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14 pages, 2362 KiB  
Article
Exploring the Chemical Profile, In Vitro Antioxidant and Anti-Inflammatory Activities of Santolina rosmarinifolia Extracts
by Janos Schmidt, Kata Juhasz and Agnes Bona
Molecules 2024, 29(7), 1515; https://doi.org/10.3390/molecules29071515 (registering DOI) - 28 Mar 2024
Abstract
In this study, the phytochemical composition, in vitro antioxidant, and anti-inflammatory effects of the aqueous and 60% ethanolic (EtOH) extracts of Santolina rosmarinifolia leaf, flower, and root were examined. The antioxidant activity of S. rosmarinifolia extracts was determined by 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and [...] Read more.
In this study, the phytochemical composition, in vitro antioxidant, and anti-inflammatory effects of the aqueous and 60% ethanolic (EtOH) extracts of Santolina rosmarinifolia leaf, flower, and root were examined. The antioxidant activity of S. rosmarinifolia extracts was determined by 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays. The total phenolic content (TPC) of the extracts was measured by the Folin–Ciocalteu assay. The anti-inflammatory effect of the extracts was monitored by the Griess assay. The chemical composition of S. rosmarinifolia extracts was analysed using the LC-MS technique. According to our findings, 60% EtOH leaf extracts showed the highest Trolox equivalent antioxidant capacity (TEAC) values in both ABTS (8.39 ± 0.43 µM) and DPPH (6.71 ± 0.03 µM) antioxidant activity assays. The TPC values of the samples were in good correspondence with the antioxidant activity measurements and showed the highest gallic acid equivalent value (130.17 ± 0.01 µg/mL) in 60% EtOH leaf extracts. In addition, the 60% EtOH extracts of the leaves were revealed to possess the highest anti-inflammatory effect. The LC-MS analysis of S. rosmarinifolia extracts proved the presence of ascorbic acid, catalpol, chrysin, epigallocatechin, geraniol, isoquercitrin, and theanine, among others, for the first time. However, additional studies are needed to investigate the direct relationship between the chemical composition and physiological effects of the herb. The 60% EtOH extracts of S. rosmarinifolia leaves are potential new sources of natural antioxidants and anti-inflammatory molecules in the production of novel nutraceutical products. Full article
16 pages, 4358 KiB  
Article
Algorithm for Point Cloud Dust Filtering of LiDAR for Autonomous Vehicles in Mining Area
by Xianyao Jiang, Yi Xie, Chongning Na, Wenyang Yu and Yu Meng
Sustainability 2024, 16(7), 2827; https://doi.org/10.3390/su16072827 (registering DOI) - 28 Mar 2024
Abstract
With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) [...] Read more.
With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) to contain dust points, leading to a significant decline in its detection performance, which makes it easy for vehicles to have failures at the perceptual level. In order to solve this problem, this study proposes a LiDAR point cloud denoising method for the quantitative analysis of laser reflection intensity and spatial structure. This method uses laser reflectivity as the benchmark template, constructs the initial confidence level template and initially screens out the sparse dust point cloud. The results are analyzed through the Euclidean distance of adjacent points, and the confidence level in the corresponding template is reduced for rescreening. The experimental results show that our method can significantly filter dust point cloud particles while retaining the rich environmental information of data. The computational load caused by filtering is far lower than that of other methods, and the overall operation efficiency of the system has no significant delay. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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20 pages, 6592 KiB  
Article
Integrated Proteomics and Metabolomics of Safflower Petal Wilting and Seed Development
by Delphine Vincent, Priyanka Reddy and Daniel Isenegger
Biomolecules 2024, 14(4), 414; https://doi.org/10.3390/biom14040414 (registering DOI) - 28 Mar 2024
Abstract
Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist [...] Read more.
Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist in variety and product development, has not yet been undertaken. In this study, an integrated proteome and metabolic analysis have shown a high complexity of lipophilic proteins and metabolites differentially expressed across organs and tissues during seed development and petal wilting. We demonstrated that these approaches successfully discriminated safflower reproductive organs and developmental stages with the identification of 2179 unique compounds and 3043 peptides matching 724 unique proteins. A comparison between cotyledon and husk tissues revealed the complementarity of using both technologies, with husks mostly featuring metabolites (99%), while cotyledons predominantly yielded peptides (90%). This provided a more complete picture of mechanisms discriminating the seed envelope from what it protected. Furthermore, we showed distinct molecular signatures of petal wilting and colour transition, seed growth, and maturation. We revealed the molecular makeup shift occurring during petal colour transition and wilting, as well as the importance of benzenoids, phenylpropanoids, flavonoids, and pigments. Finally, our study emphasizes that the biochemical mechanisms implicated in the growing and maturing of safflower seeds are complex and far-reaching, as evidenced by AraCyc, PaintOmics, and MetaboAnalyst mapping capabilities. This study provides a new resource for functional knowledge of safflower seed and potentially further enables the precision development of novel products and safflower varieties with biotechnology and molecular farming applications. Full article
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20 pages, 2337 KiB  
Article
Building Minimized Epigenetic Clock by iPlex MassARRAY Platform
by Ekaterina Davydova, Alexey Perenkov and Maria Vedunova
Genes 2024, 15(4), 425; https://doi.org/10.3390/genes15040425 (registering DOI) - 28 Mar 2024
Abstract
Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the [...] Read more.
Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array. Full article
(This article belongs to the Section Epigenomics)
27 pages, 2365 KiB  
Article
Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms
by Vasileios Laitsos, Georgios Vontzos, Dimitrios Bargiotas, Aspassia Daskalopulu and Lefteri H. Tsoukalas
Energies 2024, 17(7), 1625; https://doi.org/10.3390/en17071625 (registering DOI) - 28 Mar 2024
Abstract
The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial [...] Read more.
The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering 2024)
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18 pages, 2475 KiB  
Article
Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint
by Mazen A. Al-Sinan, Abdulaziz A. Bubshait and Zainab Aljaroudi
Buildings 2024, 14(4), 934; https://doi.org/10.3390/buildings14040934 (registering DOI) - 28 Mar 2024
Abstract
Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information [...] Read more.
Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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24 pages, 933 KiB  
Article
500 Drivers of S&P 500′s Profitability: Implications for Investment Strategy and Risk Management
by Marek Nagy, Katarina Valaskova, Erika Kovalova and Marcel Macura
Economies 2024, 12(4), 77; https://doi.org/10.3390/economies12040077 (registering DOI) - 28 Mar 2024
Abstract
The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous [...] Read more.
The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches. Full article
(This article belongs to the Special Issue Financial Market Volatility under Uncertainty)
20 pages, 2297 KiB  
Article
The Characterization of Biodiversity and Soil Emission Activity of the “Ladoga” Carbon-Monitoring Site
by Evgeny Abakumov, Timur Nizamutdinov, Darya Zhemchueva, Azamat Suleymanov, Evgeny Shevchenko, Elena Koptseva, Anastasiia Kimeklis, Vyacheslav Polyakov, Evgenia Novikova, Grigory Gladkov and Evgeny Andronov
Atmosphere 2024, 15(4), 420; https://doi.org/10.3390/atmos15040420 (registering DOI) - 28 Mar 2024
Abstract
The global climate crisis forces mankind to develop carbon storage technologies. “Ladoga” carbon monitoring site is part of the Russian climate project “Carbon Supersites”, which aims to develop methods and technologies to control the balance of greenhouse gases in various ecosystems. This article [...] Read more.
