy is the application of the Newton Sum Method, which surfaces as a compelling exemplar. This method serves as a pivotal conduit through which the model surpasses its prior constraints, allowing it to venture into the realm of calculations entailing higher exponents. Implications and Future Research: These findings not only contribute to AI's mathematical competencies but also emphasize the need for pioneering new methods, rules, theorems, or formulas to further enhance AI systems like ChatGPT. Future research could explore the development of novel mathematical techniques tailored to AI systems, thus expanding their capabilities across diverse problem-solving domains.

Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.

The reflection and transmission spectra of a metamaterial formed by omega-shaped elements with pre-calculated optimal parameters on a silicon substrate have been recorded in the terahertz range at oblique incidence for the s- and p- polarizations of the incident wave. The spectra were interpreted within the dipole radiation theory of electromagnetic waves. Both measurement results and analysis provide evidence sup-porting the presence of a pronounced polarization anisotropy impact in the reflection and transmission of the metamaterial. The potential of these materials to be utilized in the development of devices that control the polarization properties of THz radiation across a wide spectral range is examined.

BACKGROUNDː The so-called Low Cardiac Output Syndrome (LCOS) is one of the most common complications in pediatric patients with congenital heart disease undergoing corrective surgery. LCOS requires high concentrations of inotropes to support cardiac contractility and improve cardiac output, allowing for a better systemic perfusion. To date, serum lactate concentrations and central venous oxygen saturation (ScVO2) are the most commonly used perfusion markers, but they are not completely reliable to identify a state of global tissue hypoxia. The study aims to evaluate whether the venoarterial carbon dioxide difference/arterial-venous oxygen difference ratio [P(v-a)CO2/C(a-v)O2] can be a good index to predict the development of LCOS in the aforemen-tioned patients, so as to treat it promptly. METHODSː This study followed a population of 98 children undergoing corrective cardiac surgery from June 2018 to October 2020 at the Department of Cardiac Surgery of University Hospital Inte-grated Trust and their subsequent admission at the Postoperative Cardiothoracic Surgery Intensive Care Unit. During the study, central arterial and venous blood gas analysis were carried out before and after cardiopulmonary bypass (CPB) (pre-CPB and post-CPB), at admission to intensive care unit, before and after extubation and at any time of instability or modification of the patient’s clinical and therapeutic conditions. RESULTSː The data analysis shows that 46.9% of the children developed a LCOS (in line with the current literature) but that there is no statistically significant association between the P(v-a)CO2/C(a-v)O2 ratio and LCOS onset. Despite the limits of statistical significance, however, a 31% increase in the ratio emerged from the pre-CPB phase to the post-CPB phase when LCOS is present. CONCLUSIONSː This study confirms a statistically significant association between the most used markers in adult patients (serum lactate concentration, ScVO2 and oxygen extraction ratio-ERO2) measured in the pre-CPB phase and the incidence of LCOS onset, especially in patients with he-modynamic instability before surgery.

Respiratory syncytial virus (RSV) RNA synthesis takes place in cytoplasmic viral factories also called inclusion bodies (IBs), which are membrane-less organelles concentrating the viral RNA polymerase complex. IBs assembly is driven by liquid-liquid phase separation promoted by interactions between the viral nucleoprotein N and the phosphoprotein P. We recently demonstrated that cyclopamine (CPM) inhibits RSV multiplication by disorganizing and hardening IBs. Although a single mutation in the viral transcription factor M2-1 induced resistance to CPM, the mechanism of action of CPM still remains to be characterized. Here, using FRAP experiments on reconstituted pseudo-IBs both in cellula and in vitro, we first demonstrated that CPM activity depends on the presence of M2-1 together with N and P. We show that CPM impairs the competition between P and RNA binding to M2-1. As mutations on both P and M2-1 induced resistance against CPM activity, we suggest that CPM may affect the dynamics of the M2-1−P interaction, thereby affecting the relative mobility of proteins contained in RSV IBs. Overall, our results reveal that stabilizing viral protein-protein interactions is an attractive new antiviral approach. They pave the way for the rational chemical optimization of new specific anti-RSV molecules.

