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Search Results (1,041)

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Keywords = incipient

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17 pages, 1204 KiB  
Article
The Great Wanderer: The Phylogeographic History of the Bicolor Pyramid Ant (Dorymyrmex bicolor Wheeler, 1906) in Central Veracruz, Mexico
by Maria Gómez-Lazaga and Alejandro Espinosa de los Monteros
Insects 2025, 16(8), 785; https://doi.org/10.3390/insects16080785 (registering DOI) - 31 Jul 2025
Viewed by 209
Abstract
The goal of phylogeography is to explain how microevolutionary forces shape the gene pool of a lineage into the geography. In this study we have evaluated the amount of genetic variation in 13 populations of Dorymyrmex bicolor distributed in a mountainous region in [...] Read more.
The goal of phylogeography is to explain how microevolutionary forces shape the gene pool of a lineage into the geography. In this study we have evaluated the amount of genetic variation in 13 populations of Dorymyrmex bicolor distributed in a mountainous region in Central Veracruz, Mexico. To do so, we sequenced fragments from the mitochondrial COI, COII, and nuclear LWRh genes. Segregated sites were found only at the mitochondrial markers, recovering a total of 21 different haplotypes. The nucleotide diversity ranged from 0 to 0.5% at the different sampling sites. Phylogenetic and spatial analyses of molecular variance revealed a weak but significant phylogeographic structure associated with lowland and mountainous zones. Molecular clock analysis suggests that radiation in the mountain area started 7500 years ago, whereas lineage radiation in the lowland started more recently, around 2700 years ago. The phylogeographic structure is incipient, with nests from lowlands more closely related to mountain nests than to other lowland nests, and vice versa. This seems to be consistent with a model of incomplete lineage sorting. The obtained patterns appear to be the result of restricted gene flow mediated by a complex topographic landscape that has been shaped by a dynamic geologic history. Full article
(This article belongs to the Special Issue Ant Population Genetics, Phylogeography and Phylogeny)
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24 pages, 3694 KiB  
Article
Enhancing the Distinguishability of Minor Fluctuations in Time Series Classification Using Graph Representation: The MFSI-TSC Framework
by He Nai, Chunlei Zhang and Xianjun Hu
Sensors 2025, 25(15), 4672; https://doi.org/10.3390/s25154672 - 29 Jul 2025
Viewed by 228
Abstract
In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. Existing time series classification (TSC) methods struggle to achieve high classification [...] Read more.
In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. Existing time series classification (TSC) methods struggle to achieve high classification accuracy when these minor fluctuations serve as the primary distinguishing feature. This limitation arises because the low-amplitude variations of these fluctuations, compared with trends, lead the classifier to prioritize and learn trend features while ignoring the minor fluctuations crucial for accurate classification. To address this challenge, this paper proposes a novel graph-based time series classification framework, termed MFSI-TSC. MFSI-TSC first extracts the trend component of the raw time series. Subsequently, both the trend series and the raw series are represented as graphs by extracting the “visible relationship” of the series. By performing a subtraction operation between these graphs, the framework isolates the differential information arising from the minor fluctuations. The subtracted graph effectively captures minor fluctuations by highlighting topological variations, thereby making them more distinguishable. Furthermore, the framework incorporates optimizations to reduce computational complexity, facilitating its deployment in resource-constrained sensor systems. Finally, empirical evaluation of MFSI-TSC on both real-world and publicly available datasets demonstrates its effectiveness. Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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22 pages, 12545 KiB  
Article
Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
by Danni Li, Longting Chen, Hanbin Zhou, Jinyuan Tang, Xing Zhao and Jingsong Xie
Appl. Sci. 2025, 15(15), 8270; https://doi.org/10.3390/app15158270 - 25 Jul 2025
Viewed by 221
Abstract
The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the [...] Read more.
The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article
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20 pages, 6594 KiB  
Article
Intelligent Diagnosis Method for Early Weak Faults Based on Wave Intercorrelation–Convolutional Neural Networks
by Weiting Zhong and Bao Pang
Electronics 2025, 14(14), 2808; https://doi.org/10.3390/electronics14142808 - 12 Jul 2025
Viewed by 238
Abstract
Rolling bearings are widely used in rotating machinery, and their health status is crucial for the safe operation of the equipment. The research on relevant fault diagnosis algorithms is a hot topic in the field. As a leading deep learning paradigm, Convolutional Neural [...] Read more.
