Search Results (839)

Computer vision and deep learning are essential in diverse fields such as autonomous driving, medical imaging, face recognition, and object detection. However, enhancing low-light remote sensing images remains challenging for both research and real-world applications. Low illumination degrades image quality due to sensor limitations and environmental factors, weakening visual fidelity and reducing performance in vision tasks. Common issues such as insufficient lighting, backlighting, and limited exposure create low contrast, heavy shadows, and poor visibility, particularly at night. We propose QWR-Dec-Net, a quaternion-based Retinex decomposition network tailored for low-light image enhancement. QWR-Dec-Net consists of two key modules: a decomposition module that separates illumination and reflectance, and a denoising module that fuses a quaternion holistic color representation with wavelet multi-frequency information. This structure jointly improves color constancy and noise suppression. Experiments on low-light remote sensing datasets (LSCIDMR and UCMerced) show that QWR-Dec-Net outperforms current methods in PSNR, SSIM, LPIPS, and classification accuracy. The model’s accurate illumination estimation and stable reflectance make it well-suited for remote sensing tasks such as object detection, video surveillance, precision agriculture, and autonomous navigation. Full article

Background: Healthcare is a large contributor to greenhouse gas (GHG) emissions, contributing to climate change and health impairments. However, the magnitude of health and climate benefits of local and regional GHG mitigation strategies has not been well quantified. Few studies have demonstrated the use of public tools for this purpose in healthcare facilities. Methods: We evaluated several renewable energy and energy efficiency scenarios focused on one academic medical center in New York State. We used the Environmental Protection Agency’s (EPA) publicly available AVoided Emissions and geneRation Tool to estimate avoided GHG and health-harmful air pollutant emissions. The economic value of the resulting avoided health and climate damages was quantified using EPA’s CO-Benefits Risk Assessment screening tool. Results: Transitioning one healthcare institution to 100% solar energy and improving energy efficiency by 25% could yield approximately $807,000 to $1.5 million in annual health savings, with an additional $2.3 million benefits in avoided climate damages. There is an approximate $108.5–$196.6 million in annual climate and health benefits when extrapolating these energy solutions to hospitals across the same state. Conclusions: There are significant health savings from healthcare GHG mitigation strategies. This application of publicly available and accessible tools demonstrates ways to integrate climate and health benefits into local decision-making around climate change mitigation and sustainability efforts. Full article

Bio-electric fields—manifested as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG)—are fundamental to modern medical diagnostics but often suffer from severe data imbalance, scarcity, and environmental noise. Generative Adversarial Networks (GANs) offer a powerful, nonlinear solution to these modeling hurdles. This review presents a comprehensive survey of GAN methodologies specifically tailored for bio-electric signal processing. We first establish a theoretical foundation by detailing GAN principles, training mechanisms, and critical structural variants, including advancements in loss functions and conditional architectures. Subsequently, the paper extensively analyzes applications ranging from high-fidelity signal synthesis and noise reduction to multi-class classification. Special attention is given to clinical anomaly detection, specifically covering epilepsy, arrhythmia, depression, and sleep apnea. Furthermore, we explore emerging applications such as modal transformation, Brain–Computer Interfaces (BCI), de-identification for privacy, and signal reconstruction. Finally, we critically evaluate the computational trade-offs and stability issues inherent in current models. The study concludes by delineating prospective research avenues, emphasizing the necessity of interdisciplinary synergy to advance personalized medicine and intelligent diagnostic systems. Full article

Lightweight, lead-free polymer–mineral composites have attracted increasing interest as radiation-attenuating materials for applications where reduced mass and environmental compatibility are required. In this work, the $\gamma$-ray attenuation behavior of poly(ether ether ketone) (PEEK) reinforced with natural palygorskite and pumice was evaluated at filler concentrations of 10–40 wt%. Photon interaction parameters, including the linear attenuation coefficient ($\mu$), half-value layer (HVL), mean free path ($\lambda$), and effective atomic number ($Z_{\mathrm{eff}}$), were computed over the energy range 15 keV–15 MeV using the Phy-X/PSD platform and validated through full Geant4 Monte Carlo transmission simulations. At 15 keV, $\mu$ increased from $1.46{\mathrm{cm}}^{-1}$ for pure PEEK to $4.21{\mathrm{cm}}^{-1}$ and $8.499{\mathrm{cm}}^{-1}$ for the 40 wt% palygorskite- and pumice-filled composites, respectively, reducing the HVL from 0.69 cm to 0.24 cm and 0.11 cm. The corresponding $Z_{\mathrm{eff}}$ values increased from 6.5 (pure PEEK) to 9.4 (40 wt% palygorskite) and 15.3 (40 wt% pumice), reflecting the influence of higher-Z oxide constituents in pumice. At higher photon energies, the attenuation curves converged as Compton scattering became dominant, although pumice-filled PEEK retained marginally higher $\mu$ and shorter $\lambda$ up to the MeV region. These findings demonstrate that natural mineral fillers can enhance the photon attenuation behavior of PEEK while retaining the known thermal stability and mechanical performance of the polymer matrix as reported in the literature, indicating their potential use as lightweight, secondary radiation-attenuating components in medical, industrial, and aerospace applications. Full article

