Groundbreaking Method Research Articles

Governing chemical reactions and mechanisms of hydrogen generation during in-situ combustion gasification of heavy oil

The global push for sustainable energy solutions has highlighted the importance of producing carbon-zero hydrogen (H2) directly from petroleum reservoirs. In-situ combustion gasification (ISCG) presents a groundbreaking method to harness the potential of heavy oil reserves for clean hydrogen production. Although simulations have demonstrated the considerable promise of ISCG, the key reactions controlling hydrogen generation require experimental validation, and the underlying mechanisms remain largely unexplored. This research aims to describe the chemical reactions and mechanisms responsible for hydrogen generation during the ISCG of heavy oil. Using a specially designed kinetic cell, we conducted combustion and gasification experiments with heavy oil and coke. The findings revealed that clay minerals in the reservoir sand act as catalysts in the oxidation reactions of heavy oil by shifting reactions to lower temperatures by approximately 20 °C. Hydrogen production began at 450 °C and peaked at 900 °C, with coke gasification and the water-gas shift reaction being the primary mechanisms. Additionally, methane was produced due to hydrogen consumption via methanation reactions, and minerals in the reservoir sands were found to inhibit hydrogen production by increasing hydrogen consumption and methane generation at temperatures above 800 °C. Controlling the reservoir temperature within an optimal range between 450 and 800 °C can enhance hydrogen generation by managing the process mechanisms. This study provides a detailed examination of the ISCG process for heavy oil, paving the way for future development of kinetic models to simulate hydrogen production through ISCG. It also emphasizes the significance of mechanistic control in enhancing hydrogen generation and suppressing hydrogen consumption reactions.

Determination of bioequivalence between generic and reference drugs using laser-induced breakdown spectroscopy

BackgroundIn vitro bioequivalence studies are strictly limited to the comparison of dissolution performance to a reference drug. These studies are performed without considering the chemical similarity between the generic and reference drug formulations. This work has focused on developing a groundbreaking method based on the laser-induced breakdown spectroscopy (LIBS) technique for the in vitro bioequivalence determination of immediate-release solid oral dosage form generic drugs and as an alternative method for establishing the biowaiver of in vivo generic drug studies. ResultsThe novel LIBS-based methodology to determine in vitro bioequivalence is fast, easy to perform, and can be carried out without the requirement of tedious and complicated sample pre-treatment, nor expensive instrumentals and reagents, almost directly on the drug samples. Furthermore, the proposed methodology demonstrated that it is enough to identify the spectrochemical similarity of the formulation between generic drugs to a reference drug through the chemometric study of their LIBS spectra, based on the determination of the differentiation and similarity factors, f1 and f2, respectively, used in the pharmaceutical industry in this purpose. After analysing their LIBS spectra, the generic drugs selected for this work have all been shown to be in vitro bioequivalent, given their f1 values of less than 15 and f2 values greater than 50, according to the technical regulations on which the American and European medicines agencies are based for the approval of registration for generic immediate-release solid oral dosage form drugs. This has been evidenced even for drugs from Class III and Class IV of the biopharmaceutical classification system, whose active principle nominal concentration is very low as 0.1 and 0.25 mg/tablet, respectively. Significancefor the first time the LIBS technique has been successfully used in an advanced application for the pharmaceutical industry. The proposed method constitutes a reliable and specialized methodology for the establishment of formulation similarity between two drugs, without the requirement of separate identification of each of their components, which is a new and potential tool to determine the in vitro bioequivalence for generic immediate-release solid oral dosage form drugs.

Facile Preparation of Tannic Acid-Gold Nanoparticles for Catalytic and Selective Detection of Mercury(II) and Iron(II) Ions in the Environmental Water Samples and Commercial Iron Supplement.

