Weather Factors Research Articles

INOVASI PRODUK OLAHAN DAUN MANGROVE MENJADI CEMILAN PEYE MANGROVE DALAM MENINGKATKAN NILAI JUAL PADA PELAKU UKM DESA KRONJO KABUPATEN TANGERANG PROVINSI BANTEN

The economy of the people in Indonesia is currently still not at the stage of stability, because there are still many home industry business actors who have modest capital and choose to develop home industry businesses from generation to generation. In this community service activity, the team carried out activities in micro businesses on processed mangrove leaf products that were used as peyek. One of the community activities in Kronjo Village, Tangerang Regency, Banten Province whose life is still around the coast is not difficult to find mangrove trees. In the production of processed products in the form of mangrove peyek, difficulties were found that resulted in the production of mangrove peyek not being maximized, including: raw materials for young mangrove leaves which are difficult to obtain due to bad weather factors or long drought and the community around the beach does not know that mangrove leaves can be used as snacks (snacks). Therefore, there is a need for socialization and a touch of innovation in making mangrove leaf peyek so that the public is more familiar with this processed product. The purpose of this PKM is to innovate more innovative mangrove leaf processing. The method carried out in this PKM activity is by providing assistance, increasing production quantity, providing socialization to the community about processed mangrove leaf products, and introducing online marketing through applications or websites. The final result that is expected in this PKM activity is an increase in the sales of mangrove farmers and the public is more familiar with processed products from mangroves so that new SME marketing can be created that is more innovative and can be developed to a sustainable stage.

Association of meteorological variables with leaf spot and fruit rot disease incidence in eggplant and YOLOv8-based disease classification

Eggplant is one of the major vegetables consumed worldwide. Several fungal, bacterial, and viral diseases challenge the yield and quality of eggplant. The incidence of plant diseases is strongly influenced by weather factors such as temperature, humidity, rainfall, and wind speed. Mattu Gulla (MG) is a GI-tagged traditional variety of eggplant grown in Mattu village of the Udupi district in Karnataka state, India, with a cultural legacy of more than four centuries. In this study, we investigated the relationships between weather parameters and disease incidence in Mattu Gulla. Leaf spot (LS) and fruit rot (FR) are the major diseases affecting this plant variety. The influence of plant age and weather parameters on the modulation of the disease incidence (%) [DI (%)] of leaf spot and fruit rot was recorded and analyzed via correlation and regression. Prediction equations for disease incidence was derived via regression. A significant negative correlation was observed between the leaf spot DI (%) and minimum temperature (Min. temp), and a positive correlation was observed between the DI (%) and fruit rot. In the case of FR, the DI (%) is also significantly positively correlated with wind speed (WS), temperature, maximum relative humidity (RH I), rainfall (RF), and wind speed (WS). An RH I of 86–87 % was favorable for the incidence of fruit rot in the field. Regression analysis revealed a significant association between Min. temp and leaf spot DI (%), and in the case of fruit rot DI (%), the association was with Min. temp and WS. An android application, “Leaf Guard,” has been developed for AI-based disease detection in eggplant. During testing, the accuracy of the trained model reached 98.2 %.

Population dynamics of major insect pest of lentil and correlation with weather factors

Population dynamics of major insect pest of lentil and correlation with weather factors

Surface weathering process of earthen heritage under dry-wet cycles: A case study of Suoyang City, China

Dry-wet cycle is a key factor in surface weathering of earthen heritage, which remains insufficiently explained. It involves the interaction of humidity, stress, and damage. Using the RFPA (realistic failure process analysis) numerical method, this study reproduced the processes of humidity diffusion, deformation, stress, and damage evolution under dry-wet cycles in the soil site of Suoyang City, China. The numerical results indicate that the drying phase following rainfall has the most significant deteriorative impact on the earthen heritage. The evaporation of surface moisture during this phase causes volume shrinkage, which in turn generates tensile stress and leads to the formation of numerous desiccation cracks. Desiccation cracks provide channels for moisture diffusion, which further exacerbates generation of the cracks, leading to a mutual promotion between the two phenomena. Furthermore, during the wetting phase, the model elements undergo hygroscopic expansion, resulting in a slight increase in strain and displacement. Previously formed cracks may exhibit temporary narrowing or closure, but will revert during the subsequent drying phase. Ultimately, the overall displacement increases with the number of dry-wet cycles. The findings provide a theoretical foundation for protection against surface weathering and other damage in earthen heritage in arid regions.