Background/Objectives: Caffeic acid is a hydroxycinnamic acid that has a wide range of applications in the medical field. The synthesis of caffeic acid using microbial fermentation technology is an environmentally friendly method. Methods: By engaging various enzymes, specifically 4-hydroxyphenylacetate 3-monooxygenase (HpaB), sourced from diverse bacterial strains, we successfully engineered a functional version of this enzyme within Escherichia coli, enabling the production of caffeic acid. In addition to the two common tyrosine ammonia lyases (TAL) and HpaC, different combinations of HpaB demonstrated varying abilities in converting the substrate L-tyrosine into the desired product, caffeic acid. Results: Under shake-flask culture conditions, the highest yield of caffeic acid was achieved with an enzyme mixture containing HpaB from Escherichia coli, reaching 75.88 mg/L. Enhancing the activity of the rate-limiting enzyme through engineering could potentially increase caffeic acid titer. This study aims to conduct a semi-rational design of HpaB through structure-based approaches to screen for mutants that can enhance the production of caffeic acid. Initially, the predicted three-dimensional structure of HpaB was generated using AlphaFold2, and subsequent analysis was conducted to pinpoint the critical mutation sites within the substrate-binding pocket. Five key amino acid residues (R113, Y117, H155, S210 and Y461) located in the vicinity of the flavin adenine dinucleotide binding domain in HpaB from Escherichia coli could be instrumental in modulating enzyme activity. Subsequently, the mutant S210G/Y117A was obtained by iterative saturation mutagenesis, which increased the titer of caffeic acid by 1.68-fold. The caffeic acid titer was further improved to 2335.48 mg/L in a 5 L fermenter. The findings show that the yield of caffeic acid was significantly enhanced through the integration of semi-rational design and fermentation process optimization. Full article

Blink monitoring has demonstrated significant application value in fields such as safety assessments, medical monitoring, and intelligent technologies. Traditional eye monitoring methods are limited by restricted adaptability, insufficient comfort, or potential risks. MEMS-based ultrasonic technology, as a non-contact approach, has garnered attention due to its strong environmental adaptability, privacy, and security. However, existing designs require high-sensitivity processing circuits and are incompatible with standard fabrication processes. This work proposes a dual-electrode piezoelectric micro-mechanical ultrasonic transducer (PMUT) design based on aluminum nitride (AlN) piezoelectric thin films, integrated into a glasses device to enable real-time blink activity monitoring. The design successfully identifies blink states through time-of-flight (TOF) pulse-echo technology and dynamic unsupervised learning methods. Fabricated using cost-effective standard multi-user MEMS processes, this device offers distinct merits in terms of wearability comfort, information security, biosafety, and reliability. Full article