Tannic acid (TA), a plant-derived polyphenol rich in hydroxyl groups, serves as both a reducing agent and stabilizer for synthesizing gold nanoparticles (TA-AuNPs). This study presents a groundbreaking method that utilizes TA to fabricate TA-AuNPs and develop two distinct colorimetric detection systems for mercury (Hg2+) and iron (Fe2+) ions. The first detection system leverages the interaction between TA-AuNPs and Hg2+ to enhance the peroxidase-like activity of TA-AuNPs, facilitating the production of hydroxyl radicals upon reaction with hydrogen peroxide, which subsequently oxidizes 3,3',5,5'-tetramethylbenzidine (TMB) into a blue-colored product (ox-TMB). The second system capitalizes on TA-AuNPs to catalyze the Fenton reaction between Fe2+ and hydrogen peroxide in the presence of 2, 6-pyridinedicarboxylic acid, boosting the generation of hydroxyl radicals that oxidize TMB into a blue-colored ox-TMB. Absorbance measurements at 650 nm display a linear relationship with Hg2+ concentrations ranging from 0.40 to 0.60 μM (R2 = 0.99) and Fe2+ concentrations from 0.25 to 2.0 μM (R2 = 0.98). The established detection limits for Hg2+ and Fe2+ are 18 nM and 96 nM, respectively. Applications to real-world samples achieved an excellent spiked recovery, spanning 101.6% to 108.0% for Hg2+ and 90.0% to 112.5% for Fe2+, demonstrating the method's superior simplicity, speed, and cost-effectiveness for environmental monitoring of these ions compared to existing techniques.

Network quality prediction in a designated area using GPS data

This study introduces a groundbreaking method for predicting network quality in LTE and 5G environments using only GPS data, focusing on pinpointing specific locations within a designated area to determine network quality as either good or poor. By leveraging machine learning algorithms, we have successfully demonstrated that geographical location can be a key indicator of network performance. Our research involved initially classifying network quality using traditional signal strength metrics and then shifting to rely exclusively on GPS coordinates for prediction. Employing a variety of classifiers, including Decision Tree, Random Forest, Gradient Boosting and K-Nearest Neighbors, we uncovered notable correlations between location data and network quality. This methodology provides network operators with a cost-effective and efficient tool for identifying and addressing network quality issues based on geographic insights. Additionally, we explored the potential implications of our study in various use cases, including healthcare, education, and urban industrialization, highlighting its versatility across different sectors. Our findings pave the way for innovative network management strategies, especially critical in the contexts of both LTE and the rapidly evolving landscape of 5G technology.

Advanced AI and renewable energy sources for unified rotor angle stability control

Maintaining a reliable electricity supply amidst the integration of diverse energy sources necessitates optimizing the stability of power systems. This paper introduces a groundbreaking method to enhance the efficiency and resilience of power grids. The increasing dependence on renewable energy sources poses significant challenges to traditional power networks, thereby demanding innovative solutions to uphold their stability and security. To address these challenges, we propose an architecture that seamlessly unifies dynamic, transient, and static rotor angle stability (RAS) controls into a single, streamlined system. Utilizing reinforcement learning and real-time decision-making, we present Lazy Deep Q Networks (LDQNs) as a novel approach to RAS control. LDQNs provide real-time rotor angle instructions to RAS devices, enabling precise and efficient stability management. The incorporation of mass-distributed energy storage further augments the system's responsiveness and flexibility, mitigating fluctuations and promoting overall stability. This study advances the application of AI methods to RAS control, building on prior research in frequency and voltage stability frameworks. The proposed system outperforms conventional RAS control methods by integrating LDQNs with mass-distributed energy storage, offering superior performance and adaptability. Case studies validate the effectiveness of the unified RAS framework, demonstrating its advantages over traditional approaches across various power system configurations.

3DMOUSEneST: a volumetric label-free imaging method evaluating embryo-uterine interaction and decidualization efficacy.

