Value Of Experiments Research Articles

Improved sand cat swarm optimization with deep learning based enhanced malicious activity recognition for cybersecurity

The main concept of a smart city is to join manual items with electronics, software, sensors, and network connectivity for data contact via Internet of Things (IoT) gadgets. IoT improves production and efficiency by cleverly utilizing remote management, but security as well as privacy risk, rises. Cyber threats are progressing every day, affecting inadequate actions of safety and privacy. In a smart city, the exposed action of an individual or business can keep the whole city in danger. Owing to the trust of numerous modules of smart cities on data and communication techniques, cybersecurity tasks like information leakage and mischievous cyber-attacks. Malicious Activity Detection in the area of cybersecurity for smart cities is dominant in protecting the consistent organization of urban atmospheres. Leveraging innovative machine learning (ML) and deep learning (DL) approaches, mainly anomaly detection methods permits the identification of abnormal patterns and probable threats within the massive streams of data produced by smart city methods. So, this paper designs a Fusion of Optimization Algorithms and DL for Enhanced Malicious Activity Recognition (FOADL-EMAR) technique for cybersecurity in smart cities. The purpose of the FOADL-EMAR technique is to recognize the presence of malicious activities using optimal DL techniques in the environment of a smart city. To accomplish this, the FOADL-EMAR technique uses a Z-score data normalization model to measure the input dataset into a useful format. In addition, the FOADL-EMAR technique uses an improved sand cat swarm optimization (ISCSO) algorithm that can be applied to select an optimal subset of features. An ensemble of three DL techniques is used namely long short-term memory (LSTM), deep neural networks (DNNs), and extreme learning machine (ELM) for malicious activity detection. The Harris Hawks Optimization (HHO) approach has been applied to improve the detection results of the ensemble models. The simulation analysis of the FOADL-EMAR methodology occurs utilizing a benchmark database. The experimentation values demonstrate the improved detection outcomes of the FOADL-EMAR algorithm over other existing methods in terms of different measures.

Apical pH regulates calcium-activated chloride secretion across human airway epithelia

Anion secretion across airway epithelia contributes to host defense by increasing airway surface liquid (ASL) volume and pH. Anion channels in the apical membrane of airway epithelia include Ca2+-activated Cl− channels (CaCCs) and cystic fibrosis transmembrane conductance regulator (CFTR) channels. Mutations in the CFTR gene that encode nonfunctional CFTR channels cause cystic fibrosis (CF); thus, activating CaCCs may restore anion secretion in people with CF. ATP secreted into the ASL activates purinergic receptors, leading to an increase in intracellular Ca2+ (Cai2+) and the opening of CaCCs. Data from docking simulations suggest that the protonation state of ATP influences its affnity for purinergic receptors, therefore we tested the hypothesis that ASL pH influences the effcacy of ATP to activate CaCCs in airway epithelia. In saline solutions buffered with 5 mM ATP, titration experiments revealed a pKa of ~6.9 at 37°C. Thus, ATP exists in its protonated ATP3− and deprotonated ATP4− in ASL that can range from pH 6 to pH 7.5. To determine the effect of pH on ATP-stimulated Cl− secretion through CaCCs, we measured Cl− secretion across CF epithelia generated by several ATP concentrations with apical pH-6.00, -6.70, and -7.40 solutions. As pH became alkaline, the EC50 for the relationship between [ATP] and Cl− secretion decreased. Using the Henderson-Hasselbalch equation and the pKa for ATP, we determined the relationship between ATP4− and Cl− secretion. This analysis revealed that regardless of apical pH values, the relationship between [ATP4−] and Cl− secretion aligned to a single experimental fit. These data suggest that pH regulates the CaCC response by deprotonating ATP. To examine the pH dependence of purinergic receptor activation, we measured ATP-induced Cai2+ responses in acutely dissociated human airway epithelial cells loaded with FURA-2 and perfused with pH-7.40 or -6.50 solutions. Separate experiments measuring intracellular pH in pH-7.40 or 6.50 solutions informed Kd values for FURA-2 experiments. With an [ATP] that induces similar levels of Cl− secretion from pH 6.00 to pH 7.40 (100 μM ATP), we observed similar Cai2+ responses for pH-7.40 and -6.50 solutions. However, when we repeated the experiment with an [ATP] that induces pH-sensitive Cl− secretion (10 μM ATP), we observed larger Ca2+ responses with pH-7.40 solutions than with pH-6.50 solutions. These data indicate that pH influences purinergic receptor activation near the pKa of ATP, a finding consistent with ATP4− activating purinergic receptors. These data have implications in CF and airway inflammation. In CF, decreased CFTR-mediated HCO−3 secretion acidifies the ASL, thus a large portion of ATP will exist in the protonated ATP3− species requiring a higher total [ATP] to open CaCCs. Inflammatory cytokines that cause airway inflammation increase the ASL pH, therefore a larger portion of ATP will exist in the deprotonated ATP4− species requiring less total [ATP] to open CaCCs. Cystic Fibrosis Foundation Research Development Program. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

