Minimum Mean Research Articles

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Climate change is one of the worst environmental issues, with a negative impact on most developing countries across the globe and in some regions, including Ethiopia. This study seeks to establish the temporal and spatial changes in rainfall for the period 1987–2021. In this study, ordinary statistical measures, such as the mean and coefficient of variation, precipitation concentration index (PCI), and standardized anomaly index (SAI), were applied to investigate the rainfall variation. Concerning the estimation of the spatiotemporal distribution and magnitude of changes in, non-parametric Mann-Kendall (MK) tests, Sen’s slope estimator, and innovative trend analysis (ITA) were also conducted in ArcGIS 10.8 environment and XLSTAT/R. The study showed significant fluctuations in rainfall in the Wolaita zone, with minimum mean annual rainfall in 1997 and maximum rainfall in 2003. All but the Belg season had more negative seasonal anomalies than positive ones. The annual rainfall for each of the AEZs in different parts of the country ranged from one another; it was 969.03 mm in a southeast lowland AEZ and 1648.75 mm in the northwest highland AEZ. Rainfall was not uniformly distributed throughout the year and study area; the highland AEZs received more rainfall in the Belg and Kermit seasons than in the lowland seasons. In the ordinary kriging results, the extent of variability in the CV of the mean annual rainfall for each zone was identifiable. The southwest lowland AEZ produced a CV of 24.22% with a decrease in rainfall amounts, and the northeast highland AEZ produced a CV of 31.63% of the rainfall distribution amounts. This area includes lowland and highland AEZs of the northeastern part of the study area’s rainfall, which is moderately distributed by PCI. This information is helpful when attempting to associate development and cropping systems with temporal and spatial climatic patterns with respect to rainfall for agro-climatological activities and projects or flood regulatory measures.

Advanced beamforming and reflection control in intelligent reflecting surfaces with integrated channel estimation

AbstractIntelligent Reflecting Surfaces (IRS) enhance wireless communication by optimising signal reflection from the base station (BS) towards users. The passive nature of IRS components makes tuning phase shifters difficult and direct channel measurement problematic. This study presents a machine learning framework that directly maximises the beamformers at the BS and the reflective coefficients at the IRS, bypassing conventional methods that estimate channels before optimising system parameters. This is achieved by mapping incoming pilot signals and data, including user positions, with a deep neural network (DNN), guiding an optimal setup. User interactions are captured using a permutation‐invariant graph neural network (GNN) architecture. Simulation results show that implicit channel estimation method requires fewer pilots than standard approaches, effectively learns to optimise sum rate or minimum‐rate targets, and generalises well. Specifically, the sum rate for GDNNet (GNN + DNN) improves by over linear minimum mean square error (LMMSE) and by over perfect CSI concerning the number of users, and by over LMMSE and by over perfect CSI concerning pilot length. Offering a feasible solution with reduced computing complexity for real‐world applications, the proposed GNN + DNN method outperforms conventional model‐based techniques such as LMMSE and approaches the performance of perfect CSI, demonstrating its high effectiveness in various scenarios.

PREDICTION BASED ON MACHINE LEARNING OF TOOTH SENSITIVITY FOR IN-OFFICE DENTAL BLEACHING

To develop a supervised machine learning model to predict the occurrence and intensity of tooth sensitivity (TS) in patients undergoing in-office dental bleaching testing various algorithm models. Retrospective data from 458 patients were analyzed, including variables such as the occurrence and intensity of TS, basal tooth color, bleaching material characteristics (concentration and pH), intervention details (number and duration of applications), and patient age. Classification and regression models were evaluated using 5-fold cross-validation and assessed based on various performance parameters. For the predictive classification task (occurrence of TS), the developed models achieved a maximum area under the receiver operating characteristic curve (AUC) of 0.76 [0.62-0.88] on the test data, with an F1-score of 0.80 [0.71-0.87]. In cross-validation, the highest AUC reached 0.86 [0.84-0.88], and the highest F1-score was 0.78 [0.75-0.83]. For predicting TS intensity, the regression models demonstrated a minimum mean absolute error (MAE) of 1.76 [1.45-2.06] and a root mean square error (RMSE) of 2.38 [2.06-2.69] on the test set. During cross-validation, the lowest MAE was 1.84 [1.67-2.03], with an RMSE of 2.39 [2.20-2.58]. The supervised machine learning model for estimating the occurrence and intensity of TS in patients undergoing in-office bleaching demonstrated good predictive power. The Gradient Boosting Classifier and Support Vector Machine Regressor algorithms stood out as having the greatest predictive power among those tested. These models can serve as valuable tools for anticipating tooth sensitivity in this patient population, facilitating better post-treatment management and control.