Rolling bearings are widely used in rotating machinery, and their health status is crucial for the safe operation of the equipment. The research on relevant fault diagnosis algorithms is a hot topic in the field. As a leading deep learning paradigm, Convolutional Neural Networks (CNNs) have demonstrated remarkable effectiveness in bearing fault diagnosis. However, conventional CNNs encounter significant limitations in accurately identifying and classifying early-stage bearing faults, primarily due to two challenges: (1) the diagnostic accuracy is highly susceptible to variations in the input signal length and segmentation strategies and (2) incipient faults are characterized by extremely low signal-to-noise ratios (SNRs), which obscure fault signatures. To address these challenges, we propose a Waveform Intersection-CNN (WI-CNN)-based intelligent diagnosis method for early faults. This approach integrates Gramian Angular Field theory to construct high-resolution fault signatures, enabling the CNN-based diagnosis of incipient bearing faults. Validation using the Case Western Reserve University dataset demonstrates an average diagnostic accuracy exceeding 98%. Furthermore, we established a custom test platform to develop a hybrid diagnosis strategy for 10 distinct fault types. Comparative studies against two conventional CNN diagnostic methods confirm that our approach delivers superior diagnostic precision, a faster iteration speed, and enhanced algorithmic robustness. The empirical findings demonstrate that the model achieves an accuracy of 99.67% during training and 98.167% in the testing phase. Crucially, the proposed method offers exceptional simplicity, computational efficiency, and practical applicability, facilitating its widespread implementation. Full article
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29 pages, 3791 KiB  
Article
Production of Sustainable Synthetic Natural Gas from Carbon Dioxide and Renewable Energy Catalyzed by Carbon-Nanotube-Supported Ni and ZrO2 Nanoparticles
by João Pedro Bueno de Oliveira, Mariana Tiemi Iwasaki, Henrique Carvalhais Milanezi, João Lucas Marques Barros, Arnaldo Agostinho Simionato, Bruno da Silva Marques, Carlos Alberto Franchini, Ernesto Antonio Urquieta-González, Ricardo José Chimentão, José Maria Corrêa Bueno, Adriana Maria da Silva and João Batista Oliveira dos Santos
Catalysts 2025, 15(7), 675; https://doi.org/10.3390/catal15070675 - 11 Jul 2025
Viewed by 477
Abstract
The production of synthetic natural gas in the context of power-to-gas is a promising technology for the utilization of CO2. Ni-based catalysts supported on carbon nanotubes (CNTs) were prepared through incipient wetness impregnation and characterized using N2 adsorption, X-ray diffraction [...] Read more.
The production of synthetic natural gas in the context of power-to-gas is a promising technology for the utilization of CO2. Ni-based catalysts supported on carbon nanotubes (CNTs) were prepared through incipient wetness impregnation and characterized using N2 adsorption, X-ray diffraction (XRD), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), and temperature-programmed reduction (TPR). The catalysts were tested for CO2 methanation in the 200–400 °C temperature range and at atmospheric pressure. The results demonstrated that the catalytic activity increased with the addition of the CNTs and Ni loading. The selectivity towards CH4 was close to 100% for the Ni/ZrO2/CNT catalysts. Reduction of the calcined catalyst at 500 °C using H2 modified the surface chemistry of the catalyst, leading to an increase in the Ni particles. The CO2 conversion was dependent on the Ni loading and the temperature reduction in the NiO species. The 10Ni/ZrO2/CNT catalyst was highly stable in CO2 methanation at 350 °C for 24 h. Thus, CNTs combined with Ni and ZrO2 were considered promising for use as catalysts in CO2 methanation at low temperatures. Full article
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27 pages, 7944 KiB  
Article
Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges
by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez and Juan P. Amezquita-Sanchez
Infrastructures 2025, 10(7), 177; https://doi.org/10.3390/infrastructures10070177 - 8 Jul 2025
Viewed by 236
Abstract
Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection [...] Read more.
Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Bridge Engineering)
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30 pages, 5474 KiB  
Article
Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning
by Andrew Adewunmi Adekunle, Issouf Fofana, Patrick Picher, Esperanza Mariela Rodriguez-Celis, Oscar Henry Arroyo-Fernandez, Hugo Simard and Marc-André Lavoie
Energies 2025, 18(13), 3535; https://doi.org/10.3390/en18133535 - 4 Jul 2025
Viewed by 431
Abstract
Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a [...] Read more.
Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO2/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article
(This article belongs to the Section F: Electrical Engineering)
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19 pages, 1369 KiB  
Review
Current State of Arsenic, Fluoride, and Nitrate Groundwater Contamination in Northern Mexico: Distribution, Health Impacts, and Emerging Research
by Mélida Gutiérrez, María Teresa Alarcón-Herrera, María Socorro Espino-Valdés and Luz Idalia Valenzuela-García
Water 2025, 17(13), 1990; https://doi.org/10.3390/w17131990 - 2 Jul 2025
Viewed by 493
Abstract
The plateaus of north-central Mexico have an arid to semiarid climate and groundwater naturally contaminated with inorganic arsenic (iAs) and fluoride (F). Like other arid and semiarid areas, this region faces great challenges to maintain a safe supply of drinking and irrigation water. [...] Read more.
The plateaus of north-central Mexico have an arid to semiarid climate and groundwater naturally contaminated with inorganic arsenic (iAs) and fluoride (F). Like other arid and semiarid areas, this region faces great challenges to maintain a safe supply of drinking and irrigation water. Studies conducted in the past few decades on various locations within this region have reported groundwater iAs, F, and nitrate-nitrogen (NO3-N), and either their source, enrichment processes, health risks, and/or potential water treatments. The relevant findings are analyzed and condensed here to provide an overview of the groundwater situation of the region. Studies identify volcanic rocks (rhyolite) and their weathering products (clays) as the main sources of iAs and F and report that these solutes become enriched through evaporation and residence time. In contrast, NO3-N is reported as anthropogenic, with the highest concentrations found in large urban centers and in agricultural and livestock farm areas. Health risks are high since the hot spots of contamination correspond to populated areas. Health problems associated with NO3-N in drinking water may be underestimated. Removal technologies of the contaminants remain at the laboratory or pilot stage, except for the reverse osmosis filtration units fitted to selected wells within the state of Chihuahua. A recent approach to supplying drinking water free of iAs and F to two urban centers consisted of switching from groundwater to surface water. Incipient research currently focuses on the potential repercussions of irrigating crops with As-rich water. The groundwater predicaments concerning contamination, public health impact, and irrigation suitability depicted here can be applied to semiarid areas worldwide. Full article
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21 pages, 4431 KiB  
Article
Enhancing the K-Poisoning Resistance of Heteropoly Acid-Modified Ce/AC Catalyst for Low-Temperature NH3-SCR
by Tongyue Zhou, Tianlong Xiong, Mengyang Fan, Qiao Chen, Yongchun Deng and Jianjun Li
Processes 2025, 13(7), 2069; https://doi.org/10.3390/pr13072069 - 30 Jun 2025
Viewed by 296
Abstract
The combustion of biomass fuels releases alkali metals, which induce severe catalyst deactivation due to alkali metal (K) poisoning in low-temperature ammonia selective catalytic reduction (NH3-SCR) systems. To address this issue, this study developed a series of heteropoly acid (HPA)-modified Ce/AC [...] Read more.
