Search Results (136)

This article reviews the state of the art of laser ablation and deposition techniques applied so far to more than 50 elements, including metals, metalloids and lanthanides, yielding a wide variety of compounds in the form of thin films. Laser deposition processes have been performed in high-vacuum (HV) reactors at pressure values ranging between 10⁻¹ and 10⁻⁵ Pa, namely pulsed laser deposition (PLD), or, under different reactive gas ambient (O₂, N₂, CH₄, NH₃ and many others), so-called reactive pulsed laser deposition (RPLD), with the aim to form thin films with desirable chemical compositions. While a few metals have not been deposited as pure metallic films because they have no immediate technological interest, others, like alkali and alkaline earth metals, cannot be deposited in pure metallic form due to their very strong reactivity with oxygen, water vapor and hydrogen molecules which are always present, even in ultra-high-vacuum (UHV) systems, at pressure values of 10⁻⁵–10⁻¹⁰ Pa. Furthermore, elements of the Mendeleev periodic table with an atomic number higher than 88, such as actinides and synthetic elements, are dangerous to handle and deposit in the form of thin films due to their high radioactivity; therefore, they are excluded from this review. The inclusion of the non-metal thin films of carbon (C) and related chemical compounds prepared by PLD and RPLD in the present review is justified by the extensive research and the numerous scientific articles reported in the field. All the results obtained by PLD and RPLD techniques so far are discussed and presented in tabular format to guide the reader. Full article

Metal–organic frameworks (MOFs) show great potential for post-combustion carbon capture, yet their practical application is often constrained by challenges such as powder handling difficulties, limited structural stability during shaping processes, and performance degradation under high-humidity conditions. In this study, Mg-gallate was structured into millimeter-sized Mg-gallate/CA composite beads via the ionotropic gelation method, and then a hydrophobic layer of vinyltrimethoxysilane (VTMS) was constructed on the bead surface by chemical vapor deposition. The synthesized Mg-gallate/CA and V-Mg-gallate/CA are characterized by XRD, FT-IR, and other techniques, and their CO₂ adsorption behavior, adsorption–desorption kinetics, breakthrough performance, and cyclic stability are systematically evaluated. At 298 K and 0.1 bar, the CO₂ adsorption capacity of Mg-gallate/CA reached 94.2% of that of Mg-gallate powder. The microporous–microporous hierarchical structure constructed by the ionotropic gelation method improved the CO₂ capture efficiency of the composite beads by 16.7% at 0.1 bar. V-Mg-gallate/CA maintained a high dynamic CO₂ adsorption capacity of 2.87 mmol/g for a 10 vol.% CO₂/90 vol.% N₂ gas mixture at 298 K under 80% RH, corresponding to 2.04 times the capacity of Mg-gallate/CA, and retained 98.8% of its initial adsorption capacity at 0.1 bar after 10 cycles. Combining ionotropic gelation shaping with surface hydrophobic modification represents an effective strategy for developing MOF-based adsorbents suitable for post-combustion CO₂ capture. Full article

Anaerobic digestion (AD) plays an important role in the circular bioeconomy by converting organic waste into renewable methane and nutrient-rich fertilizer. However, consistent, high-quality biomethane production is hindered by four main factors: hydrolysis limitations, fluctuating feedstock quality, microbial instability, and the high cost/energy demand of purification. This review explores three key areas that improve biomethane production: (i) process intensification (pretreatments and advanced reactors), (ii) microbial regulation through additives, and (iii) biogas upgrading for pipeline use. Anaerobic digestion can be greatly improved by combining thermal or hybrid pretreatments, staged digestion, high-solids technology, and electrochemical systems. These methods speed up hydrolysis and help the system handle higher amounts of organic material more effectively. However, actual performance benefits depend on specific substrate characteristics, heat integration, and control complexity. Optimizing the C:N ratio, buffering capacity, and trace-element supplementation, while simultaneously diluting toxic inhibitors, makes co-digestion an effective and adaptable approach to enhancing anaerobic digestion processes. Additives like carbon, iron nanoparticles, enzymes, and buffers can optimize digestion, but their performance is highly dependent on dosage and substrate. Additionally, they lack validation in long-term, industrial-scale applications. Conventional physicochemical techniques continue to be standard for generating high-quality biomethane, but biological methanation and microalgal systems are playing a growing role in integrating Power-to-Gas technology and using CO₂ efficiently. Critical research needs to focus on four areas: (1) standardized reporting metrics, (2) AI-enabled monitoring and control, (3) coupled techno-economic and life-cycle analysis (TEA-LCA), and (4) long-term pilot or full-scale validation. Overall, comprehensive optimization of the entire flow is more effective than improving isolated parts. Full article

