Rainy Weather Research Articles

Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather

Energy consumption on freeway significantly contributes to environmental pollution. Rainy weather, as a common adverse condition, will exert a negative impact on car-following behavior of vehicles on freeway and further affect their energy consumption. The emergence of connected automated vehicles (CAVs) has created an opportunity to mitigate these impacts. This paper aims to propose an optimization strategy for CAVs that can reduce energy consumption during car-following behavior on freeway under different rainy weather conditions. To begin with, a calibrated car-following model for regular vehicles (RVs) on freeway in rainy weather was used to derive an optimization strategy for CAVs that have vehicle-to-vehicle communication capability to stabilize traffic flow with smoothed speed fluctuations. The proposed optimization strategy for CAVs was then subjected to simulation experiments to validate its effectiveness. Results indicate that energy consumption on freeway in rainy weather is closely linked to speed fluctuations. Frequent speed fluctuations during car-following behavior could cause more energy consumption. The proposed optimization strategy for CAVs is capable of reducing energy consumption in rainy weather by smoothing speed fluctuations. CAVs equipped with this optimization strategy shows an energy-saving of 34.69%–61.11% compared to RVs under various rainy weather conditions.

Prediction of the Hydrological Drought Index (HDI) based on Rain Simulation Data using an Artificial Neural Network (ANN) in the Randugunting Watershed, Rembang Regency

Randugunting Watershed, with the main river Randugunting River, originates from the North Limestone Mountains of Rembang Regency. Because of its cyclical nature, the river flow discharge is continually subject to change. The discharge rises during rainy weather and falls during dry weather. Due to this issue, Rembang will likely experience drought throughout the dry season. The objectives of this study are to: (1) determine the drought index and its sharpness based on the Hydrological Drought Index Method (HDI) and (2) make drought predictions using the HDI based on discharge for the years 2023–2026 using simulated rain data with Artificial Neural Networks (ANN). Calculation of regional rainfall using the Thiessen Polygon Method. To find simulated rain and drought prediction based on the ANN with Backpropagation algorithm using Mathlab software. From the analysis indicate that the number of months that would experience wet conditions are decreasing from 7 months in 2023 and 2024 to 6 months in 2025 and only 3 months in 2026. In the anticipated drought from 2023 to 2026, there will be 14 dry months, 8 very dry months, and 3 extremely dry months. The examination of the hydrological drought’s duration revealed that the typical drought period fell between May and November.

Synthetic rain image generation via CycleGAN

In the domain of autonomous driving and computer vision, the formidable challenge of adverse weather conditions, particularly rainy weather, profoundly impacts image quality and visibility. Rain streaks pose a significant impediment to accurate object detection, especially concerning pedestrians and vehicles on the road. While existing solutions have focused on training rain removal models on paired images, acquiring such data with congruent backgrounds has been a substantial hurdle. Three distinct images, each featuring a diverse background, were deliberately chosen for the rain removal experiment. The deliberate selection of these varied backgrounds allowed us to rigorously assess the efficacy of our rain removal method across diverse environmental contexts. The compelling results obtained from this experiment affirm the method's ability to effectively mitigate rain line across a spectrum of backgrounds, thus establishing its robustness and versatility in real-world scenarios. The resulting images are then employed to train our rain removal model, marking a significant advancement in our endeavor. Our innovative approach stands poised to revolutionize rain removal techniques, aligning synthetic and authentic rain images more closely in real-world scenarios.

Determinants, Motivators and Barriers for Utilitarian Cycling in Starter Cities: Case of Battaramulla, Sri Lanka

In the 1960s, a counter-cultural movement promoting utilitarian bicycle usage emerged in Europe and North America, challenging the dominance of cars, and advocating for sustainable urban mobility. despite the emergence of this movement, many developing cities have low cycling modal share and encounter obstacles in promoting utilitarian cycling. This study delves into workers attitudes toward using bicycles as an alternative mode of transportation. Employing a Binary logistic regression model and incorporating the Relative Importance Index (RII), the research identifies key determinants of utilitarian cycling. The findings highlight significant community willingness (71%) to bike to work, despite perceived barriers. Safety concerns (RII - 0.88) and environmental factors (RII = 0.60), such as rain and heat, emerge as prominent deterrents. Societal perceptions linking cycling to lower social status (vanity) (RII = 0.35), bicycle affordability (RII = 0.35), and work-related factors (RII = 0.48), such as non-cycle-friendly work attire and insufficient office parking safety, play relatively minor roles in discouraging bicycle use. Using the logistic regression model, the study predicts factors influencing the willingness to use bicycles for utilitarian transportation, revealing negative impacts from the absence of cycling infrastructure, motor traffic, rainy weather, and a lack of a cycling community.