Mechanism for allocating delay to constituent activities in project management

Project delays are common in construction due to unavoidable factors such as severe weather and human factors such as poor planning. These delays originate from the constituent activities within the project. A critical challenge in project management is accurately assigning delays to specific activities. However, this issue remains poorly understood. This study aims to address the allocation of delays to constituent activities by developing a mechanism using the Shapley value. We also propose solution methods to effectively solve the delay allocation problem. Moreover, we conduct numerical experiments that demonstrate the efficiency and applicability of the proposed mechanism and solution methods. This study can provide guidelines for allocating project delays (e.g., identifying the activity that contributes most to project delay) and project appraisal. Overall, this study contributes to a better understanding of the allocation of project delays, providing reference for project management and decision-making in the construction industry.

Impact of weather factors on various soybean varieties under different sowing dates

Impact of weather factors on various soybean varieties under different sowing dates

Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

Research on Changes in Travel Insurance Premiums Driven by Climate Change: A Case Study of Hong Kong Region

This research aims to study the impacts of climate change on worldwide general travel insurance in Hong Kong and to provide a scientific basis for premium pricing strategies in a climate-driven context. The study adopts both qualitative and quantitative research methods to reveal the relationship between climate change and travel insurance. The qualitative analysis part constructs a theoretical framework through literature review. Taking into account the current state of research, the impact of climate change on the demand for travel insurance as well as the challenges and opportunities of premium pricing strategies are discussed in depth. The quantitative analysis section collected data on flights at the Hong Kong International Airport (departures, delays, and cancellations) and travel insurance claim occurrence. The weather conditions were also checked through the weather website to analyze the correlation between climate factors and insurance claims. The findings indicated that climate change has a significant impact on the demand for travel insurance, especially during the prolonged rainy season in Hong Kong, when extreme weather events lead to an increased risk of cancellation or delay of travel plans. Meanwhile, the quantitative analysis results demonstrated a significant correlation between weather factors and flight status and insurance occurrence, which provided empirical evidence for risk assessment and premium pricing of worldwide travel insurance in Hong Kong.

Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors

ABSTRACT The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coupled model for PV power prediction based on TSO-LSTM-XGBoost. This model considers the weather factor using the tuna swarm algorithm(TSO) to optimize the long and short-term memory network model(LSTM) to overcome the shortcomings of blindness and time-consuming in the process of randomly selecting the parameters of the LSTM model. At the same time, using the extreme gradient boosting model (XGBoost), the algorithm is improved and corrected for the large prediction error in cloudy and rainy weather, and the weighted error method is used to couple the model to obtain the final prediction results. Finally, the accuracy of the proposed model is verified by comparing the PV system and meteorological data of a certain region in Shenzhen, China. The results show that the proposed TSO-LSTM-XGBoost coupled model has a value of 71.98 for MAE in cloudy days and 82.09 for MAE in rainy days, and the prediction accuracy is better than PSO-LSTM and LSTM.

The effects of weather factors on road traffic casualties: Analysis on provincial panel data of China from 2006 to 2021

IntroductionRoad traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. MethodThis study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. ResultsThe findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. ConclusionsThese results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

Assessing The Impact of Rising Cost of Materials On Construction Activities in Enugu, Enugu State, Nigeria

This study focused on assessing the impact of rising cost of building materials on construction activities in Enugu, Enugu state, Nigeria. The aim of this study is to assess the impact of rising cost of materials on construction activities in Enugu, Nigeria in order to proffer a solution to the rise in the cost of these materials used in everyday construction in Enugu state. With the following objectives; to identify the factors that lead to increasing cost of material in Enugu State, Nigeria; to assess the impact of rising cost of materials on construction activities in Enugu; to proffer solutions to the rise in the cost of building materials. The research design employed was primary and secondary which encompasses descriptions and quantitative analysis on a randomly selected sample of receipt of building materials from the suppliers over the years. The population of the study is 223 professionals in the field and sample size of 143 which was gotten using Taro Yamane formula. The findings of the study reveals that the factors that lead to increasing cost on the building construction activities in Enugu State are; fluctuations in material costs, design changes and scope creep, regulatory compliance and permitting, project delays, weather and environmental factors, technology and innovation costs, interest rates and financing costs, and others. Finally, the study recommends that there will be need for relevant authorities in the building industry to re-examine and educate the people on its existence and equally generate the possible ways of reducing the increasing cost of building materials so as to enhance the building construction industry in Nigeria.

