Falls among older adults remain a critical patient safety concern globally. Conventional walkers frequently fail to accommodate end-user needs, particularly in low-to-middle-income country (LMIC) settings. Understanding stakeholder design requirements is essential for developing contextually appropriate, technology-enhanced assistive devices. To explore multidisciplinary stakeholder perspectives on the design requirements for a sensor-integrated smart walker for fall prevention among older adults in Indonesia. A qualitative descriptive approach was employed within the empathize and define phases of a design thinking framework. Purposive sampling recruited 13 participants across three stakeholder groups: older adult walker users (n=5), informal caregivers (n=5), and healthcare professionals (n=3). Semi-structured interviews were conducted between October and December 2025 in Bandung, West Java. Data were analyzed using Braun and Clarke's reflexive thematic analysis. Cognitive screening used a standardized, licenced assessment tool. Twenty themes were identified across stakeholder groups: seven from older adults, eight from caregivers, and five from healthcare professionals. Convergent needs included a pre-impact fall warning system, design simplicity, structural durability, and affordability. Culturally specific findings included the Sundanese preference for human-accompanied walking (papah), rejection of wheeled designs, and narrow-bathroom (jamban) navigation challenges. Healthcare professionals emphasized BPJS affordability constraints and regulatory integration. Nine prioritized design requirements were derived from cross-stakeholder synthesis. These findings establish an empirically grounded, culturally responsive design foundation for the TEMAN JALAN smart walker - demonstrating that effective assistive technology for LMIC settings requires ground-up needs assessment rather than adaptation of high-income-country prototypes. The identified requirements will directly inform the subsequent ideation, prototyping, and clinical testing phases of this design thinking project, with implications for allied health-led innovation in fall prevention globally. Falls are among the most dangerous and common accidents experienced by older adults. Walking frames (walkers) help prevent falls, but the standard designs currently available in Indonesia are often too bulky for small bathrooms, lack any form of warning system, and do not consider local living conditions. This study asked older adults who use walkers daily, their caregivers, nurses, and a doctor specializing in elderly care about what they need from a better walker. Interviews took place at a residential care home and a hospital clinic in Bandung, Indonesia. Older adults said they feel safer with a walker but struggle in tight bathroom spaces and wish for an alarm that warns them before they fall. Some preferred being physically guided by another person — a cultural practice called papah in the Sundanese community. Caregivers reported that walkers reduced their physical workload but emphasized that proper training was essential during the first days of use. Nurses and the doctor stressed that most patients rely on government health insurance (BPJS) and cannot afford expensive devices, and that any new technology must fit into hospital procedures. These perspectives are now being used to design a smart walker called TEMAN JALAN that uses motion sensors to detect when someone is about to fall and sends alerts to caregivers.
With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model's architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems.
Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from high-risk human papillomavirus infection. Approximately 90% of cervical cancers can be prevented due to the disease's slow progression, which allows for a 10-year window for the detection and treatment of precancerous lesions. The World Health Organization called for global action toward the elimination of cervical cancer. One of the main strategies for cervical cancer elimination is to achieve screening coverage of at least 70% of women aged 35-45 years and to ensure that 90% of women diagnosed with precancerous lesions or invasive cervical cancer receive appropriate treatment by 2030. One of the main strategies for cervical cancer elimination is to achieve screening coverage of at least 70% of women aged 35-45 years and to ensure that 90% of women diagnosed with precancerous lesions or invasive cervical cancer receive appropriate treatment by 2030. The aims of the study were a comparative analysis of conventional Pap smear cytology and artificial intelligence (AI)-based analysis using Smart Scope for screening of cervical cancer. A prospective, cross-sectional study was conducted among 128 women at a tertiary care center. Participants underwent cervical screening using Smart Scope imaging, followed by AI-based analysis. The findings were compared with conventional methods using appropriate statistical tests. In this prospective cross-sectional pilot study involving 128 samples, AI-assisted screening demonstrated a high specificity (96.0%) and negative predictive value (99.2%) for cervical cancer detection, effectively identifying 89.8% of cases as negative for intraepithelial lesion or malignancy. AI enabled rapid image analysis and gynecologist-dependent diagnostic variability and minimized the subjectivity inherent in human interpretation. AI enabled rapid image analysis, reduced gynecologist-dependent diagnostic variability, and minimized the subjectivity inherent in human interpretation. AI-based image analysis shows promise as an adjunct tool in cervical cancer screening but lacks the reliability to function as a standalone modality. Further refinement and integration with the clinical context are warranted to improve its screening effectiveness.