Operation Risk Simulation and Interaction Impact of Stormwater and Sewage Systems Based on Storm Water Management Model

With the increasing urbanization rate, higher demand is placed on drainage systems, necessitating analysis of their risk profiles. While many studies focus on the individual hydraulic modeling of stormwater and wastewater systems when considering operational risks, they often overlook the exacerbating effects of combined stormwater and wastewater flows on each other’s risks. In this study, we constructed a model of the combined stormwater and wastewater drainage system in Lijiao, Guangdong Province. We analyzed the operational risks of both stormwater and sewage networks in different scenarios, focusing on full-load pipes, overload pipes, node overflows, and minimum flow velocities. Furthermore, we compared the exacerbating effects of sewage and stormwater on each other’s network operational risks. Simulation results indicated that, for sewage networks with combined sewer sections, the lengths of full-load pipes and overload pipes increased by an average of 27.4% and 16.3%, respectively, during rainy weather scenarios compared to dry weather. After considering the inflow of sewage into combined sewer sections, the average length of full-load pipes increased by 3.8 km, overload pipe length increased by 1.1 km, and overflow volume increased by 2.26 × 104 m3. As the return period of rainfall events increases, these inter-system impacts gradually diminish. Therefore, when modeling drainage systems and assessing operational risks, particularly during low return period rainfall events, the inclusion of both stormwater and sewage in combined sewer networks should be comprehensively considered.

STViT: Improving Self-Supervised Multi-Camera Depth Estimation with Spatial-Temporal Context and Adversarial Geometry Regularization (Student Abstract)

Multi-camera depth estimation has recently garnered significant attention due to its substantial practical implications in the realm of autonomous driving. In this paper, we delve into the task of self-supervised multi-camera depth estimation and propose an innovative framework, STViT, featuring several noteworthy enhancements: 1) we propose a Spatial-Temporal Transformer to comprehensively exploit both local connectivity and the global context of image features, meanwhile learning enriched spatial-temporal cross-view correlations to recover 3D geometry. 2) to alleviate the severe effect of adverse conditions, e.g., rainy weather and nighttime driving, we introduce a GAN-based Adversarial Geometry Regularization Module (AGR) to further constrain the depth estimation with unpaired normal-condition depth maps and prevent the model from being incorrectly trained. Experiments on challenging autonomous driving datasets Nuscenes and DDAD show that our method achieves state-of-the-art performance.

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

AbstractIn response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

Green Energy Harvesting and Management Systems in Intelligent Buildings for Cost-Effective Operation

Nowadays, the rise of Internet of Things (IoT) devices is driving technological upgrades and transformations in the construction industry, the integration of IoT devices in buildings is crucial for both the buildings themselves and the intelligent cities. However, large-scale IoT devices increase energy consumption and bring higher operating costs to buildings. Therefore, harvesting the ambient cost-effective and clean energy sources is essential for the future development of intelligent buildings. In this work, we investigate the feasibility of integrating a typical triboelectric droplet energy harvester (DEH) into buildings. We demonstrate the energy harvesting capabilities of DEH on different sloped roof surfaces and complex curved building surfaces by simulating rainy weather with various rainfall intensities. The results indicate energy harvesting efficiency increases with larger tilt angles, which guides future smart architectural designs. This work is significant for the future integration of diversified, all-weather green energy collection and management systems, including raindrop energy, wind power generation, and solar energy, which will contribute to energy conservation and cost control in the next generation of smart buildings.

A class of photosensitizer highly effective to control bacterial infection in plants even on rainy days with dim light

Bacterial infections of plants are widespread and highly virulent, causing serious economic losses in agricultural production worldwide. These infections become particularly prevalent during rainy weather, characterized by high humidity and low light intensity (about 2–10% of a sunny day). Here we identified a class of photosensitizers that were effective in inhibiting plant bacterial infections under rainy weather with dim light. One of these compounds, compound 1, demonstrated exceptional efficacy by eliminating more than 4 logs (99.99%) of either Xanthomonas perforans or Xanthomonas citri subsp. citri (Xcc) strain at a concentration of 6.25 μM under rainy weather conditions with a light exposure of 28.51 J/cm2. For Clavibacter michiganensis (Cm), compound 1 demonstrated a comparable bactericidal effect at a lower concentration of 3.13 μM. Furthermore, compound 1 was effective in controlling plant leaf infections during rainy weather. Moreover, compound 1 exhibited a potent inhibitory effect on the biofilms pertinent to tomato spot disease and citrus canker under natural illumination on rainy days. Additionally, compound 1 displayed remarkable resilience to rain-wash on plant leaves, retaining 41.20% of its initial concentration following exposure to three simulated rain events. These findings provide a new strategy for plant pathogen disease management highlight its feasibility even during rainy weather.