果园环境中巡检机器人的 SLAM 和路径规划方法研究

Orchard robots play a crucial role in agricultural production. Autonomous navigation serves as the foundation for orchard robots and eco-unmanned farms. Accurate sensing and localization are prerequisites for achieving autonomous navigation. However, current vision-based navigation solutions are sensitive to environmental factors, such as light, weather, and background, which can affect positioning accuracy. Therefore, they are unsuitable for outdoor navigation applications. LIDAR provides accurate distance measurements and is suitable for a wide range of environments. Its immunity to interference is not affected by light, colour, weather, or other factors, making it suitable for low objects and complex orchard scenes. Therefore, LiDAR navigation is more suitable for orchard environments. In complex orchard environments, tree branches and foliage can cause Global Positioning System (GNSS) accuracy to degrade, resulting in signal loss. Therefore, the major challenge that needs to be addressed is generating navigation paths and locating the position of orchard robots. In this paper, an improved method for Simultaneous Localization and Mapping (SLAM) and A-star algorithm is proposed. The SLAM and path planning method designed in this study effectively solves the problems of insufficient smoothness and large curvature fluctuation of the path planned in the complex orchard environment, and improves the detection efficiency of the robot. The experimental results indicate that the method can consistently and accurately fulfil the robot's detection needs in intricate orchard environments.

Association of weather variables with pathogens contributing to conjunctivitis worldwide.

To identify weather variables associated with pathogens contributing to infectious conjunctivitis globally. Sample collection and pathogen identification from patients with acute infectious conjunctivitis was performed from 2017 to 2023. We linked pathogens identified from 13 sites across 8 countries with publicly available weather data by geographic coordinates. Mixed effects logistic regression analysis was performed to estimate the associations between temperature, precipitation, and relative humidity exposures, and the prevalence of infection types (RNA virus, DNA virus, bacteria, and fungus). 498 cases from the United States, India, Nepal, Thailand, Burkina Faso, Niger, Vietnam, and Israel were included in the analysis. 8-day average precipitation (mm) was associated with increased odds of RNA virus infection (odds ratio (OR)=1.47, 95% confidence interval (CI): 1.12 to 1.93, P=0.01) and decreased odds of DNA infection (OR=0.62, 95% CI: 0.46 to 0.82, P<0.001). Relative humidity (%) was associated with increased odds of RNA virus infections (OR=2.64, 95% CI: 1.51 to 4.61, P<0.001), and fungal infections (OR=2.35, 95% CI: 1.19 to 4.66, P=0.01), but decreased odds of DNA virus (OR=0.58, 95%CI: 0.37 to 0.90, P=0.02) and bacterial infections (OR=0.42, 95% CI: 0.25 to 0.71, P<0.001). Temperature (°C) was not associated with ocular infections for any pathogen type. This study suggests that weather factors affect pathogens differently. Particularly, humidity and precipitation were predictors for pathogens contributing to conjunctivitis worldwide. Additional work is needed to clarify the effects of shifts in weather and environmental factors on ocular infectious diseases.

Examining the potential influences of weather factors and particulate matter pollution on adverse pregnancy outcomes in the Kavre district, Nepal

Examining the potential influences of weather factors and particulate matter pollution on adverse pregnancy outcomes in the Kavre district, Nepal

Pelestarian Gapura Wringin Lawang Sebagai Potensi Sarana Edukasi di Trowulan

This research focuses on the preservation of the Wringin Lawang Gate, a cultural heritage site in Trowulan, Mojokerto, which has historical value as a relic of the Majapahit Kingdom. A descriptive qualitative approach is used to evaluate the physical condition, accessibility, community participation, and educational potential contained in this site. The findings show that although the arch structure is still sturdy, there is damage to certain parts due to age and weather factors. Access to this site is quite good, but the supporting facilities are still limited. Educational activities at the Wringin Lawang Gate are still not optimal and require the development of a more structured program. The participation of local communities in site preservation already exists, but needs to be increased through active involvement in education and conservation activities. Recommendations for site managers include improving educational programs through modern technology such as augmented reality, as well as the development of supporting facilities to maximize the potential of the Wringin Lawang Gate as an effective and sustainable educational facility.

A Model for Determining the Thermal Comfort of Fishermen’s Houses on Coasts with Humid Tropical Climate

Fishermen’s houses on the coastal area of Dusun Karama Tengah, located at coordinates 119° 23’ 0” with 119° 23’ 9” BT, and 5° 13’ 28” LS with 5° 13’ 36” LS, with a humid tropical climate, has poor thermal comfort, and is not in the 22,8°C-27,1°C comfort category. Residents and house occupants feel stifled, hot, and uncomfortable during the day. Environmental factors, use of materials, construction, and the architecture of the fishing houses greatly contribute to the thermal comfort, apart from weather and climate factors. The aim of this research is to develop an innovative model for determining the thermal comfort of the fishermen’s homes. This research uses survey methods and observations of several factors that contribute to the thermal comfort during the day. It also uses expert system analysis techniques, certainty factors, based on Geographic Information System (GIS). A total of 107 fishermen’s housing units studied had poor thermal comfort, especially at 11:00-13:00 WITA or at 03:00-05.00 GMT. The poor thermal comfort of the fishermen’s houses is because they do not apply the principles of eco-building, green-architecture, and the lack of: facade, overhang, insulation, trees and parks, as well as the large use of low-density building projects in the form of zinc and triplex ceilings. This research has not taken into account the level of heat reduction of the house materials and the environment, so it is expected to be done in the upcoming research.