Healthcare fraud poses significant risks to insurance systems, undermining both financial sustainability and equitable access to care. Accurate detection of fraudulent claims is therefore critical to ensuring the integrity of healthcare insurance operations. However, the increasing sophistication of fraud techniques and limited data availability have undermined the performance of traditional detection approaches. To address these challenges, this paper proposes WCGAN-GA-RF, an integrated fraud detection framework that synergistically combines Wasserstein Conditional Generative Adversarial Network with gradient penalty (WCGAN-GP) for synthetic data generation, genetic algorithm-based feature selection (GA-RF) for dimensionality reduction, and Random Forest (RF) for classification. The proposed framework was empirically validated on a real-world dataset of 16,000 healthcare insurance claims from a Chinese healthcare technology firm, characterized by a 16:1 class imbalance ratio ($5.9\%$ fraudulent samples) and 118 original features. Using a stratified $80/20$ train–test split with results averaged over five independent runs, the WCGAN-GA-RF framework achieved a precision of $96.47\pm 0.5\%$, a recall of $97.05\pm 0.4\%$, and an F1-score of $96.26\pm 0.4\%$. Notably, the GA-RF component achieved a $65\%$ feature reduction (from 80 to 28 features) while maintaining competitive detection accuracy. Comparative experiments demonstrate that the proposed approach outperforms conventional oversampling methods, including Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN), particularly in handling high-dimensional, severely imbalanced healthcare fraud data. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the adaptability required to handle fluctuations in raw gas composition and operating conditions. This review aims to systematically analyze modern control strategies to identify the most influential parameters and effective methodologies for enhancing process efficiency. The methods involve a comparative assessment of classical PID control against advanced intelligent approaches, including adaptive control, fuzzy logic, and machine learning (ML) models, based on a synthesis of the recent literature and industrial case studies. The key finding is that data-driven and intelligent methods (e.g., neural networks, adaptive fuzzy controllers) demonstrate superior performance in achieving precise parameter adjustment, improving responsiveness, and optimizing energy consumption compared to traditional static systems. Such an integrated strategy transforms decision-making into a multivariable optimization framework with objectives encompassing minimizing pollutants, lowering energy usage, and enhancing end-product specifications. The present work argues for employing methodologies like systemic analyses and advanced computational techniques—particularly artificial neural networks—to forecast gas stream attributes. Full article

Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng–Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R²), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models. Full article

The application of advanced imaging techniques, particularly computed tomography (CT), photogrammetric scanning, and three-dimensional reconstructions of body surfaces and skeletal remains, is becoming a crucial component of Forensic Anthropology. These tools enable a non-invasive and highly standardized analysis of both intact cadavers and human remains recovered from terrestrial or aquatic environments, providing reliable support in identification processes, traumatological reconstruction, and the assessment of taphonomic processes. In the context of estimating the Post-Mortem Interval (PMI) and the Post-Mortem Submersion Interval (PMSI), digital imaging allows for the objective and reproducible documentation of morphological changes associated with decomposition, saponification, skeletonization, and taphonomic patterns specific to the recovery environment. Specifically, CT enables the precise assessment of gas accumulation, transformations in residual soft tissues, and structural bone modifications, while photogrammetry and 3D reconstructions facilitate the longitudinal monitoring of transformative processes in both terrestrial and underwater contexts. These observations enhance the reliability of PMI/PMSI estimates through integrated models that combine morphometric, taphonomic, and environmental data. Beyond PMI/PMSI estimation, imaging techniques play a central role in anthropological bioprofiling, facilitating the estimation of age, sex, and stature, the analysis of dental characteristics, and the evaluation of antemortem or perimortem trauma, including damage caused by terrestrial or fauna. Three-dimensional documentation also provides a permanent, shareable archive suitable for comparative analyses, ensuring transparency and reproducibility in investigations. Although not a complete substitute for traditional autopsy or anthropological examination, imaging serves as an essential complement, particularly in cases where the integrity of remains must be preserved or where environmental conditions hinder the direct handling of osteological material. Future directions include the development of AI-based predictive models for PMI/PMSI estimation using automated analysis of post-mortem changes, greater standardization of imaging protocols for aquatic remains, and the use of digital sensors and multimodal techniques to characterize microstructural alterations not detectable by the naked eye. The integration of high-resolution imaging and advanced analytical algorithms promises to further enhance the reconstructive accuracy and interpretative capacity of Forensic Anthropology. Full article