An intelligent recommendation strategy for integrated online courses in vocational education based on short-term preferences

With the swift advancement of online teaching in vocational education, an increasing number of web-based course materials are being made available to students, granting them the freedom to select resources that suit their personal needs. To optimize the effectiveness of artificial intelligence-enabled smart vocational education, this study presents a course recommendation model centered on learning behaviors and interests. The model utilizes short-term preferences reconstruction behavior contribution to identify fluctuations in learners' interests in real-time. A model for recommending courses is proposed based on short-term preferences and enhancements to learning behavior. Its purpose is to tackle the issue of generalization arising from sparsity and weak correlation in learning behavior. The outcomes demonstrated the model put forth in the study achieved higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) values in comparison experiments with multiple models. Hence, this suggested that creating a novel component of historical learning behavior, powered by dynamic interest factors, could resolve the issue of changing learning interests and enhance the efficacy of course recommendation models. Furthermore, the introduction of a correlation mapping network enables the forward mapping transformation from weak to strong learning behavior, thus improving and optimizing input for the agent strategy, reducing data sparsity, and enhancing the performance and generalization of the course recommendation model.

Long-term soil quality and C stock effects of tillage and cover cropping in a conservation agriculture system

Conservation and regenerative agriculture are widely considered as approaches aiming at addressing challenges in relation to climate change and soil quality. In that respect, no tillage (NT) and use of cover crops (CCs) are seen as key elements in achieving the sustainability goals of these approaches, but the long-term individual and interactive effects of these elements remain unknown. We examined the impact of tillage (NT and mouldboard ploughing) combined with a fodder radish CC in continuous cereal cropping. Soil was sampled in the 0–10 and 10–20 cm layer after two decades of treatments in the CENTS experiment at AU Viborg (Denmark) situated on a sandy loam with 9% clay. We assessed soil structural quality, SOC stocks, clay dispersibility (CD), wet stability of aggregates (WSA) and soil pore characteristics. Neither tillage nor cover cropping affected the SOC stock in the 0–20 cm soil layer. No tillage improved CD, WSA and plant available water capacity in 0–10 cm depth as compared to ploughing. The marked improvement in CD and WSA of NT soils could not be explained by SOC per se, but rather positive effects ascribed to absence of disturbance. In contrast, soil porosity, especially in 10–20 cm depth, the fraction of soil volume represented by >30 µm pores and gas diffusivity decreased, and NT soils resulted in a less good soil structural quality. The inclusion of CCs improved soil structural quality and the functionality of the soil macropore system. Hence, CCs have the potential to alleviate negative effects of NT on pore characteristics at macroscale. Furthermore, we found that the positive effects of NT on CD and WSA and of CCs on pore characteristics at macroscale were much more pronounced after long- (20 yrs NT; 13 yrs CC) than after medium-term (10 yrs NT; 5 yrs CC) underlining the value of long-term conservation agriculture experiments.

Personalized Dynamic Pricing Based on Improved Thompson Sampling

This study investigates personalized pricing with demand learning. We first encode consumer-personalized feature information into high-dimensional vectors, then establish the relationship between this feature vector and product demand using a logit model, and finally learn demand parameters through historical transaction data. To address the balance between learning and revenue, we introduce the Thompson Sampling algorithm. Considering the difficulty of Bayesian inference in Thompson Sampling owing to high-dimensional feature vectors, we improve the basic Thompson Sampling by approximating the likelihood function of the logit model with the Pólya-Gamma (PG) distribution and by proposing a Thompson Sampling algorithm based on the PG distribution. To validate the proposed algorithm’s effectiveness, we conduct experiments using both simulated data and real loan data provided by the Columbia University Revenue Management Center. The study results demonstrate that the Thompson Sampling algorithm based on the PG distribution proposed outperforms traditional Laplace approximation methods regarding convergence speed and regret value in both real and simulated data experiments. The real-time personalized pricing algorithm developed here not only enriches the theoretical research of personalized dynamic pricing, but also provides a theoretical basis and guidance for enterprises to implement personalized pricing.

