Search Results (9,923)

Background/Objectives: The skin is a protective barrier against pathogens such as herpes simplex virus type 1 (HSV-1), which causes recurrent and highly prevalent skin infections worldwide. The increasing resistance of HSV-1 to conventional treatments has driven the search for new therapeutic alternatives. In this context, the essential oil of Lippia graveolens (EOL) has demonstrated promising antiviral activity; however, its high volatility limits direct skin application. To overcome this, polymeric nanoparticles (NPs) loaded with EOL were developed to improve its availability and antiviral efficacy. Methods: Nanoformulations were prepared by nanoprecipitation, and their antiviral activity against HSV-1 was evaluated using the plaque reduction assay. The effect of the nanoformulations on skin barrier integrity was assessed using an ex vivo porcine skin model and non-invasive techniques. Results: The NP-EOL exhibited physicochemical properties favorable for skin deposition, including a particle size around 200 nm, a polydispersity index of ≤ 0.2, and negative zeta potential. Moreover, NP-EOL showed 1.85-fold higher antiviral activity against HSV-1 compared with free EOL, while also reducing cytotoxicity in Vero cells. Conclusions: Results demonstrated that the NPs promoted skin hydration without altering pH or transepidermal water loss, suggesting they do not disrupt skin homeostasis. This study supports the potential of NP-based systems as effective topical delivery vehicles for EOL, representing a promising therapeutic alternative against HSV-1 skin infections. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Electric vehicle (EV) battery thermal management systems have gradually improved owing to the increasing power demand of EVs. This study aims to optimize the channel geometry of sandwich double-sided cold plates for EV battery cooling under 100% state of charge and 2C-rate charging conditions. For precise and accurate optimization, the conventional one-dimensional analysis model of the sandwich double-sided cold plate was converted into a three-dimensional computational fluid dynamics (CFD) model. Non-dimensional parameters were selected as the main variables of the channel geometry, and nine additional channel shapes were derived based on them. Battery modules with the derived channel shapes were subjected to CFD analysis in the Reynolds number range of 500 to 20,000. The goodness factor was calculated from these correlations, and optimization was performed using the Taguchi method. The results revealed that the wetted area of the channel had a greater impact on battery cooling than the number of channels. This study proposed more generalized design guidelines by employing non-dimensionalized parameters across a wide range of Reynolds numbers. The rectangular channel-based correlations developed in this study showed improved prediction accuracy compared to conventional annular pipe-based correlations and are expected to be applicable to various battery thermal management system designs in the future. Full article

Traditionally, duct sizing in ventilation systems is based on balancing pressure losses across all branches, with fan selection performed subsequently. However, this sequential approach is inadequate for systems with distributed fans in the central duct network, where pressure losses can vary significantly. Consequently, when designing the system topology, fan placement and duct sizing must be considered together. Recent research has demonstrated that discrete optimisation methods can account for multiple load cases and produce ventilation layouts that are both cost- and energy-efficient. However, existing approaches usually concentrate on component placement and assume that duct sizing has already been finalised. While this is sufficient for later design stages, it is unsuitable for the early stages of planning, when numerous system configurations must be evaluated quickly. In this work, we present a novel methodology that simultaneously optimises duct sizing, fan placement, and volume flow controller configuration to minimise life-cycle costs. To achieve this, we exploit the structure of the problem and formulate a mixed-integer linear program (MILP), which, unlike existing non-linear models, significantly reduces computation time while introducing only minor approximation errors. The resulting model enables fast and robust early-stage planning, providing optimal solutions in a matter of seconds to minutes, as demonstrated by a case study. The methodology is demonstrated on a case study, yielding an optimal configuration with distributed fans in the central fan station and achieving a 5 reduction in life-cycle costs compared to conventional central designs. The MILP formulation achieves these results within seconds, with linearisation errors in electrical power consumption below 1.4%, confirming the approach’s accuracy and suitability for early-stage planning. Full article

Time-lapse imaging (TLI) has emerged as a transformative technology in in vitro fertilization (IVF). This is because it offers continuous, non-invasive embryo assessment through morphokinetic profiling. It demonstrates key advantages such as reduced embryologist subjectivity, detection of dynamic anomalies, and improved implantation rates in niche populations. However, its clinical utility remains debated. Large trials and meta-analyses reveal no universal improvement in live birth rates compared to conventional methods. Key challenges underlying the outcome include algorithm generalizability, lab-specific protocol variability, and high costs. Nevertheless, TLI shows promise in specific contexts. For instance, Preimplantation Genetic Testing for Aneuploidies (PGT-A) cycles where it reduces unnecessary biopsies by predicting euploidy. However, even in this, its benefits are marginal in unselected populations. This review synthesizes evidence to highlight that TLI’s value is context-dependent, not universal. As such, adoption must be cautious to avoid resource misallocation without significant IVF outcome improvements. In future, personalized protocols, integration with non-invasive biomarkers, and multicenter collaboration are crucial to optimize TLI’s potential in assisted reproduction. Full article

