Application of Chlorophyll Fluorescence Analysis Technique in Studying the Response of Lettuce (Lactuca sativa L.) to Cadmium Stress.

To reveal the impact of cadmium stress on the physiological mechanism of lettuce, simultaneous determination and correlation analyses of chlorophyll content and photosynthetic function were conducted using lettuce seedlings as the research subject. The changes in relative chlorophyll content, rapid chlorophyll fluorescence induction kinetics curve, and related chlorophyll fluorescence parameters of lettuce seedling leaves under cadmium stress were detected and analyzed. Furthermore, a model for estimating relative chlorophyll content was established. The results showed that cadmium stress at 1 mg/kg and 5 mg/kg had a promoting effect on the relative chlorophyll content, while cadmium stress at 10 mg/kg and 20 mg/kg had an inhibitory effect on the relative chlorophyll content. Moreover, with the extension of time, the inhibitory effect became more pronounced. Cadmium stress affects both the donor and acceptor sides of photosystem II in lettuce seedling leaves, damaging the electron transfer chain and reducing energy transfer in the photosynthetic system. It also inhibits water photolysis and decreases electron transfer efficiency, leading to a decline in photosynthesis. However, lettuce seedling leaves can mitigate photosystem II damage caused by cadmium stress through increased thermal dissipation. The model established based on the energy captured by a reaction center for electron transfer can effectively estimate the relative chlorophyll content of leaves. This study demonstrates that chlorophyll fluorescence techniques have great potential in elucidating the physiological mechanism of cadmium stress in lettuce, as well as in achieving synchronized determination and correlation analyses of chlorophyll content and photosynthetic function.

Improving denitrification estimation by joint inclusion of suspended particles and chlorophyll a in aquaculture ponds.

The denitrification process in aquaculture systems plays a crucial role in nitrogen (N) cycle and N budget estimation. Reliable models are needed to rapidly quantify denitrification rates and assess nitrogen losses. This study conducted a comparative analysis of denitrification rates in fish, crabs, and natural ponds in the Taihu region from March to November 2021, covering a complete aquaculture cycle. The results revealed that aquaculture ponds exhibited higher denitrification rates compared to natural ponds. Key variables influencing denitrification rates were Nitrate nitrogen (NO3--N), Suspended particles (SPS), and chlorophyll a (Chla). There was a significant positive correlation between SPS concentration and denitrification rates. However, we observed that the denitrification rate initially rose with increasing Chla concentration, followed by a subsequent decline. To develop parsimonious models for denitrification rates in aquaculture ponds, we constructed five different statistical models to predict denitrification rates, among which the improved quadratic polynomial regression model (SQPR) that incorporated the three key parameters accounted for 80.7% of the variability in denitrification rates. Additionally, a remote sensing model (RSM) utilizing SPS and Chla explained 43.8% of the variability. The RSM model is particularly valuable for rapid estimation in large regions where remote sensing data are the only available source. This study enhances the understanding of denitrification processes in aquaculture systems, introduces a new model for estimating denitrification in aquaculture ponds, and offers valuable insights for environmental management.

Multi-site and long-term estimation of phosphorus dynamics for paddy surface water in China based on new MLEpaddy-P.

Paddy surface water serves as the primary source of artificial drainage and rainfall runoff leading to phosphorus (P) loss from paddy fields. The quantification of P dynamics in paddy surface water on a large scale is challenging due to the spatiotemporal heterogeneity of influencing factors and the limitations of field measurements. Based on 1226 data sets from 33 field sites covering the three main rice-growing regions of China (the Southeast Coast, the Yangtze River Basin, and the Northeast Plain), we analyzed the spatiotemporal characteristics of P attenuation in paddy surface water and its influencing factors. A new multi-site and long-term phosphorus estimation model for paddy (MLEpaddy-P) was proposed to evaluate the total phosphorus (TP) dynamics at national scale by improving the initial concentration (C0) and attenuation coefficient (k) of the first-order kinetic model (Ct=C0∙e-k(t-1)). Our study showed that: (1) Fertilizer amounts, soil organic matter content, soil Olsen-P content, soil pH, and soil total phosphorus are the primary factors affecting the variation of C0 and k; (2) Yangtze River Basin possessed the highest C0 (6.87 ± 12.97 mg/L) and high k ≤ 7 (0.262 in 1-7 days after fertilization), followed by Southeast Coast (4.15 ± 5.33 mg/L; 0.263) and Northeast Plain (1.33 ± 1.50 mg/L; 0.239), respectively; (3) MLEpaddy-P performed well in daily TP dynamics estimation at national scale with R2 of 0.74-0.85; (4) Middle and lower reaches of the Yangtze River Basin were the critical regions with high TP concentration due to high fertilizer amount and soil Olsen-P content. The new universal model realizes the multi-site and long-term estimation of P dynamics while greatly saving multi-site monitoring costs. This study provides a basis for early warning and targeted control of P loss from paddies.

Utilising raw psycho-physiological data and functional data analysis for estimating mental workload in human drivers.

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.

This study aimed to estimate the mental workload of human drivers using physiological signals and Functional Data Analysis (FDA). By comparing models using raw data and extracted features, the results show that the FDA with raw data achieved a high accuracy of 90%, outperforming the model with extracted features (73%).

Maternal exposure to fine particulate matter and congenital heart defects during preconception and pregnancy period: A cohort-based case-control study in the Taiwan maternal and child health database.