Evaluation of total flavonoids, total phenolic content and biological activity of Iraqi Lipedium sativum L. crude extract obtained by optimized ultrasound assisted extraction conditions

Lepidium sativum L. also known as garden cress belong to the family Brassicaceae. The plant species composed of various phytochemicals as well as powerful nutraceutical potential and possess several bioactivities like, hepatoprotective, antioxidant, anticancer, antimicrobial, hypoglycemic, gastrointestinal and bone healing activities. This research paper presents an investigation into the total flavonoids (TFC), total phenolic content (TPC) and biological activity of Iraqi Lepidium sativum L. The study aimed to optimize ultrasound-assisted extraction conditions to obtain a crude extract with enhanced bioactive components. Three variables were examined including methanol concentration, extraction time and ultrasound frequency. The optimum yields of extract, TFC and TPC were (3.22 ± 0.049 g/10 g of dry plant), (17.03 ± 0.060 mg RE/g) and (10.96±0.020 mg GAE/g) respectively. The optimal extraction conditions contributed to these values of experiment 2 and 3 were 70% methanol, 10 min and 40 KHz and 70% methanol, 15 min and 40 KHz respectively. The lowest IC50 values of optimized methanolic extracts of Iraqi Lepidium sativum aerial parts against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals were 31.84 µg/mL for TFC and 35.85 µg/mL for TPC . For the first time, the study provided data about the phenolic and flavonoid contents of the Iraqi plant and optimized conditions for extraction by UAE technique using single factor experiment. The plant can be acknowledged as a potential nutraceutical or functional food rich in antioxidants to combat many diseases.

Optimizing Soybean Crop Performance through the Integrated Application of Organic and Chemical Fertilizers: A Study on Alkaline Soil in Afghanistan

The excess application of chemical fertilizer contributes to environmental pollution. Therefore, this study aims to explore the integrated use of animal manure and chemical fertilizer to identify a more sustainable and environmentally friendly alternative to nitrogen fertilizer. Two experiments were conducted in 2018 and 2019 at Kabul University, Afghanistan, using an LD04-13265 soybean variety. The integration of animal manure and chemical fertilizer was categorized into six treatments: (1) control (no fertilizer), (2) animal manure low dose (AMLD), (3) animal manure high dose (AMHD), (4) chemical fertilizer (CF), (5) CF 50% + AMLD, and (6) CF 75% + AMLD. The results revealed that AMHD and its integration with 75% chemical fertilizer greatly influenced the vegetative growth of soybean plants in experiments I and II. The number of pods per plant and pod length significantly (p < 0.05) increased under the CF 75% + AMLD treatment. In experiment I, soybean yield increased the most with the CF treatment, followed by CF 75% + AMLD. In contrast, in experiment II, the highest yield per hectare was observed in the CF 75% + AMLD treatment, followed by CF. The CF 75% + AMLD treatment significantly increased the SPAD value in both experiments. Consequently, a strong relationship was observed between the SPAD value and yield (r = 0.74) in experiment I and between SPAD and pod length (r = 0.82) in experiment II. Incorporating animal manure with chemical fertilizer significantly impacted soybean growth and yield, offering a potential possibility for reducing reliance on nitrogen fertilizer application to mitigate environmental pollution.

