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In this paper, the technical-economic framework for designing of water pumping system based on photovoltaic clean energy with water tank storage is presented to supply drinking water of customers for remote areas. The objective function is to minimize the net present cost (NPC) (as economic index) including initial investment costs, maintenance, and replacement costs, and reliability constraint is defined as customer’s water not supplied probability (CWNSP) as technical index. A meta-heuristic intelligent water drops algorithm (IWDA) is proposed to optimize the solar water pumping system considering NPC and CWNSP with high accuracy and speed of optimization in achieving the global solution. The simulation results show that the proposed method is capable of responding to customer’s water demand by optimally sizing components and water storage tank based on IWDA which is inspired based on flowing the water drops in rivers by achieving the lowest cost with optimal reliability. The NPC of the system with CWNSP equal to 3.17 % is obtained 0.24 M$ for 6-m-high water extraction. The results showed that with increasing the water extraction height, the NPC increased, and the reliability also weakened. Moreover, the superiority of the IWDA is confirmed compared with particle swarm optimization (PSO) in designing a water pumping system with the lowest NPC.

Intrusion detection system (IDS) is an essential cyber security tool which is used to detect abnormal activity on a network or a host. A general approach towards designing IDS models is to use classifiers as detection units. But a large feature space including noisy, redundant and irrelevant features often leads to low detection and high misclassification rates by the classifier. To address this drawback, the process of selecting most relevant key features for classification is highly important. The objective of this work is to optimize the process of feature selection in a way that improves the accuracy of the classifier. This paper presents an IDS model wherein an intelligent water drops (IWD) algorithm-based feature selection method is proposed. This method uses the IWD algorithm, a nature-inspired optimization algorithm for the feature subset selection along with support vector machine as a classifier for evaluation of the features selected. The experiments are conducted using KDD CUP’99 dataset, and the performance is compared with earlier designs. The experimental results show that the proposed model performs better in terms of higher detection rate, low false alarm rate and improved accuracy than the existing approaches.

Artificial intelligence (AI) models have been effectively applied to predict/forecast certain variable in several engineering applications, in particular, where this variable is highly stochastic in nature and complex to identify utilizing classical mathematical model, such as river streamflow. However, the existing AI models, such as multi-layer perceptron neural network (MLP-NN), are basically incomprehensible and facing problem when applied for time series prediction or forecasting. One of the main drawbacks of the MLP-NN model is the ability of the used default optimization algorithm [gradient decent algorithm (GDA)] to search for the optimal weight and bias values associated with each neuron within the MLP-NN architecture. In fact, GDA is a first-order iteration algorithm that usually trapped in local minima, especially when the time series is highly stochastic as in the river streamflow historical records. As a result, the overall performance of the MLP-NN model experienced inaccurate prediction or forecasting for the desired output. Moreover, due to the possibility of overfitting with MLP model which may lead to poor performance of prediction of the unseen input pattern, there is need to introduce new augmented algorithm capable of identifying the complexity of streamflow data and improve the prediction accuracy. Therefore, in this study, a replacement for the GDA with advanced optimization algorithm, namely intelligent water drop (IWD), is proposed to enhance the searching procedure for the global optima. The new proposed forecasting model is, namely MLP-IWD. Two different historical rivers streamflow data have been collected from Nong Son and Thanh My stations on the Vu Gia Thu Bon river basin for period between (1978 and 2016) in order to examine the performance of the proposed MLP-IWD model. In addition, in order to evaluate the performance of the proposed MLP-IWD model under different conditions, four different scenarios for the model input–output architecture have been investigated. Results showed that the proposed MLP-IWD model outperformed the classical MLP-NN model and significantly improve the forecasting accuracy for the river streamflow. Finally, the proposed model could be generalized and applied in different rivers worldwide.