(1) Background: Breast cancer screening remains limited by mammography, particularly in younger women, in dense breast tissue, and in the detection of interval cancers. The PHI-BRA Smart Bra was developed as a wearable, non-invasive device combining thermography and bioimpedance for frequent breast monitoring. This first-in-human study aimed to assess the feasibility and in vivo diagnostic performance of the PHI-BRA system in discriminating between women with and without breast lesions. (2) Methods: A prospective feasibility study was conducted between March 2023 and February 2024. A calibration cohort (n = 15) was used to define the discrimination model, followed by an analysis cohort (n = 26; 13 with breast lesions and 13 without). Thermal and bioimpedance signals were acquired using the PHI-BRA device. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, with mammography as the reference standard. (3) Results: In the analysis cohort, the temperature-based model achieved an area under the ROC curve (AUC) of 80.8% (95% CI [63.2-98.3]). At the optimal threshold, sensitivity was 84.6% (95% CI [61.5-100]) and specificity was 76.9% (95% CI [53.8-100]). Exploratory bioimpedance analyses showed lower sensitivity but high specificity, mainly limited by sensor contact stability. No adverse events were reported. (4) Conclusions: This first-in-human study demonstrates an initial exploration of the feasibility and safety of a wearable thermography-based approach for breast lesion discrimination. The results support further clinical validation of a multimodal wearable system as a complementary tool to existing breast cancer screening strategies.
This study aimed to develop a novel fluorescent sensor based on Eu3+ complex-doped protein crystal (EC-PC) for the efficient detection of metal ions in blood. By meticulously controlling the crystallization and annealing conditions in the co-crystallization strategy, the crystal growth processes were optimized to obtain doped Eu3+ complex-co-protein crystalline (EC-PC) structures. Thus, through co-crystallization of hen egg white lysozyme (HEWL) as a model protein and Eu3+ complex as fluorescent center, we successfully prepared Eu3+ complex-doped-HEWL co-crystals (EC-HC) with excellent fluorescent properties. Further treatment with 4% glutaraldehyde cross-linking enhanced the structural stability of the co-crystals. Moreover, the characteristic of sensitive, selective quenching of EC-PC fluorescence by biologically relevant cations, such as Cu2+, Zn2+, Mg2+, Ca2+ and Fe3+ ions, set up a smart sensing system in blood. For example, the fluorescence intensity of the crystals at 610 nm, as measured by a UV-visible spectrophotometer, decreases dose-dependently with the concentration of copper ions, thereby validating the sensor's high sensitivity to copper ion detection. Significantly, we also found that this hybrid protein-based sensor did not induce hemolysis, at various volume concentrations, confirming good anticoagulation in blood. This research not only provides a new perspective on the application of Eu3+ complex-doped protein crystals in the field of biosensing but also offers a new strategy for the detection of biologically relevant cations in blood. Future work will focus on further optimizing the sensor's performance and exploring its potential applications in clinical sample analysis.
Smart sensing is becoming central to plant science because it supports crop management decisions that reflect dynamic plant-environment interactions rather than field-level averages. Nitrogen fertilization is one of the most important decisions in precision agriculture, but site-specific prescriptions are often difficult to trust because spatial and seasonal variability can make point recommendations unstable. We propose an uncertainty-aware nitrogen prescription framework that combines yield-response modeling with conformal prediction intervals and propagates uncertainty into profit bounds under an environmental proxy penalty. Nitrogen rates are selected by maximizing the lower confidence bound of profit, and the framework allows abstention when competing rates are indistinguishable or uncertainty is excessive. Evaluation used a leakage-free group split of 10,000 samples from a Kaggle yield dataset. The selected tree-ensemble model achieved RMSE 1.531 and MAE 1.214. Conformal intervals reached empirical coverage of 0.912 at the 0.90 target and 0.963 at the 0.95 target. Expected-profit optimization gave mean profit 4.68 with mean N 121.34, whereas the risk-aware strategy gave mean profit 4.54 with mean N 112.07 and lower variability. Abstention withheld recommendations for 18.4% and 27.9% of cases at 0.90 and 0.95 coverage. The framework supports conservative, trustworthy nitrogen decisions and promotes practical nutrient stewardship in variable agricultural fields.