A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R2) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact.

Evaluation of phthalate acid esters (PAEs) in kefir samples by using magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI)/GC–MS method: A health risk assessment study

In this study, by magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI) −gas chromatography/mass spectrometry (GC/MS) method was applied to analyze six PAE compounds in kefir samples. The results obtained from kefir samples showed that the lowest and highest mean of PAEs were associated to dimethyl phthalate (DMP was not detected or nd) and di-n-octyl phthalate (DNOP, 2.12 µg/kg), respectively. Also according to results the average of DEHP (Bis(2-ethylhexyl)phthalate) and total PAEs in all samples were 1.31 ± 0.72 and 4.58 ± 1.87 µg/kg, respectively. Furthermore, maximum and minimum mean of total PAEs were detected in the packages with a volume of 1000 mL (5.47 ± 2.03 μg/kg) and packages with a volume of 1500 mL (3.69 ± 1.23 μg/kg), respectively, and the maximum and minimum mean total PAE (μg/kg) were related to samples with a date less than the expiration date (5.42 ± 1.46) and samples with a date greater than the production date (3.73 ± 1.91), respectively. The level of these contaminants was lower than the existing standard levels. The assessment of human health risks, comprising risks of non-carcinogenic and carcinogenic were calculated with Monte Carlo simulation (MCS) and sensitivity analysis under certain removal efficiencies from 5 to 95 %. The result of the carcinogenic risk for DEHP showed that adults are not threatened by the consumption of kefir (1.06E-05), while children are exposed to negligible risk the consumption of kefir (1/48E-04). In all samples, the total non-carcinogenic risk of PAE compounds was less than 1 (TTHQ was 2.03E-04 and 4.33E-05 for children and adults, respectively), this values presenting there would be unlikely risks of non-carcinogenic of PAEs in kefir for consumers. In conclusion, it can be stated it doesn’t pose any threat to Iranian consumers (adults and children).

The Spatiotemporal Characteristics and Driving Factors of Soil Degradation in the Black Soil Region of Northeast China

The Northeastern Black Soil Region in China is recognized as one of the three major black soil regions globally and is often regarded as a cornerstone of national food security. However, prolonged agricultural practices have led to increasingly severe soil degradation, and the mechanisms and driving factors behind the degradation of soil quality remain unclear. Therefore, this study examines the historical and current characteristics of soil quality, focusing on major influencing factors, such as the 70-year history of reclamation and climate change. By accessing different databases, reviewing the relevant literature, and performing Pearson correlation and redundancy analyses (RDA), this study investigated the variation patterns of significant soil quality indicators and their driving factors in the 0–20 cm soil layer along the latitudinal direction (Nenjiang, Beian, Hailun, and Harbin) in the typical black soil region of Northeast China. The main conclusions are as follows: the soil organic matter (SOM) content experienced a rapid decline in the 30 years preceding cultivation (1950~1980), with the greatest decline rate in the Beian area (about 1.10 g/kg per year). The SOM in the Beian, Hailun, and Harbin areas decreased from north to south, changing at rates of 9.40–21.67 g/kg/degree and 0.15–0.34 g/kg/m with latitude and elevation, respectively. Elevation impacts the annual rate of change in soil quality indicators through its influence on the annual mean maximum temperature (AMXT) and annual atmospheric pressure (AP). AMXT and AP exhibit a linear relationship with elevation, based on which regression models were established. The key factors influencing soil quality indicators in the black soil region include cultivation years (Y), annual mean maximum and minimum temperatures (AMXT and AMNT), annual relative humidity (ARH), and AP. An increase in chemical fertilizer application is among the critical factors affecting soil pH. Additionally, the extensive use of agricultural machinery can reduce soil porosity and cause water and salt accumulation, ultimately leading to a decline in soil pH. This study offers theoretical support for mitigating soil degradation in Northeast China’s black soil region, thereby contributing to national food security and promoting sustainable development.

Automatic regularization for linear MMSE filters

In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the Bayesian approach, the regularization parameter is found from the observed signals via simple closed-form fixed-point iteration. The proposed automatic regularization approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.

