Computers and artificial intelligence affect every field of life nowadays. In medical image interpretation automatic decision making using algorithms are used increasingly in every sub-field and computer aided diagnosis (CAD) is one of the main tools available to medical science today. CAD systems are used as an augmented option for both the medical practitioner and the patients, with image analysis and interpretation being of primary importance. In particular, spatial relations are used in knowledge representation, and these relations can be used for effective medical image interpretation. In this paper, we put forth an algorithm for defining non-small cells lungs cancer (NSCLC) stages in lungs images interpretation using topological spatial relations. We show an application case study in event motion predictions for lung cancer staging scoring - tumor, nodes and metastasis (TNM) - with combined topological and directional relations.

Omega-3 polyunsaturated fatty acids (ω3-PUFAs), including docosahexaenoic acid (DHA), have been shown to exert anticancer effects by inducing apoptotic cell death. However, the mechanism for DHA-induced cell death in lung cancer is not fully understood. Here, we show that DHA induces apoptosis in two human EGFR mutant non-small cell lung cancer (NSCLC) cell lines, and that DHA-induced cell death is accompanied by SIRT6 activation and attenuated Hedgehog (Hh) signaling. Knockdown of SIRT6 using siRNAs inhibited DHA-induced apoptosis, whereas SIRT6 overexpression increased apoptotic cell death. DHA-induced SIRT6 activation was associated with downregulation of Hh signaling, and knockdown of SIRT6 resulted in augmentation of Hh signaling. Pretreatment of NSCLC cells with a Smoothened agonist prevented DHA-induced decreases in the levels of Hh signaling proteins and increases in cleaved PARP levels. Moreover, endogenous production of ω3-PUFAs in PC9 cells via fat-1 expression resulted in elevated SIRT6 levels and reduced levels of Hh signaling molecules, including Gli, following DHA treatment. Overall, these results implicate that ω3-PUFAs induce apoptosis by downregulating Hh signaling via SIRT6 activation in human EGFR mutant NSCLC cells. These findings suggest that ω3-PUFAs potentially represent an effective therapy for the chemoprevention and treatment of NSCLC.

ABSTRACT This study focused for socio-economic benefit of Western shoreline of Lake Ziway ecosystem. The data collected from two woredas they are Adami Tulu Jidu Kombolch (ATJK) and Dugda woreda of fishermen and small scale irrigation users. Lake Ziway great importance food and water for both groups of respondents and additionally sources of raw material, energy, cultivation, organic fertilizers, genetic and medicinal plants. Lake Ziway also has a major economic benefit for both groups of respondents. The sampled fishermen cached mean amount of 2,524Kg per year with minimum and maximum amount of fish 504Kg and 16,800Kg per year respectively and with this fish catching they got average income of 51,398 Birr ($2,570) per year with range of 7,200 Birr ($360) and 288,000 Birr ($14,400) per year. As like of fishermen small scale irrigation also got economic benefit with their production of cereal crops, fruits and vegetables. They produce in average 13.47Quintal of cereal crop and 69.56Quintal of fruits and vegetables per year and they got average income of 7,727 Birr ($386) and with range of 13,714 Birr ($686) per year respectively. this incurred that wetland ecosystem has a lot of socio-economic benefit for the people live near by specially for developing countries like Ethiopia they are more dependent on natural ecosystem like of Lake Ziway. Because of its high importance, we have to protect and conserve and use sustainably of Lake Ziway and similar wetland ecosystems. Key Words: Wetland ecosystem service; Lake Ziway; Socioeconomic benefit; Fishermen; Small scale irrigation users

The effect of spanwise wing non-planarity, implemented in conjunction with a Gurney flap is presented. Testing was undertaken in a low speed wind tunnel using a rectangular wing with an aspect ratio of 3. The outer 1/3 of the wing was non-planar which took the form of dihedral or a circular arc. A 2% high Gurney flap was implemented such that it could extend over the entire span, or the planar inboard section. Loads were measured using a sting balance. The data shows that non-planarity increases the maximum lift coefficient and the wing’s lift curve slope. Gurney flap lift modulation was enhanced in the presence of non-planarity. The addition of Gurney flaps caused a greater minimum drag coefficient increment for the non-planar wings. The Gurney flaps reduced the lift dependent drag of the wings; however, the minimum drag coefficient was observed to increase. The Gurney flaps reduced the maximum lift to drag ratio (L/D) for the non-planar wings; however, the flat wing showed a small L/D increment with flap addition.

The growing urgency for sustainable energy solutions necessitates a deeper understanding of the environmental impacts of renewable technologies. This article aims to synthesize and analyze Life Cycle Assessments (LCA) in this domain, providing a comprehensive perspective. We systematically categorize 2,923 articles into four communities: (1) Photovoltaic systems, (2) Wind energy systems, (3) Materials for auxiliary industry of photovoltaic and wind systems, and (4) Solar thermal systems. A comparative analysis is conducted to identify methodological consistencies and disparities across these communities. The findings reveal diverse methodological approaches and a range of environmental impacts, highlighting the complexities in assessing renewable energy systems. The article underscores the significance of material selection in photovoltaic and wind systems, providing a critical overview of the current state of LCA research in renewable energy and stressing the need for standardized methodologies. It also identifies gaps in recent research, offering insights for future studies focused on integrating environmental, economic, and social considerations in renewable energy assessments.As a case study, an LCA was used to evaluate two energy supply methods for a sensor network: one connected to the public grid and another powered by a photovoltaic panel. The off-grid system, while more environmentally friendly by reducing reliance on non-renewable energy sources, posed challenges due to the high environmental impact of battery production and disposal. Integrating environmental assessments provides a robust framework for making informed decisions on sustainable technologies. The findings are critical for projects that balance technological needs with sustainability goals.

Deterministic simulations using the SWAN model were conducted along a stretch of the Tietê-Paraná waterway in the Ilha Solteira dam reservoir, São Paulo, Brazil, to assess the water waves generated by winds. Simultaneously employs a probabilistic metamodeling approach (PCE) and Sobol indices, to quantify uncertainty of the five involved physical parameters (wind velocity and direction, bottom friction (JONSWAP), whitecapping and depth-induced breaking index). In the deterministic part, Janssen’s formulation yielded the most successful and accurate estimations of wave heights. Probabilistic analysis underscored the importance of wind data and the whitecapping parameter in model calibration, with a significant influence of wind speed indicated by Sobol indices. The results not only refined the model calibration process, but also allowed the creation of contour maps for wave height and period that offer practical pre-project applications. This work advances understanding of wind wave hydrodynamics deterministically and probabilistically, with good concordance and performance indices compared to field data, despite being scarce.

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M7.8 and M7.5 Kahramanmaras earthquake sequence on February 6, 2023, the closed strong magnetic storm occurred on November 7, 2022, SYM/H=-117 nT. Storm started at 08:04 UT. At this time, the high latitudinal part of Turkey's longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth's environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from some days to some months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable.

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

To address global warming, there is a worldwide effort to establish carbon-neutral energy plans. Given the characteristics of renewable energy generation, a diverse 'Energy Mix' consisting of various energy sources such as wind and solar power is crucial. In particular, the wind power is expected to continue expanding globally due to its sustainable nature. Furthermore, the expan-sion of offshore wind farm is making essential and a rapidly growing industry sector to be achieving carbon neutrality with limited land for construction. To facilitate the construction and operation of offshore wind farms, specialized vessels with dynamic positioning (DP) systems are essential such as Wind Turbine Installation Vessels (WTIVs) and Service Operating Vessels (SOVs). The purpose of this study is to predict and analyze the scale and structural changes in the fleet capacity of DP vessel, which is essential for offshore wind power construction as the offshore wind power industry develops using System Dynamics (SD) modeling. Specifically, this study aims to construct a demand forecasting model for DP vessel used in offshore wind farm con-struction based on SD methodology. The study aims to identify influencing factors by analyzing dynamic changes in system configurations over time and establishing causal relationships based on feedback structures. Instead of traditional time series analysis methods, causal relation-ship-based simulations were conducted for quantitative analysis. The results provide insights into the changes in the DP vessel market structure due to the emergence of the new industry and highlight areas that require improvement throughout the industry.

A significant electromotive force is induced in the rotor circuit of a doubly fed induction generator (DFIG) due to its high vulnerability to grid faults. Therefore, the system performance must be increased with appropriate control actions that can successfully offset such abnormalities in order to provide consistent and stable operations during grid disturbances. In this regard, this paper presents a solution based on a combination of an energy storage-based crowbar and a rotor-side crowbar that makes the effective transient current and voltage suppression for wind-driven DFIG possible. The core of the solution is its ability to restrict the transient rotor and stator overcurrents and DC-link overvoltages within their prescribed limits, aiming to protect the DFIG and power converters, and consequently, improve the system’ ability to ride-through faults. Further, the capacity of an energy storage device for transient suppression is estimated. Finally, simulation results show that the proposed approach is effective in achieving transient control objectives precisely and maintaining a stable grid connection during the faults.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since the calm weather is mostly noticed at the local site, making the dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward-back propagation neural network with 12-month measurements at Fujairah site (UAE). Both daily and monthly wind speed data sets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Due to the random nature of wind speed at the local site, a two-layer with 4 neurons per layer neuro network is used to locate the most variable and intense speed during 8-hours in the day. Forecasted wind speed is determined with errors arising from different sources such as the utilization of only 3rd-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.

Wave buoy measurements were carried out near the north-eastern Black Sea coast at the natural reserve Utrish in 2020-2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 meters offshore and depths of 18 and 42 meters. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today's understanding of wind-wave interaction. It is shown that the main fraction of the wind energy flux goes to the direct Kolmogorov-Zakharov cascade to high wave frequencies and then to dissipation in small scales. Less than 1% of the wind energy flux is directed to low frequency band (the so-called inverse Kolmogorov-Zakharov cascade), thus, providing wave energy growth.

The common dandelion uses a bundle of drag-enhancing bristles (the pappus) that enables seed dispersal over formidable distances; however, the scaling laws of aerodynamic drag underpinning pappus-mediated flight remains unresolved. In this paper, we study the aerodynamic shape of dandelion, derive the scaling law of resistance, determine the Vogel exponent. In particular, we find that the total drag coefficient is proportional to the -2/3 power of the dandelion pappus Reynolds number, and obtain the terminal velocity of the dandelion seed under gravitation field.