In this study the electromechanical response of a cantilever composite beam with surface-bonded piezoelectric patches is examined, focusing on interface stresses that may initiate delamination. A thermodynamically consistent electroelastic framework was specialized to the linear piezoelectric law used in finite element software, and a two-dimensional (2D) finite element model was developed and validated under static actuation. The predicted tip displacement was compared against the analytical Euler-Bernoulli solution across all seven mesh levels of the convergence study; findings indicated that the converged ANSYS 17.1 result (h = 5 × 10-5 m) differed from the analytical value by 5.8%, a discrepancy attributed to the plane-strain assumption and the neglect of shear deformation in the Euler-Bernoulli formulation. To resolve the delamination-critical behavior, three-dimensional (3D) models were built using SOLID185/SOLID5 and SOLID186/SOLID226 elements. Interfacial peel σy and shear τxy stresses were evaluated along lengthwise (PATH1) and transverse (PATH2) paths at the patch-core interface, with maximum interface stresses occurring along the transverse PATH2 near the free end, where strong three-dimensional edge effects developed. Both element sets predicted a similar tip displacement, but the SOLID186/SOLID226 elements yielded peak interface stresses approximately 19% higher in peel and 87% higher in shear along the critical transverse PATH2. These findings demonstrate that element choice minimally affects global stiffness but significantly influences local interface stress prediction, providing practical guidance for the selection of appropriate models when assessing the delamination risk in piezoelectric-actuated composite beams.
Accurate estimation of dynamic environmental phenomena through intelligent sensing systems plays a critical role in enabling reliable monitoring and decision-making in complex real-world scenarios. With the rapid development of artificial intelligence-driven sensing technologies and Internet of Things systems, modern agricultural monitoring is evolving from isolated data acquisition toward intelligent, multimodal perception and decision-making. However, traditional approaches predominantly rely on single data sources, making it difficult to simultaneously capture plant phenotypic variations and environment-driven mechanisms, thereby limiting model applicability in complex field scenarios. To address this issue, a multimodal pest density estimation framework, namely the Pest Density Estimation Framework (PDEF), is proposed, which integrates UAV-based imagery, trap monitoring data, and environmental sensor measurements. In this framework, crop canopy damage features are extracted using convolutional neural networks, while temporal encoding is employed to model dynamic environmental variations. Cross-modal feature alignment and environment-aware enhancement mechanisms are further introduced to achieve deep integration of multi-source information, enabling the construction of a unified feature representation space and improving estimation accuracy. Extensive experiments conducted on a constructed multimodal agricultural dataset demonstrate that the proposed method achieves MAE, RMSE, and MAPE values of 5.47, 7.62, and 14.9%, respectively, significantly outperforming the Transformer-based fusion model (MAE 6.01, RMSE 8.16). Meanwhile, the coefficient of determination reaches R2=0.84, indicating superior fitting capability and stability. In multimodal combination experiments, the three-modality fusion reduces error metrics by more than 20% on average compared with single-modality models, validating the effectiveness of multi-source collaborative modeling. From the perspective of integrating plant phenotypic analysis and environmental perception, this study provides a novel AI-driven intelligent sensing framework for pest monitoring and crop management, contributing to improved pest prediction capability and enhanced intelligence in agricultural production systems. This study further provides practical implications for agricultural economics and supply chain optimization by enabling data-driven decision-making through intelligent sensing systems.
Against the background of accelerated green energy development and the deep integration of intelligent sensing technologies, wind power forecasting is evolving toward a multimodal sensor collaborative perception paradigm within nonlinear multi-source integrated energy systems. To address the limitations of conventional methods, including the lack of dynamic importance modeling and constrained stability under complex wind conditions, a forecasting framework based on multimodal sensor importance perception is proposed. This study emphasizes the framework's role in decoding the complex nonlinear dependencies between atmospheric drivers and turbine responses. Through a multimodal feature encoding architecture, unified temporal representations of meteorological environments and turbine operational states are established. A sensor-importance-aware attention mechanism and a cross-modal relational modeling strategy are introduced to adaptively allocate contributions under varying contexts. Furthermore, prediction compensation and uncertainty characterization modules are integrated to enhance robustness. Systematic experiments on real-world multi-source data validate the method. Overall performance comparisons demonstrate that MAE, RMSE, and MAPE reach 30.48, 42.37, and 9.16 percent, respectively, with the coefficient of determination R2 achieving 0.957, significantly outperforming the Transformer baseline. In multi-horizon tasks, the model exhibits superior error accumulation suppression, with twelve-step forecasting errors remaining at 41.27 and 56.48. These findings reveal that the framework captures the context-dependent nonlinear mapping of energy systems, providing effective technical support for green energy dispatch and intelligent sensing applications.