Accuracy of two LiDAR-based augmented reality apps in breast height diameter measurement

Accurate measurement of the diameter at the breast height (DBH) is essential in forestry-related science and practice, but its measurement is currently done by labor-intensive tools such as calipers or devices designed to measure the girth. With the development in light detection and ranging (LiDAR) and augmented reality (AR) technologies, and their integration in low-cost mobile platforms, affordable proximal measurement applications were released on the market. This study examines the accuracy in DBH measurement of Arboreal Forest (hereafter DA) and Measure (hereafter DM) apps, by taking as a reference the measurements done by an accurate forestry caliper (hereafter DC). A number of 615 trees were considered, of which 395 were broadleaved (DBH between 10 and 73 cm, averaging 39.73 ± 9.91 cm) and 220 were coniferous (DBH between 25 and 89 cm, averaging 52.47 ± 12.81 cm), and measurements were taken under sunny, cloudy and rainy weather. Comparison was done in terms of agreement (Bland and Altman's method), dependence (least square simple ordinary and regression through origin), correlation (Spearman's, Pearson's and Kendall's tests), and difference (mean absolute error - MAE, root mean squared error - RMSE, and bias - BIAS). Besides a close-to-perfect fit, strong association in data, and a good degree of agreement, the results indicated the presence of centimeter-level differences when comparing DM against DC (MAE = 0.715 cm, RMSE = 0.879 cm, BIAS = 0.333 cm) and DA against DC (MAE = 0.953 cm, RMSE = 1.246 cm, BIAS = -0.108 cm). When comparing DA against DM the differences were slightly higher (MAE = 1.175 cm, RMSE = 1.531 cm, BIAS = –0.446 cm). The magnitude in differences found is rather caused by the application used and not by the environmental conditions. Further studies may consider larger data samples to provide better estimates as well as checking the limits in measurement capabilities of these apps.

Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill-slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi-spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel-based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10-fold cross-validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer's Accuracy (PA) and User's Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90–0.93 and 0.81–0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low-slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different from previous studies. The sample purification strategy proposed provides a more reliable regional scale terrace distribution map compared to the existing product, and is potentially transferable to other mountainous areas as a robust approach for rapid identification of terrace.

Car-Following Strategy Involving Stabilizing Traffic Flow with Connected Automated Vehicles to Reduce Particulate Matter (PM) Emissions in Rainy Weather

On highways, it is commonplace to observe car-following behavior among vehicles. Unfortunately, this behavior results in significant particulate matter (PM) emissions, which greatly contribute to environmental pollution. Additionally, adverse weather conditions such as rain can negatively affect vehicles’ car-following behavior and have further influences on their PM emissions. The technology of connected automated vehicles (CAVs) offers a promising solution for mitigating these negative influences. This paper investigates the effect of various rainy weather conditions on PM emissions during car-following behavior on highways and proposes a CAV car-following strategy to reduce these emissions. Firstly, we employed a calibrated car-following model of traditional vehicles to perform simulation experiments, examining characteristics of PM emissions under four levels of rain and two simulation scenarios. Secondly, based on the relationship between PM emissions and speed fluctuations, we proposed a CAV car-following strategy by stabilizing traffic flow to smooth speed fluctuations. The proposed CAV car-following strategy was then validated through simulation experiments, and its effectiveness in reducing PM emissions under rainy conditions was assessed. The results indicate that higher speed fluctuations during car-following behavior lead to more PM emissions in rainy weather. By utilizing the proposed car-following strategy, CAVs can significantly reduce PM emissions in rain conditions, with average reductions of 41.07%, 59.46%, 49.60%, and 71.66% under very light rain, light rain, moderate rain, and heavy rain conditions, respectively. The findings of this paper facilitate the assessment of PM emissions fluctuations in different rainy weather conditions, which in turn can contribute to the development of more effective PM emissions control strategies. The proposed CAV car-following strategy can smooth speed fluctuations, and improve traffic flow stability, thus reducing PM emissions in rainy weather. It has the potential to mitigate environmental pollution from the transportation sector.