Rising sensitivity of the loads with respect to power quality has grown-up the consideration of power system study and power quality enhancement systems. The fluctuation of voltage outside the normal working range due to faults may lead to unsuitable interruption of wind turbines. This thesis present 1.5MW grid connected Adama Ⅰ Wind Farm in Ethiopia with the objectives to capture the optimal power from the wind and ensure voltage source for sensitive load. This paper deals with the effective voltage sag mitigation and Harmonic distortion effectively met IEEE 519-1992 standard under all Phase to ground fault compensation by using Dynamic Voltage Restorer (DVR), to regulate the terminal voltage of the wind farm and safe operation of sensitive load. The DVR utilizes a feed forward control-based algorithm to generate PWM. The simulation results are going to be carried out by MATLAB-Simulink to verify the operation and effectiveness of DVR during balanced voltage sag and swell conditions.

Large structures such as wind turbines are subject to environmental factors and varying operational loads which may result in structural damage, making components of these large structures prone to performance and mechanical degradation. The use of high-definition optical vision sensors in digital image correlation (DIC) allow for the application of a non-destructive image registration technique in which it measures finite three-dimensional deformations on surfaces through correlations of a unique pattern or set of unique localized patterns. However, the physical placement of an artificial marker such as a unique speckled pattern on the surface of the structure is time-consuming and often impractical for large structures. Therefore, we propose a novel auto-mated methodology that searches and segments salient and unique regions of an image as well as for all subsequent images to assist in performing efficient displacement measurements for vibrational study and structural health monitoring purposes. Our algorithm is validated on a con-trolled set of images, as well as on a small structure and large real-world wind turbine, which suggests the algorithm’s efficacy without the use of artificial markers for large structural health monitoring.

The North Sea Offshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of offshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic benefits of the North Sea Offshore Grid using two case studies: a power-based offshore grid, where only investments in power assets are allowed (i.e. offshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen offshore grid, where investments in offshore hydrogen assets are also permitted (i.e. offshore electrolysers, new hydrogen pipelines and retrofitted natural gas pipelines). We compare these scenario results with a business as usual scenario, in which offshore wind is connected radially to the shore and no offshore grid is deployed. All scenarios are run with the IESA-NS model, without any specific technology ban and under open optimization. This paper also presents a novel methodology, combining Geographic Information Systems and Energy System Models, to cluster offshore spatial data and define meaningful offshore regions and offshore hub locations. This novel methodology is applied to the North Sea region to define nine offshore clusters taking into account offshore spatial claims, and identifying suitable areas for single-use and multi-use of space for renewable energy purposes. The scenario results show that the deployment of an offshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn €) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn €) in the power-and-hydrogen based. In the most extreme scenario (H2) an offshore grid permits to integrate 283 GW of HVDC connected offshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario (P1) the offshore grid integrates 59 GW of HVDC connected offshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of offshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%. Finally, we also find that, when imported hydrogen is available at 2 €/kg (including production and transport costs), large investments in an offshore grid are not optimal anymore. In contrast, at import costs over 4 €/kg imported hydrogen is not competitive.

Regional Climatic Comfort Index (CCI) deteriorated significantly due to the climate change and anthropogenic interference. Knowledge regarding the long-term temporal dynamics of CCI in typical regions should be strengthened. In this study, we analyze the temporal and spatial evolution of CCI from 1969 to 2018 in Guangdong Province, based on meteorological indicators, including heat, humidity, wind and cloth loading etc.. Additionally, the population exposure to climate unconformity was examined since 1990 with the help of population data. Our study found that: (1) the warming and humidifying of the summer climate served as the main driving force for the continuous deterioration of CCI, with the comfortable days decreased by 1.06d/10a and the extremely muggy days increased by 2.83d/10a; (2) spatially, the lowest climate comfortability concentrated in southwestern Guangdong with more than 50 uncomfortable days each year, while the climate comfortability in northeastern Guangdong tends to deteriorated whit higher rate, which can reach as high as 6d/10a; (3) in summer, the population exposure to uncomfortable climate highly centralized in the Pearl River Delta, Shantou, Jieyang, and the surrounding areas, and both area and population exposure showed increasing trends. Particularly, Shenzhen held the highest growth rate of population exposure with an increase rate of 2.94 million/10a; (4) although the discomfort distribution and deterioration rate vary across the province, the spatial heterogeneity of comfortability is diminishing in Guangdong Province. This study will provide scientific reference for regional urban planning, thermal environment improvement, local resident health risk analysis, and key strategy implementation, etc.

Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.

The Haar wavelet decomposition technique is used to detect the planetary magnetosphere boundaries and discontinuities. We use the magnetometer data from the CASSINI and MESSENGER spacecraft to identify the abrupt changes in the magnetic field when the spacecraft crossed the magnetospheric bow shocks and magnetopauses of Saturn and Mercury, respectively. The results confirm that the Haar transform can efficiently identify the planetary magnetosphere boundaries characterized by the abrupt magnetic field changes. It is suggested that this technique can be applied to detect the planetary boundaries as well as the discontinuities such as the shock waves in the interplanetary space.

Air pollution is responsible for any adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the present work is to study the behavior of two bio-climatic indexes and statistical characteristics of the air quality index for Sofia city - the capital of Bulgaria for the period 2008 - 2014. The study is based on WRF-CMAQ model system simula-tions with a spatial resolution of 1 kilometer. The air quality is estimated by the air quality index, taking into account the influence of different pollutants and the thermal conditions by two indi-ces, respectively, for hot and cold weather. Results: It was found that half of the heat and cold index categories are present in the simulations. Their distribution has some spatial features. All air quality categories are present in the domain, with dominance only of the O3 and PM10. Conclu-sions: It was found that Sofia is not so hot and air polluted place, but in some situations, people have to have some concerns when intend to be outdoors for a prolonged time.

The present study aims to develop a risk-based approach to find optimal solutions for life extension management for offshore wind farms based on Markowitz’s modern portfolio theory, adapted from finance. The developed risk-based approach assumes that the offshore wind turbines (OWT) can be considered as cash-producing tangible assets providing positive return from the initial investment (capital) with a given risk attaining the targeted (expected) return. In this regard, the present study performs a techno-economic life extension analysis within the scope of the multi-objective optimisation problem. The first objective is to maximise the return from the overall wind assets, while the latter aims to minimise the risk associated with obtaining the return. In formulating the multi-dimensional optimisation problem, the life-extension assessment considers the results of a detailed structural integrity analysis, free-cash-flow analysis, and probability of project failure, local and global economic constraints. Further, the risk is identified as the variance from the expected mean of return on investment. The risk-return diagram is utilised to classify the OWTs of different classes using an unsupervised machine learning algorithm. The optimal portfolios for the various required rate of return are recommended for different stages of life extension.

There is an accelerating requirement for ocean sensing where autonomous vehicles can play an essential role in assisting engineers, researchers, and scientists with environmental monitoring and collecting oceanographic data. This paper is performed to develop a rigid sail for the autonomous sailing drone. Our study aims to numerically analyze the aerodynamic characteristics of curvy twin sail and compare it with wing sail. Because racing regulations limit the sail shape, only the two-dimensional geometry was open for an optimization. Therefore, this study’s first objective was to identify the aerodynamic performance of such curvy twin sails. Simultaneously, a secondary objective was to estimate the effect of the sail’s spacing and shapes. A viscous Navier-Stokes flow solver is used for the numerical aerodynamic analysis. The 2D aerodynamic investigation is a preliminary evaluation. The results have shown that the curvy twin sail designs have improved lift, drag, and driving force coefficient compared to the wing sails. The spacing between the port and starboard sails of curvy twin sail is an important parameter. The spacing is 0.035L, 0.07L, and 0.14L shows the lift coefficient reduction because of dramatically stall effect, while flow separation is improved with spacing is 0.21L, 0.28L, and 0.35L. Significantly, the spacing 0.28L shows the maximum high pressure at the lower area and the small low pressure area at leading edges, so the highest lift is generated.

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are inter-correlated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error of the retrieved U10 was reduced from 1.42 m/s to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.

The aim of work is to derive an explicit expression for a function of vertical mixing induced by wind-waves. To this end, in the Navier-Stokes equations, a current is decomposed into four constituents: the mean flow, the wave-orbital motion, the wave-induced turbulent and the background turbulent currents. This decomposition allows separating the wave-induced Reynolds stress, Rw, from the background one, Rb. To make a statistical closure for Rw, the Prandtl approach for the background turbulent fluctuations is used that results in an implicit expression for the wave-induced vertical mixing function, Bv. Expression for Bv is specified based on the author’s results for the eddy viscosity found earlier in the frame of the three-layer concept for a wavy air–sea interface, used for modelling wind-drift currents [1]. Finally, the explicit parameterization for Bv(a, u*, z) is found as a linear function in both the wave amplitude at depth z, a(z), and the friction velocity in the air, u*. The linear dependence of function Bv(a) on the wave amplitude provides the enhanced vertical mixing induced by wind–waves in comparison with function Bv(a) having the cubic dependence found in [2], as far as the wind-wave amplitude a(z) decays exponentially with depth.

The underground complicated testing environment and the fan operation instability cause large random errors and outliers of the wind speed signals. The outliers and large random errors result in distortion of mine wind speed monitoring, which possesses safety hazards in mine ventilation system. Application of Kalman filter in velocity monitoring can improve the accuracy of velocity measurement and eliminate the outliers. Adaptive Kalman Filter was built by automatically adjusting process noise covariance and measurement noise covariance depending on the differences between measured and expected speed signals. We analyzed the fluctuation of airflow flow using data of wind speed flow and distribution characteristics of the tunnel obtained by the Laser Doppler Velocimetry system (LDV) studies. A state-space model was built based on the tunnel airflow fluctuations and wind speed signal distribution. The adaptive Kalman Filter was calculated according to the actual measurement data and the Expectation Maximization (EM) algorithm. The adaptive Kalman filter was used to shield fluid pulsation while preserving system-induced fluctuations. Using the Kalman filter to treat offline wind speed signal acquired by LDV, the reliability of Kalman filter wind speed state model and the characteristics of adaptive Kalman Filter were investigated. Results showed that the adaptive Kalman filter effectively eliminated the outliers and reduced the root-mean-squares error (RMSE), and the adaptive Kalman filter had better performance than the traditional Kalman filter in eliminating outliers and reducing RMSE. Field experiments in online wind speed monitoring were conducted using the optimized adaptive Kalman Filter. Results showed that adaptive Kalman filter treatment could monitor the wind speed with smaller RMSE compared with LVD monitor. The study data demonstrated that the adaptive Kalman filter is reliable and suitable for online signal processing of mine wind speed monitor.

