Opportunities For Applications Research Articles

Bioethics curriculum for undergraduate medical students: an evaluation study utilizing mixed methods approach

BackgroundThe undergraduate bioethics curriculum introduced in a private medical college in Pakistan in 1988 and revised in 2009 has evolved over time to incorporate globally relevant innovations, including integration of bioethics spirally within an existing problem-based learning curricular framework. The present evaluation study shares the results of this integrated bioethics curriculum delivered for 10 years across the five-year undergraduate medical curriculum. The study assessed the effectiveness of the curriculum in terms of student achievement, appropriateness of course contents and efficiency of instructional methods.MethodsThe study utilized a mixed method sequential explanatory design. The quantitative method was used in the first phase to gather data by utilizing a structured online questionnaire. This was followed by the second phase of qualitative methods to explain the findings of the first phase and enrich the data gathered. This phase was based on focus group discussions and document review.ResultsStudent and faculty responses showed the curriculum contents to be relevant, informative, and appropriate as per learning objectives and student achievement. Multi-modal instructional methods used were stated to be effective and engaging; small group teaching and shorter sessions suggested to be preferable for fostering discussion and maintaining student engagement and attention. Large class formats were stated to be less effective. Students affirmed the contribution of bioethics education to their personal and professional development and ethical positioning. The majority of students agreed that the curriculum contributed to their knowledge acquisition (60.3—71.2%), skill development (59.41—60.30%) and demonstration of ethical/professional behavior (62.54—67.65%). The ranges indicate agreement with related sets of questions.Participants suggested that the curriculum could be further strengthened by better integration in clinical years, role modelling and providing opportunities for application in clinical health care settings. Moreover, topics like ethical issues related to the use of social media, public health ethics and ethics and law were suggested as additions to the existing curriculum. These findings have regional and global relevance for the development and assessment of effective bioethics curricula.ConclusionAn effective bioethics curriculum for undergraduate medical education should run longitudinally across the 5 year curriculum and be integrated in the modules and clerkships. Basic acquisition of knowledge and skills takes place in Years 1 & 2 with reinforcement and application in Years 3–5. Learning embedded in an integrated curriculum can help students recognize, critically analyze and address ethical dilemmas. Involvement and commitment of the clinical faculty is essential for reinforcing the ethical principles and concepts learnt in the earlier years.

A tri-modal dielectric elastomer actuator integrating linear actuation, sound generation, and self-sensing capabilities

This paper presents a concept of multi-modal dielectric elastomer actuator (DEA) that leverages a single voltage input to concurrently work as linear actuator and loudspeaker, while also integrating self-sensing capabilities. Low-frequency linear actuation is obtained by inducing tangential stretching of the DEA membrane surface, whereas high-frequency sound generation is concurrently achieved through transverse structural vibrations of the DEA membrane surface. Multi-mode actuation is combined with a new self-sensing paradigm: measuring the current signal arising from the dynamic acoustic excitation and processing it in real-time with capacitance estimation algorithms, the actuator low-frequency displacement can be reconstructed with no need for additional transducers or dedicated probing signals. The performance of the proposed self-sensing approach is evaluated using complex multi-harmonic driving signals, with a focus on analyzing the correlation between capacitance estimates and the low-frequency stroke of the device. Concurrent self-sensing and multi-mode actuation are finally demonstrated in a number of application scenarios, in which the intensity/frequency of the DEA acoustic output is adjusted in closed-loop as a function of externally induced deformations, such as impacts with obstacles, or interactions with a user. The multi-modality paradigm pursued in this work paves the way to new application opportunities, such as multi-sensory user interfaces (e.g. audio-tactile buttons), or highly integrated sensor-actuator units able to sense their state during operation and provide feedback (e.g., acoustic signaling) accordingly.