The Mediterranean mixed coniferous and broad-leaved forest of Moarda (Palermo) was affected by a large wildfire in summer 2020. In spring 2021, burned and unburned loam soil sites were sampled and the water drop penetration time (WDPT) and ethanol percentage (EP) tests applied to assess the influence of wetting-drying processes and soil water content on post-fire soil water repellency (SWR) as well as its vertical distribution. According to the WDPT test, the surface layer of the natural unburned soils was severely hydrophobic at intermediate soil water contents roughly corresponding to wilting point and SWR reduced either for very dry conditions (air- or oven-dried conditions) or wetter conditions close to field capacity. For these soils, EP test yielded results in agreement with WDPT. An influence of the wetting/drying cycle was detected as, for a given soil water content, WDPT was generally higher for the drying than the wetting process. The surface of burned soils was always wettable independently of the soil water content. The vertical distribution of SWR was modified by wildfire and the maximum hydrophobicity layer, that was located at the surface of the unburned soils, moved to a depth of 2–4 cm in the soils of burned sites. The results confirmed that wildfire can induce destruction of soil water repellency (SWR) naturally occurring at the surface of forest soils and create a shallow hydrophobic layer that may increase overland flow and erosion risk.

Electrochemical migration (ECM) on the surface of printed circuit boards (PCBs) continues to pose a significant reliability risk in electronics. Nevertheless, the existing literature lacks studies that address the solder mask and solder pad design aspects in the context of ECM. Therefore, the objective of this study was to assess the impact of solder mask type with varying roughness and solder pad design on the susceptibility to ECM using a water drop test and thermal humidity bias test. Hot air solder leveling-coated PCBs were tested. Furthermore, the ECM tests were conducted on PCBs with applied no-clean solder paste to evaluate the influence of flux residues on the resulting ECM behavior. The results indicated that the higher roughness of the solder mask significantly contributes to ECM inhibition through the creation of a mechanical barrier for the dendrites. Furthermore, lower ECM susceptibility was also observed for copper-defined pads, where a similar effect is presumed. However, the influence of the no-clean flux residues can prevail over the effects of the solder mask. Therefore, the use of a rough solder mask and a copper-defined pad design is recommended if the PCB is to be washed from flux residues after the soldering process.

Supercooled liquid water drops, with temperatures below freezing point, are common in high‐altitude clouds. These drops, despite being in a metastable state, can remain liquid for extended periods if temperatures are above the homogeneous nucleation point. Impact of such liquid drops with a cold solid surface is one of the reasons for ice accretion, which in many cases can represent a safety hazard. The study of supercooled drop impact dynamics is key to developing materials that provide resistance against the formation and accumulation of ice. In this work, the impact of supercooled water drops on dry icephobic coatings based on gradient polymers deposited via initiated chemical vapor deposition (iCVD) under several conditions is analyzed. Experimental results show that coated surfaces potentially decrease the freezing probability upon impact. The gradient polymer surfaces with higher roughness and lower wettability do not increase the freezing probability upon impact but result in rebound and eventual roll off the surface, indicating that surface hydrophobic properties prevailed over the impact. The findings demonstrate the remarkable efficacy of gradient polymer coatings in inhibiting drop freezing, even under high wind velocities, and provide insights for the design of durable and effective anti‐icing coatings across diverse applications. This study explores supercooled water drop impacts on gradient polymer coatings across different wind speeds. It finds that increasing the top layer thickness significantly reduces freezing probability, even at high‐impact velocities. The study highlights that surface roughness and wettability alone do not promote freezing, underscoring the importance of understanding microstructure and hydrophobicity for effective icephobic performance.

It is not well understood if wildfires induce soil water repellency in broadleaf deciduous forests, such as those endemic to the Blue Ridge Mountains of the eastern United States. In 2016, widespread wildfires provided an opportunity to study soil water repellency in this region. We selected sites in four locations with low to moderate burn severities, along with unburned controls. We estimated soil water repellency using water drop penetration time measurements from the surface (i.e. ash or organic) layer to ~5 cm within the underlying mineral layer. Two months after the fires, water repellency was detected in all locations and was greater in more severely burned sites. One location had the greatest water repellency in surface ash (frequency of occurrence: 68–74%), whereas the other locations showed greatest repellency at the ash–mineral interface (40–96%). Unburned soils rarely showed repellency (0–18%). Burned soils also exhibited water repellency 1 year post fire. The study results suggest that combustion of non-resinous foliage within litter layers can cause water repellency in deciduous forests, meaning that this condition is not exclusive to coniferous and dryland forests. The duration of impact depends on fire severity, and may enhance overland flow and sediment transport in affected landscapes.