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.

Typhoon Durian forming over the Western North Pacific Ocean and entering into the South China Sea (SCS), caused extreme and widespread damages in 2006. In this research, sensitivity analyses on different physical parameterization schemes of the Weather Research and Forecasting Atmospheric Model (WRF-ATM) have been utilized to study typhoon Durian. Model accuracy and performance testing were investigated with different initial conditions during the tropical cyclone simulation in the SCS. The initial and boundary conditions (IBCs) for all experiments were derived from the European Centre for Medium Range Weather Forecasts (ECMWF), Re-Analysis Interim (ERAI), and the National Centers for Environmental Prediction (NCEP) with Final (FNL) analysis data compiled through the WRF-ATM model. The sensitivity analysis results indicated a major improvement for the cumulus scheme by using the Grell-Devenyi scheme along with the PBL scheme of Yonsei University, mixed-phase microphysics scheme of the WRF Single Moment 5-class and IBCs for ECMWF-ERAI of TC simulation under the context of Wind-Pressure Relationships. This predicted better track and intensity comparing with these of the Joint Typhoon Warning Center. The results revealed that the TC track and intensity were well simulated by the WSM5-GD combination for the WRF-ATM model with an intensity error of 1.69 hPa for minimum surface level pressure, maximum wind speed of 1.83 knots and average track error of 25 km in 72 hours. The simulations showed that the potential track and intensity error decreased with the delayed IBCs, suggesting that the model simulation is more dependable when the coast is approached by the TC.

This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.

A three-dimensional, unstructured grid, hydrodynamic and suspended-sediment transport model (i.e., SELFE-SED) was developed to simulate temporal and spatial variations of suspended sediment and was applied to the subtropical subalpine Tsuei-Feng Lake (TFL) of Taiwan. The model was validated with measured water level and suspended‑sediment concentration in 2009, 2010, and 2011. The overall model simulation results are in quantitative agreement with the observational data. The validated model was then applied to explore the most important parameter that affects the suspended-sediment concentration and to investigate the effect of wind stress on the mean current and suspended‑sediment distribution in this shallow lake. Modeling results of sensitivity analysis reveal that the settling velocity is a crucial parameter and erosion rate is less important in the suspended-sediment transport model. Remarkable lake circulation was found based on the strength of wind speed and wind direction. Strong wind would result in higher mean current in the top layer and suspended-sediment distribution in the top and bottom layers. This study demonstrated that the wind stress played a significant influence on mean circulation and suspended-sediment transport in a shallow lake.

Optimizing the NACA0015 airfoil which is widely applied in small-scale vertical axis wind turbine to make it has a better aerodynamic performance. In the optimization process, using CST parameterization method to perturb the airfoil geometry, the thickness and camber of the airfoil are selected as the constraint, and the value of the maximum tangential force coefficient is chosen as the objective function, the genetic algorithm based on non-dominated sorting (NSGA-II)is selected as an optimization method, calculates the aerodynamic performance of the airfoil by applying the approach of combining XFOIL program and Viterna-Corrigan post-stall mode ,and establishes the optimizing process by the optimization software modefrontier for NACA0015 airfoil’s muti-point optimization, validate the airfoil’s performance with CFD finally. The result illustrates that, by comparing with the NACA0015 airfoil, the optimized airfoil’s lift to drag ratio is improved over a wide range of attack angles, the stall performance is more gentle. The maximum lift coefficient, the maximum lift-drag ratio and the maximum tangential force coefficient are increased by 7.5%,9 and 8.87%, respectively. The optimized airfoil has a wide variable condition performance, more suitable for the operating conditions of a vertical axis wind turbine. Finally, predict the rotor efficiency with optimized airfoil and NACA0015 airfoil for different tip speed ratios and different solidities with multiple streamtube model, the result shows the rotor with optimized airfoil has a higher efficiency.

The terrestrial subsurface harbors unique microbial communities that play important biogeochemical roles and allows studying a yet unknown fraction of the Earth’s biodiversity. The Saint-Leonard cave in Montreal city (Canada) is of glaciotectonic origin. Its speleogenesis traces back to the withdrawal of the Laurentide Ice Sheet, 13 000 years ago, during which the moving glacier dislocated the sedimentary rock layers. Our study is the first to investigate the microbial communities of the cave. By using amplicon sequencing, we analyzed the taxonomic diversity and composition of bacterial, archaeal and eukaryotes communities living in the groundwater (0.1µm- and 0.2µm-filtered water), in the sediments and in surface soils. We identified a microbial biodiversity typical of cave ecosystems. Communities were mainly shaped by habitat type and harbored taxa associated with a wide variety of lifestyles and of metabolic capacities. Although we found evidence of a geochemical connection between the above soils and the cave’s galleries, our results suggest that the community assembly dynamics are driven by habitat selection rather than dispersal. Furthermore, we found that the cave’s groundwater, in addition to being generally richer in microbial taxa than sediments, contained a considerable diversity of ultra-small bacteria and archaea.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

Patient-derived tumor organoids (PDTOs) are a novel preclinical model of non-small cell lung cancer (NSCLC) for studying tumor biology and precision medicine, which recapitulates tumor morphology and gene expression profile. However, some issues such as low establishment rates, long-term production, and the loss of the immune microenvironment have hindered their widespread practical use. In this study, we investigated the efficacy of a recently introduced free-floating organoid production method to address these issues. The free-floating approach provided PDTO establishment success rate of over 90 % resulting in organoid formation within a short period of one week. These organoids retained the elements of the parental tumor morphology and contained stroma and immune cell populations, as confirmed by flow cytometry analysis. Importantly, we found that the cytokine and growth factor expression profiles of the free-floating organoids correlated with those of the original tumors in 58 % of cases, indicating the preservation of tumor-associated signaling pathways. Obtained organoids exhibited different responses to anticancer chemotherapeutics that might be a result of variable expression of drug resistance-associated genes. Considering the high establishment rate and short time of free-floating organoid production, this technique provides a valuable tool for a more accurate evaluation of therapeutic strategies and cancer biology studies.

Small island developing states (SIDS) traditionally face a set of challenges like the narrow economic structure, environmental issues, and dependence on a few ranges of economic activities forcing them to open the economy to the exterior. Therefore, their development model, like in Cape Verde depends on migration, remittances, dependence on aid, tourism, and state employment. The current research offers an insight about the nature of Cape Verde’s economy as a SIDS and the degree to which its economy depends on tourism and government services, both of which are supported through aid programs. Understanding Cape Verde’s development model is important to clarify the challenges the country faces, and its development needs to gather a long-term resilience and to understand if it is changing from a MIRAB (Migrations, Remittances, Aid and Bureaucracy) model into another one.

Serine/threonine kinase (AKT) signaling regulates diverse cellular processes and is one of the most important aberrant cell survival mechanisms associated with tumorigenesis, metastasis, and chemoresistance. Targeting AKT has become an effective therapeutic strategy for the treatment of many cancers. AKT3 (PKBγ), the least studied isoform of the AKT family, has emerged as a major contributor to malignancy. AKT3 is frequently overexpressed in human cancers, and many regulatory oncogenic or tumor suppressor small non-coding RNAs (ncRNAs), including microRNAs (miRNAs), have recently been identified to be involved in regulating AKT3 expression. Therefore, a better understanding of regulatory miRNA/AKT3 networks may reveal novel biomarkers for diagnosis of patients with cancer and provide invaluable information for developing more effective therapeutic strategies. The aim of this review is to summarize current research progress in the isoform-specific functions of AKT3 in human cancers and the roles of dysregulated miRNA/AKT3 in specific types of human cancers.

Mathematical models of non-small cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of NSCLC. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

Postprandial hyperlipidemia showing postprandial increase in serum triglyceride (TG) is atherogenic and associated with the development of coronary artery disease (CAD). To diagnose postprandial hyperlipidemia, the oral fat loading test (OFLT) should be performed, however, this test is very time-consuming and is difficult to perform. Ele-vated serum TG levels reflect an increase in TG-rich lipoproteins (TRL) such chylomi-crons (CM), very low-density lipoproteins (VLDL) and their remnants (CM remnant [CMR] and VLDL remnant [VLDLR]). Understanding of elevation in CMR and/or VLDLR can lead us to understand the existence of postprandial hyperlipidemia. The measurements of apo B48 which is a constituent of CM and CMR, non-fasting TG which includes TG content in all lipoproteins including CM and CMR, non-high-density lipoprotein-cholesterol (non-HDL-C) which includes TRL and low-density lipoprotein, and remnant cholesterol, are useful to suspect the existence of postprandial hyperlipidemia. Postprandial hyperlipidemia is observed in patients with familial type III hyperlipoproteinemia, familial combined hyperlipidemia, chronic kidney disease, metabolic syndrome and type 2 diabetes. Postprandial hyperlipidemia is closely related to postprandial hyperglycemia, and insulin resistance may be an in-ducing and enhancing factor for both postprandial hyperlipidemia and postprandial hyperglycemia. Remnant lipoproteins and metabolic disorders associated with post-prandial hyperlipidemia have various atherogenic properties such as induction of in-flammation and endothelial dysfunction. A healthy diet, calorie restriction, weight loss and exercise, positively impact postprandial hyperlipidemia. Anti-hyperlipidemic drugs such pemafibrate, bezafibrate, ezetimibe and eicosapentaenoic acid have shown to improve postprandial hyperlipidemia. Anti-diabetic drugs including metformin, alpha-glucosidase inhibitors, pioglitazone, dipeptidyl-peptidase-4 inhibitors and glu-cagon like peptide 1 analogues, have shown to ameliorate postprandial hyperlipidemia. Although sodium glucose cotransporter-2 inhibitors have not been proven to reduce postprandial hyperlipidemia, they reduced fasting apo B48 and remnant lipoprotein cholesterol. In conclusion, it is important to appropriately understand the existence of postprandial hyperlipidemia and to connect it to optimal treatments.