The combustion of biomass fuels releases alkali metals, which induce severe catalyst deactivation due to alkali metal (K) poisoning in low-temperature ammonia selective catalytic reduction (NH3-SCR) systems. To address this issue, this study developed a series of heteropoly acid (HPA)-modified Ce/AC catalysts prepared via incipient wetness impregnation. The low-temperature NH3-SCR performance (80–200 °C) of these catalysts was systematically evaluated, with particular emphasis on their denitrification activity and K-poisoning resistance. The silicotungstic-acid (TSiA)-modified Ce/Ac (TSiA-Ce/AC) catalyst showed an improvement (>20%) in NO conversion activity under the K poisoning condition. The superior K-poisoning resistance of the TSiA-Ce/AC catalyst was attributed to the high density of Brønsted acidic sites and the strong K binding affinity of TSiA, which together protected active sites and preserved the standard SCR reaction pathway under K contaminations. This study proposes a novel strategy for enhancing catalyst K resistance in low-temperature NH3-SCR systems. Full article
(This article belongs to the Special Issue Advances in Metal Catalyst: Synthesis and Application)
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38 pages, 4787 KiB  
Article
Modeling and Simulation of Internal Incipient Faults in Electrical Transformers Using a Bond Graph Approach
by Arthur Cleary-Balderas, Gilberto Gonzalez-Avalos, Gerardo Ayala-Jaimes and Aaron Padilla Garcia
Energies 2025, 18(13), 3307; https://doi.org/10.3390/en18133307 - 24 Jun 2025
Viewed by 213
Abstract
Power transformers are a key piece of equipment located between the points of supply and consumption of electrical energy. Due to their continuous exposure to the environment, they may be subject to failure. Thus, the modeling of transformers subject to incipient faults using [...] Read more.
Power transformers are a key piece of equipment located between the points of supply and consumption of electrical energy. Due to their continuous exposure to the environment, they may be subject to failure. Thus, the modeling of transformers subject to incipient faults using a bond graph approach is presented in this study. In particular, incipient faults in the primary and secondary windings with respect to ground and a turn-to-turn fault in the primary winding are modeled. In order to develop a mathematical model capturing the incipient faults in transformers including magnetic saturation effects, a junction structure for the system applied to the bond graph model is proposed. The steady-state responses of the faulted transformer models using a bond graph approach are presented, leading to the proposal of a method for fault analysis in transformers with DC supply sources. Simulation results for the transformers with the different faults are presented, validating the results obtained according to expressions derived from the bond graph models. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 4th Edition)
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21 pages, 3033 KiB  
Article
An Experience with Pre-Service Teachers, Using GeoGebra Discovery Automated Reasoning Tools for Outdoor Mathematics
by Angélica Martínez-Zarzuelo, Álvaro Nolla, Tomás Recio, Piedad Tolmos, Belén Ariño-Morera and Alejandro Gallardo
Educ. Sci. 2025, 15(6), 782; https://doi.org/10.3390/educsci15060782 - 19 Jun 2025
Viewed by 524
Abstract
This paper presents an initial output of the project “Augmented Intelligence in Mathematics Education through Modeling, Automatic Reasoning and Artificial Intelligence (IAxEM-CM/PHS-2024/PH-HUM-383)”. The starting hypothesis of this project is that the use of technological tools, such as mathematical modeling, visualization, automatic reasoning and [...] Read more.
This paper presents an initial output of the project “Augmented Intelligence in Mathematics Education through Modeling, Automatic Reasoning and Artificial Intelligence (IAxEM-CM/PHS-2024/PH-HUM-383)”. The starting hypothesis of this project is that the use of technological tools, such as mathematical modeling, visualization, automatic reasoning and artificial intelligence, significantly improves the teaching and learning of mathematics, in addition to fostering positive attitudes in students. With this hypothesis in mind, in this article, we describe an investigation that has been developed in initial training courses for mathematics teachers in several universities in Madrid, where students used GeoGebra Discovery automated reasoning tools to explore geometric properties in real objects through mathematical paths. Through these activities, future teachers modeled, conjectured and validated geometric relationships directly on photographs of their environment, with the essential concourse of the automated discovery and verification of geometric properties provided by GeoGebra Discovery. The feedback provided by the students’ answers to a questionnaire concerning this novel approach shows a positive evaluation of the experience, especially in terms of content learning and the practical use of technology. Although technological, pedagogical and disciplinary knowledge is well represented, the full integration of these components (according to the TPACK model) is still incipient. Finally, the formative potential of the approach behind this experience is highlighted in a context where Artificial Intelligence tools have an increasing presence in education, as well as the need to deepen these three kinds of knowledge in similar experiences that articulate them in a more integrated way. Full article
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17 pages, 2398 KiB  
Article
Mesoporous SBA-15-Supported Ceria–Cadmium Composites for Fast Degradation of Methylene Blue in Aqueous Systems
by Dănuţa Matei, Abubakar Usman Katsina, Diana-Luciana Cursaru and Sonia Mihai
Water 2025, 17(12), 1834; https://doi.org/10.3390/w17121834 - 19 Jun 2025
Viewed by 478
Abstract
A composite photocatalyst of ceria–cadmium supported on mesoporous SBA-15 silica was synthesized and employed for the aqueous methylene blue (MB) degradation. The composites were prepared using an incipient wetness impregnation technique and a conventional sol–gel approach with triblock copolymer P123 as a structure-directing [...] Read more.