Stimuli-responsive nanostructures are a revolutionary breakthrough in the controlled delivery of drugs, allowing for their precise spatiotemporal control. These intelligent materials are designed to respond to internal stimuli (such as pH, redox gradients, enzymatic activity) or external cues (such as temperature, light, magnetic fields), thus offering greater flexibility and functionality in biomedical applications. Recent achievements have been aimed at the development of multi-stimuli-responsive systems, which utilize a combination of several stimuli to achieve sophisticated control, greater stability, and greater therapeutic accuracy in complex biological media. At the same time, the incorporation of artificial intelligence (AI) into the design and optimization of these nanostructures has brought about real, data-driven breakthroughs. Thus, supervised machine learning algorithms have been employed to predict the drug-loading efficiency and gene-delivery ability of lipid and polymeric nanoparticles based solely on their compositional characteristics, thus facilitating the rational selection of optimal formulations without the need for extensive experimental screening. Moreover, AI-based modeling tools have been shown to possess the capability to predict complete drug release profiles in response to varying pH or redox environments, thus enabling the pre-optimization of release kinetics tailored to specific pathological microenvironments. With the integration of patient-specific biological information such as genomic signatures and biomarker profiles, AI-assisted approaches also allow for the personalization of carrier composition and sensitivity to stimuli. This review offers a thorough examination of the latest developments in stimuli-responsive nanostructures and their integration with AI. This complementary combination is revolutionizing the way carriers are designed, shifting from trial-and-error methods to predictive and personalized drug delivery systems, thus propelling the development of next-generation precision nanomedicine.
Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine's design and verify compliance with environmental regulations through the vehicle's emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle's temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time.
[This corrects the article DOI: 10.1016/j.jpi.2025.100535.].
Diabetic chronic wounds (particularly diabetic foot ulcers) are difficult to heal due to factors such as high glucose levels, infection, and inflammatory imbalance. In severe cases, they can lead to tissue necrosis and amputation. Hydrogel materials, as moist wound dressings, possess high water content, biocompatibility, and tunability, making them an important platform for promoting diabetic wound healing. In recent years, novel smart hydrogels have been developed to integrate multiple functions. They respond to abnormal stimuli in the wound microenvironment-such as acidic pH, high glucose levels, or excessive reactive oxygen species-to trigger the release of drugs, delivering on-demand antimicrobial, antioxidant, and anti-inflammatory effects. Simultaneously, they modulate immune responses (promoting macrophage polarization toward the M2 type) and stimulate angiogenesis, creating a microenvironment conducive to tissue regeneration. Some hydrogels incorporate antimicrobial agents, anti-biofilm components, or photothermal/photodynamic agents to effectively eliminate drug-resistant pathogens and control infections. Others serve as carriers for delivering stem cells and their exosomes, enhancing cell survival rates and releasing growth factors to accelerate wound healing. This review systematically summarizes recent advances in hydrogel strategies for diabetic wound treatment, focusing on stimulus-responsive hydrogels, antimicrobial and immune modulation mechanisms, pro-angiogenic and oxygen-supplying therapies, smart dressings and monitoring technologies, integration of stem cells and exosomes, as well as hydrogel injection, self-healing, and adhesion properties. Based on this, we analyze challenges and prospects for clinical translation of these strategies. Collectively, functionalized hydrogels hold promise as multifunctional therapeutic platforms for diabetic non-healing wounds. They offer a multi-pronged approach to disrupt the vicious cycle of "infection-inflammation-tissue destruction" thereby achieving more efficient wound healing.