: Molecular targeting strategies have been used for years to control cancer progression and are often based on targeting various enzymes involved in metabolic pathways. Keeping that in mind, it is essential to know the role of each enzyme in a particular metabolic pathway. In this review, we are providing in-depth knowledge of various enzymes such as ceramidase, sphingosine kinase, sphingomyelin synthase, dihydroceramide desaturase, and ceramide synthase, which are associated with multiple types of cancer that depend on ceramide metabolism. Focus has also been given to discussing physicochemical properties of well-studied inhibitors of natural products origin and their related structures for these enzymes. Targeting ceramide metabolism exhibited promise in mono and combination therapies at preclinical stages to prevent cancer progression and paved the way for the significance of sphingolipid metabolism in cancer treatments. Targeting ceramide metabolizing enzymes will help the medicinal chemist to design potent and selective small molecules for treating cancer progression at various levels.

Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection as well as to distribute the computing load to edge devices. However, even though wireless communications is a essential part for implementing FL in edge networks, there have been a few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks, and it can be exploited as a guideline for designing the network facilitating the wireless FL.

Lung cancer is the leading cause of cancer death worldwide, with non-small cell lung cancer (NSCLC) making up 80% of cases. Some genetic factors leading to NSCLC development include genetic mutations and PD-L1 expression. PD-L1 proteins are targeted in an NSCLC treatment called targeted gene therapy. However, this treatment is effective in a low percentage of patients. This study aimed to create machine learning models to use features like the number of mutations and the level of PD-L1 proteins in cancer cells, along with others, to predict whether a patient will receive clinical benefit from gene therapy treatment. This was done by downloading and merging datasets from cbioportal.org to create a sample size for the model. Features with high correlations to clinical benefit were identified. Three machine-learning models were created using these features to predict clinical benefits in patients, and each model’s accuracy was evaluated. All three models were accurate between 55-85%, with two of the models averaging an accuracy around 75%. Doctors can use these models to more accurately predict whether gene therapy treatment is likely to work in a patient before prescribing it to them.

Change detection with heterogeneous remote sensing images (Hete-CD) plays an important role in practical applications, especially when homogenous remote sensing images are unavailable. However, bitemporal heterogeneous remote sensing images (HRSIs) cannot compare directly to measure change magnitude, and many deep learning methods require large amounts of samples to train the module. Moreover, labeling many samples for land cover change detection with HRSIs is time-consuming and labor-intensive. Therefore, acquiring satisfactory performance of Hete-CD remains a challenge for deep learning networks with very small training samples. In this study, we promote a novel deep-learning framework for Hete-CD to obtain satisfactory performance while the initial samples are very small. We initially design a multiscale network with select kernel-attention module to focus on capturing different change targets with various sizes and shapes. Then, a simple yet effective non-parametric sample-enhanced algorithm based on the Pearson correlation coeffi-cient is promoted to explore potential samples around each initial sample. Finally, the proposed network and sample-enhanced algorithm are fused into one iterative framework to improve the change detection performance with very small samples. Experimental results conducted on four pairs of actual HRSIs indicated that the proposed framework can achieve competitive accuracies with very small samples for initialization when compared with some state-of-the-art methods. For example, the improvement is approximately 3.38% and 1.99% when compared with the selected traditional methods and deep learning methods, respectively.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.

One of the stated goals of the Common Agricultural Policy reforms has been to provide a fairer distribution of payments across and within Member States, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of Direct Payments. The structural factors that underlie this inequity, notably the preponderance of specific types of farming systems, are investigated in this research. A logit model was developed using Agricultural Census data at the Commune level, using the evolution of the percentage of farmers receiving Direct Payments in Portugal as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to DP between 2009 and 2019. In traditional Mediterranean farming systems, however, access to DP has been restricted to a smaller proportion of farmers. However, there appears to have been some redistribution in the previous two CAP programming cycles, from bigger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other types of production, notably vineyards.

Phagosomal escape and intracellular survival, often accompanied by Small Colony Variants (SCVs) formation, are typical features of infections caused by S. aureus. The survival in macro-phages favours S. aureus dissemination and complicates treatment. RAW 264.7 murine macro-phages infected with S. aureus USA300 and treated with erythromycin and 20mM carnosine, alone and in combination, were used as experimental model. SCVs were isolated from all treat-ment conditions, but only those undergoing the pressure of combined erythromycin and carnosine for 48 hours were stable for at least six passages on blood agar. Nucleic acid extraction was car-ried out for S. aureus USA300 wild-type and stable SCVs. Whole Genome Sequencing (WGS) was performed using Illumina DNA Prep and Illumina MiSeq, and quantitative reverse transcription PCR was performed. WGS analysis did not yield mutations pointing to differences between S. au-reus USA300 and stable SCVs, therefore the focus was shifted to evaluating gene expression vari-ations. Genes such as zur, mntR, uhpt, fur, sdrE were shown to be significantly up-regulated in SCVs compared to S. aureus USA300 wild-type, suggesting a global change that allows adapta-tion to intracellular persistence, including protection from inflammatory response and evasion of the immune system.

The DNA methylation profile of breast cancer differs from that in healthy tissue and can be used as a diagnostic and prognostic biomarker. Aim of the study: to compare gene methylation in small malignant breast tumors less than 2 cm and in healthy tissue and fibroadenoma. Methylation of the following 15 genes was studied: MAST1, PRDM14, ZNF177, DNM2, SSH1, AP2M1, CACNA1E, CPEB4, DLGAP2, CCDC181, GCM2, ITPRIPL1, POM121L2, KCNQ1, TIMP3. Methods: analysis was made by our modified MS-HRM method followed confirmation of the results by pyrosequencing. The genes were selected from publications that studied DNA methylation in breast cancer with high genome coverage. The study group included 48 samples of breast cancer, the control group included 24 samples of fibroadenoma and 24 samples of healthy tissue. Results: significant differences were found in methylation of 8 genes: CCDC181, GSM2, ITPRIPL1, ZNF177, CACNA1E, DLGAP2, TIMP3 (all р<0.001), and PRDM14 (р=0.002). The most accurate diagnostic value, based on logistic regression, was shown with the compound of two genes – CCDC181 and ZNF177 (AUC=0.99) in pyrosequencing analysis. Conclusion: small breast cancer tumors have a specific DNA methylation profile that distinguishes them from healthy tissue and benign proliferative lesions.

There is a growing consensus that to effectively adapt to climate change, cities need user-friendly tools and reliable high-resolution biophysical and socio-economic data for analysis, mapping, modeling, and visualization. This study examines availability of various types of information used in climate adaptation plans of 40 municipalities with population less than 300,000 people in the United and in France, probing into the choice and usage of relevant information by small municipalities. We argue that non-climatic spatial data, such as population demographic and socio-economic patterns, urban infrastructure, and environmental data must be integrated with climate tools and datasets to inform effective vulnerability assessment and equitable climate adaptation planning goals. Climate adaptation plans frequently fail to address the existing structural inequalities and environmental injustices in urban infrastructure and land use. Adaptation methodological approaches should be reassessed in the context of much needed societal transformation. Lessons learned from our studies offer valuable insights for potential development of the national and state-level climate adaptation information services for cities.

The large production of non-degradable petrol-based plastics has become a major global issue due to its environmental pollution. Biopolymers produced by microorganisms such as polyhydroxyalkanoates (PHAs) are gaining potential as a sustainable alternative, but the high cost associated to their industrial production has been a limiting factor. Post-transcriptional regulation is a key step to control gene expression in changing environments and has been reported to play a major role in numerous cellular processes. However, limited reports are available concerning the regulation of PHA accumulation in bacteria, and many essential regulatory factors still need to be identified. Here, we review studies where the synthesis of PHA has been reported to be regulated at the post-transcriptional level, and we analyze the RNA-mediated networks involved. Finally, we discuss the forthcoming research on riboregulation, synthetic and metabolic engineering which could lead to improved strategies for PHAs synthesis in industrial production, thereby reducing the costs currently associated with this procedure.

A sufficient amount of tissue sample is required to perform next-generation sequencing (NGS) with a high success rate, but the majority of patients with advanced non-small-cell lung cancer (NSCLC) are diagnosed with small biopsy specimens. Biopsy samples were collected from 184 patients with bronchoscopically diagnosed NSCLC. The tissue surface area, tumor cell count, and tumor content rate of each biopsy sample were evaluated. The impact of the cut-off criteria for the tissue surface area (≥ 1 mm2) and tumor content rate (≥ 30%) on the success rate of Oncomine Dx Target Test (ODxTT) was evaluated. The mean tissue surface area of the transbronchial biopsies was 1.23 ± 0.85 mm2 when small endobronchial ultrasonography with a guide sheath (EBUS-GS) was used, 2.16 ± 1.49 mm2 with large EBUS-GS, and 1.81 ± 0.75 mm2 with endobronchial biopsy (EBB). The proportion of samples with a tissue surface area of ≥ 1 mm2 was 48.8% for small EBUS-GS, 79.2% for large EBUS-GS, and 78.6% for EBB. Sixty-nine patients underwent ODxTT. The success rate of DNA sequencing was 84.1% and that of RNA sequencing was 92.7% over all patients. The success rate of DNA (RNA) sequencing was 57.1% (71.4%) for small EBUS-GS (n = 14), 93.4% (96.9%) for large EBUS-GS (n = 32), 62.5% (100%) for EBB (n = 8), and 100% (100%) for endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) (n = 15). Regardless of the device used, a tissue surface area of ≥ 1 mm2 is adequate for samples to be tested with NGS.

Primary hyperoxalurias (PHs) are a group of inherited alterations of the hepatic glyoxylate metabolism. PHs classification based on gene mutations parallel a variety of enzymatic defects, and all involve the harmful accumulation of calcium oxalate crystals that produce systemic damage. These geographically widespread rare diseases have a deep impact in the life quality of the patients. Until recently, treatments were limited to palliative measures and kidney/liver transplants in the most severe forms. Efforts made to develop pharmacological treatments succeeded with the biotechnological agent lumasiran, a siRNA product against glycolate oxidase, which has become the first effective therapy to treat PH1. However, small molecule drugs have classically been preferred since they benefit from experience and have better pharmacological properties. The development of small molecule inhibitors designed against key enzymes of glyoxylate metabolism is on the focus of research. Enzyme inhibitors are successful and widely used in several diseases and their pharmacokinetic advantages are well known. In PHs, effective enzymatic targets have been determined and characterized for drug design and interesting inhibitory activities have been achieved both in vitro and in vivo. This review describes the most recent advances towards the development of small molecule enzyme inhibitors in the treatment of PHs, introducing the multi-target approach as a more effective and safe therapeutic option.

