Search Results (2,641)

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

Plastic waste, particularly polyethylene terephthalate (PET), poses a growing environmental challenge. This study investigates the feasibility of incorporating recycled PET into clay bricks as a sustainable alternative in construction. Bricks were fabricated with 0%, 5%, 10%, and 15% PET content. Clay characterization included particle size distribution, Atterberg limits, and moisture content. Physical and mechanical tests evaluated dimensional variability, void percentage, warping, water absorption, suction, unit compressive strength ($f_b^{\prime }$), and prism compressive strength ($f_m^{\prime }$). Statistical analysis (Shapiro–Wilk, p < 0.05) validated the results. PET addition improved physical properties—reducing water absorption, suction, and voids—while slightly compromising mechanical strength. The 15% PET mix showed the best overall performance ($f_b^{\prime }$ = 24.00 kg/cm²; $f_m^{\prime }$ = 20.40 kg/cm²), with uniform deformation and lower absorption (18.7%). Recycled PET enhances key physical attributes of clay bricks, supporting its use in eco-friendly construction. However, reduced compressive strength limits its structural applications. Optimizing PET particle size, clay type, and firing conditions is essential to improve load-bearing capacity. Current formulations are promising for non-structural uses, contributing to circular material strategies. Full article

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

Thick active layers are crucial for scalable production of organic photodetectors (OPDs). However, most OPDs with active layers thicker than 200 nm typically exhibit decreased photocurrents and responsivities due to exciton diffusion and prolonged charge transport pathways. To address these limitations, we designed and synthesized PFBDT-8ttTPD, a fluorinated polymer donor. The strategic incorporation of fluorine effectively enhanced the charge carrier mobility, enabling more efficient charge transport, even in thicker films. OPDs combining PFBDT−8ttTPD with IT−4F or Y6 non-fullerene acceptors showed a substantially lower dark current density (J_d) for active layer thicknesses of 250−450 nm. Notably, J_d in the IT-4F-based devices declined from 8.74 × 10⁻⁹ to 4.08 × 10⁻¹⁰ A cm⁻² under a reverse bias of −2 V, resulting in a maximum specific detectivity of 3.78 × 10¹³ Jones. Meanwhile, Y6 integration provided near-infrared sensitivity, with the devices achieving responsivity above 0.48 A W⁻¹ at 850 nm and detectivity over 10¹³ Jones up to 900 nm, supporting broadband imaging. Importantly, high-quality thick films (≥400 nm) free of pinholes or defects were fabricated, enabling scalable production without performance loss. This advancement ensures robust photodetection in thick uniform layers and marks a significant step toward the development of industrially viable OPDs. Full article

The nominal compositions of Fe₆₅Co_10−xNi_10−xCr₁₅Si_2x (x = 1, 2, and 3 at.%) medium-entropy alloys (MEAs) were designed and fabricated by vacuum arc melting. Their microstructure, hardness, and mechanical properties were systematically characterized. Corrosion behavior was evaluated in 3.5 wt.% NaCl solution by potentiodynamic polarization and electrochemical impedance spectroscopy. The investigated MEAs exhibit a dual-phase microstructure composed of face-centered cubic (FCC) and body-centered-cubic (BCC) phases. With increasing Si content, yield strength and ultimate tensile strength increase, while uniform elongation decreases. Hardness also increases with increasing Si content. For the x = 3 MEA, the yield strength, ultimate tensile strength, and hardness of are ~518 MPa, ~1053 MPa, and 262 ± 4.8 HV, respectively. The observed strengthening can be primarily attributed to solid solution strengthening effect by Si. Polarization curves indicate that the x = 3 MEA exhibits the best corrosion resistance with the lowest corrosion current density ((0.401 ± 0.19) × 10⁻⁶ A × cm⁻²) and corrosion rate ((4.65 ± 0.19) × 10^–2 μm × year⁻¹)). Equivalent electric circuit analysis suggests the formation of a stable passive oxide film on the MEAs. This conclusion is supported by the capacitive behavior, high impedance values (> 10⁴ Ω cm²) at low frequencies, and phase angles within a narrow window of 80.05°~80.64° in the medium-frequency region. The passive-film thickness was calculated and the corrosion morphology was analyzed by SEM. These results provide a reference for developing high-strength, corrosion-resistant, medium-entropy alloys. Full article