Traditional polymer-dispersed liquid crystal (PDLC) faces limitations in smart dimming applications due to high driving voltage and poor high-temperature stability. In this study, a high-birefringence liquid crystal (QYPDLC-901) was used to prepare PDLC films with liquid crystal contents ranging from 72 wt% to 80 wt%, achieved through synergistic regulation of a low-functional acrylic polymer system and a low-intensity curing process. The effects of liquid crystal content, cell gap, and temperature on electro-optical properties were systematically investigated. Optimal performance was obtained at a liquid crystal content of 77 wt%, with a low threshold voltage of 2.9 V, saturation voltage of 7 V, fast response (rise time 4.2 ms, decay time 47 ms), and a favorable balance between high on-state and low off-state transmittance. Microstructural analysis revealed that the superior performance results from uniform droplet dispersion and low interfacial energy. Furthermore, the PDLC exhibited excellent switching stability from 23 °C to 90 °C, maintaining a maximum transmittance of 93% at 90 °C, with increases of only 0.4 V in threshold voltage and 0.1 V in saturation voltage. This study provides an experimental basis for designing smart dimming devices suitable for low-voltage driving and extreme environments.
Foodborne pathogens present a major threat to global public health. However, conventional detection methods and equipment are often unsuitable for the on-site and timely monitoring of these pathogens. To overcome this critical limitation and establish a rapid detection workflow, we developed the portable smart Ai BOX (artificial intelligence BOX). This device is a compact, palm-sized, internet of things (IoT)-enabled instrument that utilizes isothermal fluorescence diagnostics and weighs only 180 g. The Ai BOX features an optimized minimalist industrial design, ultralow power consumption, and a high-sensitivity optical sensing system. The device performs real-time fluorescence detection, with results automatically interpreted and transmitted to a dedicated mobile application (APP) via an integrated smart camera, enabling comprehensive food monitoring. Furthermore, the incorporation of artificial intelligence and machine learning (ML) algorithms significantly enhances the processing capability of the RPA-CRISPR/Cas12a fluorescence signal, thereby ensuring superior detection accuracy. The Ai BOX is ideally suited for on-site point-of-care testing (POCT) of foodborne pathogens. By integrating the one-pot-RPA-CRISPR/Cas12a method, the device achieves an exceptionally low limit of detection (LOD) of 1 × 101 CFU/mL for Listeria monocytogenes. In tests using simulated samples, it demonstrated 100% sensitivity and specificity. Consequently, the Ai BOX exhibits promising application potential for diverse public and personal health scenarios, including the detection of meat adulteration, food contamination, and wastewater monitoring.
The need for reliable preventive medicine tools is growing, especially for diseases with long diagnostic delays, such as endometriosis, which can take several years to diagnose. In this context, cellulose acetate nanofibrous membranes were prepared via electrospinning, to create the absorbent core of a smart wearable in the form of a sanitary pad, intended to support electronic diagnostic devices. A multi-layered structure was opted for, with each layer acting in a specific way according to its position within the pad, regarding mainly absorbency and porosity. The membranes were ultralight and highly absorbent, with single membranes showing an absorbency of 20-70 times their initial weight, and multi-layered membranes 15-30 times. Morphological evaluation of the pad was used as the basis for the optimization of the fabrication parameters, while liquid absorption capacity confirmed the pad's high absorbency. Additionally, chemical and toxicological assessments indicated in vitro biocompatibility of the pad. The potential of the electrospinning process in the fabrication of menstrual hygiene pads is shown by these results. Future studies should focus on the integration of smart devices within the pad, as well as their functionality and effectiveness.
The ubiquity of resource-constrained Internet-of Things (IoT) nodes creates an urgent demand for network intrusion detection systems (NIDSs) optimized for edge devices with limited computing power. In this paper, we propose a new NIDS system based on Mamba. NIDS-Mamba uses a dynamic sparse attention and a lightweight state space to jointly learn from short-term anomaly and long-term attack patterns. We use standardized NF-UNSW-NB15 and NF-CSE-CIC-IDS2018 datasets to verify the effectiveness of this NIDS-Mamba model. We find that this NIDS-Mamba model is very effective in dealing with extreme class imbalance problems. In the NF-CSE-CIC-IDS2018 dataset, the model achieves 98.32% accuracy, 96.98% F1-score, and an AUC of 0.9996. Most notably, the model is very robust in handling extreme class imbalance problems in the NF-UNSW-NB15 dataset. It achieves 97.03% G-Mean, 0.7915 MCC, and 0.9983 AUC, far exceeding other baseline models. Compared to Transformer-based baselines, NIDS-Mamba achieves nearly an order-of-magnitude improvement in throughput while maintaining a parameter footprint compatible with edge deployment constraints. The proposed architecture effectively mitigates the quadratic complexity and memory wall inherent in standard Transformers, ensuring compatibility with Limited RAM and strict energy constraints. The proposed model achieves a compact design with 1.12 million parameters and a peak inference memory of 5.4 MB, ensuring its feasibility for edge-based IoT nodes. These properties make NIDS-Mamba a strong candidate for deployment on IoT gateways and edge sensor nodes in smart home, industrial IoT, and critical infrastructure scenarios.