Hybrid supervised hierarchical control of a biological wastewater treatment plant.

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

Weather-Based Arthritis Tracking: A Mobile Mechanism for Preventive Strategies

Arthritis is a common joint disorder characterised by symptoms such as swelling, pain, stiffness, and limited joint movement. It primarily affects older individuals, women, and athletes. The advent of information technology has created opportunities for patients to manage their health conditions more effectively. Research indicates that weather can affect arthritis symptoms, with many patients experiencing severe discomfort during rainy weather due to the expansion of already inflamed tissues. However, there is currently no mobile application mechanism available that combines weather forecasting with health recommendations for arthritis patients, which means that patients may not have access to important information that could help them manage their symptoms. Furthermore, few research workflows have focused on weather conditions in online arthritis treatment systems. This research aims to develop a weather-based mobile system for arthritis tracking that provides health advice and alerts based on current and forecast weather conditions, as well as features to help patients track how weather affects their arthritis. This system utilises several tools for its development. The Flutter Framework is used for creating mobile apps, while Firebase is chosen as the cloud-hosted database. Visual Studio Code and Android Studio are utilised as the code editor and Android emulator, respectively. Information about weather forecasts is retrieved via the OpenWeather API. The application mechanism will feature a user-friendly interface to help users stay updated on weather forecasts, and it will collect data in a reliable and user-centric manner for generating robust evidence on health outcomes.

An advanced accident avoidance system based on imbalance-control ensemble and deep learning fusion design

The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems.

Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery

Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

Relationships Between Weather and Cycling


 
 
 As cycling gains more traction and attention as a beneficial mode of urban transportation, different factors have emerged in different regions of the world to influence its adoption and promotion. One of the most wide-spread and colloquial of these factors is that of the weather or climate of the region, and how it may facilitate or, more often, dissuade cycling practices. Although it may appear intuitive that “poor” weather shall negatively impact the amount of cycling that occurs in a region, the reliability of this intuition is challenged by example locations where cycling in “poor” winter conditions is the norm rather than the exception, or where cyclists are relatively indifferent towards rainy weather. As such, the aim of this literature review is to identify the real effects of weather on cycling behaviour in different locations and uncover relationships between secondary covariates and responses to weather. Rainfall, temperature, and wind are identified as the three most important measurable weather parameters that affect cycling behaviour. In most locations, cycling rates drop significantly when it starts to rain, temperature thresholds vary between locations, and the effect of wind is only significant when above 5 Beaufort. Several studies also reveal factors that interact with measurable weather parameters to influence behaviour in significant and seemingly complex ways. These include the trip purpose, traveller sociodemographic, the characteristics of the built environment, and perceptions of normality. The identification of these covariates and a discussion about interrelations amongst them contributes to an explanation of why weather seems to affect cyclists in some places more than in others, while also highlighting directions for future research. 
 
 

Relative Humidity Dominances in Negative Air Ion Concentration: Insights from One–Year Measurements of Urban Forests and Natural Forests

Forests are one of the most important sources of negative oxygen ions (NAIs). NAIs have been recognized as beneficial for both physical and mental well–being, and higher concentrations of NAIs have been associated with improved health. However, the environmental factors that predominantly influence NAI concentration and their relationship with NAIs remain uncertain. This study aims to investigate the dominant factors and their impact on NAI concentration by observing NAIs and various environmental factors in two different environments (natural forest and urban forest) in the Beijing region over a one–year period. Through our investigation, we aimed to identify the key factor as well as other influential variables affecting NAI concentration. Our analysis encompassed the examination of dynamic concentration changes over multiple time scales, revealing uniform trends in both forest types. Notably, natural forests consistently demonstrated higher NAI concentration across these time scales, attributable to greater vegetation density and the stability of the forest microenvironment. By utilizing regression, correlation analysis, and structural equation analysis, we determined that relative humidity (RH) has the most significant effect on NAI concentration. Notably, both NAI concentration and RH displayed similar patterns across multiple time scales. When considering hourly average daily variation, the lowest values for both NAI concentration and RH were observed at noon, followed by an increase that persisted throughout the night. Seasonal average variation showed that both NAI concentration and RH peaked in the summer, followed by autumn. In terms of daily average annual variation, summer exhibited more days with high NAI concentration and high RH, which can be attributed to the increased rainfall during that season. Rainy weather was found to contribute to higher NAI concentration and RH levels. Furthermore, our findings revealed that on a daily scale, high RH and high NAI concentration occurred more frequently under conditions of high air temperature and low wind speed. However, the air quality index demonstrated only a minor effect in urban forest, while net radiation exhibited no significant influence on NAI concentration and RH. The fitted equations and trends of the aforementioned environmental factors with NAI concentration and RH were found to be comparable. The path analysis further corroborates these conclusions. The findings of this study indicate that RH is the primary factor driving the fluctuations in NAI concentration across various time scales, including hourly, daily, and seasonal variations. The study revealed that wind speed indirectly impacts NAI concentration by modulating RH. In contrast, air temperature influences NAI concentration both indirectly through RH and directly. The environmental factors affecting NAI concentration in the two types of forests are similar, but the degrees vary; in urban forests, wind speed, air quality index, and RH are slightly higher, while in natural forests, air temperature is slightly higher. This discovery further enhances our understanding of the underlying mechanisms and dynamic changes in NAI concentration within urban forests and natural forests. Moreover, it confirms the reliability and effectiveness of using RH as an indicator to monitor changes in NAI concentration over time.