The global climate crisis forces mankind to develop carbon storage technologies. “Ladoga” carbon monitoring site is part of the Russian climate project “Carbon Supersites”, which aims to develop methods and technologies to control the balance of greenhouse gases in various ecosystems. This article shows the condition of soil and vegetation cover of the carbon polygon “Ladoga” using the example of a typical southern taiga ecosystem in the Leningrad region (Russia). It is revealed that soils here are significantly disturbed as a result of agrogenic impact, and the vegetation cover changes under the influence of anthropogenic activity. It has been found that a considerable amount of carbon is deposited in the soils of the carbon polygon; its significant part is accumulated in peat soils (60.0 ± 19.8 kg × m−2 for 0–100 cm layer). In agrogenically disturbed and pristine soils, carbon stocks are equal to 12.8 ± 2.9 kg × m−2 and 8.3 ± 1.3 kg × m−2 in the 0–100 cm layer, respectively. Stocks of potentially mineralizable organic matter (0–10 cm) in peat soils are 0.48 ± 0.01 kg × m−2; in pristine soils, it is 0.58 ± 0.06 kg × m−2. Peat soils are characterized by a higher intensity of carbon mineralization 9.2 ± 0.1 mg × 100 g−1 × day−1 with greater stability. Carbon in pristine soils is mineralized with a lower rate—2.5 ± 0.2 mg × 100 g−1 × day−1. The study of microbial diversity of soils revealed that the dominant phyla of microorganisms are Actinobacteria, Bacteroidetes, and Proteobacteria; however, methane-producing Archaea—Euryarchaeota—were found in peat soils, indicating their potentially greater emission activity. The results of this work will be useful for decision makers and can be used as a reference for estimating the carbon balance of the Leningrad region and southern taiga boreal ecosystems of the Karelian Isthmus. Full article
(This article belongs to the Special Issue Advances in CO2 Capture and Absorption)
13 pages, 3543 KiB  
Article
How Pseudomonas nitroreducens Passivates Cadmium to Inhibit Plant Uptake
by Yakui Chen, Yongquan Yu, Xiaoyu Fang, Yinhuan Zhou and Diannan Lu
Appl. Sci. 2024, 14(7), 2857; https://doi.org/10.3390/app14072857 (registering DOI) - 28 Mar 2024
Abstract
Cadmium (Cd) has been widely used in industry applications, leading to water and soil contamination. This study investigated the potential ability of Pseudomonas nitroreducens (11830) to perform the biosorption of cadmium from aqueous solution and soil. The biosorption characteristics were described using equilibrium [...] Read more.
Cadmium (Cd) has been widely used in industry applications, leading to water and soil contamination. This study investigated the potential ability of Pseudomonas nitroreducens (11830) to perform the biosorption of cadmium from aqueous solution and soil. The biosorption characteristics were described using equilibrium isotherm and kinetic studies. The Langmuir adsorption isotherm indicated a better fit with the experimental data (R2 = 0.980), with a maximum capacity of 160.51 mg/g at 30 °C in an initial aqueous solution of 300 mg/L Cd2+. The experiments followed a pseudo-second-order kinetics model (R2 > 0.99), especially at a low initial concentration. The biosorption mechanisms involved were determined through scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS) and protein analysis. The SEM and TEM figures showed that the morphology of cells changed before and after the adsorption of Cd, and the EDS confirmed that Cd was absorbed on the surface of the cell. The analysis of proteins indicated that the protein species increased after the stimulation of Cd, which further confirmed the biosorption mechanism. A pot experiment confirmed that 11830 could passivate the cadmium in soil and reduce its uptake and utilization by Houttuynia cordata Thunb (H. cordata). This work demonstrates the potential application of microorganisms in inhibiting the accumulation of Cd in crops. Full article
(This article belongs to the Special Issue Environmental Pollution and Bioremediation Technology)
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36 pages, 14539 KiB  
Article
Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring
by Jacopo Corona, Stefano Tondini, Duccio Gallichi Nottiani, Riccardo Scilla, Andrea Gambaro, Wilmer Pasut, Francesco Babich and Roberto Lollini
Sensors 2024, 24(7), 2176; https://doi.org/10.3390/s24072176 (registering DOI) - 28 Mar 2024
Abstract
The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users’ health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of [...] Read more.