UN 2030 Sustainable Development Goal 6 presents difficulties for small island developing states such as the Kingdom of Tonga, which relies on rainwater and groundwater lenses for freshwater supply. Planning and managing water resources to supply demands in dispersed small islands under variable climate and frequent extreme events is challenging. Tensions between water planning using top-down versus bottom-up processes have long been recognized. Tonga’s overarching national planning instrument is the Tonga Strategic Development Framework, 2015-2025 (TSDFII). This identifies desired national outcomes and is used to direct and resource Ministries and address international and regional commitments. Water supply was a low priority in the three-month consultations that led to TSDFII. Community Development Plans (CDPs), developed by rural villages throughout Tonga’s five Island Groups over nine years, involved participation from 80% of each village population who ranked local priorities. Analysis of priorities in 117 available village CDPs reveals improvements to village water supply was the highest overall priority in all five Island Groups and ranked within the top three priorities by 76% of all villages, with women, youth and men returning figures of 83%, 66% and 80% respectively. The mismatch between top-down and bottom-up priorities appears to result from an urban/rural divide.

COVID-19 is now a major global health crisis, can lead to severe food crisis unless proper measures are not taken. Though a number of scientific studies have addressed the possible impacts of COVID-19 in Bangladesh on variety of issues, problems and food crises associated with aquatic resources and communities are missing. Therefore, this study aimed at bridging the gap in the existing situation and challenges of COVID-19 by linking its impact on aquatic food sector and small-scale fisheries with dependent population. The study was conducted based on secondary data analysis and primary fieldwork. Secondary data focused on COVID-19 overview and number of confirmed, recovered and death cases in Bangladesh; at the same time its connection with small-scale fisheries, aquatic food production, demand and supply was analyzed. Community perceptions were elicited to present how the changes felt and how they affected aquatic food system and small-scale fisheries and found devastating impact. Sudden illness, reduced income, complication to start production and input collection, labor crisis, transportation abstraction, complexity in food supply, weak value chain, low consumer demand, rising commodity prices, creditor’s pressure were identified as the primary affecting drivers. Dependent people felt the measures taken by the Government should be based on protecting the health and food security, although it could be detrimental to economic growth in the short term. The study provides insight into policies adopted by the policy makers to mitigate the effects of the pandemic on aquatic food sector and small-scale fisheries.

Activating mutations in the Epidermal Growth Factor Receptor gene occur as early cancer-driving clonal events in a subset of patients with non-small cell lung cancer (NSCLC) and result in increased sensitivity to EGFR-tyrosine-kinase-inhibitors (EGFR-TKIs). Despite very frequent and often prolonged clinical response to EGFR-TKIs, virtually all advanced EGFR-mutated (EGFRM+) NSCLCs inevitably acquire resistance mechanisms and progress at some point during treatment. Additionally, 20-30% of patients do not respond or respond for a very short time (< 3 months) because of intrinsic resistance. While several mechanisms of acquired EGFR-TKI-resistance have been determined analyzing tumor specimens obtained at disease progression, the factors causing intrinsic TKI-resistance are less understood. However, recent comprehensive molecular-pathological profiling of advanced EGFRM+ NSCLC at baseline has illustrated the co-existence of multiple genetic, phenotypic, and functional mechanisms that may contribute to tumor progression and cause intrinsic TKI-resistance. Several of these mechanisms have been further corroborated by preclinical experiments. Intrinsic resistance can be caused by mechanisms inherent EGFR or by EGFR-independent processes, including genetic, phenotypic or functional tumor changes. This comprehensive review describes the identified mechanisms connected with intrinsic EGFR-TKI-resistance and differences and similarities with acquired resistance and among clinically implemented EGFR-TKIs of different generations. Additionally, the review highlights the need for extensive pre-treatment molecular profiling of advanced NSCLC for identifying inherently TKI-resistant cases and designing potential combinatorial targeted strategies to treat them.

Here we evaluate the chemistry of mineral soil solutions collected by suction lysimeters in a small mountain catchment that was affected by acidification-related spruce die-back. The aim was to obtain new insights into spatial patterns of nutrient partitioning during acidification recovery. This was achieved by comparing nutrient concentrations in soil solutions, collected along a V-shaped valley, with those in runoff. Five nests of suction lysimeters were installed in the 33-ha UDL catchment at different topographic positions (hilltops, slopes and valley). Following a 1-year equilibration, monthly samples of soil solutions were collected over a 2-year period. In the vicinity of each lysimeter nest, soil pits were excavated for a study of soil chemistry. Soil solutions were analyzed for SO42-, NO3-, DOC, NH4+, Na+, K+, Ca2+, Mg2+, and total dissolved Al concentrations, DOC and pH. For a P release estimation, ammonium oxalate extraction of soil samples was performed. Comparison of soil water data from this study with other European studies indicated that major environmentally relevant chemical species at UDL had concentrations similar to median concentrations across numerous previously acidified sites. CEC (≤ 58 meq kg-1) and BS (≤ 13 %), however, were significantly lower at UDL than at other European sites, documenting incomplete recovery from acidification. Spatial trends and seasonality in soil water chemistry support belowground inputs from mineral-stabilized legacy pollutants and soil nutrients. Seasonal variability of soil water sulfate and nitrate was significant. High nitrate in soil solutions during summer originated during the preceding dormant season, and high sulfate concentrations observed during the winter originating from recycled organic S during the summer. Higher concentrations of SO42-, NO3-, and base cations in runoff than in soil solutions may be explained by lateral surficial runoff leaching pollutants and nutrients from very shallow soil horizons. Nearly 30 years after peak acidification, solutions in mineral subsoil at UDL exhibited similar concentrations of SO42-, Ca2+ and Mg2+ as median values at the Pan-European International Co-operative Program (ICP) Forest sites, yet NO3- concentrations were an order of magnitude higher than the ICP sites. Calcium applied on soil surface by liming 32 to 11 years ago affected runoff more than soil solutions, suggesting contribution of both shallow soil water and groundwater to runoff.

Natural and synthetic small molecules from the NCI Developmental Therapeutics Program (DTP) were employed in molecular dynamics-based docking with DNA repair proteins whose RNA-Seq based expression was associated with overall cancer survival (OS) after adjustment for the PCNA metagene. The compounds employed were required to elicit a sensitive response (vs. resistance) in more than half of the cell lines tested for each cancer. Methodological approaches included peptide sequence alignments and homology modeling for 3D protein structure determination, ligand preparation, docking, toxicity and ADME prediction. Docking was performed for unique lists of DNA repair proteins which predict OS for AML, cancers of the breast, lung, colon, and ovaries, GBM, melanoma, and renal papillary cancer. Results indicate hundreds of drug-like and lead-like ligands with best-pose binding energies less than -6 kcal/mol. Ligand solubility for the top 20 drug-like hits approached lower bounds, while lipophilicity was acceptable. Most ligands were also blood-brain barrier permeable with high intestinal absorption rates. While the majority of ligands lacked positive prediction for Herg channel blockage and Ames carcinogenicity, there was considerable variation for predicted fathead minnow, honey bee, and Tetrahymena pyriformis toxicity. The computational results suggest the potential for new targets and mechanisms of repair inhibition and can be directly employed for in vitro and in vivo confirmatory laboratory experiments to identify new targets of therapy for cancer survival.

The increasing use of energy resources recovered from the subsurface environments and the resulting carbon imbalance in the environment has motivated the need to develop thermodynamically downhill pathways to convert and store CO₂ as water insoluble calcium or magnesium carbonates. While previous studies extensively explored aqueous routes to produce calcium and magnesium carbonates from CO₂, there has been a limited scientific understanding of the phase evolution and textural changes during the direct gas-solid conversion routes to produce calcium carbonate from calcium hydroxide, which is one of the abundant constituents of alkaline industrial residues. With increasing interest in developing integrated pathways for capturing, converting and storing CO₂ from dilute flue gases, understanding the compositions of the product phases as they evolve is essential for evaluating the efficacy of a given processing route. Therefore, in this study, we investigate the phase evolution and the corresponding textural changes as calcium hydroxide is converted to calcium carbonate under the continuous flow of CO₂ at an ambient pressure of 1 atm and on heating continuously from 30 °C to 500 °C using in-operando wide angle X-ray scattering (WAXS), small angle X-ray scattering (SAXS), and ultra-small angle X-ray scattering (USAXS) measurements.

In this paper, a small wind power generator is proposed with a new straight shape stator and rotor. The advantages of the proposed structure are introduced through a comparison between the basic and the proposed models. By comparing the pole slot combination of the proposed generator, the combination with optimal cogging torque characteristics was selected. The electromagnetic characteristics of proposed shape are analyzed for design variables using a finite element analysis (FEA) of ANSYS Maxwell. The final model of the proposed structure is designed by considering cogging torque and the generator electromagnetic characteristics. The electromagnetic and structural simulations of the final model are performed to satisfy the required performance of generator and mechanical safety. To verify the FEA results of the final model, a prototype is manufactured, experimented, and compared with the FEA results.