Precise sensor integration is crucial for autonomous vehicle (AV) navigation, yet traditional extrinsic calibration remains costly and labor-intensive. This study proposes an automated calibration approach that uses metaheuristic algorithms (Simulated Annealing (SA), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)) to independently optimize rotational and translational parameters, reducing cross-compensation errors. Bayesian optimization is used offline to define the search bounds (and tune hyperparameters), accelerating convergence, while computer vision techniques enhance automation by detecting geometric features using a checkerboard reference and a Huber estimator for noise handling. Experimental results demonstrate high accuracy with a single-pose acquisition, supporting multi-sensor configurations and reducing manual intervention, making the method practical for real-world AV applications. Full article

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANN–LSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions. Full article

This review explores strategies to enhance meat quality in poultry, focusing on both management and genetic methods. Poultry meat quality is influenced by many factors, including rearing conditions, nutrition, animal welfare, and post-slaughter processing. Key management factors such as stocking density, ventilation, temperature, and humidity are emphasized for their significant impact on bird welfare and the resulting meat texture, color, and microbial stability. Welfare-enhancing practices like gentle handling, environmental enrichment, and thermal comfort are highlighted for their direct effects on stress levels and meat properties such as water-holding capacity and pH. Innovations in slaughtering and chilling techniques, including electrical and gas stunning and rapid chilling, are shown to preserve meat quality and prevent common defects like pale, soft, and exudative (PSE) or dark, firm, and dry (DFD) meat. The review also underscores the importance of hygiene protocols, hazard analysis and critical control points (HACCP) systems, and traceability technologies to ensure food safety and foster consumer trust. On the genetic front, it discusses conventional selection, marker-assisted selection (MAS), and genomic selection (GS) as tools for breeding birds with better meat quality traits, including tenderness, intramuscular fat, and resistance to conditions like woody breast. Functional genomics and gene editing are identified as the leading edge of future advances. Ultimately, the review advocates for an integrated approach that balances productivity, quality, animal welfare, and sustainability. As consumer expectations increase, the poultry industry must adopt precise, science-based strategies across the entire production process to reliably deliver high-quality meat products. Full article

Accurate identification of lithology is considered very important in oil and gas exploration because it has a direct impact on the evaluation and development planning of any reservoir. In complex reservoirs where extreme class imbalance occurs, as critical minority lithologies cover less than 5%, the identification accuracy is severely constrained. Recent deep learning methods include convolutional neural networks, recurrent architectures, and transformer-based models that have achieved substantial improvements over traditional machine learning approaches in identifying lithology. These methods demonstrate great performance in catching spatial patterns and sequential dependencies from well log data, and they show great recognition accuracy, up to 85–88%, in the case of a moderate imbalance scenario. However, when these methods are extended to complex reservoirs under extreme class imbalance, the following three major limitations have been identified: (1) single-scale architectures, such as CNNs or standard Transformers, cannot capture thin-layer details less than 0.5 m and regional geological trends larger than 2 m simultaneously; (2) generic imbalance handling techniques, including focal loss alone or basic SMOTE, prove to be insufficient for extreme ratios larger than 50:1; and (3) conventional Transformers lack depth-dependent attention mechanisms incorporating stratigraphic continuity principles. This paper is dedicated to proposing a geological-constrained multi-scale Transformer framework tailored for 1D well-log sequences under extreme imbalance larger than 50:1. The systematic approach addresses the extreme imbalance by deep-feature fusion and advanced class-rebalancing strategies. Accordingly, this framework integrates multi-scale convolutional feature extraction using 1 × 3, 1 × 5, 1 × 7 kernels, hierarchical attention mechanisms with depth-aware position encoding based on Walther’s Law to model long-range dependencies, and adaptive three-stage class-rebalancing through SMOTE–Tomek hybrid resampling, focal loss, and CReST self-training. The experimental validation based on 32,847 logging samples demonstrates significant improvements: overall accuracy reaches 90.3% with minority class F1 scores improving by 20–25% percentage points (argillaceous siltstone 73.5%, calcareous sandstone 68.2%, coal seams 65.8%), and G-mean of 0.804 confirming the balanced recognition. Of note, the framework maintains stable performance even when there is extreme class imbalance at a ratio of up to 100:1 with minority class F1 scores above 64%, representing a two-fold improvement over the state-of-the-art methods, where former Transformer-based approaches degrade below. This paper provides the fundamental technical development for the intelligent transformation of oil and gas exploration, with extensive application prospects. Full article