Effective interplay between the uterus and the embryo is essential for pregnancy establishment; however, convenient methods to screen embryo implantation success and maternal uterine response in experimental mouse models are currently lacking. Here, we report 3DMOUSEneST, a groundbreaking method for analyzing mouse implantation sites based on label-free higher harmonic generation microscopy, providing unprecedented insights into the embryo-uterine dynamics during early pregnancy. The 3DMOUSEneST method incorporates second-harmonic generation microscopy to image the three-dimensional structure formed by decidual fibrillar collagen, named 'decidual nest', and third-harmonic generation microscopy to evaluate early conceptus (defined as the embryo and extra-embryonic tissues) growth. We demonstrate that decidual nest volume is a measurable indicator of decidualization efficacy and correlates with the probability of early pregnancy progression based on a logistic regression analysis using Smad1/5 and Smad2/3 conditional knockout mice with known implantation defects. 3DMOUSEneST has great potential to become a principal method for studying decidual fibrillar collagen and characterizing mouse models associated with early embryonic lethality and fertility issues.

Modulating endothelial cell dynamics in fat grafting: the impact of DLL4 siRNA via adipose stem cell extracellular vesicles.

In the context of improving the efficacy of autologous fat grafts (AFGs) in reconstructive surgery, this study delineates the novel use of adipose-derived mesenchymal stem cells (ADSCs) and their extracellular vesicles (EVs) as vehicles for delivering delta-like ligand 4 (DLL4) siRNA. The aim was to inhibit DLL4, a gene identified through transcriptome analysis as a critical player in the vascular endothelial cells of AFG tissues, thereby negatively affecting endothelial cell functions and graft survival through the Notch signaling pathway. By engineering ADSC EVs to carry DLL4 siRNA (ADSC EVs-siDLL4), the research demonstrated a marked improvement in endothelial cell proliferation, migration, and lumen formation, and enhanced angiogenesis in vivo, leading to a significant increase in the survival rate of AFGs. This approach presents a significant advancement in the field of tissue engineering and regenerative medicine, offering a potential method to overcome the limitations of current fat grafting techniques.NEW & NOTEWORTHY This study introduces a groundbreaking method for enhancing autologous fat graft survival using adipose-derived stem cell extracellular vesicles (ADSC EVs) to deliver DLL4 siRNA. By targeting the delta-like ligand 4 (DLL4) gene, crucial in endothelial cell dynamics, this innovative approach significantly improves endothelial cell functions and angiogenesis, marking a substantial advancement in tissue engineering and regenerative medicine.

Multimodal insights into granger causality connectivity: Integrating physiological signals and gated eye-tracking data for emotion recognition using convolutional neural network

This study introduces a groundbreaking method to enhance the accuracy and reliability of emotion recognition systems by combining electrocardiogram (ECG) with electroencephalogram (EEG) data, using an eye-tracking gated strategy. Initially, we propose a technique to filter out irrelevant portions of emotional data by employing pupil diameter metrics from eye-tracking data. Subsequently, we introduce an innovative approach for estimating effective connectivity to capture the dynamic interaction between the brain and the heart during emotional states of happiness and sadness. Granger causality (GC) is estimated and utilized to optimize input for a highly effective pre-trained convolutional neural network (CNN), specifically ResNet-18. To assess this methodology, we employed EEG and ECG data from the publicly available MAHNOB-HCI database, using a 5-fold cross-validation approach. Our method achieved an impressive average accuracy and area under the curve (AUC) of 91.00 % and 0.97, respectively, for GC-EEG-ECG images processed with ResNet-18. Comparative analysis with state-of-the-art studies clearly shows that augmenting ECG with EEG and refining data with an eye-tracking strategy significantly enhances emotion recognition performance across various emotions.

Mechanism Analysis of Rock Vitrification by Strip Laser Scanning.