Metallic cultural heritage artifacts are highly susceptible to multi-factor electrochemical degradation, driven by chloride ions, humidity, acidic deposition, and heterogeneous material interfaces. Traditional conservation materials, including organic and inorganic coatings and corrosion inhibitors, often exhibit limited interfacial compatibility, poor long-term stability, and insufficient multifunctionality. Recent advances in protective materials-including nano-enhanced coatings, self-healing systems, smart-responsive polymers, green biodegradable formulations, and metal-organic framework (MOF)-based composites-offer multifunctional, long-lasting, and minimally invasive solutions. These materials enhance corrosion inhibition, barrier performance, structural reinforcement, and environmental responsiveness, while enabling in situ sensing, reversible application, and ethical deployment. Laboratory evaluation, accelerated aging tests, and field verification demonstrate their efficacy in preserving artifact integrity and aesthetics. This review systematically discusses degradation mechanisms, limitations of traditional materials, and the mechanisms, applications, and future perspectives of novel functional coatings, providing a roadmap for scientifically optimized and ethically responsible conservation of metallic heritage.
Bacterial infection and biofilm formation synergistically hinder wound healing by perpetuating inflammation and evading conventional treatments. Monotherapeutic strategies often fail to simultaneously eradicate resilient biofilms and rectify the dysregulated wound microenvironment. To overcome these limitations, we developed a multifunctional and targeted nanoplatform for synergistic antibacterial therapy and immunomodulation. The smart nanoplatform (CCP-DFO(Fe)) was constructed with a triple-component architecture: a photothermal Cu7S4 core pre-loaded with chlorogenic acid (CGA), enveloped by a thermo-responsive poly(N-vinylcaprolactam) (PVCL) shell, and surface-functionalized with deferoxamine-iron (DFO(Fe)) via amide coupling for active bacterial targeting. The nanoplatform exhibits effective bacterial targeting via DFO(Fe)-mediated siderophore mimicry, enabling preferential accumulation at infection sites. Under NIR irradiation, CCP-DFO(Fe) nanoplatform exhibits efficient photothermal conversion, rapidly elevating the temperature to 44.3 °C within 4 min, which induces the sudden collapse of the PVCL shell from a uniform swollen state to a phase-separated state, leading to shell disruption and consequent exposure of the CGA-loaded Cu7S4 nanoparticles (CSC). Under physiological conditions, the CSC nanoplatform gradually releases Cu2+ and CGA, which, together with the photothermal effect, synergistically exert potent antibacterial activity. As a result, the nanoplatform achieves highly effective bacterial eradication, reducing the survival rates of both E. coli and S. aureus to below 5%, along with pronounced anti-biofilm activity. Beyond its antibacterial activity, the released CGA further exerts antioxidant and anti-inflammatory effects by scavenging reactive oxygen species and promoting macrophage polarization toward the pro-healing M2 phenotype, thereby facilitating inflammation resolution. In an infected rat wound model, CCP-DFO(Fe) combined with NIR irradiation achieved 98.56 ± 1.08% wound closure by day 14, with nearly complete bacterial eradication, while simultaneously promoting angiogenesis and collagen deposition. This integrated nanoplatform combines targeted antibacterial activity, biofilm disruption, and inflammation resolution into a single system, demonstrating significant potential for treating infected and chronic wounds.
Diabetic foot ulcers are serious skin wounds that affect many people with diabetes, often leading to severe infections or even the loss of a limb. This paper explores how artificial intelligence -computer programs that can learn from data-is changing the way doctors find and treat these wounds. By reviewing 68 recent studies, we looked at how these smart technologies analyze different types of medical images to help patients. Our findings show that AI can help doctors identify health risks much earlier than traditional methods. These computer tools are also excellent at measuring how a wound is healing and predicting which treatments will work best for each individual. Because AI can spot tiny patterns in images that the human eye might miss, it makes medical care more precise and consistent. In conclusion, using AI to manage diabetic foot wounds offers a powerful way to improve patient health. By helping doctors make better, data-driven decisions, this technology can lead to faster healing and reduce the risk of serious complications for people living with diabetes.