A composite photocatalyst of ceria–cadmium supported on mesoporous SBA-15 silica was synthesized and employed for the aqueous methylene blue (MB) degradation. The composites were prepared using an incipient wetness impregnation technique and a conventional sol–gel approach with triblock copolymer P123 as a structure-directing agent for SBA-15 preparation, enabling the uniform dispersion of CeO2 and Cd species within the SBA-15 framework. The physicochemical properties of both CeO2/SBA-15 and Cd-CeO2/SBA-15 composites were analyzed using small-angle and wide-angle XRD, FT-IR spectroscopy, SEM, TEM, EDX spectroscopy, N2 physisorption at 77 K, and UV-Vis spectroscopy. The findings revealed that the SBA-15 support retained its well-ordered hexagonal mesostructure in both the ceria–SBA-15 and SBA-15-supported cadmium–ceria (Cd-CeO2) composites. The highest degradation efficiency of 96.40% was achieved under optimal conditions, and kinetic analysis using the Langmuir–Hinshelwood model indicated that the MB degradation process followed pseudo-first-order kinetics, with a strong correlation coefficient (R2 = 0.9925) and a rate constant (k) of 0.02532 min−1. Under irradiation, the Cd-CeO2/SBA-15 composites exhibited superior photocatalytic activity compared to the pristine components, owing to the synergistic interaction between ceria and cadmium, enhanced light absorption, and improved charge carrier separation. The recyclability test demonstrated that the degradation efficiency decreased slightly from 96.40% to 94.86% after three cycles, confirming the stability and reusability of Cd-CeO2/SBA-15 composites. The photocatalytic process demonstrated a favorable electrical energy per order (EE/O) value of 281.8 kWh m−3, indicating promising energy efficiency for practical wastewater treatment. These results highlight the excellent photocatalytic performance and durability of the synthesized Cd-CeO2/SBA-15 composites, making them promising candidates for facilitating the photocatalytic decomposition of MB and other dye molecules in water treatment applications. Full article
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42 pages, 4411 KiB  
Review
Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review
by Blanca Vazquez, Mariano Rojas-García, Jocelyn Isabel Rodríguez-Esquivel, Janeth Marquez-Acosta, Carlos E. Aranda-Flores, Lucely del Carmen Cetina-Pérez, Susana Soto-López, Jesús A. Estévez-García, Margarita Bahena-Román, Vicente Madrid-Marina and Kirvis Torres-Poveda
Diagnostics 2025, 15(12), 1543; https://doi.org/10.3390/diagnostics15121543 - 17 Jun 2025
Viewed by 1039
Abstract
Background/Objectives: Cervical cancer (CC) is the fourth most common cancer among women worldwide. This study explored the use of machine learning (ML) and deep learning (DL) in the prediction, diagnosis, and prognosis of CC. Methods: An electronic search was conducted in the PubMed, [...] Read more.
