UNASSIGNED: Scientific control of wind speed between tobacco leaves can improve quality of tobacco after cured in bulk curing barn.
UNASSIGNED: Regarding the light orange color after cured tobacco and high power consumption caused by inaccurate control of the current circulating fan, and different requirements for wind speed during the tobacco curing, this paper designed a variable frequency drive system (VDS) to control the rotation speed of the circulating fan. With the design of anti-interference components, the VDS is integrated with the curing controller.
UNASSIGNED: The curing adoption of anti-interference for the VDS device will not affect the normal operation of the tobacco curing controller during tobacco curing. Compared with the traditional circulating fan with high speed and low speed options, the VDS device can accurately control the air speed during the tobacco curing process, and improve the economic characters and internal quality of cured tobacco. This paper is also to provide an alternative method in terms of ventilation and humidity removal for industrial and agricultural production.

This study investigates the relationship between wind speed, climatic conditions, and road accidents in Iran, focusing on the type of accidents and collisions. The research aims to identify the causes of accidents and provide insights for prediction and decision-making purposes. The study adopts a developmental research approach, analyzing road accident data and wind speed data. Logistic regression is employed to investigate the correlation between wind speed and the type of accidents and collisions. Data mining techniques, specifically the J48 decision tree algorithm, are used to examine the relationship patterns among wind speed, climatic conditions, collision types, and accident types. Additionally, texts and articles related to atmospheric hazards and road accidents are studied, and interviews are conducted with road accident experts and drivers to extract insights into the causes of road accidents in Iran. The findings indicate that wind speed does not have a direct and significant effect on the type of accidents (fatal or non-fatal), but it does influence the type of collisions in road accidents. The decision tree analysis reveals patterns in the relationships between weather conditions, wind speed, collision types, and accident types, enabling the prediction of collision probabilities in different scenarios. The causes of road accidents in Iran are categorized into human factors, secondary causes, and unique causes. Based on the findings, several recommendations are proposed to enhance road safety and reduce accidents in Iran.

As the world warms, it will be tempting to relate the biological responses of terrestrial animals to air temperature. But air temperature typically plays a lesser role in the heat exchange of those animals than does radiant heat. Under radiant load, animals can gain heat even when body surface temperature exceeds air temperature. However, animals can buffer the impacts of radiant heat exposure: burrows and other refuges may block solar radiant heat fully, but trees and agricultural shelters provide only partial relief. For animals that can do so effectively, evaporative cooling will be used to dissipate body heat. Evaporative cooling is dependent directly on the water vapour pressure difference between the body surface and immediate surroundings, but only indirectly on relative humidity. High relative humidity at high air temperature implies a high water vapour pressure, but evaporation into air with 100% relative humidity is not impossible. Evaporation is enhanced by wind, but the wind speed reported by meteorological services is not that experienced by animals; instead, the wind, air temperature, humidity and radiation experienced is that of the animal\'s microclimate. In this Commentary, we discuss how microclimate should be quantified to ensure accurate assessment of an animal\'s thermal environment. We propose that the microclimate metric of dry heat load to which the biological responses of animals should be related is black-globe temperature measured on or near the animal, and not air temperature. Finally, when analysing those responses, the metric of humidity should be water vapour pressure, not relative humidity.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

Human activities and climatic elements, including temperature, humidity, and wind speed, have an impact on natural dust deposition. Therefore, this study aims to investigate the effects of wind speed, relative humidity, and ambient temperature on the performance of soiled photovoltaic panels in Al Seeb, Oman. The study was conducted by exposing the solar PV panels to outdoor sunlight for a duration of two months. Parameters such as solar radiation, voltage, current, solar panel temperature, wind speed, relative humidity, and ambient temperature were collected in a short time interval. It was observed that the dust densities of 20.7 g/m2, 27 g/m2, and 41.3 g/m2 resulted in electrical power reductions of 18 %, 33 %, and 40 % for the panels uncleaned for one week, two weeks, and three weeks, respectively. The effect of daily dust resulted in an energy reduction of 14 %. Moreover, dust deposition decreases when the wind speed increases, resulting in a higher power output and vice versa. The higher the humidity, the stronger the dust\'s adhesion to the surface, resulting in more deposition and reduced power output. The maximum power output of 82.3 W was achieved at the wind speed of 10 m/s, 34.9 % relative humidity, and ambient temperature of 38.5 °C.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

The rise in temperatures and changes in other meteorological variables have exposed millions of people to health risks in Bangladesh, a densely populated, hot, and humid country. To better assess the threats climate change poses to human health, the wet bulb globe temperature (WBGT) is an important indicator of human heat stress. This study utilized high-resolution reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) to analyze the spatiotemporal changes in outdoor WBGT across Bangladesh from 1979 to 2021, employing Liljegren\'s model. The study revealed an increase in the annual average WBGT by 0.08-0.5 °C per decade throughout the country, with a more pronounced rise in the southeast and northeast regions. Additionally, the number of days with WBGT levels associated with high and extreme risks of heat-related illnesses has shown an upward trend. Specifically, during the monsoon period (June to September), there has been an increase of 2-4 days per decade, and during the pre-monsoon period (March to May), an increase of 1-3 days per decade from 1979 to 2021. Furthermore, the results indicated that the escalation in WBGT has led to a five-fold increase in affected areas and a three-fold increase in days of high and extreme heat stress during the monsoon season in recent years compared to the earlier period. Trend and relative importance analyses of various meteorological variables demonstrated that air temperature is the primary driver behind Bangladesh\'s rising WBGT and related health risks, followed by specific humidity, wind speed, and solar radiation.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5 minutes, 30 minutes, and 1 hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.