In this paper, the feasibility and potential assessment (PA) of wind power in six different areas MehalMeda, Eneware, DebreBerhan, AlemKetema, GundoMekel, and Majet are investigated. The data is collected from the National Metrological Agency (NMA) in Addis Ababa and from the districts of the case study areas and analyzed using MATLAB and HOMER software. Using the data collected at 10 meter height, wind direction using a wind rose, the wind frequency distribution, and wind turbine characteristics have been analyzed. Moreover, to obtain a more realistic wind potential study, the wind speed at a 10 meter height is extrapolated to a height of 50 meter in order to determine wind speed, wind power density, and wind energy density. The average wind speed of the zone at 10m and 50m heights are 1.58 m/s and 1.99 m/s, 3.06 m/s and 3.85 m/s, 4.75 m/s and 5.98 m/s, 2.29 m/s and 2.88 m/s, 1.01 m/s and 1.27 m/s and 4.07 and 5.12m/s at Alemketema, Debreberhan, Eneware, Gundemeskel, Majete and Mehalmeda respectively. The extrapolated result shows that the maximum wind speed and power densities at a height of 50 meter for Eneware, MehalMeda and Debre Berhan are 10.4 m/s, 689 W/m2, 8.9m/s, 429 W/m2 and 6.3m/s, 152.2 W/m2 respectively. The energy densities at a height of 50 meter for AlemKetema, DebreBerhan, Eneware, GundoMekel, Majete, and MehalMeda are 181.06 kWh/m2*yr , 1332.83 kWh/ m2*yr, 6078.91 kWh/ m2*yr, 857.76 kWh/ m2*yr, 58.78 kWh/ m2*yr, and 3939.42 kWh/ m2*yr respectively. As per the international standard for wind power and wind speed classification, considering the extrapolated height, Eneware, MehalMeda, and DebreBerhan have feasible wind potential for both small-scale and large-scale wind farm implementation, whereas Majete, AlemKetema, and GundoMeskel of the North Shewa Zone in the Amhara region have poor wind potential.

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen enriched compressed natu-ral gas (HCNG). It is also injected into the natural gas pipeline network to realize the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load peak and trough period is adjusted, and large-scale consumption of renewa-ble energy and the reduction of carbon emissions are realized. First of all, a fine model of hydro-gen production and hydrogen use equipment is established to analyze the impact of adding hy-drogen mixing on the economy and low-carbon of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utiliza-tion rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment opera-tion cost, carbon emission cost, wind curtailment cost and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natu-ral gas was established. The model was linearized and solved by using MATLAB and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Previous studies have demonstrated that cold pools play a pivotal role in the initiation and organization of convection, yet their influence on the evolution of GFs remains inadequately understood. A destructive wind event associated with a rearward gust front (RGF, 8 grade gale appeared after passing GF) and a prior gust front (PGF, 10 grade gale appeared before passing GF) over the north coast of China on 10 June 2016 was analyzed. Using multiple observation data as well as the four-dimensional Variational Doppler Radar Data Assimilation System (VDRAS), we found that the intensity of the GFs increased with increasing cold pool strength, depth and propagation speed. Two schematic models were proposed to explain the discrepancy between GFs and associated cold pools. The depth and intensity of the cold pool in RGF are found to be relatively shallower and weaker, leading to a correspondingly reduced strength in both outflow and convergence. In contrast, the enhanced vertical shear and boundary northeaster inflow of PGF generate intensified and more organized downdrafts, resulting in a deeper cold pool, robust outflow, and convergence. These findings offer novel insights into the diversity of GFs and associated cold pools.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

A Savonius wind turbine is one of the simplest vertical-axis turbines. Several researchers have been attracted to it because of its simple structure, low noise, appropriate torque, and independence from wind direction. SR3345 and SR5050 blade profiles will be investigated for their effects on the performance of Savonius wind turbines. First, the fabricated wind turbine is placed in front of an open circuit wind tunnel with a velocity of 6 m/s. Furthermore, governing relationships are used to calculate power and torque coefficients as well as tip speed. In this study, the power and torque coefficients of the turbine are compared with the two profiles considered based on overlap ratios between 0 and 0.3. Despite its increased stability, the central shaft reduced the power coefficient of the turbine. For blade SR3345, the best performance occurs at tip speed ratios of less than 1, while for blade SR5050, the best performance occurs at higher tip speed ratios. Both blades SR3345 and SR5050 achieve the highest power coefficients at overlap ratios of 0 and 0.18 when a central shaft is present.

This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical economic and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university's electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, environmental factors, and addressing unmet load, this research contributes to the advancement of renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University in its search of energy resilience and environmental surveillance which has a total peak load of 9.958MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of 30M$, with a levelized energy cost (LCOE) of 0.00274$/kWh. In addition, the minimum desired load will be unmeted on some days of September.

The accurate of model attitude angel plays an important role on the aerodynamic test results in the wind tunnel test. The original method applies the spherical coordinate system transformation to obtain attitude angel calculation. The model attitude angel is obtained by coordinate transformation and spherical surface mapping applying the nominal attitude angel (the balance attitude angel in the wind tunnel coordinate system) indicated by the mechanism. First, the coordinate transformation of this method is not only complex but also difficult to establish the transformed relationship between the space coordinate systems especially after many steps of coordinate transformation, moreover it cannot realize the iterative calculation of the interference relationship between attitude angels; Second, during the calculate process to solve the problem the arc is approximately used to replace the straight line, the angel for the tangent value, and the inverse trigonometric function is applied. Therefore, in the calculation of attitude angel, the process is complex and inaccurate, which can be solved approximately when calculating small attack angel. However, with the advancing development of modern aerodynamic unsteady research, the aircraft tends to develop high or super large attack angel and unsteady research field. According to engineering practice and vector theory, the concept of vector angel coordinate system is proposed for the first time, and the vector angel coordinate system of attitude angel is established. With the iterative correction calculation and avoiding the problem of approximate and inverse trigonometric function solution, the model attitude calculation process is carried out in detail, which validates that the calculation accuracy and accuracy of model attitude angels are improved. Based on engineering and theoretical methods, a vector angel coordinate system is established for the first time, which gives the transformation and angel definition relations between different flight attitude coordinate systems, that can accurately calculate the attitude angel of the corresponding coordinate system and determine its direction, especially in the channel coupling calculation, the calculation of the attitude angel between the coordinate systems is only related to the angel, and has nothing to do with the change order s of the coordinate system, which simplifies the calculation process.

This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model (Technical Report Risø-M; No. 2411, 1983), which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.

Steady-state Reynolds-Averaged Navier-Stokes (RANS) simulations are performed for a leading-edge inflatable wing for airborne wind energy applications. Expanding on previous work where only the inflatable leading edge tube was considered, eight additional inflatable strut tubes that support the wing canopy are now included. The shape of the wing is considered to be constant. The influence of the strut tubes on the aerodynamic performance of the wing and the local flow field is assessed, considering flow configurations with and without side-slip. The simulations show that the aerodynamic performance of the wing decreases with increasing side-slip component of the inflow. On the other hand, the chordwise struts have little influence on the integral lift and drag of the wing, irrespective of the side-slip component. The overall flow characteristics are in good agreement with previous studies. In particular, it is confirmed that at a low Reynolds number of Re=10^5, a laminar separation bubble exists on the suction side of this hypothetical rigid wing shape with perfectly smooth surface. The destruction of this bubble at low angles of attack impacts negatively on the aerodynamic performance.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.

This work presents an alternative to harnessing wind energy with an Electromagnetic Frequency Regulator (EFR) coupled to a hydrostatic transmission and associated with a horizontal axis wind turbine, a bidirectional frequency inverter and a secondary energy source, in a hybridized system. The hydrostatic transmision is composed by a fixed displacement axial piston pump and a variable displacement, swash plate, axial piston motor. Feedback linearization was used as a technique to control the motor geometric displacement, and a prediction algorithm for the steady state rotations of the armature and the electromagnetic field has been developed. A 5\,kW project was simulated on a Scilab platform, with combinations of constant or variable load, and constant or variable wind speed. The results indicated that the system was able to supply the generator load, adapting to fluctuations in wind speed. The possibility of storing wind energy through the inverter has also been proven. The system can accumulate energy in batteries during the fastest wind regimes, to use it when the turbine power is lower than the load.

This work presents an application called APPMAR 1.0 based on Python ® environment, built to perform the downloading, treatment and analysis of meteorological and marine information. This application is composed of two main modules: the first module allows the downloading of information from the database (NOAA - WW3); the second module uses the principles of statistical mathematics for the treatment of waves and wind. The importance of this simple application is based on the free and agile access to meteorological and marine information for a coastal project. The determination of representative conditions of sea states ultimately will govern the process of design of coastal and oceanic infrastructure. The analysis of historical time series of local waves and winds allows the evaluation of average regimes or operational design, the ultimate limit states or extreme design, and the storms or design by persistence. In spite that the former analysis is a common task for coastal engineers, the codes generated are seldom shared for public use. In summary, for operational purposes is useful to have a freeware that can assist in the data processing for decision making and forcing of the mathematical models that are part of the common practice of coastal, oceanic and offshore engineering. This application has been tested in the Caribbean area of Colombia where meteorological and marine information are scarce.

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

The Wind Energy Conversion System (WECS) plays an inevitable role across the world. In particular, the attention for Permanent Magnet Synchronous Generators (PMSGs) connected with wind farm is popular. This paper deals with the literature review that describes the recent advances, progresses and innovatory trends on PMSGs for WECS. Comparison between geared and direct-driven conversion systems and the classification of electrical machines used in WECS are discussed. A detailed analysis on the design aspects considering various topologies of PMSGs are encompassed in the literature. The PMSG design and optimization problems are solved by field computation techniques and optimized by using Soft Computing (SC) techniques .The three-dimensional, finite element software platform for the analysis and design of PMSGs is discussed. This paper also deals with the interdisciplinary modeling, analysis, and optimization of PMSG using Finite Element Analysis (FEM) and SC techniques. Finally, PMSGs are reviewed and compared for further exploration.

This research investigates the economic resilience of small and medium enterprises (SMEs) during financial crises, focusing on the factors that enable these businesses to survive and recover from economic downturns. Employing a qualitative methodology, the study examines various aspects of SME operations, including financial management, innovation, leadership, government support, geographical location, social capital, and contingency planning. The findings reveal that effective financial management—encompassing cash flow management, debt restructuring, and diversified funding sources—is crucial for maintaining stability during financial crises. Innovation and adaptability also play significant roles, with SMEs that embraced new technologies and market strategies demonstrating greater resilience. Leadership emerged as a critical factor, with leaders who communicated effectively and managed stress contributing to their organization's ability to navigate crises. Government support provided essential relief, although its impact varied depending on accessibility and timeliness. Geographical location influenced resilience, with urban SMEs having better access to resources, while rural SMEs benefited from strong community networks. Social capital, including relationships with suppliers and customers, was vital for providing stability and support. Contingency planning and risk management were identified as essential for preparing for and responding to disruptions. The research highlights that resilience is a multifaceted attribute resulting from the interplay of financial prudence, adaptability, effective leadership, supportive policies, geographic factors, and strong social networks. These insights offer valuable lessons for SME owners, policymakers, and support organizations, emphasizing the need for a comprehensive approach to building and sustaining resilience in the face of economic challenges.