Exploring contactless techniques in multimodal emotion recognition: insights into diverse applications, challenges, solutions, and prospects

In recent years, emotion recognition has received significant attention, presenting a plethora of opportunities for application in diverse fields such as human–computer interaction, psychology, and neuroscience, to name a few. Although unimodal emotion recognition methods offer certain benefits, they have limited ability to encompass the full spectrum of human emotional expression. In contrast, Multimodal Emotion Recognition (MER) delivers a more holistic and detailed insight into an individual's emotional state. However, existing multimodal data collection approaches utilizing contact-based devices hinder the effective deployment of this technology. We address this issue by examining the potential of contactless data collection techniques for MER. In our tertiary review study, we highlight the unaddressed gaps in the existing body of literature on MER. Through our rigorous analysis of MER studies, we identify the modalities, specific cues, open datasets with contactless cues, and unique modality combinations. This further leads us to the formulation of a comparative schema for mapping the MER requirements of a given scenario to a specific modality combination. Subsequently, we discuss the implementation of Contactless Multimodal Emotion Recognition (CMER) systems in diverse use cases with the help of the comparative schema which serves as an evaluation blueprint. Furthermore, this paper also explores ethical and privacy considerations concerning the employment of contactless MER and proposes the key principles for addressing ethical and privacy concerns. The paper further investigates the current challenges and future prospects in the field, offering recommendations for future research and development in CMER. Our study serves as a resource for researchers and practitioners in the field of emotion recognition, as well as those intrigued by the broader outcomes of this rapidly progressing technology.

Compact meta-differentiator for achieving isotropically high-contrast ultrasonic imaging

Ultrasonic imaging is crucial in the fields of biomedical engineering for its deep penetration capabilities and non-ionizing nature. However, traditional techniques heavily rely on impedance differences within objects, resulting in poor contrast when imaging acoustically transparent targets. Here, we propose a compact spatial differentiator for underwater isotropic edge-enhanced imaging, which enhances the imaging contrast without the need for contrast agents or external physical fields. This design incorporates an amplitude meta-grating for linear transmission along the radial direction, combined with a phase meta-grating that utilizes focus and spiral phases with a first-order topological charge. Through theoretical analysis, numerical simulations, and experimental validation, we substantiate the effectiveness of our technique in distinguishing amplitude objects with isotropic edge enhancements. Importantly, this method also enables the accurate detection of both phase objects and artificial biological models. This breakthrough creates new opportunities for applications in medical diagnosis and nondestructive testing.

Recent Advances of Aminopeptidases-Responsive Small-Molecular Probes for Bioimaging.

Aminopeptidases, enzymes with critical roles in human body, are emerging as vital biomarkers for metabolic processes and diseases. Aberrant aminopeptidase levels are often associated with diseases, particularly cancer. Small-molecule probes, such as fluorescent, fluorescent/photoacoustics, bioluminescent, and chemiluminescent probes, are essential tools in the study of aminopeptidases-related diseases. The fluorescent probes provide real-time insights into protein activities, offering high sensitivity in specific locations, and precise spatiotemporal results. Additionally, photoacoustic probes offer signals that are able to penetrate deeper tissues. Bioluminescent and chemiluminescent probes can enhance in vivo imaging abilities by reducing the background. This comprehensive review is focused on small-molecule probes that respond to four key aminopeptidases: aminopeptidase N, leucine aminopeptidase, Pyroglutamate aminopeptidase 1, and Prolyl Aminopeptidase, and their utilization in imaging tumors and afflicted regions. In this review, the design strategy of small-molecule probes, the variety of designs from previous studies, and the opportunities of future bioimaging applications are discussed, serving as a roadmap for future research, sparking innovations in aminopeptidase-responsive probe development, and enhancing our understanding of these enzymes in disease diagnostics and treatment.

Evolutionary Perspectives on Neural Network Generations: A Critical Examination of Models and Design Strategies

Abstract: In the last few years, Neural Networks have become more common in different areas due to their ability to learn intricate patterns and provide precise predictions. Nonetheless, creating an efficient neural network model is a difficult task that demands careful thought of multiple factors, such as architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different deep neural networks (DNN) in image recognition, natural language processing, and time series analysis. In addition, the text explores the advantages of selecting optimal values for various components of an Artificial Neural Network (ANN). These components include the number of input/output layers, the number of hidden layers, the type of activation function used, the number of epochs, and the model type selection. Setting these components to their ideal values can help enhance the model's overall performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

Natural deep eutectic solvents (NADES) for the extraction of bioactives: emerging opportunities in biorefinery applications