Nitrogen (N) applications can play a key role in achieving profitable wheat production in the low-fertility soils prevalent under rainfed conditions. The model-predicted N partitioning revealed that ammonia-nitrogen (NH4-N) contributed little to the plant N nutrition, and its concentration in the soil remained below 2 ppm throughout the crop season except immediately after the NH4-N based fertilizer application. Nitrate-nitrogen (NO3-N) gradually increased (maximum 144 ppm) with time, especially in the 0-40 cm horizons, and contributed to most N uptake during both seasons at both locations. The N losses from the soil at the medium rainfall site (3.5-20.5 kg ha-1) were mainly attributed to NH4-N volatilization (Nv) and NO3-N leaching (NL) below the crop root zone. Water productivity (8-40 kg ha-1 mm-1) and N use efficiency (31-41 kg kg-1) showed immense variability induced by climate, water availability, and N dynamics in the soil. These results suggest that combining water balance and N modeling can help manage N applications to optimize wheat production and minimize N losses in rainfed agriculture.

Pure monocular 3D reconstruction is an ill-posed problem that has attracted the research community's interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion, so many methods have been proposed. However, most of these methods were not evaluated in large datasets, under various motion patterns, had not been tested under the same metrics, and most of them had not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine learning-based methods, and two updates of the best methods, to identify the advantages and limitations of each category of the taxonomy and test if the addition of machine learning or the updates made on those methods improved them significantly. Thus, we evaluated each algorithm under the TUM-Mono benchmark and performed an inferential statistical analysis to identify significative differences through its metrics. Results determined that sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly improves the performance of geometric-based methods from different perspectives.

Composite biomaterials that combine osteoconductive and osteoinductive properties are a promising approach for bone tissue engineering (BTE) since it allows osteogenesis stimulation while mimicking the extracellular matrix (ECM) morphology. In this context, the aim of the present research work was to produce polyvinylpyrrolidone (PVP) nanofibers containing mesoporous bioactive glass (MBG) 80S15 nanoparticles. These composite materials were produced by electrospinning technique. The design of experiments (DOE) was used to estimate the optimal electrospinning parameters to reduce average fiber diameter. The polymeric matrices were thermally crosslinked under different conditions, and the fiber’s morphology was studied using scanning electron microscopy (SEM). Evaluation of the mechanical properties of nanofibrous mats revealed a dependence on thermal crosslinking parameters and on the presence of MBG 80S15 particles inside the polymeric fibers. Degradation tests indicated that the presence of MBG led to a faster degradation of nanofibrous mats. On the other hand, MBG presence also leads to higher swelling capacity. The assessment of in vitro bioactivity in simulated body fluid (SBF) was performed in MBG pellets and PVP/MBG (1:1) composites to assess whether the bioactive properties of MBG 80S15 were kept when it was incorporated into PVP nanofibers. FTIR and XRD analysis along with SEM–EDS results indicated that a hydroxy-carbonate apatite (HCA) layer was formed on the surface of MBG pellets and nanofibrous webs after soaking in SBF over different time periods. In general, the materials revealed no cytotoxic effects on Saos-2 cell line. The overall results for the produced materials show the potential of the composites to be used in BTE.