Traditional pork meat products produced through artisanal smoking and drying techniques are highly appreciated for their distinctive sensory characteristics; however, such practices may raise concerns regarding both chemical and microbiological safety. The present study aimed to assess the occurrence of selected polycyclic aromatic hydrocarbons (PAHs) and hygiene- and safety-related microorganisms in traditionally processed pork meat products collected from local markets and small-scale producers. A total of 140 samples were analyzed for four marker PAHs—benzo[a]pyrene (BaP), benz[a]anthracene (BaA), benzo[b]fluoranthene (BbF), and chrysene (Chr)—using gas chromatography–mass spectrometry (GC–MS). Microbiological contamination was evaluated through standard plate count techniques, and the presence of Listeria monocytogenes and Salmonella serovars was determined using selective isolation methods, followed by PCR confirmation of pathogenic strains. PAH concentrations varied widely: BaP (0.3–1.8 µg/kg), BaA (0.5–2.4 µg/kg), BbF (0.8–3.1 µg/kg) and Chr (0.4–2.0 µg/kg), with ΣPAH₄ (Sum of PAH4, referring to the total concentration of the four-priority polycyclic aromatic hydrocarbons) ranging from 2.5 to 8.3 µg/kg. Smoked sausages showed the highest contamination (BaP: 1.8 µg/kg; ΣPAH₄: 8.3 µg/kg), significantly exceeding levels in dry-cured ham (BaP: 1.2 µg/kg; ΣPAH₄: 6.1 µg/kg) and smoked bacon (BaP: 0.9 µg/kg; ΣPAH₄: 5.4 µg/kg) (Kruskal–Wallis, p < 0.0001). Although all samples complied with the EU ΣPAH₄ limit (12 µg/kg), 15% exceeded the BaP limit of 2.0 µg/kg, primarily among artisanal sausages. Microbiological analyses revealed total coliform counts between 1.5 × 10² and 6.2 × 10⁴ CFU/g, while Enterobacteriaceae ranged from 2.0 × 10² to 4.9 × 10⁴ CFU/g. Samples obtained from unregulated producers exhibited higher bacterial loads, indicating suboptimal hygiene during processing and storage. A moderate positive correlation was identified between total coliform and Enterobacteriaceae counts (r = 0.59, p < 0.05). Moreover, Salmonella serovars was detected in ten sausage samples, and Listeria monocytogenes was confirmed in three samples of traditional products. Overall, the findings suggest that although PAH contamination generally complied with EU safety limits, occasional exceedances of benzo[a]pyrene and elevated microbial indicators underscore the need for stricter control of smoking parameters, fuel sources, and hygienic handling. Implementation of standardized smoking protocols and good manufacturing practices (GMP) is recommended to enhance the safety and quality of traditional pork meat products Full article

Mask optimization is a critical technique for enhancing imaging performance in digital micromirror device (DMD)-based maskless lithography. Conventional algorithms, however, often suffer from slow convergence and limited adaptability, particularly when handling complex multi-feature mask patterns. To address these challenges, this study proposes a hybrid Genetic–Hippo Optimization (GA-HO) algorithm that integrates the global exploration capability of the Genetic Algorithm (GA) with the local exploitation efficiency of the Hippocampus Optimization (HO) Algorithm. The approach employs grayscale modulation for adaptive mask optimization and introduces a global–local cyclic search mechanism to balance exploration and exploitation throughout the optimization process. Simulation results demonstrate that the GA-HO hybrid algorithm achieves a more pronounced improvement in overall optimization performance compared with the standard GA. In complex multi-line mask optimization, the standard GA achieves approximately a 18% enhancement in optimization accuracy, whereas the GA-HO algorithm achieves around a 30% improvement. Moreover, the GA-HO algorithm exhibits a smoother convergence curve, greater stability, and superior robustness. The hybrid method effectively suppresses linewidth variations and corner distortions caused by optical proximity effects (OPE), maintaining high imaging fidelity and stable optimization outcomes even under challenging mask conditions. Overall, the proposed GA-HO framework demonstrates excellent efficiency, adaptability, and precision, providing a reliable and high-performance solution for DMD-based maskless lithography. This work offers a strong theoretical and algorithmic foundation for advancing high-resolution, high-efficiency, and low-cost micro/nanofabrication technologies, highlighting the potential of heuristic hybrid optimization strategies for practical lithography applications. Full article

Air transportation has accelerated international trade, and the efficient use of cargo aircraft capacity supports logistics operations, reduces expenses, and benefits the environment. In this study, we formulate a mathematical programming model to solve the cargo aircraft capacity optimization problem and propose simplified approaches for practical applications. We investigate Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA), and Large Neighborhood Search (LNS) techniques. MILP yields optimal solutions for small instances but cannot handle large-scale, real-world problems due to excessive computation time; therefore, we combine the GA and LNS. The GA provides acceptable solutions rapidly, and LNS refines them by exploring larger solution spaces. Thus, this hybrid approach leverages the GA’s exploration capability and LNS’s exploitation ability to produce high-quality solutions efficiently. Our experimental results show that the hybrid GA-LNS method outperforms the MILP and single approaches in terms of capacity usage, loading duration, and computational time. This study provides an applicable model with practical constraints and guidelines for air cargo and cost reduction, operational efficiency, and safety. Full article