The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users’ health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system. Full article
(This article belongs to the Special Issue Integrated Sensor Systems for Environmental Applications)
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19 pages, 2306 KiB  
Article
Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method
by Young-Jun Park and Chang-Yong Yi
Appl. Sci. 2024, 14(7), 2859; https://doi.org/10.3390/app14072859 (registering DOI) - 28 Mar 2024
Abstract
In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the [...] Read more.
In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
17 pages, 467 KiB  
Article
The Impact of Carbon Emissions Trading on the Total Factor Productivity of China’s Electric Power Enterprises—An Empirical Analysis Based on the Differences-in-Differences Model
by Gezi Chen, Zhenhua Hu, Shijin Xiang and Ailan Xu
Sustainability 2024, 16(7), 2832; https://doi.org/10.3390/su16072832 (registering DOI) - 28 Mar 2024
Abstract
Based on the panel data of China’s listed electric power enterprises, this paper adopts the differences-in-differences model to empirically analyze the pilot policy of carbon emissions trading’s impact on the total factor productivity of power enterprises in 2013. The study finds that the [...] Read more.
Based on the panel data of China’s listed electric power enterprises, this paper adopts the differences-in-differences model to empirically analyze the pilot policy of carbon emissions trading’s impact on the total factor productivity of power enterprises in 2013. The study finds that the carbon trading pilot policy has a significant positive effect on the total factor productivity of power companies, and the two possible impact mechanisms are external cost compensation and additional income, and internal low-carbon technology innovation and resource allocation optimization. The conclusions above have been further confirmed by the parallel trend test and robustness test. The heterogeneity analysis demonstrates that there are differences in the regression results between state-owned enterprises and nonstate-owned enterprises. The possible reason is that state-owned enterprises are more likely to be affected by the carbon emissions trading system, and their asset-heavy model puts greater pressure on carbon emission reduction. Therefore, their demand for low-carbon technology innovation is more urgent; areas with stricter carbon emission verification are more sensitive to the implementation of carbon trading, and a reasonable increase in carbon verification can make the carbon trading market more effective. Based on the research results, this paper proposes to speed up the improvement of the national carbon trading market system, enhance the diversity and richness of the main market, improve the liquidity of the carbon trading market, broaden financing channels for electric power enterprises, and improve the carbon market supervision mechanism. Full article
21 pages, 435 KiB  
Article
Enhancing Internal Control Mechanisms in Local Government Organizations: A Crucial Step towards Mitigating Corruption and Ensuring Economic Development
by Paraskevi Boufounou, Nikolaos Eriotis, Theodoros Kounadeas, Panagiotis Argyropoulos and John Poulopoulos
Economies 2024, 12(4), 78; https://doi.org/10.3390/economies12040078 (registering DOI) - 28 Mar 2024
Abstract
Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) [...] Read more.
Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) emerges as paramount. This paper presents a comprehensive analysis of the internal control landscape within LGO revenue departments, focusing on factors influencing its effectiveness and proposing strategies for improvement. Drawing upon survey data and regression analyses, this study highlights the crucial role of robust internal control mechanisms in combating corruption and fostering economic development. The findings underscore the importance of competent personnel, legislative compliance, interdepartmental collaboration, and technology utilization in enhancing internal control practices. Despite existing legislation, gaps in internal control implementation persist, including understaffing, inadequate procedures, and limited access to information. This study emphasizes the transformative potential of effective internal audit measures in mitigating corruption at the local level, thereby contributing to broader economic growth and societal well-being. Recommendations for strengthening the internal control structures within LGOs include the formal establishment of internal audit functions, adherence to professional standards, and the promotion of information system utilization. By addressing the corruption and inefficiencies within LGOs, this research underscores the pivotal role of institutional effectiveness in promoting transparency, accountability, and sustainable economic progress. Full article
14 pages, 645 KiB  
Systematic Review
Retinal Structural and Vascular Changes in Patients with Coronary Artery Disease: A Systematic Review and Meta-Analysis
by Alexandra Cristina Rusu, Karin Ursula Horvath, Grigore Tinica, Raluca Ozana Chistol, Andra-Irina Bulgaru-Iliescu, Ecaterina Tomaziu Todosia and Klara Br?nzaniuc
Life 2024, 14(4), 448; https://doi.org/10.3390/life14040448 (registering DOI) - 28 Mar 2024
Abstract
Background: Retinal microvascular anomalies have been identified in patients with cardiovascular conditions such as arterial hypertension, diabetes mellitus, and carotid artery disease. We conducted a systematic review and meta-analysis (PROSPERO registration number CRD42024506589) to explore the potential of retinal vasculature as a biomarker [...] Read more.