BACKGROUND: Few studies have explored the association between maternal exposure to particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5) and congenital heart defects occurring before and during pregnancy. We aimed to investigate the association and the critical time windows between the maternal exposure to PM2.5 and congenital heart defects. METHOD: We conducted a cohort-based case-control study of 507,960 participants obtained from the Taiwan Maternal and Child Health Database between 2004 and 2015. We applied satellite-based spatiotemporal models with 1-km resolution to calculate the average PM2.5 concentration during preconception and the specific periods of pregnancy. We also performed conditional logistic regression with distributed lag non-linear models (DLNMs) to assess the effects of weekly average PM2.5 on both congenital heart defects and their isolated subtypes, as well as the concentration-response relationships. RESULTS: In DLNMs, exposure to PM2.5 (per 10 µg/m3) during weeks 7-12 before conception and weeks 3-9 after conception was associated with congenital heart defects. The strongest association at 12 weeks before conception (odds ratio [OR] = 1.026, 95% confidence intervals [CI]: 1.012-1.040) and 7 weeks after conception (OR = 1.024, 95% CI: 1.012-1.036) for every 10 µg/m3 increase in PM2.5 concentration. In modification analysis, strongest associations were observed for low SES. CONCLUSIONS: Our study revealed that exposure to ambient PM2.5 raises the risk of congenital heart defects, particularly among individuals with lower socioeconomic status. Moreover, our findings suggest that preconception exposure to PM2.5 may be a crucial period for the development of congenital heart defects.

State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles.

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination.

In computational color constancy, regressing illumination is one of the most common approaches to manifesting the original color appearance of an object in a real-life scene. However, this approach struggles with the challenge of accuracy arising from label vagueness, which is caused by unknown light sources, different reflection characteristics of scene objects, and extrinsic factors such as various types of imaging sensors. This article introduces a novel learning-based estimation model, an aggregate residual-in-residual transformation network (ARiRTN) architecture, by combining the inception model with the residual network and embedding residual networks into a residual network. The proposed model has two parts: the feature-map group and the ARiRTN operator. In the ARiRTN operator, all splits perform transformations simultaneously, and the resulting outputs are concatenated into their respective cardinal groups. Moreover, the proposed architecture is designed to develop multiple homogeneous branches for high cardinality, and an increased size of a set of transformations, which extends the network in width and in length. As a result of experimenting with the four most popular datasets in the field, the proposed architecture makes a compelling case that complexity increases accuracy. In other words, the combination of the two complicated networks, residual and inception networks, helps reduce overfitting, gradient distortion, and vanishing problems, and thereby contributes to improving accuracy. Our experimental results demonstrate this model's outperformance over its most advanced counterparts in terms of accuracy, as well as the robustness of illuminant invariance and camera invariance.

An Interface Setup Optimization Method Using a Throughput Estimation Model for Concurrently Communicating Access Points in a Wireless Local Area Network.

The IEEE 802.11 wireless local-area network (WLAN) has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining access points (APs). To address this problem, we studied the AP transmission power optimization method, which selects the maximum or minimum power supplied to each AP so that the average signal-to-interference ratio (SIR) among the concurrently communicating APs is maximized.However, this method requires measurements of receiving signal strength (RSS) under all the possible combinations of powers. It may need intolerable loads and time as the number of APs increases. It also only considers the use of channel bonding (CB), although non-CB sometimes achieves higher performance under high levels of interference. In this paper, we present an AP interface setup optimization method using the throughput estimation model for concurrently communicating APs. The proposed method selects CB or non-CB in addition to the maximum or minimum power for each AP. This model approach avoids expensive costs of RSS measurements under a number of combinations. To estimate the RSS at an AP from another AP or a host, the model needs the distance and the obstacles between them, such as walls. Then, by calculating the estimated RSS with the model and calculating the SIR from them, the AP interface setups for a lot of APs in a large-scale wireless network can be optimized on a computer in a very short time. For evaluation, we conducted extensive experiments using Raspberry Pi for APs and Linux PCs for hosts under 12 network topologies in three buildings at Okayama University, Japan, and Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. The results confirm that the proposed method selects the best AP interface setup with the highest total throughput in any topology.

Adaptive Inertial Sensor-Based Step Length Estimation Model.

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.

Integrating optimization and damage estimation to increase economic benefit and ensure food security under seasonal land variability.

Rapidly increasing population and rising food demand necessitates expansion of agricultural mainland. However, with accelerated urbanization, agricultural land resources are difficult to expand. The riverine sandbars offer a vast fertile terrain that is conducive to agricultural food production and could support the global food demands despite urbanization. Nevertheless, hydroclimatic factors like streamflow and precipitation have an impact on the availability of agricultural land and potential crop damage in riverine ecosystems. The present study determines the agricultural economic benefits from cultivation in riverine sandbars through optimization framework under stochastic streamflow. A damage estimation model was developed to evaluate the economic losses an optimally planned riverine agricultural area would suffer, if the flow variation exceeds a certain threshold. The findings showed that economic benefit of ∼130 million rupees could be achieved with ∼22 million rupees of additional benefit from the proposed optimization approach. This additional benefit was in reference to the selective cropping approaches, which the farmers did not account. Furthermore, the damage estimation model could comprehend the losses under fluctuating streamflow in subsequent years that was found to vary between 5 and 34 percent. Therefore, this framework of integrating optimization and damage estimation approaches contribute to a better understanding of optimally utilizing the riverine sandbars, thereby improving the socio-economic status of marginalized communities and providing potential additional land resources to sustain food security and production. The study also highlighted the need of crop insurance facilities to assess and manage risks that could provide financial support to farmers, cover crop loss and damage arising from hydroclimatic variabilities.