This study evaluates lead-free high-density polyethylene (HDPE) composites reinforced with high-Z oxides (Bi₂O₃, WO₃, Gd₂O₃, TeO₂, and a Bi₂O₃/WO₃ hybrid) as lightweight materials for gamma-ray and fast-neutron shielding. A hybrid computational framework combining Phy-X/PSD with Geant4 Monte Carlo simulations was used to obtain key shielding parameters, including the linear and mass attenuation coefficients (μ, μ/ρ), half-value layer (HVL), mean free path (MFP), effective atomic number (Z_eff), effective electron density (N_eff), exposure and energy-absorption buildup factors (EBF, EABF), and fast-neutron removal cross section (Σ_R). The incorporation of heavy oxides produced a pronounced improvement in gamma-ray attenuation, particularly at low energies, where the linear attenuation coefficient increased from below 1 cm⁻¹ for neat HDPE to values exceeding 130–150 cm⁻¹ for Bi- and W-rich composites. In the intermediate Compton-scattering region (≈0.3–1 MeV), all oxide-reinforced systems maintained a clear attenuation advantage, with μ values around 0.12–0.13 cm⁻¹ compared with ≈0.07 cm⁻¹ for pure HDPE. At higher photon energies, the dense composites continued to outperform the polymer matrix, yielding μ values of approximately 0.07–0.09 cm⁻¹ versus ≈0.02 cm⁻¹ for HDPE due to enhanced pair-production interactions. The Bi₂O₃/WO₃ hybrid composite exhibited attenuation behavior comparable, and in some regions slightly exceeding, that of the single-oxide systems, indicating that mixed fillers can effectively balance density and shielding efficiency. Oxide addition significantly reduced exposure and energy-absorption buildup factors below 1 MeV, with a moderate increase at higher energies associated with secondary radiation processes. Fast-neutron removal cross sections were also modestly enhanced, with Gd₂O₃-containing composites showing the highest values due to the combined effects of hydrogen moderation and neutron capture. The close agreement between Phy-X/PSD and Geant4 results confirms the reliability of the dual-method approach. Overall, HDPE composites containing about 60 wt.% oxide filler offer a practical compromise between shielding performance, manufacturability, and environmental safety, making them promising candidates for medical, nuclear, and aerospace radiation-protection applications. Full article

Asthma is a common chronic disease in children, contributing to significant morbidity and healthcare utilization worldwide. The integration of artificial intelligence (AI) and machine learning (ML) into pediatric asthma care is rapidly advancing, offering new opportunities for early diagnosis, risk stratification, and personalized management. AI-driven tools can analyze complex clinical, genetic, and environmental data to identify asthma phenotypes and endotypes, predict exacerbations, and support timely interventions. In pediatric populations, these technologies enable non-invasive diagnostic approaches, remote monitoring through wearable devices, and improved medication adherence via smart inhalers and digital health platforms. Despite these advances, challenges remain, including the need for pediatric-specific datasets, transparency in AI decision-making, and careful attention to data privacy and equity. The integration of AI in pediatric asthma care and into the clinical decision system can offer personalized treatment plans, reducing the burden of the disease both for patients and health professionals. This is a narrative review on the applications of AI and ML in pediatric asthma care. Full article

Polyhydroxyalkanoates (PHAs), a family of biodegradable polyesters produced through microbial fermentation of carbon-rich residues, are emerging as attractive alternatives to petroleum-based plastics. Their appeal lies in their exceptional biocompatibility, inherent biodegradability, and tunable physicochemical properties across diverse applications. These materials are environmentally friendly not just at the end of their life, but throughout their entire production–use–disposal cycle. This mini-review presents an update on the expanding biomedical relevance of PHAs, with emphasis on their utility in tissue engineering and drug delivery platforms. In addition, current clinical evaluations and regulatory frameworks are briefly discussed, underscoring the translational potential of PHAs in meeting unmet medical needs. As the healthcare sector advances toward environmentally responsible and patient-focused innovations, PHAs exemplify the convergence of waste valorization and biomedical progress, transforming discarded resources into functional materials for repair, regeneration, and healing. Full article

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN–HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN–HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors. Full article

Valorizing fruit and vegetable residues as renewable sources of bioactive compounds (BCs) is critical for advancing sustainable biotechnology. This review (i) assesses the occurrence, diversity and functionality of BCs in 20 edible plant residues; (ii) compares and classify them by botanical family and residue type; (iii) reviews and evaluates the efficiency of conventional and green extraction and characterization techniques for recovering phytochemical and isolating phenolics (e.g., flavonoids and anthocyanins), carotenoids, alkaloids, saponins, and essential oils; and (iv) examines the BCs’ environmental, medical, and industrial applications. It synthesizes current knowledge on the phytochemical potential of these crops, highlighting their role in diagnostics, biomaterials, and therapeutic platforms. Plant-derived nanomaterials, enzymes, and structural matrices are employed in regenerative medicine and biosensing. Carrot- and pumpkin-based nanoparticles accelerate wound healing through antimicrobial and antioxidant protection. Spinach leaves serve as decellularized scaffolds that mimic vascular and tissue microenvironments. Banana fibers are used in biocompatible composites and sutures, and citrus- and berry-derived polyphenols improve biosensor stability and reduce signal interference. Agro-residue valorization reduces food waste and enables innovations in medical diagnostics, regenerative medicine, and circular bioeconomy, thereby positioning plant-derived BCs as a cornerstone for sustainable biotechnology. The BCs’ concentration in fruit and vegetable residues varies broadly (e.g., total phenolics (~50–300 mg GAE/g DW), anthocyanins (~100–600 mg C3G/g DW), and flavonoids (~20–150 mg QE/g DW)), depending on the crop and extraction method. By linking quantitative food waste hotspots with phytochemical potential, the review highlights priority streams for the circular-bioeconomy interventions and outlines research directions to close current valorization gaps. Full article