Background/Objectives: Cervical cancer (CC) is the fourth most common cancer among women worldwide. This study explored the use of machine learning (ML) and deep learning (DL) in the prediction, diagnosis, and prognosis of CC. Methods: An electronic search was conducted in the PubMed, IEEE, Web of Science, and Scopus databases from January 2015 to April 2025 using the search terms ML, DL, and uterine cervical neoplasms. A total of 153 studies were selected in this review. A comprehensive summary of the available evidence was compiled. Results: We found that 54.9% of the studies addressed the application of ML and DL in CC for diagnostic purposes, followed by prognosis (22.9%) and an incipient focus on CC treatment (22.2%). The five countries where most ML and DL applications have been generated are China, the United States, India, Republic of Korea, and Japan. Of these studies, 48.4% proposed a DL-based approach, and the most frequent input data used to train the models on CC were images. Conclusions: Although there are results indicating a promising application of these artificial intelligence approaches in oncology clinical practice, further evidence of their validity and reproducibility is required for their use in early detection, prognosis, and therapeutic management of CC. Full article
(This article belongs to the Special Issue Machine-Learning-Based Disease Diagnosis and Prediction)
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17 pages, 2081 KiB  
Article
Efficiency of Microwave-Assisted Surface Grafting of Ni and Zn Clusters on TiO2 as Cocatalysts for Solar Light Degradation of Cyanotoxins
by Andraž Šuligoj, Mallikarjuna Nadagouda, Gregor Žerjav, Albin Pintar, Dionysios D. Dionysiou and Nataša Novak Tušar
Catalysts 2025, 15(6), 590; https://doi.org/10.3390/catal15060590 - 14 Jun 2025
Viewed by 579
Abstract
Herein, we report on the synthesis of Ni and Zn clusters on the surface of TiO2 as well as their bimetallic NiZn analogs. The materials were prepared by incipient wet impregnation of colloidal TiO2 followed by microwave (MW) irradiation to graft [...] Read more.
Herein, we report on the synthesis of Ni and Zn clusters on the surface of TiO2 as well as their bimetallic NiZn analogs. The materials were prepared by incipient wet impregnation of colloidal TiO2 followed by microwave (MW) irradiation to graft the clusters to TiO2 surface. The materials were further immobilized onto glass slides and exhibited high surface area, high mechanical stability, and porosity with accessible pores. The main species responsible for visible light degradation of microcystin LR via the interface charge transfer (IFCT) of excited e to surface metal clusters were found to be O2•− and h+. The optimal nominal grafting concentration was 0.5 wt.% for Ni and 1.0 wt.% for Zn, while for the bimetal modification (NiZn), the optimal nominal concentration was 0.5 wt.%. Compared to monometallic, bimetallic grafting showed a lower kinetic constant, albeit still improved compared to bare TiO2. Bimetal-modified titania showed a lower photocurrent compared to single metal-grafted TiO2 and poorer interfacial charge transport, namely, more recombination sites—possibly at the interface between the Ni and Zn domains. This work highlights the efficiency of using MW irradiation for grafting sub-nano-sized metallic species to TiO2 in a homogeneous way. However, further strategies using MW irradiation for the structural design of bimetallic cocatalysts can be implemented in the future. Full article
(This article belongs to the Special Issue Commemorative Special Issue for Prof. Dr. Dion Dionysiou)
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20 pages, 2001 KiB  
Article
Sustainability in Civil Construction: Study of Companies in Mossoró, Rio Grande do Norte, Brazil
by Ingrid Eduarda Alves Paiva and Jorge Luís de Oliveira Pinto Filho
Reg. Sci. Environ. Econ. 2025, 2(2), 15; https://doi.org/10.3390/rsee2020015 - 12 Jun 2025
Viewed by 808
Abstract
The growing relevance of sustainable practices has driven organizations from various sectors to adapt their activities to current socio-environmental demands. In the construction sector, this demand is even more pronounced due to the high consumption of natural resources and the significant generation of [...] Read more.
The growing relevance of sustainable practices has driven organizations from various sectors to adapt their activities to current socio-environmental demands. In the construction sector, this demand is even more pronounced due to the high consumption of natural resources and the significant generation of solid waste. However, questions remain about the extent to which companies in this sector understand and incorporate sustainable practices into their routines. This study investigates the level of knowledge and the adoption of sustainable practices by residential building construction companies registered with the Civil Construction Industry Union of Mossoró/RN. A qualitative-quantitative approach was adopted, using questionnaires and photographic records collected during on-site visits. The data reveal an incipient adoption of Environmental Management Systems (EMSs) and limited knowledge about ESG principles, highlighting structural and cultural barriers to sustainability in the sector. Nevertheless, isolated initiatives related to waste reduction and the adoption of more efficient practices were observed. The study concludes that strengthening technical training, promoting management systems, and aligning with contemporary demands are relevant strategies to foster sustainability and competitiveness in the construction sector. Full article
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