Comparative Technical and Economic Analyses of Hydrogen-Based Steel and Power Sectors

Decarbonizing the current steel and power sectors through the development of the hydrogen direct-reduction iron ore–electric arc furnace route and the 100% hydrogen-fired gas turbine cycle is crucial. The current study focuses on three clusters of research works. The first cluster covers the investigation of the mass and energy balance of the route and the subsequent application of these values in experiments to optimize the reduction yield of iron ore. In the second cluster, the existing gas turbine unit was selected for the complete replacement of natural gas with hydrogen and for finding the most optimal mass and energy balance in the cycle through an Aspen HYSYS model. In addition, the chemical kinetics in the hydrogen combustion process were simulated using Ansys Chemkin Pro to research the emissions. In the last cluster, a comparative economic analysis was conducted to identify the levelized cost of production of the route and the levelized cost of electricity of the cycle. The findings in the economic analysis provided good insight into the details of the capital and operational expenditures of each industrial sector in understanding the impact of each kg of hydrogen consumed in the plants. These findings provide a good basis for future research on reducing the cost of hydrogen-based steel and power sectors. Moreover, the outcomes of this study can also assist ongoing, large-scale hydrogen and ammonia projects in Uzbekistan in terms of designing novel hydrogen-based industries with cost-effective solutions.

An image encryption algorithm based on multi-layered chaotic maps and its security analysis

This study introduces a novel symmetric image encryption algorithm that employs a multi-layered architecture of chaotic maps, incorporating both confusion and diffusion components. The selected chaotic maps, namely Aizawa, Ricker, Sine-Circle, and Chirikov, have been well-established as effective sources of chaos in the existing literature. Within the proposed algorithm, these chaotic maps are strategically utilised in distinct layers to enhance the security of the encrypted image. The algorithm demonstrates applicability to real-world scenarios, distinguished by its simplicity, high-speed performance, and low computational complexity. The efficacy of the method is underlined by its dependence on both original images and secret keys, rendering it resilient against brute force and differential attacks. Rigorous experimentation, encompassing correlation, histogram analysis, NBCR, NPCR, UACI, PSNR, and NIST values, establishes the proposed encryption algorithm's robustness and security against diverse attack modalities, including differential and statistical approaches. These findings position the algorithm as a compelling solution for safeguarding sensitive information in various practical applications.

Prediction of fatigue crack behaviour of carbon fibre reinforced asphalt using fracture testing and modelling of the adhesive zone CZM

ABSTRACT Mechanical behaviour and fatigue failure characteristic of asphalt mixtures is complex under cyclic loading conditions and require better recognition due to the importance of these infrastructures. Fatigue tests are usually time-consuming, expensive, and require experience. These challenges increase when the mixtures contain a variety of additives that complicate the recognition of material behaviour. In this study, carbon fibres were investigated as additives with high tensile strength. After failure tests on SCB samples(5 different percentages of fibres and 5 different lengths), 1.5% fibres and 15 mm length were used as optimum values for other experiments for 3 fracture models (I,II, and mixed I&II modes). Monotonic and cyclic failure tests of mixtures containing carbon fibres are presented by Abaqus modelling to predict fatigue behaviour of these materials. For this purpose, the Cohesion Zone Model (CZM) was selected for modelling and the failure parameters obtained from the experiments were validated by the results of the modelling. Comparison of experimental and modelling results in the study of mulmouthle outputs indicates that the predicted and measured fatigue behaviour have good conformity. Achieving such models reduces the need for expensive and time-consuming experiments and provides a useful tool for examining other effective parameters.

Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution

Abstract. In accordance with progression in current capabilities towards high-resolution approaches, applying a convective-permitting resolution to global aerosol models helps comprehend how complex cloud–precipitation systems interact with aerosols. This study investigates the impacts of a double-moment bulk cloud microphysics scheme, i.e., NICAM Double-moment bulk Water 6 developed in this study (NDW6-G23), on the spatiotemporal distribution of aerosols in the Nonhydrostatic ICosahedral Atmospheric Model as part of the version-19 series (NICAM.19) with 14 km grid spacing. The mass concentrations and optical thickness of the NICAM-simulated aerosols are generally comparable to those obtained from in situ measurements. However, for some aerosol species, especially dust and sulfate, the differences between experiments of NDW6 and of the NICAM single-moment bulk module with six water categories (NSW6) were larger than those between experiments with different horizontal resolutions (14 and 56 km grid spacing), as shown in a previous study. The simulated aerosol burdens using NDW6 are generally lower than those using NSW6; the net instantaneous radiative forcing due to aerosol–radiation interaction (IRFari) is estimated to be −1.36 W m−2 (NDW6) and −1.62 W m−2 (NSW6) in the global annual mean values at the top of the atmosphere (TOA). The net effective radiative forcing due to anthropogenic aerosol–radiation interaction (ERFari) is estimated to be −0.19 W m−2 (NDW6) and −0.23 W m−2 (NSW6) in the global annual mean values at the TOA. This difference among the experiments using different cloud microphysics modules, i.e., 0.26 W m−2 or 16 % difference in IRFari values and 0.04 W m−2 or 16 % difference in ERFari values, is attributed to a different ratio of column precipitation to the sum of the column precipitation and column liquid cloud water, which strongly determines the magnitude of wet deposition in the simulated aerosols. Since the simulated ratios in the NDW6 experiment are larger than those of the NSW6 result, the scavenging effect of the simulated aerosols in the NDW6 experiment is larger than that in the NSW6 experiment. A large difference between the experiments is also found in the aerosol indirect effect (AIE), i.e., the net effective radiative forcing due to aerosol–cloud interaction (ERFaci) from the present to preindustrial days, which is estimated to be −1.28 W m−2 (NDW6) and −0.73 W m−2 (NSW6) in global annual mean values. The magnitude of the ERFaci value in the NDW6 experiment is larger than that in the NSW6 result due to the differences in both the Twomey effect and the susceptibility of the simulated cloud water to the simulated aerosols between NDW6 and NSW6. Therefore, this study shows the importance of the impacts of the cloud microphysics module on aerosol distributions through both aerosol wet deposition and the AIE.

Systematic Face Pareidolia Generation Method Using Cycle‐Consistent Adversarial Networks

Pareidolia is a psychological tendency of perceiving a face in non‐face stimulus. As a majority of people globally experience this tendency, it has been extensively studied and measured in terms of tendencies, such as frequencies. However, no study has investigated the systematic manipulation of stimulus owing to the lack of a systematic image‐generation method. Therefore, herein, we generated face pareidolia stimuli using a face data set with annotated data. We employed cycle‐consistent adversarial networks (CycleGAN), an image‐to‐image‐style translation framework, to generate stimuli for translating natural‐image styles from face images. We manipulated the weight of the cycle‐consistency loss in the CycleGAN via an experiment to evaluate the image generated using the CycleGAN. Thus, we found that the weight value of the evaluation experiment correlated with the pareidolia‐inducing power when the preprocessing of the face data set was applied to the blur process. As a result, we achieved to systematically generate pareidolia stimuli. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

STGAFormer: Spatial–temporal Gated Attention Transformer based Graph Neural Network for traffic flow forecasting

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS). However, the dynamic temporal variations in traffic flow, especially in potential occurrence of unexpected incidents, pose challenges to the prediction of traffic flow. This paper proposes a Spatial–temporal Gated Attention Transformer (STGAFormer) model based Graph Neural Network(GNN), leveraging the encoder architecture of the transformer. The gated temporal self-attention in the model, a novel module, can improve the model’s ability to make long-term predictions and handle sudden traffic incidents by enhancing the extraction of both local and global temporal features. Additionally, this paper proposes a distance spatial self-attention module to extract spatial features, which employs thresholding to selectively identify crucial features from both nearby and distant regions. In this way, the model’s ability to assimilate critical spatial information is promoted. Moreover, our model incorporates a diverse range of inputs, including traffic flow attributes, periodicity, proximity adjacency matrix, and adaptive adjacency matrix. Experiments from four real datasets demonstrate that STGAFormer achieves state-of-the-art performance, especially the MAE value of the PeMS08 dataset experiment is improved by 3.82%. This method offers valuable insights and robust support for future transportation planning.

Establishment of a Generalizable Industrial Crop Model for Microwave Extraction of Unsaturated Fatty Acids.