Channel Mixer Layer: Multimodal Fusion Toward Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content

AbstractThe spatiotemporal distribution of Total Electron Content (TEC) in ionosphere determines the refractive index of electromagnetic wave leading to the radio signal scintillation and deterioration. Thanks to the development of machine learning for video prediction, spatiotemporal predictive models are applied on the future TEC map prediction based on the graphic features of past frames. However, output result of graphic prediction is unable to properly respond to the external factor variations such as solar or geomagnetic activity. Meanwhile, there is still neither standard data ‐set nor comprehensive evaluation framework for spatiotemporal predictive learning of TEC map sequences leading to the comparisons unfair and insights inconclusive. In this research, a new feature‐level multimodal fusion method named as channel mixer layer for machine reasoning is proposed that can be embedded into the existing advanced spatiotemporal sequence prediction models. Meanwhile, all performance benchmarks are accomplished on the same running environment and newly proposed largest scale data set. Experiment results suggest that the multimodal fusion prediction of existing model backbones by proposed method improves the prediction accuracy up to 15% with almost the same computational complexity compared to that of graphic prediction without auxiliary factors input, having the real‐time inference speed of 34 frames/second and minimum mean absolute error of 0.94/2.63 TEC unit during low/high solar activity period respectively. The channel mixer layer embedded models can respond to the variations of auxiliary external factors more correctly than previous multimodal fusion methods such as concatenation and arithmetic, which is regarded as the evidence of state‐of‐the‐art machine reasoning ability.

Safety and Efficacy of BioMime Sirolimus-Eluting Stent System in All-Comers Real-World Population With Coronary Artery Stenosis: MILES Global Registry.

This study evaluated the safety and efficacy of BioMime sirolimus-eluting stent (SES) system, with an ultra-low strut thickness (65 µm), in real-world all-comers population with coronary artery stenosis (CAD). This was a post-marketing, multicenter, single-arm, observational clinical registry among patients undergoing intervention for CAD. Patients were clinically followed up at 1, 9, 12, and 24 months after the index percutaneous coronary intervention. Four major indications, namely long stents of > 30 mm, stents with diameters of 4 and 4.5 mm, bifurcation subgroup, and chronic total occlusion (CTO) were evaluated as pre-specified subsets. A total of 771 patients (1,079 treated lesions) from 23 sites were included in this study. The mean length and diameter of the implanted stents were 25.57 ± 9.35 mm and 3.00 ± 0.44 mm, respectively. The mean minimum lumen diameter before and after the procedure was 1.00 ± 1.69 mm and 2.96 ± 1.35 mm, respectively. The cumulative rates of major adverse cardiovascular events (MACEs) and stent thrombosis (ST) at 1, 9, 12, and 24 months were 1.05%, 3.13%, 4.04%, 5.64% and 0%, 0.13%, 0.28%, 0.28%, respectively. In a subset with > 30 mm long stents, the cumulative rate of MACEs was 0.4%, 4.6%, 5.12%, and 7.01% at 1, 9, 12, and 24 months, respectively. The corresponding rates of ST were 0%, 0.42%, 0.43%, and 0.44%, indicating constant rate of ST after 9 months. In a subset of 4 and 4.5 mm diameter stents, the cumulative rate of MACEs was high (0%, 6.25%, 6.25%, and 10.41%) at 1, 9, 12, and 24 months, respectively. However, there was no case of ST until 24 months. In patients with bifurcation lesions, the cumulative rates of MACEs and ST were 2.46%, 6.32%, 11.53%, 16.21% and 0%, 1.27%, 1.28%, 1.35% at 1, 9, 12, and 24 months follow-up. In patients with chronic total occlusion, the cumulative rates of MACEs and ST were 0.79%, 5.04%, 6.83%, 7.07% and 0%, 0.84%, 0.85%, 0.88% at 1, 9, 12, and 24 months, respectively, indicating constant rate of ST after 9 months. The BioMime SES demonstrated good safety and efficacy outcomes at 24-month follow-up, with low rates of MACEs and ST in patients with CAD in the real-world setting.