Next-generation cancer immunotherapy increasingly combines tumor-targeting antibodies or antibody–drug conjugates (ADCs) with immune effector cells to enhance therapeutic precision. However, many existing approaches rely on genetic modification or complex manufacturing, limiting their clinical scalability and rapid deployment. To address this issue, we developed an antibody capture protein (ACP)-based surface engineering platform that enables the rapid, reversible, and non-genetic functionalization of NK cells with therapeutic antibodies or ADCs. This approach uses a DMPE-PEG-lipid conjugate to anchor thiolated protein A (ACP) to the NK cell membrane via hydrophobic insertion, thereby stably and selectively binding to the Fc region of IgG molecules. Using this strategy, we developed ACP-modified NK cells (AC-NKs) that can selectively capture therapeutic antibodies (trastuzumab (TZ), trastuzumab-emtansine (T-DM1), and sacituzumab (SZ)) pre-bound to each target antigen on tumor cells and induce antigen-specific cytotoxic responses. The resulting AC-NKs exhibited enhanced tumor recognition and cytotoxicity against HER2-positive and Trop-2-positive cancer cells in vitro. Compared with conventional combination therapies, AC-NKs enhanced immune activation, as demonstrated by effective delivery of cytotoxic agents, enhanced cancer cell engagement, and upregulation of CD107a expression. Notably, the system supports multiple antigen targeting and tunable antibody loading, enabling adaptation to tumor heterogeneity and resistant phenotypes. This platform might also provide a simple, scalable, and safe method for rapidly developing programmable immune cell therapies without genetic modification. Its versatility supports multi-antigen targeting and broad applicability across NK and T cell therapies, offering a promising path toward personalized, off-the-shelf chemoimmunotherapy. Full article

Structured quantitative data, such as survey responses in human resource management research, are often analysed using machine learning methods, including logistic regression. Although these methods provide accurate statistical predictions, their results are frequently abstract and difficult for non-specialists to comprehend. This limits their usefulness in practice, particularly in contexts where eXplainable Artificial Intelligence (XAI) is essential. This study proposes a domain-independent approach for the autonomous classification and interpretation of quantitative data using visual processing. This method transforms individual responses based on rating scales into visual representations, which are subsequently processed by Convolutional Neural Networks (CNNs). In combination with Class Activation Maps (CAMs), image-based CNN models enable not only accurate and reproducible classification but also visual interpretability of the underlying decision-making process. Our evaluation found that CNN models with bar chart coding achieved an accuracy of between 93.05% and 93.16%, comparable to the 93.19% achieved by logistic regression. Compared with conventional numerical approaches, exemplified by logistic regression in this study, the approach achieves comparable classification accuracy while providing additional comprehensibility and transparency through graphical representations. Robustness is demonstrated by consistent results across different visualisations generated from the same underlying data. By converting abstract numerical information into visual explanations, this approach addresses a core challenge: bridging the gap between model performance and human understanding. Its transparency, domain-agnostic design, and straightforward interpretability make it particularly suitable for XAI-driven applications across diverse disciplines that use quantitative response data. Full article

Traditional river quality models struggle to accurately predict river water quality in watersheds dominated by non-point source pollution due to computational complexity and uncertain inputs. This study addresses this by developing a novel coupling model integrating a gradient boosting algorithm (Light GBM) and a long short-term memory network (LSTM). The method leverages Light GBM for spatial data characteristics and LSTM for temporal sequence dependencies. Model outputs are reciprocally recalculated as inputs and coupled via linear regression, specifically tackling the lag effects of rainfall runoff and upstream pollutant transport. Applied to predict the concentrations of chemical oxygen demand digested by potassium permanganate index (COD) in South China’s Jiuzhoujiang River basin (characterized by rainfall-driven non-point pollution from agriculture/livestock), the coupled model outperformed individual models, increasing prediction accuracy by 8–12% and stability by 15–40% than conventional models, which means it is a more accurate and broadly applicable method for water quality prediction. Analysis confirmed basin rainfall and upstream water quality as the primary drivers of 5-day water quality variation at the SHJ station, influenced by antecedent conditions within 10–15 days. This highly accurate and stable stack coupling method provides valuable scientific support for regional water management. Full article

High parasitic capacitance from poly-insulator-poly capacitors in complementary metal oxide semiconductor (CMOS) processes presents a major bottleneck to achieving high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) in imaging systems. This study proposes a Phase-Adjustable SAR ADC that addresses this limitation through a reconfigurable architecture. The design utilizes a phase-adjustable logic unit to switch between a conventional SAR mode for high-speed operation and a noise-shaping (NS) SAR mode for high-resolution conversion, actively suppressing in-band quantization noise. An improved SAR logic unit facilitates the insertion of an adjustable phase while concurrently achieving an 86% area reduction in the core logic block. A prototype was fabricated and measured in a 0.35-µm CMOS process. In conventional mode, the ADC achieved a 7.69-bit effective number of bits at 2 MS/s. By activating the noise-shaping circuitry, performance was significantly enhanced to an 11.06-bit resolution, corresponding to a signal-to-noise-and-distortion ratio (SNDR) of 68.3 dB, at a 125 kS/s sampling rate. The results demonstrate that the proposed architecture effectively leverages the trade-off between speed and accuracy, providing a practical method for realizing high-performance ADCs despite the inherent limitations of non-ideal passive components. Full article