To define and evaluate the areas of consideration concerning in identifying the critical factors that top universities in Nueva Ecija, Philippines can be used for triangulating the courses of actions that can be applied to improve the current practices of universities towards its combat to the COVID–19 disease is the primary objective of this study. The researchers used a descriptive design of methodology by using questionnaire–checklist to scientifically describe the situation, problems, phenomenon, or program, or provide information about certain issues related to the virus outbreak. The respondents of the study were faculty and staff of five established universities in Nueva Ecija, Philippines wherein the researchers employed a non–probability sampling technique to be logically assumed as the representative of the entire population. The results of the study shown that the top universities in Nueva Ecija have made efforts to ensure the safety of university workers by complying with the Inter–Agency Task Force (IATF) protocols. It can be inferred, in reality, that there are some areas that must be improved especially when it comes to ensuring the welfare of the personnel who are still reporting to work even in this time of the pandemic. The researchers suggested an enhancement plan that can be adapted by these universities to resolve the concerns of the faculty and staff especially in reducing the spread of the virus without sacrificing the day–to–day transactions of the academic institutions.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

Nowadays, transactions carried out with digital currencies are increasing. Modern societies are asked to respond to growing challenges related to the management of digital currencies in their daily lives. However, due to the lack of digital skills of users, the management of digital currencies hides risks. To the best of our knowledge, the originality of the current research lies in the act of combining the concept of digital skills with the use of digital currencies. After all, the use of digital currencies is constantly increasing, which means that citizens should familiarize themselves with their use, an element that makes this study valuable for digital societies. Digital skills effectively contribute to the development of digital societies because they increase the employment of citizens, facilitate access to information, and contribute to the social inclusion of individuals through digital communication, while also increasing efficiency and productivity in the workplace. Also, the government and banking institutions can more effectively sensitize citizens to digital skills for more effective use of digital currencies. In this way, tax payments will be facilitated, the use of e-wallets will be safer, and e-governance will be greatly promoted, while the quality of banking services will be improved. The methodology of this study was based on the Digital Competence Framework for Modern Societies (DigComp) and was applied through a questionnaire completed by 443 respondents. The main objective was to evaluate their digital skills from the perspective of digital currency use. The analysis of the responses was carried out by using Structural Equation Modeling (SEM). The most important result from this research reveals that users of digital currencies are significantly capable of developing communication to solve everyday problems. At the same time, users of digital currencies mostly detect digital threats and effectively manage fake news without being affected by them. However, users of digital currencies consider that security issues are important, but only for transactions and not for their supporting functions. The study concludes with suggestions for improving the experience of digital currency users through individual actions, thus having a positive impact on the state and banking institutions.

The COVID-19 pandemic is the biggest public health threat facing the globe today. Multiple vaccines have been approved, however the emergence of viral variants such as the recent Omicron, raises the possibility of booster doses to achieve adequate protection. In Brazil, the CoronaVac (Sinovac) vaccine was used, however it’s important to assess the immune response to this vaccine over time. This study aimed to monitor the anti-SARS-CoV-2 antibody responses in those immunized with CoronaVac and SARS-CoV-2 infected individuals. Samples were collected between August 2020 and August 2021. Within the vaccinated cohort, some individuals had history of infection by SARS-CoV-2 prior to immunization and others not. We analyzed RBD-specific and neutralizing- antibodies. Anti-RBD antibodies were detected in both cohorts, with a peak between 45-90 days post infection or vaccination, followed by a steady decline over time. In those with previous history of COVID-19, a higher, longer, more persistent response was observed. This trend was mirrored in the neutralization assays, where infection followed by immunization resulted in higher, longer lasting responses which were conditioned on the presence of levels of RBD antibodies right before the vaccination. This supports the necessity of booster doses of CoronaVac in due course to prevent serious disease.