This paper investigates the strategic dimensions influencing the adoption of environmental inno-vations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. The study aims to understand the fundamental strategic dimensions defining eco-innovation and ex-amine how SMEs manage the complexities of incorporating these practices. Employing a mixed methods approach with documentary analysis and expert interviews; the study identifies key factors influencing EI adoption through causal maps and the Viable System Model. Findings high-light the significance of external and internal factors such as competitive pressure, economic con-straints, technological capabilities, political support, and social influence. This integrative approach emphasizes the importance of adaptability and responsiveness, presenting eco-innovation as a source of sustainable competitive advantage for SMEs.

Non-Small Cell Lung Cancer (NSCLC) is the most common pulmonary malignancy, most frequently diagnosed at an advanced stage (III/IV). Patients in the Locally-Advanced Stage Subgroup (IIIA) are relatively few, yet compose of heterogenic phenotypes posing a diagnostic and treating challenge, leading to lack of clinical guidelines regarding the optimal standard of care. Several approaches exist, with a general agreement that a combined oncological and surgical modality approach is required. In this current retrospective descriptive study, operable Stage IIIA NSCLC patients who underwent surgery between 2013-2020 were evaluated on several aspects, including initial diagnosis, neoadjuvant regimens, outcomes of surgical intervention, overall survival at 2-years and 5-years following treatment. A total of 35 patients had neoadjuvant oncological treatment (mostly chemoradiation therapy) prior to surgery, out of which 28 patients were diagnosed with Stage IIIA NSCLC. In post-operative assessment of pathological staging, downstaging was reported in 20 patients, of which 7 (25%) cases were defined as complete pathological response. The 2-years overall survival rate was 65% and the 5-years overall survival rate was 60%. The main pattern of disease recurrence was distant metastasis.

Аnalysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that holds potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods. The source code can be accessed at https://github.com/zajakin/sRNAflow under the GPL3 licence.

Different prognostic scores have been applied to identify patients with non-small cell lung cancer who have a higher probability of poor outcome. In this study we evaluated whether Naples Prognostic Score, a novel index which considers both inflammatory and nutritional values, was associated with long-term survival. The study is a retrospective propensity score matching analysis of patients who underwent curative surgery for non-small cell lung cancer from January 2016 to December 2021. The score considered four pre-operative parameters: neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, serum albumin and total cholesterol. Kaplan-Meier method and Cox regression analysis were performed to evaluate the relation between the score and disease-free survival, overall survival and cancer-related survival. A total of 260 patients were selected for the study, reduced to 154 after propensity score matching. Post-propensity Kaplan-Meier analysis showed a significant correlation between Naples Prognostic Score and overall survival (p=0.018) and cancer-related survival (p=0.007). Multivariate Cox regression analysis further validated the score as an independent prognostic indicator for both these survivals (p=0.007 and p=0.010 respectively). Naples Prognostic Score proved to be an easily achievable prognostic factor of long-term survival in patients with non-small cell lung cancer after surgical treatment.

The residual content of the only carbon source increases at the end of the exponential growth phase when P. putida BS3701 cells are cultured under nitrogen deficiency. Moreover, the mRNA quantity of some genes involved in carbon catabolism decreases. There are no available data on regulators capable of stimulating the target mRNA degradation in pseudomonads under nitrogen deficiency in the literature. We have applied both comparative genomics methods and a number of biomolecular ones to identify new small P. putida BS3701 RNAs. This is due to the fact that the formation of ncRNA-mRNA duplexes, recognized by RNases, is one of the main mechanisms for reducing the number of transcripts in a cell. We have used the following selection criteria: 1) the presence of the interest gene of the NtrC binding site and a sigma54-dependent promoter in the upstream area, 2) the conservative location of the interest gene in the P. putida and P. aeruginosa genomes, 3) increase in the product quantity of the interest gene in a cell under nitrogen deficiency on two different carbon sources (succinate and naphthalene), 4) the product detection of the interest gene in a fraction enriched with small RNAs. The expression of two new small RNAs has increased twice or more times under nitrogen deficiency on both succinate and naphthalene: expression of ng171 and ng379. ng379 contains narK RNA motif.

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Concatemeric viral DNA is packaged into Bacteriophage P22 procapsids via a headful packaging mechanism mediated by a molecular machine consisting of small (gp3) and large (gp2) terminase subunits. Although a negative stain reconstruction exists for the terminase holoenzyme, it is not clear how this complex binds dodecameric portal protein located at a 5-fold mismatch vertex. Herein, we describe new assemblies for the holoenzyme. Both native mass spectrometry and transmission electron microscopy reveal that the P22 terminase complex adopts three main assemblies, which include a nonameric S-terminase bound to two L-terminase 1(gp3)9:2(gp2), two nonameric S-terminase bound to five L-terminase 2(gp3)9:5(gp2), and three nonameric S-terminase bound to seven L-terminase 3(gp3)9:7(gp2). Native agarose gel electrophoresis shows that the terminase complex interacts with procapsid with mild crosslinking. These results herein illustrate the P22 terminase complex can adopt a variety of confirmations and assembly states.

Autoantibodies against specific lung cancer-associated antigens have been suggested for lung cancer diagnosis. This study aimed to evaluate the diagnostic performance of anti-gen-autoantibody immune complex (AIC) against its free antigens for CYFRA21-1, ProGRP, NGAL, and NSE in non-small cell lung cancer (NSCLC). In total, 85 patients with NSCLC and 120 healthy controls (HCs) were examined using a 9G DNA chip method. The ratios of AICs to their antigens and combinations of ratios consisting of two to four markers were calculated. The levels of AICs for CYFRA21-1, ProGRP, NGAL, and NSE were higher than their free antigens in all participants. The levels of each free antigen distinguished patients with NSCLC from HCs. The ratios of AIC to its antigen and seven combinations consisting of two to four ratios were significantly higher in patients with NSCLC than those in HCs. Excellent diagnostic performance was observed for all combination ratios (C4-1), with 85.9% sensitivity and 86.7% specificity at cutoff 3.51. Higher sensitivity was observed in the very early stages (0–I) and adenocarcinoma than in stages II–IV and other pathological types. The combination of all ratios of AICs and their antigens for all four markers is useful in the diagnosis of NSCLC.

Phylogenetic tree analysis is commonly used to understand the evolutionary distance between different species or strains of the same species. This work originally aimed to gain an understanding and visual depiction of the evolutionary distance in 23S rRNA gene of different Helicobacter pylori strains. Using an in-house MATLAB multiple sequence alignment and phylogenetic tree analysis software, the phylogenetic tree constructed for 23S rRNA gene reveals two major lineages with further subdivisions in each cluster. This suggests two major evolutionary forces driving the evolution of the 23S rRNA gene. In comparison, there is relatively short evolutionary distance in the 16S rRNA gene phylogenetic tree of H. pylori, suggesting lack of major evolutionary forces driving mutational changes of the gene. Overall, the data suggests that the large ribosome subunit which houses the 23S rRNA of H. pylori is undergoing significant conformational changes that may be a response to the frequent use of various antibiotics that target the ribosome of H. pylori.

Abstract: Pericyte(s) (Pcs) and resident perivascular macrophages (rPVMΦs) are positioned perfectly in the neurovascular unit (NVU) and perivascular spaces to facilitate metainflammation that results in brain endothelial cell activation and dysfunction and neuroinflammation. Their positions within the NVU allow intimate contact with one another as follows: Brain endothelial cells (BECs) and the Pcs via their shared basement membrane and physical contact peg-socket junctions with N cadherins and gap junctions Cx43; Pcs and rPVMΦs intimate contacts. Additionally, rPVMΦs have intimate contact with the astrocyte endfeet (ACef) that form the outermost membrane of perivascular spaces. Importantly, ACef have intimate contact with BECs that have intimate physical contact with neuronal axons and dendrites to complete NVU coupling. The multiplicity of intimate contacts of NVU cells allow for continuous crosstalk communications to provide brain homeostasis. While each of the cells of the NVU play important roles in the development of enlarged perivascular spaces (EPVS), this review focuses on the Pcs and rPVMΦs and discusses each of the intimate contacts and their functional significance in detail with numerous illustrations and transition electron microscopic images to demonstrate their role in the development of EPVS. EPVS are known to be biomarkers for cerebral small vessel disease and impaired glymphatic system waste clearance. Additionally, EPVS associate with mixed dementias of vascular dementia, vascular contributions to cognitive impairment and dementia, and Alzheimer’s disease that results in high economic and psychosocial cost to the global community.

In the vaginal ecosystem there is a symbiotic relationship between the microbiota and the host, particularly Candida spp., which are considered commensals of the vaginal mucosa. During pregnancy there is an increase in hormonal secretion that alters the vaginal microbiota and favors the development of Candida infection. Several studies have shown the increased prevalence of Candida during pregnancy, as well as its association with perinatal complications and current clinical practice guidelines recommend fluconazole as the treatment of choice during pregnancy in case of infection by Candida albicans and non-albicans Candida. However, in the Mexican population, this association has not been studied. So in this study, an intentional search for colonization by Candida spp. in pregnant women who attended the obstetric triage of the Hospital Regional de Alta Especialidad de Ixtapaluca during the period from May to October 2019, and they were followed up until the end of the pregnancy. Data on age, number of pregnancies, gestational age, body mass index, and perinatal outcomes were analyzed. Of 83 patients included in the study, yeasts corre-sponding to C. albicans (12) and C. glabrata (4) were isolated in 16. In the group of patients with Candida, the maternal age range was from 15 to 36 years old, of which 7 were adolescents and 2 with advanced maternal age, with an average of 2 gestations, the range of body mass index was from 19.3 to 42.4, of which 4 were overweight and 7 obese. Perinatal complications were observed in three patients, one presented chorioamnionitis and in two the neonates were small for gestational age. No association was found between colonization by Candida spp. and the number of preg-nancies, gestational age, body mass index or perinatal results. However, it was found that age (adolescence) may be a risk factor for the development of vulvovaginitis during pregnancy.