To explore the relationship between unsaturated fatty acid (UFA) content and parameters for microwave extraction, multimaterial and multiparameter testing was conducted in which five kinds of materials with different UFA contents (potato, wheat, corn, soybean, and peanut) were selected for the experiment. Four factors, namely, extraction temperature (X 1), extraction time (X 2), proportional volume of acetone (X 3), and liquid-to-solid ratio (X 4), were screened for their significant effects by using Prob > |T| values from the Plackett-Burman experiment. A microwave extraction orthogonal experiment with four factors and five levels was designed separately using Design-Expert 8.05 software and them concentrated. Microwave-accelerated methyl esterification was then performed, and the UFA content was determined via gas chromatography (flame ionization detector). The optimal extraction conditions (X 1, X 2, X 3, X 4) and the optimal UFA content of potato were 80.68°C, 10.74 min, 0.80, 3.25 mL × g-1, 1.08%; wheat: 80.81°C, 10.54 min, 0.80, 20.91 mL × g-1, 2.26%; corn: 81.18°C, 9.93 min, 0.80, 50.94 mL × g-1, 6.89%; soybean: 82.07°C, 9.07 min, 0.80, 93.87 mL × g-1, 15.81%; and peanut: 83.12°C, 8.11 min, 0.80, 168.70 mL × g-1, 33.07%. Then, the optimization results for the five kinds of materials were synthesized by Origin 8.0 software, the fitting degree of the cubic model with the four extraction factors was the highest, the determination coefficients were 0.9984, 0.9991, 0.8953, and 0.9989, the residual sums of squares were 0.2888, 0.1587, 0.8265, and 0.1864, and the correlation coefficients are ideal. The stability and accuracy of the model were verified by the orthogonal experiment of UFA extraction from rice, and the correlation coefficient between the predicted value and the actual value of the orthogonal experiment was 0.9998. This study systematically collates the optimal parameters for microwave extraction of UFA content in different crops from the perspective of multimaterial and multiparameter, which can provide a large amount of detailed basic data for microwave extraction technology.

Integrating mathematical approaches (IMAS): Novel methodology for predicting dermal absorption rates of chemicals under finite dose conditions.

Quantitative structure permeation relationship (QSPR) models have gained prominence in recent years owing to their capacity to elucidate the influence of physicochemical properties on the dermal absorption of chemicals. These models facilitate the prediction of permeation coefficient (Kp) values, indicating the skin permeability of a chemical under infinite dose conditions. Conversely, obtaining dermal absorption rates (DAs) under finite dose conditions, which are crucial for skin product safety evaluation, remains a challenge when relying solely on Kp predictions from QSPR models. One proposed resolution involves using Kroes' methodology, categorizing DAs based on Kp values; however, refinement becomes necessary owing to discreteness in the obtained values. We previously developed a mathematical model using Kp values obtained from in vitro dermal absorption tests to predict DAs. The present study introduces a new methodology, Integrating Mathematical Approaches (IMAS), which combines QSPR models and our mathematical model to predict DAs for risk assessments without conducting in vitro dermal absorption tests. Regarding 40 chemicals (76.1 ≤ MW ≤ 220; -1.4 ≤ Log Ko/w ≤ 3.1), IMAS showed that 65.0% (26/40) predictions of DA values were accurate to within twofold of the observed values in finite dose experiments. Compared to Kroes' methodology, IMAS notably mitigated overestimation, particularly for hydrophilic chemicals with water solubility exceeding 57.0 mg/cm3. These findings highlight the value of IMAS as a tool for skin product risk assessments, particularly for hydrophilic compounds.

Avaliação do modelo AquaCrop para simulação da resposta da soja sob estresse hídrico

ABSTRACT: Lately, irrigated soybean production has increased; therefore, tools that can aid water resources management must be improved. Two experiments were carried out, the first one from May to August and the second one from September to December 2019. The experimental design included randomized blocks with five treatments and four replicates. In the control treatment (SE), irrigation was carried out to meet the crop’s water demand during all growth stages. In the other treatments, irrigation was interrupted at specific crop growth stages (TI = VC-V4, TII = V5-R1, TIII = R1-R5, and TIV = R5-R7), and then restored. After calibration, the model satisfactorily estimated the canopy cover, biomass, and soybean yield, with high values of determination coefficients (r² > 0.90), low RMSE and MBE values, and high values of EF. In experiment 1, the soil water content was overestimated in the SE, TI, and TIV treatments by 0.24%, 0.78%, and 0.23%, respectively, and underestimated by 3.3% and 5.5% in the TII and TIII treatments, respectively. In experiment 2, in the SE, TI, TII, TIII, and TIV treatments, the soil water content was underestimated by 6%, 3%, 4.6%, 5.9%, and 6.7%, respectively. Real evapotranspiration was overestimated in all treatments in both experiments, with low r² values in both experiments 1 (0.11- 0.23) and 2 (0.04 - 0.21).