Arsenic pollution in potable water is a significant worldwide health concern. This study systematically evaluates current progress in adsorption technology, the most promising restorative approach, to provide a definitive framework for future research and use. The methodology entailed a rigorous evaluation of 91 peer-reviewed studies (2012–2025), classifying adsorbents into three generations: (1) Natural adsorbents (e.g., agricultural/industrial wastes), characterized by cost-effectiveness but limited capacities (0.1–5 mg/g); (2) Engineered materials (e.g., metal oxides, activated alumina), which provide dependable performance (84–97% removal); and (3) Advanced hybrids (e.g., MOFs, polymer composites), demonstrating remarkable capacities (60–300 mg/g). The primary mechanisms of removal are confirmed to be surface complexation, electrostatic interactions, and redox precipitation. Nevertheless, the critical analysis indicates that despite significant laboratory efficacy, substantial obstacles to field implementation persist, including scalability limitations (approximately 15% of materials are evaluated beyond laboratory scale), stability concerns (e.g., structural collapse of MOFs at extreme pH levels), and elevated costs (e.g., MOFs priced at approximately $230/kg compared to $5/kg for alumina). The research indicates that the discipline must transition from only materials innovation to application science. Primary objectives include the development of economical hybrids (about $50/kg), the establishment of uniform WHO testing standards, and the implementation of AI-optimized systems. The primary objective is to attain sustainable solutions costing less than $0.10 per cubic meter that satisfy worldwide deployment standards via multidisciplinary cooperation. Full article

This study investigates the influence of plasma electrolytic oxidation (PEO) on corrosion resistance of Ti6Al4V alloys produced by direct metal laser sintering (DMLS) for orthopedic implants. PEO (300 s) and flash-PEO (60 s) coatings containing Si, Ca, P, Mg and Zn were applied on both DMLS and wrought Ti6Al4V alloys. Samples, coated and uncoated, were characterized for microstructure, morphology and composition. Electrochemical behaviour was assessed by potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) in simulated body fluid (SBF) at 37 °C. Ion release was quantified by inductively coupled plasma optical emission spectroscopy (ICP-OES). DMLS alloy was more passive than wrought Ti6Al4V, releasing ~60% less Ti and ~25% less Al, but ~900% more V. For both alloys, correlation of corrosion current and ion release indicated that 98–99% of oxidized Ti remained in the passive layer. Flash-PEO produced uniform porous coatings composed of anatase and rutile with ~50% amorphous phase, while PEO yielded heterogeneous layers due to soft sparking. In both cases, coatings were the main source of ions. For the DMLS alloy, the best protection was afforded by flash-PEO, releasing 0.01 μg cm⁻² d⁻¹ Ti, 26 μg cm⁻² d⁻¹ Al, and 0.25 μg cm⁻² d⁻¹ V over 30 days. Full article

Limited-Entry Liner (LEL) technology has emerged as a transformative solution for enhancing hydrocarbon recovery in unconventional reservoirs while addressing challenges in carbon sequestration. This review examines the role of LEL in optimizing acid stimulation, hydraulic fracturing and production optimization, focusing on its ability to improve fluid distribution uniformity in horizontal wells through precision-engineered orifices. By integrating theoretical models, experimental studies, and field applications, we highlight LEL’s potential to mitigate the heel–toe effect and reservoir heterogeneity, thereby maximizing stimulation efficiency. Based on a comprehensive review of existing literature, this study identifies critical limitations in current LEL models—such as oversimplified annular flow dynamics, semi-empirical treatment of wormhole propagation, and a lack of quantitative design guidance—and aims to bridge these gaps through integrated multiphysics modeling and machine learning-driven optimization. Furthermore, we explore its adaptability for controlled CO₂ injection in geological storage, offering a sustainable approach to energy transition. This work provides a comprehensive yet accessible overview of LEL’s significance in both energy production and environmental sustainability. Full article