Wildfires affect different physical, chemical, and hydraulic soil properties, and the magnitude of their effects varies depending on intrinsic soil properties and wildfire characteristics. The objectives of this study are: to estimate the impact of heating temperature (50–900°C) on the properties of sandy soil (Arenosol) taken in 1) coniferous forests (Scots pine ) of different ages (30 and 100 years); and 2) coniferous (Scots pine ) and deciduous (alder ) forests of the same age (30 years). The forests are located in the central part of the Borská nížina lowland (western Slovakia), and the properties treated were soil organic carbon content (SOC), pH, and soil water repellency (measured in terms of water drop penetration time, WDPT). It was found that the impact of heating temperature on the properties of sandy soil is great and depends on both the age and type of forest. The SOC value decreased unevenly with temperature in all three soils, and it was higher in the 30-year-old deciduous forest soil than in the 30-year-old coniferous forest soil. The value of pH increased monotonously with temperature from 200 °C, and it was higher in 30-year-old coniferous forest soil than in the 100-year-old coniferous forest soil. SOC and WDPT in the 100-year-old coniferous forest soil were higher than SOC and WDPT in the 30-year-old coniferous forest soil. Results obtained (decrease in SOC, disappearance of SWR after heating to 400 °C, and increase in pH from heating temperature 200 °C) bring important information for post-fire vegetation restoration and post-fire management of Central European forests established on sandy soil.

Many problems concerning appropriate calibration besides camera placement are focused by various researchers during measurement operations while dealing with thermal imaging camera. For easy processing of video stream, it is greatly necessitated to correct camera on a stand yaw/pitch/roll angles by utilizing various algorithms. The task is regarded as an easy one for hot object besides obviously visible in the infrared. Heat exchange process is greatly necessitated for registering initiation from a cold object. Boundary markers set positioning is accomplished on the supervised object in addition it requires an algorithm for recognition. A fuzzy assessed spatial relations-based approach is exploited previously for visual markers set detection on a rotating steel cylinder. However, that fuzzy assessed spatial relations-based approach not producing enough detection accuracy. To mitigate the above-mentioned issue this work introduces Intelligent Water Drop Optimization based Fuzzy Inference System (IWD-FIS) on the basis of fuzzy-intrinsic shape aspects such as objects, during a source image, and also their reciprocal reference frame. In this work Otsu algorithm is used for background as well as foreground segmentation. And then Features Extraction and Object Labelling are performed. Markers detection is done by using Proposed IWT-FIS based on the extracted features. The rule conclusions, parameter optimization and Membership Function (MF) parameters are concentrated mainly through this IWD-FIS. A state-of-the-art optimization sequence for the different FIS parameters is recommended rather than presenting a new algorithm.

The continuous advancement in innovative electronic applications leads to closer interconnection spacing and higher electric field density, thus increasing the risk of electrochemical migration (ECM)-related failures. The ECM of tin (Sn) attracts great interest due to the wide use of Sn on the surface of the printed circuit board assembly. In this work, we investigated the effects of adipic acid (1 ppm—saturated concentration) on the ECM of Sn using the water drop test (WDT) at 5 V. In situ observation and ex situ characterization of ECM products were carried out using optical and electrochemical techniques. Results show that the ECM failure probability is higher at intermediate adipic acid concentrations (10 ppm, 100 ppm and 1000 ppm). The major ECM reactions include anodic corrosion and the formation of dendrites, precipitates and gas bubbles. ECM failure does not occur at higher adipic acid concentrations (≥ 5000 ppm) although the anodic corrosion becomes more severe. The complexation of Sn with adipic acid to form Sn adipate complex is suggested as the main factor suppressing ECM failure at higher concentrations (≥ 5000 ppm) by retarding ion transport. The electrochemical parameters (Ecorr and Icorr) do not correlate with the ECM failure probability. They affect the anodic dissolution stage, but not the subsequent stages in the ECM mechanism. In this study, the ion transport stage plays a more significant role in determining the ECM failure probability.