Background: Retinal microvascular anomalies have been identified in patients with cardiovascular conditions such as arterial hypertension, diabetes mellitus, and carotid artery disease. We conducted a systematic review and meta-analysis (PROSPERO registration number CRD42024506589) to explore the potential of retinal vasculature as a biomarker for diagnosis and monitoring of patients with coronary artery disease (CAD) through optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Methods: We systematically examined original articles in the Pubmed, Embase, and Web of Science databases from their inception up to November 2023, comparing retinal microvascular features between patients with CAD and control groups. Studies were included if they reported sample mean with standard deviation or median with range and/or interquartile range (which were computed into mean and standard deviation). Review Manager 5.4 (The Cochrane Collaboration, 2020) software was used to calculate the pooled effect size with weighted mean difference and 95% confidence intervals (CI) by random-effects inverse variance method. Results: Eleven studies meeting the inclusion criteria were incorporated into the meta-analysis. The findings indicated a significant decrease in the retinal nerve fiber layer (WMD −3.11 [−6.06, −0.16]), subfoveal choroid (WMD −58.79 [−64.65, −52.93]), and overall retinal thickness (WMD −4.61 [−7.05, −2.17]) among patients with CAD compared to controls (p < 0.05). Furthermore, vascular macular density was notably lower in CAD patients, particularly in the superficial capillary plexus (foveal vessel density WMD −2.19 [−3.02, −1.135], p < 0.0001). Additionally, the foveal avascular zone area was statistically larger in CAD patients compared to the control group (WMD 52.73 [8.79, 96.67], p = 0.02). Heterogeneity was significant (I2 > 50%) for most features except for subfoveal choroid thickness, retina thickness, and superficial foveal vessel density. Conclusion: The current meta-analysis suggests that retinal vascularization could function as a noninvasive biomarker, providing additional insights beyond standard routine examinations for assessing dysfunction in coronary arteries. Full article
36 pages, 1503 KiB  
Review
Optimizing Performance of Hybrid Electrochemical Energy Storage Systems through Effective Control: A Comprehensive Review
by Alejandro Clemente, Paula Arias, Levon Gevorkov, Lluís Trilla, Sergi Obrador Rey, Xavier Sanchez Roger, José Luis Domínguez-García and ?lber Filbà Martínez
Electronics 2024, 13(7), 1258; https://doi.org/10.3390/electronics13071258 (registering DOI) - 28 Mar 2024
Abstract
The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of [...] Read more.
The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of fact, an isolated storage solution’s energy and power density, lifespan, cost, and response time are its primary performance constraints. Batteries are the essential energy storage component used in electric mobility, industries, and household applications nowadays. In general, the battery energy storage systems (BESS) currently available on the market are based on a homogeneous type of electrochemical battery. However, a hybrid energy storage system (HESS) based on a mixture of various types of electrochemical batteries can potentially provide a better option for high-performance electric cars, heavy-duty electric vehicles, industries, and residential purposes. A hybrid energy storage system combines two or more electrochemical energy storage systems to provide a more reliable and efficient energy storage solution. At the same time, the integration of multiple energy storage systems in an HESS requires advanced control strategies to ensure optimal performance and longevity of the system. This review paper aims to provide a comprehensive overview of the control systems used in HESSs for a wide range of applications. An overview of the various control strategies used in HESSs is offered, including traditional control methods such as proportional–integral–derivative (PID) control, and advanced control methods such as model predictive control (MPC), droop control (DC), sliding mode control (SMC), rule-based control (RBC), fuzzy logic control (FLC), and artificial neural network (ANN) control are discussed. The paper also highlights the recent developments in HESS control systems, including the use of machine learning techniques such as deep reinforcement learning (DRL) and genetic algorithms (GA). The paper provides not only a description and classification of various control approaches but also a comparison between control strategies from the evaluation of performance point of view. The review concludes by summarizing the key findings and future research directions for HESS control systems, which is directly linked to the research on machine learning and the mix of different control type strategies. Full article
(This article belongs to the Special Issue Advances in Power Converter Design, Control and Applications)
19 pages, 12194 KiB  
Article
Rock Glacier Inventory of the Southwestern Pamirs Supported by InSAR Kinematics
by Qiqi Ma and Takashi Oguchi
Remote Sens. 2024, 16(7), 1185; https://doi.org/10.3390/rs16071185 (registering DOI) - 28 Mar 2024
Abstract
Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through [...] Read more.
Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3620 to 5210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes. The Shakhdara range to the south hosts a denser and more active population of RGs than the Shughnon range to the north, highlighting the influence of topography and precipitation. Overall, RGs in the southwestern Pamirs exhibit high activity levels, with active RGs predominating (58%). A comparison between active and transitional RGs showed no significant differences in elevation, temperature, and slope. Glacier-connected and glacier forefield-connected RGs demonstrated higher line-of-sight (LOS) velocities than talus-connected and debris-mantled slope-connected RGs, underscoring the significant impact of precipitation and meltwater on their activity. Full article
(This article belongs to the Special Issue Remote Sensing in Permafrost Region Monitoring)
22 pages, 1774 KiB  
Article
A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm
by Wenshuo Zhao, Yipeng Ning, Xiang Jia, Dashuai Chai, Fei Su and Shengli Wang
Appl. Sci. 2024, 14(7), 2852; https://doi.org/10.3390/app14072852 (registering DOI) - 28 Mar 2024
Abstract
Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, [...] Read more.
Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. Full article
16 pages, 1614 KiB  
Article
Predicting Epipelic Algae Transport in Open Channels: A Flume Study to Quantify Transport Capacity and Guide Flow Management
by Li Pan, Guoying Wu, Mingwu Zhang, Yuan Zhang, Zhongmei Wang and Zhiqiang Lai
Water 2024, 16(7), 983; https://doi.org/10.3390/w16070983 (registering DOI) - 28 Mar 2024
Abstract
The functionality of rivers and open diversion channels can be severely impacted when the epipelic algae group that grows on concrete inclined side walls, which are typical of urban rivers, joins the water flow. This study aims to increase the long-distance transport of [...] Read more.
The functionality of rivers and open diversion channels can be severely impacted when the epipelic algae group that grows on concrete inclined side walls, which are typical of urban rivers, joins the water flow. This study aims to increase the long-distance transport of epipelic algae groups in urban rivers and open diversion channels through flow scheduling and to anticipate their transport capacity with respect to water flow. Current research on contaminant movement is primarily based on mathematical models with limited data on flake epipelic algae types. A sidewall epipelic algae group in a flume was modeled using a generalized hydrodynamic experimental approach. Hydraulic experiments were conducted to study the physical movement form and transport capacity of the suspended epipelic algae group. This study suggests that the epipelic algae group will create transport movement without sedimentation when the velocity reaches 80–85% of the main flow velocity and settle to the bottom when it falls below 80%. This research can support the mathematical modelling of hydrodynamic transport, provide a research foundation for long-distance transport, and estimate potential gathering places and sediment amounts under different water flow conditions. Full article
(This article belongs to the Special Issue Advances in Hydraulic and Water Resources Research)
21 pages, 2205 KiB  
Article
Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network
by Jincun Liu, Kangji Li and Wenping Xue
Energies 2024, 17(7), 1624; https://doi.org/10.3390/en17071624 (registering DOI) - 28 Mar 2024
Abstract
Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM [...] Read more.
Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM network to improve the accuracy and robustness of PV solar power prediction. During the data clustering process, the Euclidean distance-based clustering centroids are optimized by an improved particle swarm optimization (iPSO) algorithm. For each obtained data cluster, the AdaLSTM network is utilized for model training, in which multiple LSTMs are serially combined together through the AdaBoost algorithm. For PV power prediction tasks, the inputs of the testing set are classified into the nearest data cluster by the K-nearest neighbor (KNN) method, and then the corresponding AdaLSTM network of this cluster is used to perform the prediction. Case studies from two real PV stations are used for prediction performance evaluation. Results based on three prediction horizons (10, 30 and 60 min) demonstrate that the proposed model combining the optimized data clustering and AdaLSTM has higher prediction accuracy and robustness than other comparison models. The root mean square error (RMSE) of the proposed model is reduced, respectively, by 75.22%, 73.80%, 67.60%, 66.30%, and 64.85% compared with persistence, BPNN, CNN, LSTM, and AdaLSTM without clustering (Case A, 30 min prediction). Even compared with the model combining the K-means clustering and AdaLSTM, the RMSE can be reduced by 10.75%. Full article
25 pages, 1212 KiB  
Article
Thermo-Structural Characterization of Phase Transitions in Amorphous Griseofulvin: From Sub-Tg Relaxation and Crystal Growth to High-Temperature Decomposition
by Roman Svoboda and Kate?ina Kozlová
Molecules 2024, 29(7), 1516; https://doi.org/10.3390/molecules29071516 (registering DOI) - 28 Mar 2024
Abstract
The processes of structural relaxation, crystal growth, and thermal decomposition were studied for amorphous griseofulvin (GSF) by means of thermo-analytical, microscopic, spectroscopic, and diffraction techniques. The activation energy of ~395 kJ·mol−1 can be attributed to the structural relaxation motions described in terms [...] Read more.
The processes of structural relaxation, crystal growth, and thermal decomposition were studied for amorphous griseofulvin (GSF) by means of thermo-analytical, microscopic, spectroscopic, and diffraction techniques. The activation energy of ~395 kJ·mol−1 can be attributed to the structural relaxation motions described in terms of the Tool–Narayanaswamy–Moynihan model. Whereas the bulk amorphous GSF is very stable, the presence of mechanical defects and micro-cracks results in partial crystallization initiated by the transition from the glassy to the under-cooled liquid state (at ~80 °C). A key aspect of this crystal growth mode is the presence of a sufficiently nucleated vicinity of the disrupted amorphous phase; the crystal growth itself is a rate-determining step. The main macroscopic (calorimetrically observed) crystallization process occurs in amorphous GSF at 115–135 °C. In both cases, the common polymorph I is dominantly formed. Whereas the macroscopic crystallization of coarse GSF powder exhibits similar activation energy (~235 kJ·mol−1) as that of microscopically observed growth in bulk material, the activation energy of the fine GSF powder macroscopic crystallization gradually changes (as temperature and/or heating rate increase) from the activation energy of microscopic surface growth (~105 kJ·mol−1) to that observed for the growth in bulk GSF. The macroscopic crystal growth kinetics can be accurately described in terms of the complex mechanism, utilizing two independent autocatalytic Šesták–Berggren processes. Thermal decomposition of GSF proceeds identically in N2 and in air atmospheres with the activation energy of ~105 kJ·mol−1. The coincidence of the GSF melting temperature and the onset of decomposition (both at 200 °C) indicates that evaporation may initiate or compete with the decomposition process. Full article
(This article belongs to the Section Physical Chemistry)
17 pages, 7742 KiB  
Article
Cavitation Erosion of the Austenitic Manganese Layers Deposited by Pulsed Current Electric Arc Welding on Duplex Stainless Steel Substrates
by Ion Mitelea, Daniel Muta?cu, Ion-Drago? U?u, Corneliu Marius Cr?ciunescu and Ilare Bordea?u
Crystals 2024, 14(4), 315; https://doi.org/10.3390/cryst14040315 (registering DOI) - 28 Mar 2024
Abstract
Fe-Mn-Cr-Ni alloys like Citomangan, delivered in the form of powders, tubular wires, and coated electrodes, are intended for welding deposition operations to create wear-resistant layers. Their main characteristic is their high capacity for surface mechanical work-hardening under high shock loads, along with high [...] Read more.