Metallic magnesium is a strategic material with applications in mobility, energy and medicine, due to its low density, biocompatibility and use as an anode in rechargeable batteries. However, industrial production methods—such as the thermal reduction of dolomite or the electrolysis of anhydrous MgCl₂—face environmental and operational challenges, including high temperatures, emissions, and dehydration of precursors like bischofite. In response, ionic liquids (ILs) have emerged as alternative electrolytes, offering low volatility, thermal stability and wide electrochemical windows that enable electrodeposition in water-free media. This study presents a systematic review of 32 peer-reviewed articles, applying the PRISMA 2020 methodology. The analysis is structured across three dimensions: (1) types of ILs employed, (2) operational parameters and (3) magnesium source materials. In addition to electrolyte composition, key factors such as temperature, viscosity control, precursor purity and cell architecture were identified as critical for achieving efficient and reproducible magnesium deposition. Furthermore, the use of elevated temperatures and co-solvent strategies has been shown to effectively mitigate viscosity-related transport limitations, enabling more uniform ion mobility and enhancing interfacial behavior. The use of alloy co-deposition strategies and multicomponent electrolyte systems also expands the technological potential of IL-based processes, especially for corrosion-resistant coatings or composite electrode materials. This review contributes by critically synthesizing current techniques, identifying knowledge gaps and proposing strategies for scalable, sustainable magnesium production. The findings position IL-based electrodeposition as a potential alternative for environmentally responsible metal recovery. Full article

The cycling stability and widespread practical implementation of aqueous zinc ion batteries (AZIBs) are impeded by dendrite growth and the hydrogen evolution reaction (HER). Herein, theaflavins, a low-cost organic bio-compounds and a major component of tea, were innovatively introduced as an electrolyte additive for AZIBs to address these challenges. When added into the electrolyte, theaflavins, with their strong de-solvation capability, facilitated the more uniform and stable diffusion of zinc ions, effectively suppressing dendrite formation and HER. This, in turn, significantly enhanced the coulombic efficiency (>95% in Zn/Cu system) and the stability of the zinc deposition/stripping process in Zn/Zn system. The Zn/Zn symmetric battery system stably cycled for approximately 3000 h at current densities of 1 mA/cm². Compared with H₂O molecules, theaflavins exhibited a narrower LUMO and HOMO gap and higher adsorption energy on zinc surfaces. These properties enabled theaflavins to be preferentially adsorbed onto zinc anode surfaces, forming a protective layer that minimized direct contact between water molecules and the zinc surface. This layer also promoted the electron transfer associated with zinc ions, thereby greatly enhancing interfacial stability and significantly mitigating HER. When 10 mmol/L of theaflavins was present in the electrolyte, the system exhibited lower impedance activation energy, a smoother zinc ion deposition process, reduced corrosion current, and higher HER overpotential. Furthermore, incorporating theaflavins into the electrolyte enhanced the vanadium redox reaction and accelerated zinc ion diffusion, thereby significantly improving battery performance. This work explores the design of a cost-effective electrolyte additive, providing essential insights for the progress of practical AZIBs. Full article

Electric fields (EFs) have emerged as effective, non-chemical tools for modulating microbial populations in complex matrices such as wastewater. This review consolidates current advances on EF-induced alterations in microbial structures and functions, focusing on both vegetative cells and spores. Key parameters affected include membrane thickness, transmembrane potential, electrical conductivity, and dielectric permittivity, with downstream impacts on ion homeostasis, metabolic activity, and viability. Such bioelectrical modifications underpin EF-based detection methods—particularly impedance spectroscopy and dielectrophoresis—which enable rapid, label-free, in situ microbial monitoring. Beyond detection, EFs can induce sublethal or lethal effects, enabling selective inactivation without chemical input. This review addresses the influence of field type (DC, AC, pulsed), intensity, and exposure duration, alongside limitations such as species-specific variability, heterogeneous environmental conditions, and challenges in achieving uniform field distribution. Emerging research highlights the integration of EF-based platforms with biosensors, machine learning, and real-time analytics for enhanced environmental surveillance. By linking microbiological mechanisms with engineering solutions, EF technologies present significant potential for sustainable water quality management. Their multidisciplinary applicability positions them as promising components of next-generation wastewater monitoring and treatment systems, supporting global efforts toward efficient, adaptive, and environmentally benign microbial control strategies. Full article

Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full population of possible images, they exhibit sufficient generalization to be applicable to new and previously unseen data. Since training data sets typically represent such a small sampling of any domain, the possibility of bias in their composition is very real. But what are the limits of generalization given such bias, and up to what point might it be sufficient for a real problem task? There are many types of bias as will be seen, but we focus only on one, selection bias. In vision, image contents are dependent on the physics of vision and geometry of the imaging process and not only on scene contents. How do biases in these factors—that is, non-uniform sample collection across the spectrum of imaging possibilities—affect learning? We address this in two ways. The first is theoretical in the tradition of the Thought Experiment. The point is to use a simple theoretical tool to probe into the bias of data collection to highlight deficiencies that might then deserve extra attention either in data collection or system development. Those theoretical results are then used to motivate practical tests on a new dataset using several existing top classifiers. We report that, both theoretically and empirically, there are some selection biases rooted in the physics and imaging geometry of vision that challenge current methods of classification. Full article

Underwater crack detection is critical for ensuring the safety and longevity of submerged infrastructures, yet it remains challenging due to water-induced image degradation, limited labeled data, and the poor generalization of existing models. This paper proposes a novel deep learning framework that integrates physical priors and uncertainty modeling to address these challenges. Our approach introduces a physics-guided enhancement module that leverages underwater light propagation models, and a dual-branch segmentation network that combines semantic and geometry-aware curvature features to precisely delineate irregular crack boundaries. Additionally, an uncertainty-aware Transformer module quantifies prediction confidence, reducing the number of overconfident errors in ambiguous regions. Experiments on a self-collected dataset demonstrate State-of-the-Art performance, achieving 81.2% mIoU and 83.9% Dice scores, with superior robustness in turbid water and uneven lighting. The proposed method introduces a novel synergy of physical priors and uncertainty-aware learning, advancing underwater infrastructure inspection beyond the current data-driven approaches. Our framework offers significant improvements in accuracy, robustness, and interpretability, particularly in challenging conditions like turbid water and non-uniform lighting. Full article

Background/Objectives: Next-Generation Sequencing (NGS) has transformed cancer diagnostics and treatment by enabling comprehensive genomic profiling of tumors. This review aims to summarize the current applications of NGS in oncology, highlighting its role in early detection, precision therapy, and disease monitoring. Methods: We conducted a comprehensive review of the recent literature, focusing on the application of NGS in cancer care. Results: NGS enables high-resolution genomic profiling, identifying actionable mutations (e.g., EGFR, KRAS, and ALK) and immunotherapy biomarkers (e.g., PD-L1, TMB, and MSI), guiding personalized treatment selection and improving outcomes in advanced malignancies. Liquid biopsy enhances diagnostic accessibility and enables real-time monitoring of minimal residual disease and treatment resistance. Despite these advances, widespread clinical adoption remains constrained by technical limitations (e.g., coverage uniformity and sample quality), economic challenges (high costs and complex reimbursement), and interpretative issues, including the management of variants of uncertain significance (VUSs). Conclusions: NGS is central to precision oncology, enabling molecularly driven cancer care. Integration with artificial intelligence, single-cell sequencing, spatial transcriptomics, multi-omics, and nanotechnology promises to overcome current limitations, advancing personalized treatment strategies. Standardization of workflows, cost reduction, and improved bioinformatics expertise are critical for its full clinical integration. Full article

The current study presents the development and optimisation of foam glass manufactured from recycled glass shards and expanded ground perlite, targeting enhanced structural and thermal performance for sustainable building applications. By investigating various particle size fractions (“125 μm”, “250 μm”, “500 μm”) and sintering temperatures (800–850 °C), we achieved a foam glass with superior compressive strength and uniform porosity. Notably, samples utilising a homogeneous 500 μm particle fraction sintered at 850 °C exhibited the highest compressive strength of 2.17 MPa, coupled with open porosity uniformity and stable structural matrix formation. Density values in this fraction decreased from 321 to 263 kg/m³, indicating effective foaming and well-developed open porosity that balances mechanical integrity and thermal insulation. The optimised thermal regime minimised crystalline phase formation, preserving low thermal conductivity and mechanical stability. Compared to heterogeneous composites, the homogeneous fractions demonstrated significantly improved strength-to-porosity ratios, ensuring predictable mechanical performance and competitive thermal insulation properties. These findings underline the material’s potential as a cost-effective, environmentally friendly insulation solution that meets or exceeds existing standards, with promising applications in energy-efficient construction. Full article