The process of casing the wellbore in oil and gas drilling consumes a significant amount of time and economic resources. High-energy laser rock fracturing, as an efficient and cost-effective new approach, holds the potential to create a glass-like casing by irradiating the rocks as an alternative to traditional casing. The mechanism behind the vitrification of rocks using laser irradiation, a key factor in achieving glassified casings, remains to be studied. This paper, based on experiments involving scanning sandstone with a line laser, investigates the mechanism of rock vitrification using numerical simulations and X-ray diffractometers. The results demonstrate that the sandstone surface is transformed into glass after laser scanning, with multiple scans and the application of high-speed airflow helping to reduce the formation of bubbles and other phenomena. Furthermore, the speed of laser scanning showed a negative correlation with the laser ablation depth, glass thickness, temperature diffusion rate, and temperature gradient. Based on these findings, a groundbreaking method is proposed for creating high-quality glass by moving the laser to scan the rocks multiple times, offering insights for research into laser-manufactured wellbore casings. Furthermore, this approach holds promising prospects for enhancing and embellishing the exterior of structures and for in situ environmental modifications on planetary surfaces and beyond.

Application of Deep Learning in Predictive Maintenance of Aircraft Engines

The performance and dependability of aircraft engines are vital to the aviation sector, which is a key pillar of the world economy. Ensuring the seamless functioning of these engines is crucial for both economic efficiency and safety. While reactive and preventative maintenance are examples of traditional maintenance systems, they have limits of their own. Preventive maintenance can save costs by avoiding needless inspections and part replacements, whereas reactive maintenance frequently results in unplanned outages. In this regard, deep learning-powered predictive maintenance shows up as a ground-breaking method for raising the dependability and effectiveness of aircraft engines. Predictive maintenance, or PdM, uses a variety of data sources to monitor the state of the equipment and make maintenance recommendations. Instead of using preset plans or scheduling maintenance after a breakdown has occurred, this technique seeks to prevent failures and optimize schedules depending on the actual state of the equipment. Deep learning (DL), a branch of machine learning, is modeling complicated patterns in huge datasets by using multiple-layered artificial neural networks (thus the term "deep"). It is very effective at time-series data analysis, picture identification, and natural language processing, which makes it appropriate for predictive maintenance jobs involving substantial volumes of sensor data.

Digital violence in schools: a unified theory and structural equation model to counteract cyberbullying

PurposeThis study aims to investigate the dynamics of cyberbullying among minority youths, focusing on its increasing prevalence in the digital realm, which does not necessitate technical expertise from the offenders. It explores a shift from conventional bully characteristics to individuals assuming new digital personas, merging traditional criminology with flag theory to analyze violent behavior online, specifically in educational environments, and studying the correlation between low self-control, affiliations with delinquent peers and the likelihood of cyberbullying occurrences.Design/methodology/approachThe research uses structural equation modeling to examine survey data from 237 eleventh-grade students in St. Louis public schools, focusing on the relationships between cyberbullying, low self-control and association with delinquent peers within a comprehensive theoretical framework that includes the flag theory and demographic factors, as well as using multigroup analysis to investigate racial dynamics and confirming indirect effects using bootstrapping techniques.FindingsThe research establishes strong connections between low self-control, delinquent peer involvement, and cyberbullying, supporting the flag theory in digital violence contexts, while also showing that low self-control and peer engagement play a mediating role in cyberbullying incidents among minority youth, with racial composition having no significant impact on these dynamics.Research limitations/implicationsThe study is restricted by its focus on a particular geographical area, in conjunction with its reliance on self-reported information obtained exclusively from a specific age group. This specificity raises concerns about the applicability of the findings to diverse populations. Furthermore, the study’s dependence on a three-item assessment for cyberbullying, combined with the challenges encountered in achieving complete scalar invariance during multigroup analysis, emphasizes the need for more precise measurement tools and improved methodological frameworks.Practical implicationsThis research discloses actionable insights fundamental to the advancement of cyberbullying prevention strategies. The finding that the connection between race and the effects of peer association and self-control on cyberbullying is minimal suggests that these interventions can be generally applied, transcending racial boundaries. Moreover, identifying self-control as a critical intermediary offers fresh avenues for cybercrime research, shifting the conventional focus from established predictors. By zeroing in on peer influence as a fundamental element, this study provides innovative angles to bolster the understanding of digital violence mitigation in educational settings.Social implicationsThis research emphasizes the urgent concern of cyberbullying among minority youths, along with its psychological and academic impacts. By elucidating the interaction between personal traits and social networks, the findings can guide comprehensive strategies aimed at cultivating safer digital spaces and bolstering social wellness within educational frameworks.Originality/valueThis research presents an original and ground-breaking method that combines various theories in criminology specifically in the context of cyberbullying among minority youths. By proposing a new and redefined role for self-control, which is not seen as the common main predictor but rather as a crucial mediator, this study provides fresh insights into the dynamics of cyber aggression. Through the detailed examination of the flag theory framework, the study uncovers the complex reciprocal relationship between self-control and peer engagement, revealing previously overlooked mechanisms in a broader landscape of digital violence.