Cystic Fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the CFTR gene, coding for the CFTR chloride channel. About 10% of the CFTR gene mutations are "stop" mutations, which generate a Premature Termination Codon (PTC), thus synthesizing a truncated CFTR protein. A way to bypass PTC relies on ribosome readthrough, which is the ribosome’s capacity to skip a PTC, thus generating a full-length protein. “TRIDs” are molecules exerting ribosome readthrough; for some, the mechanism of action is still under debate. We investigate a possible mechanism of action (MOA) by which our recently synthesized TRIDs, namely NV848, NV914, and NV930, could exert their readthrough activity by in silico analysis and in vitro studies. Our results suggest a likely inhibition of FTSJ1, a tryptophan tRNA-specific 2’-O-methyltransferase.

Most pivotal clinical trials in advanced non-small cell lung cancer (NSCLC) have excluded patients with poor performance status (PS), and data on the efficacy and safety of pharmacotherapy have not been fully accumulated. For NSCLC patients with PS 2 and without druggable genetic alterations, monotherapy with cytotoxic agents or carboplatin-based combination therapy is usually administered based on the results of several randomized trials. However, the evidence of cytotoxic chemotherapy for patients with PS 2 is insufficient, with limited efficacy and toxicity concerns. Immune checkpoint inhibitors (ICIs) are a promising treatment for patients with PS 2 because of lower incidence of severe toxicity compared to cytotoxic chemotherapy. Meanwhile, several reports suggest that anti-PD-1 antibodies monotherapy is less effective for patients with PS 2, especially for those with PS 2 caused by disease burden. Although the combination therapy of nivolumab and ipilimumab is a promising treatment option, there is a divergence in efficacy data between clinical trials. The standard of care for advanced NSCLC with PS 2 has not been established, and future therapeutic strategies should take into account the heterogeneity of the PS 2 population.

Abstract Kidney transplant recipients, because of a weak immune response due to the assumption of immunosuppressant are exposed to the risk of COVID-19 infection. This fact realize the problem on how to treat the severe infection without carrying the risk of acute rejection due to the reduction of the immunosuppressive drugs. The best are the prophylactic measures to be taken before transplantation as vaccination. If the patient is already transplanted, three measures may be undertaken: Vaccination, use of monoclonal antibodies, use of therapeutic antiviral small molecules. Concerning vaccination is still debated which one is the best and how many doses should be given. The surge of new virus variant is the major problem and invites to find new active vaccines. In addition, not all the transplanted patients develop antibodies. The other measure is the use of monoclonal antibodies. They may be used as prophylaxis or in the early stage of the disease. Finally, the antiviral small molecules may be used again as prophylaxis or treatment. Their major drawback are the interference with the immunosuppressive drugs and the fact that some of them cannot be administered to patients with low eGFR.

One of the most important challenges in the Machine and Deep Learning areas today is to build good models using small datasets, because sometimes it is not possible to have large ones. Several techniques have been proposed in the literature to address this challenge. This paper aims at studying the different available Deep Learning techniques and performing a thorough experimentation to analyze which technique or combination thereof improves the performance and effectiveness of the models. A complete comparison with classical Machine Learning techniques was carried out, to contrast the results obtained using both techniques when working with small datasets. Thirteen algorithms were implemented and trained using three different small datasets (MNIST, Fashion MNIST, and CIFAR-10). Each experiment was evaluated using a well-established set of metrics (Accuracy, Precision, Recall, F1, and the Matthews correlation coefficient). The experimentation allowed concluding that it is possible to find a technique or combination of them to mitigate a lack of data, but this depends on the nature of the dataset, the amount of data, and the metrics used to evaluate them.

The rural population percentage decreased from 82.7% to 68.9% in 2011, even though there is an increase in the total rural population, which stands at 833.7 million, and the rural population were now more than three times compared to the population seven decades ago. Another observation is the decrease in cultivators percentage from 71.9% to 45.1 %, while agriculture labour increase from 28.1% to 54.9% during the same period. Despite the increase in irrigated land and net area sown, the average holdings' size under the farmers is continuously decreasing, and it requires a study to look into the reasons. The research probes the role of Minimum Support Price (MSP) in supporting farmers and measuring market price above MSP needed to help marginal and small farmers remain above the poverty level. It explains how different market rates above MSP have a different impact on different categories of agriculture landholding. The study works on developing a common model that relates the impact of MSP on different farmers categories. The model can be generalized to all crops and regions and useful in designing policies that focus on uplifting the income of agricultural farmers.

The open-bundle structure of cytochrome c’ as an unfolding intermediate was determined by small-angle neutron scattering experiment (SANS). The four-α-helix bundle structure of Cyt c’ at neutral pH was transited to an open-bundle structure (at pD ~13), which is a joint-clubs consisting of four clubs (α-helices) connected by short loops. The compactly folded structure of Cyt c’ (radius of gyration, Rg = 18 Å for the Cyt c’ dimer) at neutral or mildly alkaline pD transitioned to a remarkably larger “open-bundle” structure at pD ~13 (Rg = 25 Å for the Cyt c’ monomer). Cyt c’ adopts an unstructured random coil structure at pD = 1.7 (Rg = 25 Å for the Cyt c’ monomer). Numerical partial scattering function analysis (joint-clubs) and ab initio modelling gave structures similar to the “open-bundle”, which retains the α-helices but loses the bundle structure.

Indian cities are increasingly becoming susceptible to PM10 induced health effects which have become a matter of concern for the policymakers of the country. Air pollution is engulfing the comparatively smaller cities as the rapid pace of urbanization, and economic development seems not to lose steam ever. A review of air pollution of 28 cities of India, which includes tier-I, II, and III cities of India, found to have grossly violated both WHO and NAAQS standards in respect of acceptable daily average PM10 concentrations by a wide margin. Predicting the city level PM10 concentrations in advance and accordingly initiate prior actions is an acceptable solution to save the city dwellers from PM10 induced health hazards. Predictive ability of three models, linear MLR, nonlinear MLP (ANN), and nonlinear CART, for one day ahead PM10 concentration forecasting of tier-II Guwahati city, were tested with 2016-2018 daily average observed climate data, PM10, and gaseous pollutants. The results show that the non-linear algorithm MLP with feedforward backpropagation network topologies of ANN class, giving the best prediction value when compared with linear MLR and nonlinear CART model. ANN (MLP) approach, therefore, may be useful to effectively derive a predictive understanding of one day ahead PM10 concentration level and thus provide a tool to the policymakers for improving decision-making associated with air pollution and public health.

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5~2.5 cm and a large void ratio of 18~25%. As a newly asphalt mixture, the structure characteristics differ from traditional pavement. Therefore, it is necessary to investigated the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compactness of the Marshall sample. With the same temperature and number of blows, the scattering resistance of coarse gradation (PAC-1) is better than that of fine gradation (PAC-2), and the increased asphalt viscosity significantly improves the scattering resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage and skid resistance.

The paper presents a new vision on the energy consumption management in the case of the Small and Medium Enterprises (SMEs), integrated into an advanced decision support platform, with technical and economic benefits on increasing the energy efficiency, which contains modules for database management, profiling, forecasting, and production scheduling. Inside each module, Artificial Intelligence and Data Mining techniques were proposed to remove the uncertainties regarding the dynamic of technological flows. Thus, the data management module includes the Data Mining techniques, that extract the technical details on the energy consumption needed in the development of production scheduling strategies, the profiling module uses an original approach based on clustering techniques to determine the typical energy consumption profiles required in the optimal planning of the activities, the forecasting module contains a new approach based on an expert system to forecast the total energy consumption of the SMEs, and production scheduling module integrates a heuristic optimization method to obtain the optimal solutions in flattening the energy consumption profile. The testing was done for a small enterprise from Romania, belonging to the domain of trade and repair of vehicles. The obtained results highlighted the advantages of the proposed decision support platform on the decrease in the intensity of energy consumption per unit of product, reduction of the purchase costs, and modification of the impact whom the energy bills have on the operational costs.

Ebola Virus Disease (EVD) is one of the most lethal transmissible infections characterized by a high fatality rate, and caused by members of the Filoviridae family. The recent large outbreak of EVD in West Africa (2013-2016), highlighted the worldwide danger of this disease and its impact on global public health and economy. The development of highly needed anti-Filoviridae antivirals has been so far hampered by the shortage of tools to study their life cycle in vitro, and therefore screen for potential active compounds outside a biosafety level-4 (BSL-4) containment. Importantly, the development of surrogate models to in vitro study of Filoviridae entry in a BSL-2 setting, such as viral pseudotypes and Ebola virus like particles, tremendously boosted both our knowledge on viral life cycle and the identification of promising anti-Filoviridae compounds interfering with viral entry. In this context, the combination of such surrogate systems with large-scale small molecule compounds and haploid genetic screenings, as well as rational drug design and drug repurposing approaches will prove priceless in our quest for the development of a treatment for EVD.

Discovery of inhibitors for endothelial-related transcription factors can contribute to the development of angiogenic therapies that treat diseases ranging from cardiovascular to cancer. The role of transcription factor Vezf1 in vascular development and regulation of angiogenesis has been defined by several earlier studies. Through construction of a computational model for Vezf1, work here has identified a novel small molecule drug capable of inhibiting Vezf1 from binding to its cognate DNA binding site. Using structure-based design and virtual screening of the NCI Diversity Compound Library, 12 shortlisted compounds were tested for their ability to interfere with the binding of Vezf1 to DNA using electrophoretic gel mobility shift assays. We identified one compound, T4, which has an IC50 of 20uM. Using murine endothelial cells, MSS31, we tested the effect of T4 on endothelial cell viability and angiogenesis by using tube formation assay. Our data show that addition of T4 in cell culture medium does not affect cell viability at concentrations lower or equal to its IC 50 but strongly inhibits the network formation by MSS31 in the tube formation assays. Given its potential efficacy, this inhibitor has significant therapeutic potential in several human diseases raging from wound healing to cancer.

The World Health Organization (WHO) recently added snakebite envenoming to the priority list of Neglected Tropical Diseases (NTD). It is thought that ~75% of mortality following snakebite occurs outside the hospital setting, making the temporal gap between a bite and antivenom administration a major therapeutic challenge. Small molecule therapeutics (SMTs) have been proposed as potential pre-referral treatments for snakebite to help address this gap. Herein, we discuss the characteristics, potential uses and development of SMTs as potential treatments for snakebite envenomation. We focus on SMTs that are secretory phospholipase A2 (sPLA2) inhibitors and metalloprotease (MP) inhibitors.