Geological storage of carbon dioxide (CO2) is a crucial technology for mitigating global temperature rise. Near-depleted edge-bottom water reservoirs are attractive targets for CO2 storage, as they can not only enhance oil recovery (EOR) but also provide important potential candidates for geological storage. This study investigated CO2 enhanced oil recovery and storage for a typical near-depleted edge-bottom water reservoir that had been developed for a long time with a recovery factor of 51.93%. To improve the oil recovery and CO2 storage, new production scenarios are explored. At the near-depleted stage, by comparing the four different scenarios of water injection, gas injection, water alternating gas injection, and bi-directional injection, the highest additional recovery of 8.64% is achieved by the bi-directional injection scenario. Increasing the injection pressure leads to a higher gas-oil ratio and liquid production rate. After shifting from the near-depleted to depleted stage, the most effective approach to improve CO2 storage capacity is to increase reservoir pressure. At 1.4 times the initial reservoir pressure, the maximum storage capacity is 6.52×108 m3. However, excessive pressures boosting poses potential storage and leakage risks. Therefore, lower injection rates and longer intermittent injections are expected to achieve a larger amount of long-term CO2 storage. Through the numerical simulation study, a gas injection rate of 80,000 m3/day and a schedule of 4-6 years injection with 1 year shut-in was shown to be effective for the case considered. During 31 years of CO2 injection, the percentage dissolved CO2 increased from 5.46% to 6.23% during the near-depleted period, and to 7.76% during the depleted period. This study acts as a guide for the CO2 geological storage of typical near-depleted edge-bottom water reservoirs.

The processability of conductive polymers such as polyaniline (PANI) still poses a challenge due to their properties. The use of stabilizers, compatible and able to interact with PANI, to obtain dispersible and stable colloids is described in this work. To this aim, potato starch has been used as a steric stabilizer for the preparation of polyaniline (emeraldine salt, ES)/starch biocomposites, by exploiting oxidative polymerization of aniline in aqueous solutions containing different ratios of aniline and starch (% w/w). The polyaniline/starch biocomposites were subjected to structural and spectroscopic analyses, thermal analysis, morphological analysis and electrochemical analysis. The samples were then tested for their dispersibility/solubility in various organic solvents. The results showed the formation of PANI/starch biocomposites with an overall smaller size than starch particles, with improved aqueous dispersion and solubility in organic solvents. Although X-ray diffraction and differential scanning calorimetry analyses indicated a loss of crystallinity in the biocomposites, the cyclic voltammetry tests showed that all PANI ES/starch biocomposites display improved redox exchange properties.

The aim of this study was synthesize and evaluate the effects of Poly (D, L-Lactide-co-glycolide) (PLGA) Nanoparticles (NPs) of metformin (PLGA+ Met) on inflammation, and bone loss in a ligature-induced periodontitis rat model. The prepared NPs were characterized by mean diameter, size particle, polydispensity index and encapsulation efficiency by Atomic force microscopy (AFM). Male albino Wistar rats were randomly divided into four groups of 20 rats in each group, and given the following treatments for 10 days to evaluate in vivo activity: (1) Sham: no ligature + water; (2) Positive control: ligature + water (with Periodontal disease and Diabetes); (3) ligature + PLGA+ 10 mg/kg Met (With Periodontal disease and Diabetes); and (4) ligature + PLGA+ 100 mg/kg Met (with Periodontal disease and Diabetes). Water or PLGA + Met was administered orally by gavage. Maxillae were fixed and scanned using Micro-computed Tomography (μCT) to quantify linear of bone loss. Histopathological characteristics were assessed through immunohistochemical staining for Osteocalcin, Cathepsyn K, RANKL/RANK/OPG pathway. IL-1β and TNF-α from gingival tissues were analysed by Elisa immunoassay. Quantitative RT-PCR reaction was used to evaluate gene expression of AMPK, NF-κB p-65, Hmgb1 and TAK-1 from gingival tissues. Statistical analysis was performed using one-way ANOVA at 5% significance. The mean diameter of MET-loaded PLGA nanoparticles was in a range of 457.1 ± 48.9 nm with a polydispersity index of 0.285, zeta potential: 8.16 ± 1.1 mV and entrapment efficiency (EE) was 70%. The results suggest that the addition of MET in the core slightly affected the particle sizes. Treatment with PLGA+ 10 mg/kg Met showed low inflammatory cells, decreased bone loss and integrity cement and levels of IL-1β, and TNF-α (p < 0.05) were significantly reduced. Additionally, weak staining was shown by RANKL, Cathepsyn K, OPG, and osteocalcin. Radiographically, linear measurements showed a statistically significant reduction in bone loss after treatment with PLGA+ 10 mg/kg Met compared to the positive control (p < 0.05). RT-PCR showed increased AMPK expression (p < 0.05) and decreased expression of NF-κB P65, HMGB1 and TAK-1 after PLGA+ 10 mg/kg Met (p < 0.05). The PLGA nanoparticle + 10 mg/kg Met decreased glucose levels and also decreased the inflammatory response, and bone loss in ligature-induced periodontitis in rats.