An efficient method to prepare high grafting montmorillonite with silane coupling agent and its improvements for styrene butadiene rubber composites

AbstractThe quantity of silane chemically bonded to the organoclay's surface significantly influences its dispersion within the polymer matrix, as well as the interfacial interaction. This research presents a groundbreaking method for enhancing the surface properties of montmorillonite (Mt) through the use of γ‐(methacryloxy) propyl trimethoxy silane (KH570) coupled with acid treatment, leading to increased grafting efficiency. Confirmed by FT‐IR, TGA, and XRD, the process effectively bonds organo‐silanes to Mt and achieves a high grafting level of 1.59 mequiv/g. Utilizing a latex compounding method, Mt/rubber nanocomposites are synthesized with thoroughly modified clay for peak performance. Dispersion and interfacial properties are evaluated through XRD, TEM, AFM, and DMTA. The findings reveal that the well‐modified Mt is an exceptional filler, ensuring a high level of dispersion and robust interaction with the rubber matrix. This synergy translates to a marked improvement in the mechanical properties of the composite, with notable enhancements observed in both tensile strength and elongation at break of 9.7 MPa and 1141%, respectively. This innovative approach to the graft modification of clays presents a potent method, offering the potential for the industrial‐scale manufacture of high‐performance clay‐based composites.Highlights A processing method for preparing high grafting clay with a silane coupling agent. Functionalized clay presents better nano‐dispersion in the polymeric matrix. Strong covalent interfacial interaction is constructed between clay and rubber. Tensile Strength and elongation at break of the composites are obliviously improved.

An innovative strategy for maximizing CO2 reduction in concrete through preparing carbon sequestration precursors by accelerated carbonation

Concrete possesses significant potential as a medium for CO2 sequestration. Nevertheless, conventional approaches like CO2 mixing and carbonation curing have shown limited efficacy in this regard. The present work introduces a groundbreaking method for enhancing CO2 reduction in concrete production. It involves preparing carbon sequestration precursors (CSP) through carbonation of cement reinforced by mechanochemical effects. The study investigated the kinetics and phase assemblage during the CSP production process, along with examining the impact of CSP on the reaction kinetics and strength development of cement composites. Due to the mechanochemical effects, CSP was characterized by the prevalence of metastable calcium carbonate (Cc) with a small crystalline size. Moreover, CSP not only accelerated the hydration of cement composites but also led to increased compressive strength at all ages, even with the replacement of 30 wt% of cement by CSP. The multifunctional attributes of CSP, including nucleation, reactant, and inert filler roles, contributed to its exceptional performance. Furthermore, utilizing the CSP method demonstrated a significant capacity for CO2 reduction, exceeding 36.5 %, which is more than seven times that of the traditional CO2 mixing method. The positive outcomes from this study underscore the high efficacy of the CSP method for efficient CO2 sequestration in concrete.