On the double-sided shock diffractions in quiescent and supersonic crossflows

Shock diffraction is a widespread phenomenon in aerospace applications, such as shock tunnel nozzle and jet exciter exit, impacting their performance significantly. This paper focuses on the transient evolution of double-sided shock diffraction in both quiescent and supersonic crossflows by unsteady numerical simulations. The characteristics of the shock wave and the vortex are revealed. In the quiescent flow, the double-sided shock diffraction exhibits remarkable symmetry. The diffracted shock retains a self-similar nature, but its intensity distribution displays non-uniform characteristics, which gradually weakens from the center to both sides. The vortices on both sides also exhibit symmetrical behavior, with their trajectory behaving in linear tendency. When the supersonic crossflow interacts with the diffracted shock, an upward-moving separation shock and an asymmetric diffracted shock are generated. The vortices remain confined beneath the boundary layer and exhibit different shapes. Moreover, due to the rapid motion of the separation shock, the relative Mach number is introduced into the free-interaction theory (FIT) to predict the shock angle of the separation shock. The F(x¯) values corresponding to the separation point and pressure plateau are determined to be 3.04 and 4.68, respectively. The results evaluated by modified FIT show a good agreement with the values of simulation and experiment.

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

The Hydrogeological Condition of Dibdibba aquifer between Karbala and Najaf in the middle of Iraq

The study area is located on the Dibdibba plateau in the middle of Iraq, particularly between the governorates of Karbala and Najaf. Given that the Dibdibba hydrogeological aquifer is thought to be the main source of water in this region, this site is chosen for performing the hydrogeological assessment. The study included 60 wells located on the west of the study area in Najaf Governorate. In these wells, the groundwater depths were measured in the field at the beginning of December 2021. The water stream was between 7.8 and 37.7 meters above the sea level, while the well depths ranged from 2.83 to 34 meters. Then, the spatial distribution of depths and water streams was mapped. Many wells were left by farmers because the water stream had gone down and the depth of the water had increased. The flow net of groundwater was determined to have two directions. The first direction was from the west to the east, and the second one was from the west and northwest to the southeast. The hydraulic properties of the Dibdibba aquifer in this study area were extracted by pumping tests. The experiments were carried out for five wells distributed over the area. The data was replaced with input into software for pumping tests, then analyzed by Newman's and Jacob's corrected methods for unconfined aquifers. Transmissivity values in experiments (T) ranged between 348 and 596 square meters/day, while hydraulic conductivity ranged between 14.1-22.5 m/day. The specific yield (Sy) equivalent to the storage coefficient in a confined aquifer was between 0.006 and 0.0302. The results of pumping test showed that the aquifer was of unconfined type according to the Neuman's and Jacob's corrected curves. In addition, the hydraulic property values were similar in all experiments with a slight difference, which may be related to the differences in saturated thickness, total depths, hydraulic gradient, and properties of rocks and sediments.

Fusion of the word2vec word embedding model and cluster analysis for the communication of music intangible cultural heritage

This article aims to propose a method for computing the similarity between lengthy texts on intangible cultural heritage (ICH), to facilitate the swift and efficient acquisition of knowledge about it by the public and promote the dissemination and preservation of this culture. This proposed method builds on traditional text similarity techniques. The ultimate goal is to group together those lengthy texts on ICH that exhibit a high degree of similarity. First of all, the word2vec model is utilized to construct the feature word vector of music ICH communication. This includes the acquisition of long text data on music ICH, the word segmentation of music ICH communication based on the dictionary method in the field of ICH, and the creation of a word2vec model of music ICH communication. A clustering algorithm analyzes and categorizes ICH communication within the music. This procedure involves employing text semantic similarity, utilizing a similarity calculation method based on optimized Word Mover Distance (WMD), and designing long ICH communication clustering. The main objective of this analysis is to enhance the understanding and classification of the intricate nature of ICH within the musical realm. Finally, experiments are conducted to confirm the model’s effectiveness. The results show that: (1) the text word vector training based on the word2vec model is highly accurate; (2) with the increase in K value, the effect of each category of intangible word vector is improving; (3) the final F1-measure value of the clustering experiment based on the optimized WMD is 0.84. These findings affirm the usefulness and accuracy of the proposed methodology.