Keywords: Antibacterial activity, Antioxidant activity, Bioactive compounds, Elementary analysis, Nutritional and phytochemical composition, Seaweed.

An increased demand for natural products nowadays most specifically probiotics (PRO) is evident since it comes in conjunction with beneficial health effects for the consumers. In this regard, it is well known that encapsulation could affect positively the PRO's viability throughout food manufacturing and long-term storage. This paper aims to analyze and review various multilayer strategies for encapsulation of PRO. Double-layer encapsulation of PRO by electro-hydrodynamic atomization or electrospray technology has been reported along with layer-by-layer assembly and water-in-oil-in-water (W1/O/W2) double emulsions to produce multilayer PRO-loaded carriers. Finally, their applications in food products are presented. The resistance (cover material) and viability of (PRO) to mechanical damage, during gastrointestinal transit and shelf life of these trapping systems are also described. The PRO encapsulation in double and multiple-layer coatings combined with other technologies can be examined to increase the opportunities for new functional products with amended functionalities opening a novel horizon in food technology.

Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.

To enhance the uplift capacity and facilitate the installation of multi-row perfobond connectors at shallow burial depths, this study puts forward a novel notched T-perfobond connector. The design incorporates an integrated flange at the bottom of the connector and a notch at the edge of the hole. Through pull-out model tests on four notched T-perfobond connectors, this research investigates their failure mechanisms and pull-out capacities. Utilizing the explicit dynamics method in Abaqus, a finite element model of the pull-out resistance test for the notched T-perfobond connector is established and verified against experimental data. Furthermore, a detailed parametric analysis involving 54 models is conducted, examining crucial parameters such as rib dimensions, hole geometry, flange size, notch width, bar diameter, and material properties. Based on the combined experimental and numerical results, the paper assesses the suitability of current formulas for calculating the pull-out capacity of perfobond connectors and proposes a refined calculation method specifically for notched T-perfobond connectors. All the findings in this paper can serve as a reference in the design and construction of composite structures.

Metagenomic analysis of mosquitoes allows the genetic characterization of mosquito-associated viruses in different regions of the world. This study applied the metagenomic approach to search for novel viral sequences in seven species of mosquitoes collected from the Novosibirsk region of Western Siberia. Using NGS sequencing, we identified 15 coding-complete viral polyproteins (genomes) and 15 viral-like partial sequences in mosquitoes. The complete sequences for novel viruses or partial sequences of capsid proteins, hypothetical viral proteins, and RdRp were used to identify their taxonomy. The novel viral sequences were classified within the orders Tymovirales and Picornavirales, the families Partitiviridae, Totiviridae, Tombusviridae, Iflaviridae, Nodaviridae, Permutotetraviridae, Solemoviridae, with several attributed to four unclassified RNA-viruses. The main part of the novel putative viruses and viral sequences was associated with Coquillettidia richardii mosquito. This study is intended to increase our understanding of viral diversity in mosquitoes found in the natural habitats of Siberia, characterized by very long, snowy, and cold winters.