The Levantine Megalithic Building Techniques: A Groundbreaking Method Applied to Menjez’s Monuments (Akkar, Lebanon) from the 4th–3rd Millennium BCE

The aim of this paper is to present the methodology used to study the megalithic architecture of Menjez’s monuments (Akkar, Lebanon), as part of the MEG-A Project - “First megalith builders in the northern Levant” (2022–2025). Twenty-four monuments have been investigated since 2018. The primary objective is to pioneer a comprehensive understanding of the unique Levantine megalithic building techniques and re-establish the “chaînes opératoires,” by determining the builders’ sequence of actions. This groundbreaking methodology originally developed for Western European megalithic monuments, notably in Brittany, France, has been innovatively applied and customized to suit the Levantine context, specifically focusing on the distinctive basaltic monuments of Menjez and its surrounding areas. By using photogrammetry as a tool, the researchers are able to de-construct the megalithic architecture by analyzing the different components of these monuments. Moreover, it is then possible to describe monoliths according to their place within the monument and their geological and geomorphological features. Our work has led us to consider the symbolic aspect expressed in the megalithic architecture of Menjez. Employing this groundbreaking methodology not only yields concrete answers regarding the typology of these monuments but also dramatically reshapes our perception of their construction. It establishes a precise relative chronology for the various architectural phases and, most significantly, reveals the hidden details of the raw material supply chain.

Switchable and conspicuous retroreflective sensors inspired by the wing scale of an emerald swallowtail

Butterfly wings possess distinct micro/nanostructures that contribute to their vibrant coloration, light-trapping capabilities, and sensitivity to various stimuli. These complex features have inspired the creation of diverse devices and systems, such as sensors, photovoltaics, photocatalysis, and robotics. Specifically, the wing scales of the Emerald Swallowtail (Papilio palinurus) display iridescent, polarization-sensitive, and retroreflective colors due to their hierarchical structures. However, current technologies fail to mimic these natural designs fully, limiting their practical application in everyday life. In this study, we introduce a groundbreaking method for fabricating artificial wing scales that emulate the biological structure's functionality with a much simpler geometry. By integrating self-graded lossy media into metallic micro-concavity arrays, we achieve pronounced iridescent effects in both coaxial and non-coaxial arrangements, while preserving retroreflective properties. In particular, the simplified design allows for switchable color patterns based on the viewing angle. Demonstrating the concept, we successfully employ these conspicuous retroreflectors in hydrogen gas detection and the bi-directional/switchable recognition of patterned signals.

A new approach of B-spline wavelets to solve fractional differential equations

This paper presents a groundbreaking method for solving the multi-order fractional differential (M-OFD), both linear and nonlinear, as well as fractional partial differential equations (FPDE)s. This approach involves constructing an operational matrix of fractional derivatives using linear B-spline (LB-S) wavelet functions with perfect subtlety. The new method has two crucial features. Firstly, it simplifies the problem by converting it into a set of algebraic equations, which is a significant advantage. This makes the method highly accurate and reliable. Secondly, it uses thresholding to dramatically reduce the computational workload in linear problems. This leads to lightning-fast and highly efficient problem-solving. The newly developed scheme underwent a thorough examination of its error estimates and convergence, revealing remarkable results in terms of accuracy and efficiency. The analysis provides a comprehensive understanding of the scheme’s performance, highlighting its potential as a dependable and effective method. Based on the findings, it is evident that the proposed method not only delivers exceptional precision but also operates with remarkable efficiency.

Oxygen evolution reaction kinetics and photoluminescence studies of zinc stannate (ZnSnO3/Zn2SnO4) nanoparticles synthesized via [formula omitted] mediated route