Determinants promoting and hindering physical activity in primary school children in Germany: a qualitative study with students, teachers and parents.

Determinants affecting children's physical activity (PA) at an early age are of particular interest to develop and strengthen strategies for increasing the levels of children's PA. A qualitative study was conducted to investigate the views of primary school-aged children, their teachers and parents regarding barriers and facilitators to engage in PA. Focus groups were conducted separately with primary school children, parents and teachers in a city in Northern Germany between October 2021 and January 2022. The semi- structured focus groups with children and teachers took part in person within school, whereas the focus groups with parents took place online. Data were transcribed verbatim and analysed using thematic analysis. During analysis, the socio-ecological model was identified as useful to map the determinants mentioned and was consequently applied to organize the data. Teachers (n = 10), parents (n = 18) and children (n = 46) of five primary schools in Germany participated in the focus groups. Participants of the three groups identified similar barriers and facilitators of PA in primary school-aged children, ranging across all four layers of the socio-ecological model. The barriers encountered were the preferences of children for sedentary activities (individual characteristics), the preference of parents to control their child's actions (microsystem), a lack of financial resources from parents and long sitting times in class (mesosystem), and barriers related to rainy weather and Covid-19 restrictions (exosystem). Facilitators mentioned were the childrens' natural tendency to be active (individual characteristics), involvement and co-participation of parents or peers in engaging in PA, support provided by teachers and the school (microsystem), living in rural areas, having sufficient facilities and favorable weather conditions (exosystem). A range of determinants promoting and hindering PA, ranging across all layers of the socio-ecological model were identified by children, parents and teachers in this study. These determinants need to be kept in mind when developing effective PA intervention programs for primary school-aged children. Future interventions should go beyond individual characteristics to also acknowledge the influence of childrens' social surrounding, including parents, peers and teachers, and the wider (school) environment.

ACCELERATION ANALYSIS USING THE CRASHING METHOD IN THE SDN KUTISARI 2 DEVELOPMENT PROJECT

In the construction of the Kutisari 2 SDN Project, there were several obstacles that hampered the implementation of the project, such as heavy rainy weather. The aim of the research is to determine the cost and acceleration time for the construction of the SDN Kutisari 2 Project. The research design was carried out to identify the time and costs of implementing the building construction completion project. The research was conducted at CV.Nisya Multi Bintang as the implementing contractor with a budget of Rp. 1,614,171,924 for 165 calendar days. The research subjects were contractors completing work on the Kutisari 2 Elementary School Development Project. This research used secondary data from the Draft Expenditure Budget, Schedule, weekly reports and project cost costs. Data analysis used Microsoft Project 2016 software. The results of the research stated (1) The duration of the Crashing was 8 calendar days with work that was on the Critical Work Route for the Construction of School Facilities, Infrastructure and Utilities at SDN Kutisari 2, namely water proofing coating with fiber on the 2nd floor, cleaning location to the end, B-0 work to cover 2nd floor columns, 2nd floor column cutting work, and floor screading work. (2) From the initial cost of the Kutisari 2 SDN School Facilities, Infrastructure and Utilities Construction Work Project, namely Rp. 1,164,171,924 with additional costs due to Crashing of Rp. 24,569,780 so the total costs due to the Crashing are Rp. 1,638,741,704.