Natural deep eutectic solvents (NADES) have emerged as an eco-friendly alternative for extracting bioactives, avoiding the use of flammable organic solvents and extreme temperatures and pH conditions. NADES rely on intermolecular interactions between hydrogen bonding donors (HBD) and hydrogen bonding acceptors (HBA) to form eutectic mixtures with significantly lower melting points than their individual components. These matrices are influenced by factors like water content, temperature, and component ratios. NADES high viscosity can hinder extractive efficiency, which can be mitigated by adding water or working at higher temperatures. However, excessive dilution with water may disrupt the supramolecular structure of NADES, reducing extraction efficiency. A notable feature of NADES is their fine-tunability for specific purposes. Adjusting physicochemical properties such as polarity, pH, and viscosity optimizes extraction efficiency by promoting the solubility of target molecules and interactions between the NADES and target molecules. NADES, unlike organic solvents, can partially disrupt plant and microalgae cell walls, enhancing permeability and extraction efficiency. Moreover, NADES can have a stabilising effect on bioactives and can enhance their biological activity and bioavailability. These attributes, coupled with their low environmental impact in terms of low toxicity and high biodegradability, make NADES attractive for biorefinery applications.

Beyond bNAbs: Uses, Risks, and Opportunities for Therapeutic Application of Non-Neutralising Antibodies in Viral Infection.

The vast majority of antibodies generated against a virus will be non-neutralising. However, this does not denote an absence of protective capacity. Yet, within the field, there is typically a large focus on antibodies capable of directly blocking infection (neutralising antibodies, NAbs) of either specific viral strains or multiple viral strains (broadly-neutralising antibodies, bNAbs). More recently, a focus on non-neutralising antibodies (nNAbs), or neutralisation-independent effects of NAbs, has emerged. These can have additive effects on protection or, in some cases, be a major correlate of protection. As their name suggests, nNAbs do not directly neutralise infection but instead, through their Fc domains, may mediate interaction with other immune effectors to induce clearance of viral particles or virally infected cells. nNAbs may also interrupt viral replication within infected cells. Developing technologies of antibody modification and functionalisation may lead to innovative biologics that harness the activities of nNAbs for antiviral prophylaxis and therapeutics. In this review, we discuss specific examples of nNAb actions in viral infections where they have known importance. We also discuss the potential detrimental effects of such responses. Finally, we explore new technologies for nNAb functionalisation to increase efficacy or introduce favourable characteristics for their therapeutic applications.

Progressive Insights into Metal-Organic Frameworks and Metal-Organic Framework-Membrane Composite Systems for Wastewater Management.

The sustainable management of wastewater through recycling and utilization stands as a pressing concern in the trajectory of societal advancement. Prioritizing the elimination of diverse organic contaminants is paramount in wastewater treatment, garnering significant attention from researchers worldwide. Emerging metal-organic framework materials (MOFs), bridging organic and inorganic attributes, have surfaced as novel adsorbents, showcasing pivotal potential in wastewater remediation. Nevertheless, challenges like limited water stability, elevated dissolution rates, and inadequate hydrophobicity persist in the context of wastewater treatment. To enhance the performance of MOFs, they can be modified through chemical or physical methods, and combined with membrane materials as additives to create membrane composite materials. These membrane composites, derived from MOFs, exhibit remarkable characteristics including enhanced porosity, adjustable pore dimensions, superior permeability, optimal conductivity, and robust water stability. Their ability to effectively sequester organic compounds has spurred significant research in this field. This paper introduces methods for enhancing the performance of MOFs and explores their potential applications in water treatment. It delves into the detailed design, synthesis strategies, and fabrication of composite membranes using MOFs. Furthermore, it focuses on the application prospects, challenges, and opportunities associated with MOF composite membranes in water treatment.

Large area single crystal gold of single nanometer thickness for nanophotonics

Two-dimensional single crystal metals, in which the behavior of highly confined optical modes is intertwined with quantum phenomena, are highly sought after for next-generation technologies. Here, we report large area (>104 μm2), single crystal two-dimensional gold flakes (2DGFs) with thicknesses down to a single nanometer level, employing an atomic-level precision chemical etching approach. The decrease of the thickness down to such scales leads to the quantization of the electronic states, endowing 2DGFs with quantum-confinement-augmented optical nonlinearity, particularly leading to more than two orders of magnitude enhancement in harmonic generation compared with their thick polycrystalline counterparts. The nanometer-scale thickness and single crystal quality makes 2DGFs a promising platform for realizing plasmonic nanostructures with nanoscale optical confinement. This is demonstrated by patterning 2DGFs into nanoribbon arrays, exhibiting strongly confined near infrared plasmonic resonances with high quality factors. The developed 2DGFs provide an emerging platform for nanophotonic research and open up opportunities for applications in ultrathin plasmonic, optoelectronic and quantum devices.