Being forced to live in an area following a catastrophic landslide may adversely impact people's mental well-being. Unprivileged communities such as Adivasis, Dalits, plantation workers, and people with low incomes in Kerala's hilly districts are forced to live in landslide-affected areas despite their fear. This paper examines the fears and concerns of landslide survivors from the marginalized sections of society from a broader framework of the political economy of mental health. This approach aligns with the assertion made by the World Health Organisation that individual factors do not solely determine mental health but are intricately linked to broader social, political, and economic circumstances. Data were collected through in-depth interviews with fatal landslide survivors and key informants [ ]. The study employed thematic data analysis from in-depth interviews of various fatal landslide survivors since 2018. The three major themes unveiled in the analysis are: 1. post-disaster fears, 2. The factors that triggered fear and concern and 3. Socio-economic marginality and mental health. The study asserts that individuals who are poor and unprivileged are highly susceptible to mental health issues associated with landslides.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

Currently the bimatoprost implant (DURYSTA) is approved for a single administration secondary to concern for progressive corneal endothelial cell (CEC) loss with intracameral administration. In order to better understand the cause of CEC loss and other adverse events (AEs) with intracameral administration, multimodal imaging of the implant utilizing optical coherence topography (OCT), robotic ultrasound and bio microscopy video recording was utilized. Inherently the anterior chamber is a non-confined space. In certain examples the implant assumed a round and spotted structure as it picked up iris pigment as it migrated around the anterior chamber. Accordingly, we explored delivery of the implant in the middle segment behind the iris and in front of the pseudophakic lens. We then evaluated the suitability of various imaging modalities to assess the implant in this location. For middle segment administration, the ArcScan Insight 100 robotic ultrasound provided resolution and repeatability sufficient to monitor the implant behind the iris. This preliminary study hypothesizes that DURYSTA migration in the anterior chamber is the main source of AEs. Through use of a confined location, the middle segment, the implant is unlikely to migrate and cause CEC loss while also delivering drug closer to its site of action, the ciliary body. Further efforts are underway to understand the suitability of the middle segment for serial DURYSTA administration to maximize intraocular pressure lowering while minimizing AEs.

Polyarteritis nodosa (PAN), also known as panarteritis nodosa, represents a form of necrotizing vasculitis that predominantly affects medium-sized vessels, although it is not restricted to them and can also involve smaller vessels. The clinical presentation is heterogeneous and characterized by a significant number of patients exhibiting general symptoms including asthenia, fever, and unintended weight loss. Although PAN can involve virtually any organ, it preferentially affects the skin, nervous system, and the gastrointestinal tract. Orchitis is a rare but specific manifestation of PAN. The absence of granulomas, glomerulonephritis and ANCA serves to distinguish PAN from other type of vasculitis. Major complications consist of hemorrhagic and thrombotic events occurring in mesenteric, cardiac, cerebral, and renal systems. Historically, PAN was frequently linked to hepatitis B virus (HBV) infection, but this association has dramatically changed in recent years due to declining HBV prevalence. Current epidemiological research often identifies connection between PAN and genetic syndromes as well as neoplasia. This article provides a comprehensive review of PAN, specifically focusing on the progression of its clinical manifestations over time.

The objectives of this study are threefold. First, we evaluate the applicability of the information–motivation–behavioral skills (IMB) model for predicting organic food purchase intention (OFPI) in adults with chronic conditions. Second, we examine the indirect effects of organic food knowledge, attitudes toward organic food purchase, and subjective norms on OFPI through self-efficacy. Third, we examine whether these indirect effects are moderated by gender and educational attainment. Data were collected from Indian adults with chronic conditions using a self-administered questionnaire. The results show significant indirect effects of organic food knowledge, attitude toward organic food purchase, and subjective norm on OFPI through self-efficacy. Moreover, the mediating effect of knowledge was moderated by gender and education attainment, with the effect being stronger for females and among individuals with a lower level of education. Organic food marketers, social marketers, and public health agencies promoting organic food consumption to people with chronic conditions should aim to increase their confidence in comprehending organic food. This study contributes to the literature by assessing the applicability of Fisher and Fisher’s (1992) IMB model in understanding behavioral intention toward organic food.