A groundbreaking method utilizing Aloebarbadensismiller gel to synthesize novel zinc stannate nanoparticles in two distinct phases, followed by calcination at 600 °C, has been developed. Structural, optical, and electrochemical analyses were meticulously conducted to explore the impact of phase variation. X-ray diffraction confirmed the formation of face-centered perovskite and inverse spinel cubic structures for ZnSnO3 and Zn2SnO4 nanoparticles respectively, with irregular sizes and shapes observed in both phases. Energy-dispersive X-ray spectroscopy confirmed their purity. Tauc’s plot revealed direct energy band gaps of 3.45 eV and 3.4 eV for ZnSnO3 and Zn2SnO4 respectively, with photoluminescence emission spectra displaying distinct peaks at 646 nm and 647 nm. The study identified higher defect levels in the inverse spinel structure. Notably, the synthesized nanoparticles exhibited promising characteristics for light-emitting diodes and display technology, with color-correlated temperatures (6810 and 6695 K) suggesting their potential application. Furthermore, the nanoparticles demonstrated remarkable performance in oxygen evolution reaction (OER) studies, with low overpotentials (η10) in the range of 269 mV and 273 mV along with a minimal Tafel slope of 76 mV dec−1 and stable electrochemical behavior over 45 h at a current density of 20 mA/cm2, indicating their efficacy as electrocatalysts. The current work unveils the novel synthesis of zinc stannate nanoparticles through an Aloebarbadensismiller gel, a method that stands out for its eco-friendly and cost-effective nature. We delve into uncharted territory by investigating these nanoparticles’ oxygen evolution reaction kinetics and photoluminescence properties, shedding light on their potential applications in energy conversion and sensing technologies. This pioneering study contributes to the understanding of zinc stannate nanoparticles’ behavior, paving the way for transformative advancements in various fields.

Advancing SeNP synthesis: Innovative confined environments for enhanced stability and size control

Nanotechnology's exponential growth has spurred a demand for high-quality and safe nanomaterials, prompting increased interest in cost-effective and fast colloidal syntheses that must mitigate their irreversible aggregation, an issue particularly pertaining to spherical selenium nanoparticles (SeNPs), promising materials in a wide array of technological and biological fields.This study presents a novel approach to SeNP synthesis in confined environments developed from the highly biocompatible surfactant sodium oleate (NaOl) and the amino acid l-cysteine as a selenite-reducing agent. l-cysteine@NaOl (C@NaOl) confined environments were modulable as a function of the amino acid and surfactant concentrations and yielded high-quality spherical SeNPs with enhanced stability. This approach enables generating SeNPs even under alkaline conditions and improving up to 3-fold the final SeNP yield compared to other processes. Besides, we introduce a groundbreaking method for determining SeNP size by adapting Mie's scattering theory to metalloid NPs. This innovative technique proves effective for SeNPs in the 40–100 nm range, offering a reliable alternative to conventional sizing methods. These findings provide valuable insights regarding the generation of bio- and eco-compatible confined environments and SeNPs, paving the way for developing safe, cost-effective, and environmentally friendly strategies for their synthesis with broad applications in various scientific and technological domains.

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Abstract Purpose This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions. Method The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems. Results and Conclusions The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.

Biological removal of nitrogenous waste compounds in the biofloc aquaculture system: a review

Abstract Aquaculture has experienced significant global expansion and is considered one of the fastest-growing sectors in food production. However, there exist additional challenges that restrict the capacity to achieve maximum efficiency in aquaculture systems, such as issues over water quality and shortages of appropriate live feeds. Intensive aquaculture systems involve the use of protein-rich prepared feed for feeding the cultured animals. This may give rise to the discharge of nitrogenous compounds into the water, which can pose a risk to the environment when present in excessive quantities beyond the acceptable levels. In recent years, an innovative method called biofloc technology (BFT) has become a practical solution to this issue. Undoubtedly, BFT offers a groundbreaking method for nutrient disposal that eradicates the requirement for excessive water use or equipment maintenance. Three primary types of microorganisms are crucial in alleviating the adverse impacts of nitrogen compounds in this technique. Photoautotrophs participate in the processes of removal and absorption, whereas chemoautotrophs promote nitrification and conversion. Heterotrophs contribute to the absorption process. Biofloc predominantly consists of heterotrophic bacteria, alongside algae, protozoa, rotifers, and nematodes. While there have been reviews carried out on multiple aspects of biofloc technology, there exists a lack of literature that tackles this particular field of research progress. This article discusses every aspect and techniques of biological management used for removing nitrogenous waste compounds in biofloc aquaculture systems.