Object identification (OI) is widely used in fields like autonomous driving, security, robotics, environmental monitoring, and medical diagnostics. OI using infrared (IR) images provides high visibility in low light for all-day operation compared to visible light. However, the low contrast often causes OI failure in complex scenes with similar target and background temperatures. Therefore, there is a stringent requirement to enhance IR image contrast for accurate OI, and it is ideal to develop a fully automatic process for identifying objects in IR images under any lighting condition, especially in photon-deficient conditions. Here, we demonstrate for the first time a highly accurate automatic IR OI process based on the combination of polarization IR imaging and artificial intelligence (AI) vision (Yolov7), which can quickly identify objects with a high discrimination confidence level (DCL, up to 0.96). In addition, we demonstrate that it is possible to achieve accurate IR OI in complex environments, such as photon-deficient, foggy conditions, and opaque-covered objects with a high DCL. Finally, through training the model, we can identify any object. In this paper, we use a UAV as an example to conduct experiments, further expanding the capabilities of this method. Therefore, our method enables broad OI applications with high all-day performance. Full article

Photocatalytic water sterilization has emerged as a promising sustainable technology for addressing microbial contamination across diverse sectors including healthcare, food production, and environmental management. This review examines the fundamental mechanisms and recent advances in photocatalytic water sterilization, with a particular emphasis on the differential bactericidal pathways against Gram-negative and Gram-positive bacteria. Gram-negative bacteria undergo a two-step inactivation process involving initial outer membrane lipopolysaccharide (LPS) degradation followed by inner membrane disruption, whereas Gram-positive bacteria exhibit simpler kinetics due to direct oxidative attacks on their thick peptidoglycan layer. Escherichia coli has long been used as the gold standard in photocatalytic sterilization studies owing to its aerobic nature and suitability for the colony-counting method. In contrast, Lactobacillus casei, a facultative anaerobe, can be cultured statically and evaluated rapidly using turbidity-based optical density measurements. Therefore, both organisms serve complementary roles depending on the experimental objectives—E. coli for precise quantification and L. casei for rapid, practical assessments of Gram-positive bacterial inactivation under laboratory conditions. We also describe sterilization using light alone while comparing it to photocatalytic sterilization and then discuss two innovative suspension-based photocatalyst systems: polystyrene bead-supported TiO₂/SiO₂ composites offering balanced reactivity and separability and magnetic TiO₂-SiO₂/Fe₃O₄ nanoparticles enabling rapid magnetic recovery. Future research directions should prioritize enhancing visible-light efficiency using metal-doped TiO₂ such as Cu-doped systems; improving catalyst durability; developing new applications of photocatalysts, such as protecting RO membranes; and validating scalability across diverse industrial and medical water treatment applications. Full article

Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment. Full article

In recent years, diseases, environmental pollution, and food safety issues have seriously threatened global health, generating international concern. Many existing detection strategies used to deal with the above problems have high accuracy and sensitivity, but usually rely on large-sized, complex instruments and professional technicians, which are not suitable for on-site testing. Therefore, it is imperative to develop highly sensitive, rapid, and portable analytical methods. Recently, microfluidic paper-based analytical devices (μPADs) have been recognized as a highly promising microfluidic device substrate to deal with the issues existing in medical, environmental, and food safety, etc., due to their advantages, including environmental-friendliness, high flexibility, low cost, and mature technology. This review comprehensively summarizes the recent advances in μPADs. We first overview the development of paper-based materials and their core fabrication techniques, followed by a detailed discussion on the material selection and detection mechanisms of the devices. The review also provides an assessment of the application achievements of μPADs in medical diagnostics, environmental analysis, and food safety monitoring. Finally, current challenges in the field are summarized and future research directions and prospects are proposed. Full article