Livestock production raises significant environmental concerns, necessitating the development of sustainable feeding strategies based on non-conventional forages, such as locally available vegetation. This study evaluated the effects of a pelleted concentrate containing 10% Acacia farnesiana leaves as a dietary supplement on in vitro ruminal fermentation. Four experimental diets were formulated with increasing levels of the concentrate (0%, 25%, 50%, and 75%). Analyses were performed in triplicate and included chemical composition, in vitro gas and methane production, fermentation kinetics, ammonia nitrogen concentration (N–NH₃), in vitro dry matter digestibility (IVDMD), and metabolizable energy (ME) estimation. The results revealed no significant differences (p > 0.05) in most gas production kinetic parameters, overall fermentation patterns, or metabolizable energy. In contrast, a significant increase (p < 0.05) in secondary metabolite concentrations was detected. While methane production remained unaltered (p > 0.05), a significant linear reduction was observed for IVDMD, the lag phase (L), and N–NH₃ concentration (p = 0.0064, p = 0.0036, and p < 0.0001, respectively). These findings suggest that A. farnesiana can be incorporated into ruminant concentrates without increasing methane emissions. However, in vivo trials and mechanistic studies are required to validate and further elucidate these results. Full article

Background: Autologous tooth-derived grafts have recently gained attention as an innovative alternative to conventional biomaterials for alveolar ridge preservation (ARP) and augmentation (ARA). Their structural similarity to bone and osteoinductive potential support clinical use. Methods: This systematic review was conducted according to PRISMA 2020 guidelines and registered in PROSPERO (CRD420251108128). A comprehensive search was performed in PubMed, Scopus, and Web of Science (2010–2025). Randomized controlled trials (RCTs), split-mouth, and prospective clinical studies evaluating autologous dentin-derived grafts were included. Two reviewers independently extracted data and assessed risk of bias using Cochrane RoB 2.0 (for RCTs) and ROBINS-I (for non-randomized studies). Results: Nine studies involving 321 patients were included. Autologous dentin grafts effectively preserved ridge dimensions, with horizontal and vertical bone loss significantly reduced compared to controls. Histomorphometric analyses reported 42–56% new bone formation within 4–6 months, with minimal residual graft particles and favorable vascularization. Implant survival ranged from 96–100%, with stable marginal bone levels and no major complications. Conclusions: Autologous tooth-derived biomaterials represent a safe, biologically active, and cost-effective option for alveolar bone regeneration, showing comparable or superior results to xenografts and autologous bone. Further standardized, long-term RCTs are warranted to confirm their role in clinical practice. Full article

Statistical factor models are widely applied across various domains of the financial industry, including risk management, portfolio selection, and statistical arbitrage strategies. However, conventional factor models often rely on unrealistic assumptions and fail to account for the fact that financial markets operate under multiple regimes. In this paper, we propose a regime-switching factor model estimated via a particle filtering algorithm, which is a Monte Carlo-based method well-suited for handling nonlinear and non-Gaussian systems. Our empirical results show that incorporating dynamic structure and a regime-switching mechanism significantly enhances the model’s ability to detect structure breaks and adapt to evolving market conditions. This leads to improved performance and reduced drawdowns in the equity statistical arbitrage strategies. Full article

Submarine cable systems, serving as the critical backbone of global communications, face evolving resilience threats. This work proposes a novel sensing attack that utilizes ultra-narrow-linewidth lasers to surveil these infrastructures. First, the Narrowband Jamming Attack (NJA) is introduced as an evolution of conventional physical-layer jamming. NJA is divided into three categories according to the spectral position, and the non-overlapping class represents the proposed sensing attack. Its operational principles and the key parameters determining its efficacy are analyzed, along with its deployment strategy in submarine cable systems. Finally, the sensing capability is validated via OptiSystem simulations. Results demonstrate successful localization of vibrations within the 50–200 Hz range on a 1 km fiber, achieving a spatial resolution of 1 m, and confirm the influence of vibration parameters on sensing performance. This work reveals that the proposed sensing attack has the potential to covertly monitor environmental data, thereby posing a threat to information security in submarine cable systems. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

Biofouling on offshore wind farm substructures threatens operational reliability and raises maintenance demands, underscoring the need for effective antifouling strategies. This study presents a 27-month evaluation of fouling development on a conventional non-antifouling coating and self-polishing copolymer (SPC) systems at a South Korean offshore wind farm. Biofouling coverage was assessed through long-term image analysis, and surface energy was characterized via contact angle measurements. Species analyses identified successional communities dominated by barnacles, coralline algae, and bryozoa. The conventional coating showed rapid colonization, exceeding 90% coverage within 10 months, whereas the SPC systems exhibited superior performance by suppressing settlement, though their effectiveness declined over time. Quantitative analysis revealed a clear relationship between higher surface energy and increased fouling rates, highlighting material properties as a key factor in colonization. This study provides one of the first long-term quantitative datasets from South Korean waters, advancing understanding of biofouling dynamics and informing antifouling strategies for offshore wind infrastructure. Full article