Indoor heat in Amsterdam: Comparing observed indoor air temperatures from a professional network and from a citizen science approach

Ongoing climate change is increasing summertime temperatures, and frequency and intensity of heatwaves in Europe, which can threaten human health. Relatively little is known about how quickly outdoor heat penetrates into residences during heatwaves. Long-term and systematic networks recording indoor temperatures are challenging to install and maintain, and therefore scarce. We first report on crowdsourced indoor air temperature data in residences in Amsterdam (The Netherlands) during a heatwave event in September 2023. These data complement professional long-term indoor air temperature observations in 92 houses in Amsterdam. Second, we document the lessons learnt in the design and execution of this citizen science activity. 571 indoor temperature records were collected through the citizen science crowdsourcing approach, with a median value of 28.0 °C on the warmest day in the study period, while outdoor mean minimum and maximum temperatures reached 20.6 °C and 31.1 °C respectively. The results indicate that the crowdsourcing approach reports temperatures that are significantly higher than the professional approach, which supports the need for professional indoor networks. Finally, local media attention was critical in reaching a wide audience.

Deep learning based signal processing and detection for multiple medical devices OFDM systems

In general multiple medical devices orthogonal frequency-division multiplexing (OFDM) communication systems, all the interfering medical users are legitimate but will cause disturbance to the desired user. In this work, we evaluate three deep learning (DL) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices OFDM communications systems. The bit error rates (BER) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio, signal-to-noise ratio, the number of interferences, and modulation type are investigated. Numerical results show that DL methods outperform LMMSE under different multiple medical device interference situations and are robust when the wireless channel has high variability. Also, DL methods are proven to have strong anti-interference ability and are useful in multiple medical devices OFDM systems.

A retrospective two-center cohort study of the bidirectional relationship between depression and tinnitus-related distress

BackgroundTinnitus can cause considerable psychological distress among patients, particularly if comorbidities occur. Despite a strong relationship between tinnitus-related distress and depression, the underlying mechanisms represent a long-standing question. By investigating the co-development of tinnitus-related distress and depressiveness throughout therapy, we capture the dynamic interplay of both conditions and uncover underlying common features mediating their link.MethodsLarge datasets from two different day clinics in Germany have been analyzed using a regularization method for predictor selection (analysis 1) and latent growth curve modeling (LCM; analysis 2). Tinnitus-related distress was assessed using the Tinnitus Questionnaire (TQ). All patients have been experiencing chronic subjective tinnitus with a minimum mean severity level of TQ grade 2. Treatment at both day clinics involved tinnitus management according to clinical guidelines with minor idiosyncratic differences. Analysis 1 was performed on a dataset of 500 patients who received the Jena Interdisciplinary Treatment for Tinnitus (JITT) for 5 consecutive days between 2013 and 2017. Analysis 2 was performed on a second dataset, which included 1016 patients treated at the Tinnitus Center of the Charité Universitätsmedizin Berlin for 7 days between 2011 and 2015.ResultsHere, we show a substantial bidirectional relationship between tinnitus-related distress and depression severity while emphasizing the role of somatic symptoms and perceived stress in the experience and maintenance of tinnitus awareness. The LCM provides adequate model fit (CFI = 0.993, SRMR = 0.016).ConclusionsOur results indicate enhanced therapy success in depression when tinnitus-related distress is addressed and vice versa. The combined treatment of tinnitus and depression is proposed for future treatment strategies.

Robust Control Strategy of Acoustic Micro Robots Based on Fuzzy System.

This study presents a robust control strategy for acoustic micro robots utilizing a novel interval type-three fuzzy system. Micro robots driven by acoustic forces face significant challenges in fluid environments due to complex nonlinearities, uncertainties, and disturbances. To address these issues, we propose a control framework that combines fuzzy logic and sliding mode control to enhance the stability and trajectory tracking performance of micro robots under varying fluid conditions. The interval type-3 fuzzy logic system provides increased robustness by better handling external disturbances and uncertainties compared to the robustness of the traditional methods. The experimental results from one-dimensional, two-dimensional, and three-dimensional fluid cavities demonstrate that the proposed control method significantly improves tracking accuracy, reducing the errors in complex environments. This control framework offers promising potential for the precise manipulation of micro robots in biomedical applications and other microfluidic systems. The minimum trajectory tracking control mean square error is 12.82 μm.