A Diagnostic Study of Storm Daniel's Meteorological and Environmental Characteristics in Derna, Eastern Libya, 2023

Many climate change hotspots around the world are characterized by annual droughts, floods, and fires. In September 2023, the city of Derna became a significant hotspot that garnered global attention due to severe material and human losses caused by the semi-cyclonic Storm Daniel. This study examines the weather factors influencing this storm, including sea level pressure, precipitation, sea surface temperature, and wind speed, from the 8th to the 11th of September, with a focus on northeastern Libya, where the storm peaked on the 10th. The highest total precipitation recorded in Al-Marj was approximately 379.08 mm per day. Notable precipitation anomalies for September 2023 in northeastern Libya included daily anomalies of 10.91, 14.98, and 9.23 in Benghazi, Al-Marj, and Al-Bayda, respectively, with the highest anomaly recorded in Derna at approximately 16.44.

Responses of mustard greens (Brassica juncea L.) to additional lighting duration of LED under greenhouse conditions in the tropical zone

ABSTRACT Light-emitting diodes have been beneficial for plant growth, especially for microgreens in countries with low radiation, greenhouses, and chambers. However, information about how plants respond to additional light-emitting diode lighting after 12 hours of sunlight exposure in tropical areas is limited. This study examines the effects of long-duration light-emitting diodes on mustard greens in Kien Giang province, Vietnam (105°14’33.21” E; 9°91’41.13” N). The trials were conducted in the greenhouse for two growing seasons with a completely randomized design, including 0, 2, 4, and 6 hours of assembled light-emitting diodes lighting and 4 hours of commercial light-emitting diodes lighting after 6 pm, with three replications. Results showed that weather factors in season 2 favorably influenced mustard’s growth. At 4 hours of lighting, commercial light-emitting diodes showed a higher chlorophyll index, flowering time, flowering rate, leaf number, and absolute growth rate of leaf number but less leaf and fresh shoot weight than assembled light-emitting diodes. Four hours of assembled light-emitting diodes positively affected most mustard growth, while 6 hours of assembled light-emitting diodes were superior for flowering aspects. Adding 4 hours of light-emitting diodes could increase leafy yield and shorten the harvesting time of mustard greens grown in greenhouses under tropical conditions.

Water stress combining weather condition shapes wheat yield and inter-annual yield variability: Field observations from a six-year study

Irrigation has played a pivotal role in Chinese wheat production and is becoming increasingly crucial in adapting to the changing climate. However, the benefits of water-saving wheat production in the long-term period and its response to climate change have received limited attention. In this study, a 6-year field experiment was conducted to investigate the grain yield and water use with three treatments such as Irrigation three times (I3), Irrigation two times (I2), and disposable pre-sowing irrigation (I0), and their sensitivity to weather conditions. The average yield over six years for the I2 treatment is 8.3 Mg ha−1, similar to I3 treatment while using 10.2 % less irrigation water and improving 8.0 % water use efficiency. In contrast, the grain yield in I0 treatment is 28.4 % lower than I3 treatment while consuming 36.9 % less irrigation water. Furthermore, 90.2 % of the yield decrease in I0 treatment results from the lower ear number. Water stress from jointing to flowering accounts for 58.8 % of ear number decrease. Although interannual yield variation is similar among the three treatments, the source of the variation is very different. Kernel weight explains the yield variation by 92.3 % in I3 treatment and 66.1 % in I2 treatment, while ear number accounts for 60.7 % of the variation in I0 treatment. Minimum temperature for kernel weight in both I3 and I2 treatments and rainfall for ear number in I0 treatment is the most important weather factor, respectively. In summary, this study provides valuable insights into the delicate balance between water conservation and food security while adapting to varying weather conditions.

Identifying optimal number of driving cycles to represent diverse driving conditions

Driving cycle is one of the main inputs of vehicle emission modeling. However, the variability of driving cycles due to fluctuations in weather conditions is one of the primary sources of uncertainty in vehicle emission estimation. This study aims to identify and determine an optimal number of driving cycles that can correctly represent driving patterns in diverse weather conditions. First, a multivariate multiple regression model is developed to determine the most important weather factors affecting the driving patterns. Then, similar weather conditions are identified according to these factors using unsupervised machine learning. Next, two driving cycles are constructed for diverse weather types, one for weekdays and one for weekends. Afterward, descriptive analysis and a similarity matrix are employed to determine how similar the generated driving cycles are in different weather types. Finally, 15 driving cycles are identified to represent driving patterns in diverse driving conditions.