Fe-Mn-Cr-Ni alloys like Citomangan, delivered in the form of powders, tubular wires, and coated electrodes, are intended for welding deposition operations to create wear-resistant layers. Their main characteristic is their high capacity for surface mechanical work-hardening under high shock loads, along with high toughness and wear resistance. In order to increase the resistance to cavitation erosion, hardfacing of Duplex stainless steel X2CrNiMoN22-5-3 with Citomangan alloy was performed using a new welding technique, namely one that uses a universal TIG source adapted for manual welding with a coated electrode in pulsed current. Cavitation tests were conducted in accordance with the requirements of ASTM G32—2016 standard. Comparing the characteristic cavitation erosion parameters of the manganese austenitic layer, deposited by this new welding technique, with those of the reference steel, highlights an 8–11 times increase in its resistance to cavitation erosion. Metallographic investigations by optical microscopy and scanning electron microscopy (SEM), as well as hardness measurements, were carried out to understand the cavitation phenomena. Full article
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19 pages, 3005 KiB  
Article
Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead Agriculture Research Station, Thunder Bay, Canada
by María V. Brenes Fuentes, Muditha K. Heenkenda, Tarlok S. Sahota and Laura Segura Serrano
Crops 2024, 4(2), 115-133; https://doi.org/10.3390/crops4020009 (registering DOI) - 28 Mar 2024
Abstract
Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high [...] Read more.
Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture. Full article
(This article belongs to the Special Issue Fertigation and Nutrient Management in Crops)
14 pages, 1604 KiB  
Article
Construction of a Visible-Light-Response Photocatalysis–Self-Fenton Degradation System of Coupling Industrial Waste Red Mud to Resorcinol–Formaldehyde Resin
by Xiangxiu Lv, Hao Yuan, Kaiqu Sun, Weilong Shi, Chunsheng Li and Feng Guo
Molecules 2024, 29(7), 1514; https://doi.org/10.3390/molecules29071514 (registering DOI) - 28 Mar 2024
Abstract
Heterogeneous photocatalysis–self-Fenton technology is a sustainable strategy for treating organic pollutants in actual water bodies with high-fluent degradation and high mineralization capacity, overcoming the limitations of the safety risks caused by adding external iron sources and hazardous chemicals in the homogeneous Fenton reaction [...] Read more.
Heterogeneous photocatalysis–self-Fenton technology is a sustainable strategy for treating organic pollutants in actual water bodies with high-fluent degradation and high mineralization capacity, overcoming the limitations of the safety risks caused by adding external iron sources and hazardous chemicals in the homogeneous Fenton reaction and injecting high-intensity energy fields in photo-Fenton reaction. Herein, a photo-self-Fenton system based on resorcinol–formaldehyde (RF) resin and red mud (RM) was established to generate hydrogen peroxide (H2O2) in situ and transform into hydroxy radical (OH) for efficient degradation of tetracycline (TC) under visible light irradiation. The capturing experiments and electron spin resonance (ESR) confirmed that the hinge for the enhanced performance of this system is the superior H2O2 yield (499 μM) through the oxygen reduction process (ORR) of the two-step single-electron over the resin and the high concentration of OH due to activation effect of RM. In addition, the Fe2+/Fe3+ cycles are accelerated by photoelectrons to effectively initiate the photo-self-Fenton reaction. Finally, the possible degradation pathways were proposed via liquid chromatography-mass spectrometry (LC-MS). This study provides a new idea for environmental recovery in a waste-based heterogeneous photocatalytic self-Fenton system. Full article
(This article belongs to the Special Issue Feature Papers in Photochemistry and Photocatalysis)

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