Exploring the effects of extreme events on cereal cropping systems in the Ganga River basin, Haryana

ABSTRACT Recent studies have highlighted the profound impact of global warming on climate patterns worldwide, but few have specifically addressed its consequences for crop yields. This study aims to bridge that gap by examining the trends of extreme events and their effects on agriculture in adjacent Ganga River Basin Haryana districts from 1981 to 2020, focusing on the Expert Team on Climate Change Detection, Monitoring and Indices. The study area experienced increasing mean maximum and minimum temperatures, raising drought concerns, especially in Sonipat and Panipat districts. Drought indices showed prolonged events in these areas, contrasting with shifting wet-dry patterns in Yamuna Nagar and fluctuating conditions in Karnal. An analysis from 1998 to 2020 revealed intricate relationships between climate factors and rice, wheat, and pearl millet production, with rising temperatures significantly impacting crop yields. Notably, both mean maximum and minimum temperatures have increased, with a significant daytime and nighttime warming trend. Extreme maximum temperature and diurnal temperature range indices were found to negatively impact crop yields, whereas precipitation extremes demonstrated positive correlations with yield outcomes. Collaborative efforts between policymakers and farmers to integrate climate-resilient practices and continuous monitoring are crucial for ensuring food security and sustainable farming amidst climate variability.

Low-Complexity Convolutional Neural Network for Channel Estimation

This paper presents a deep learning algorithm for channel estimation in 5G New Radio (NR). The classical approach that uses neural networks for channel estimation requires more than one stage to obtain the full channel matrix. First, the channel has to be constructed by the received reference signal, and then, the precision is improved. In contrast, to reduce the computational cost, the proposed neural network method generates the channel matrix from the information captured from a few subcarriers along the slot. This information is extrapolated by applying the Least Square technique only on the Demodulation Reference Signal (DMRS). The received DMRS placed in the grid can be seen as a 2D low-resolution image and it is processed to generate the full channel matrix. To reduce complexity in the hardware implementation, the convolutional neural network (CNN) structure is selected. This solution is analyzed comparing the Mean Square Error (MSE) and the computational cost with other deep learning-based channel estimation, as well as the traditional channel estimation methods. It is demonstrated that the proposed neural network delivers substantial complexity savings and favorable error performance. It reduces the computational cost by an order of magnitude, and it has a maximum error discrepancy of 0.018 at 5 dB compared to Minimum Mean Square Error (MMSE) channel estimation.

Effects of Autologous Platelet Concentrate Injection on Persistent Macular Hole

Purpose: To evaluate the effects of secondary pars plana vitrectomy (PPV) with autologous platelet concentrate (APC) injection in patients with a persistent macular hole that failed to close after the primary surgery.Methods: This study included 10 eyes of 10 patients who required secondary PPV and APC injection due to unsuccessful hole closure following primary macular hole surgery between February 2021 and July 2023. We measured the minimum and base diameters of the holes before primary and secondary PPV, respectively. Furthermore, we evaluated the anatomical closure of the holes and changes in best corrected visual acuity (BCVA) for 6 months following secondary PPV.Results: The mean age of the 10 patients was 61.6 ± 9.5 years. Prior to the primary PPV, the mean BCVA (LogMAR) was 1.6 ± 0.7, the mean minimum diameter of the hole was 654.4 ± 98.9 μm, and the mean base diameter of the hole was 1,135.4 ± 276.5 μm. Prior to the secondary PPV with APC injection, the mean BCVA (LogMAR) was 1.3 ± 0.7, the mean minimum diameter of the hole was 666.8 ± 270.2 μm and the mean base diameter of the hole was 845.4 ± 292.4 μm. Following secondary surgery, all holes were successfully closed, the mean BCVA (LogMAR) was 0.9 ± 0.6 and the BCVA was significantly improved (<i>p</i> = 0.007).Conclusions: PPV with APC injection can be an effective therapeutic option for persistent macular hole that failed to close following primary surgery both in terms of anatomical and functional outcomes.

Improving predictive accuracy for punching shear strength in fiber-reinforced polymer concrete slab-column connections via robust deep learning

Amidst the evolving landscape of structural engineering, this study addresses the pressing need for a robust, data-driven model in the context of alternative reinforcement techniques such as Fiber-Reinforced Polymer (FRP) bars and the application of Machine Learning (ML) methodologies for predicting the punching shear strength (PSS). Structural engineering heavily relies on accurate predictions of PSS for ensuring the safety and durability of various constructions. However, existing models often lack the comprehensiveness and precision required to effectively address the diverse and complex factors influencing PSS in FRP-reinforced concrete structures. This research explored the shear strength of FRP-reinforced concrete slabs, with a particular focus on the different types of FRP, namely Carbon Fiber-Reinforced Polymer (CFRP), Basalt Fiber-Reinforced Polymer (BFRP), Glass Fiber-Reinforced Polymer (GFRP), and Hybrid Fiber-Reinforced Polymer (HFRP). Leveraging an innovative deep learning approach, the study utilizes a dataset comprising 285 samples gathered from 56 distinct studies to train models that incorporate 11 dependent variables. This comprehensive approach aims to capture the intricate interplay between geometric, material, and other pertinent factors influencing PSS. The study employs a connection weight method to evaluate the significance of various features and introduces a novel Graphical User Interface (GUI) to enhance user interaction with the Deep Neural Network (DNN) model designed for PSS prediction. Through meticulous experimentation and analysis, the research identifies an optimal DNN architecture that achieves a minimum Mean Absolute Error (MAE) of 0.81, validated through k-fold cross-validation. Key conclusions highlight the critical influence of specific features, such as slab depth, column-to-slab area ratio, and modulus of elasticity, on the predictive accuracy of the model. Conversely, certain factors like slab area, reinforcement ratio, and concrete compressive strength negatively impact predictions. SHAP analysis further elucidates the significance of FRP type and column length-to-width ratio as pivotal factors affecting PSS predictions. These findings underscore the potential of the developed model in enhancing the dependability and practicality of structural engineering applications. Moreover, they point towards future research directions aimed at refining predictive accuracy and exploring broader applications in the field of building engineering.

The INS/GPS integrated navigation based on autonomous underwater vehicle using modified extended Kalman filter

Ensuring highhtab accuracy and robustness remains a paramount concern in the realm of underwater positioning and navigation research. This paper proposes a filtering algorithm grounded in Inertial Measurement Unit/Global Positioning System (INS/GPS) integrated navigation to effectively address the challenges posed by non-Gaussian noise, stemming from outliers and measurement inaccuracies. Currently, most filtering algorithms are developed based on the criteria of minimum mean square error (MMSE) or maximum entropy criteria (MCC). To address the challenge of handling complex non-Gaussian noises, this paper uses the Minimum Error Entropy Criterion (MEE) and Extended Kalman Filter (EKF) with Kernel Risk-Sensitive Loss (KRSL) instead of MMSE or MCC to develop an online filtering algorithm with a recursive process, and adjusts the kernel size of MEE within a reasonable range by constructing an adaptive factor to deal with outliers generated during measurement and excessive convergence caused by the inapplicability of the kernel size. Through extensive target tracking simulations and surface Autonomous Underwater Vehicle (AUV) localization experiments, results demonstrate the efficacy of the proposed algorithm. Notably, the algorithm optimizes kernel sizes for different types of noises, ensuring robust state estimation and rapid convergence without compromising filtering accuracy.

Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration.

The accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we proposed an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implemented a multi-frequency microwave sensing system for PCC measurement. This method transcended the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measured insertion loss data across the sensor cross-section at various frequencies and utilized PCA to reduce the dimensionality of high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm was applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing the measurement accuracy through multi-dimensional fusion. The experimental results demonstrated that the multi-frequency microwave sensing system effectively extracted features from the high-dimensional PCC samples and selected the optimal frequency combination. Filed experiments conducted on five coal mills showed that, within a common PCC range of 0-0.5 kg/kg, the system achieved a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system could quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.

Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area

Abstract A monthly correlation between urban vegetation growth and potential evapotranspiration (PET) is needed for better knowledge of controlling water resources and organized irrigation processes. This study aims to explore their relationship within an urban area like Baghdad, using a linear regression model to derive a best-fit line drawn in a scatterplot on a monthly time scale. Based on two different monthly data sources: weather variables (e.g., air temperature, solar radiation, and relative humidity) and Sentinel-2 satellite imagery of 2 years, 2018 and 2021, this study presented the interannual variations of PET and normalized difference vegetation index (NDVI). The choice of these years has a significant feature of climatic differences, which are arid and semi-arid, respectively. PET values were estimated by the Truc method, while the areas of vegetation (represented by NDVI) were calculated using the Geographic Information Sensing program. The results show that the maximum PET in both years was found in the summer months (June and July) with mean values of about 8.8 mm/day, while their minimum mean values of about 1.5 mm/day occurred in winter months (January and December). From the spatial distribution of NDVI, it was found that at positive pixels when NDVI >0.2, vegetation cover in March, April, and December 2018 had large areas with more than 200 km2 in 2018, while they were largest only in May 2021 with 197.8 km2. There was a linear correlation with slope (0.03) and intercept (= 1.8) and a strong correlation, R 2 = 0.72. The practical implications of the findings contribute to enhancing a solid scientific basis for improving agricultural water management, especially under dry conditions.