Ultrahigh Nonlinear Responses from MXene Plasmons in the Short-Wave Infrared Range.

Surface plasmons in 2D materials such as graphene exhibit exceptional field confinement. However, the low electron density of majority of 2D materials, which are semiconductors or semimetals, has limited their plasmons to mid-wave or long-wave infrared regime. This study demonstrates that a 2D Ti3C2Tx MXene with high electron density can not only support strong plasmon confinement with an acoustic plasmon mode in the short-wave infrared region, but also provide ultrahigh nonlinear responses. The acoustic MXene plasmons (AMPs) in the MXene (Ti3C2Tx)-insulator (SiO2)-metal (Au) nanostructure generate in the 1.5-6.0 µm wavelength range, exhibiting a two orders of magnitude reduction in wavelength compared to wavelength in free space. Furthermore, AMP resonators with patterned Au rods exhibit a record-high nonlinear absorption coefficient of 1.37 × 10-2 mW-1 at wavelength of 1.56 µm, ≈3 orders of magnitude greater than the highest value recorded for other 2D materials. These results indicate that MXenes can overcome fundamental plasmon wavelength limitations of previously studied 2D materials, providing groundbreaking opportunities in nonlinear optical applications, including all-optical processing and ultrafast optical switching.

114 Reimagining Entryways: Innovative Apprenticeship Models for New Clinical Research Professionals

OBJECTIVES/GOALS: 1. Standardize pathways, training and evaluations 2. Expose apprentices to a variety of research experiences 3. Remove barriers to hiring early talent 4. Expand opportunities for underrepresented minority applicants to obtain clinical research professional positions METHODS/STUDY POPULATION: Collaborators connected by the Clinical Research Professional Taskforce ACTS SIG conducted a landscape analysis survey to identify aspects of CRP Apprentice models and formed a Subgroup. Members will share plans for multiple apprenticeship programs, including specific training modalities and skill sets used to prepare apprentices for a successful clinical research professional career. Methods across institutions include: • Increasing awareness of the profession • Facilitating talent identification for managers • Making the business case for funding and staffing • Implementing work-based learning for fundamental competency development Survey results from CRP institutions demonstrated apprenticeships are value added to teaching how to conduct research. RESULTS/ANTICIPATED RESULTS: The landscape survey of Apprentice programs revealed multiple models in use. The newly formed Apprentice subgroup is engaging in analysis and actively working to build a standardized repository of competency-aligned, research courses and experiences for apprentices. Results will help make the business case for starting or growing programs. Subgroup members have focused on a shared goal of expanding opportunities for underrepresented minority applicants, with current outreach efforts that are extending awareness of the CRP profession. We anticipate a continuous strengthening of connections between institutions to share a variety of models to implement, develop shared tools (e.g., proficiency tests), and share existing tools to standardize pathways and training for CRP apprenticeships. #_msoanchor_1 DISCUSSION/SIGNIFICANCE: Academic Medical Centers (AMCs) need novel strategies to support clinical research portfolios.Innovative Apprenticeship Models improve efficiency and sustainability of the clinical research professional (CRP) workforce to train the next generation of CRPs in an effective and timely way.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

This research investigates the effectiveness of the Random Forest algorithm in optimizing e-commerce inventory management. In a digital business that continues to grow, inventory management is crucial for smooth operations and customer satisfaction. The Random Forest algorithm, a development of the CART method by applying bagging techniques and random feature selection, was tested to predict inventory. An experimental design is used to test the algorithm's performance algorithms performance, using data relevant to the observed inventory variables. The analysis involves evaluating the performance of algorithms in predicting and preventing stockouts. The results show that the Random Forest algorithm provides more accurate inventory predictions than traditional methods. Comparison with linear and rule-based regression reveals higher accuracy, making this algorithm a promising choice for e-commerce inventory management. These findings imply that the Random Forest Algorithm can be an effective solution in overcoming the complexity and fluctuations of digital markets. Practical recommendations include a deep understanding of the data, engagement of trained human resources, and training strategies for optimal use of these algorithms. This research also contributes to the literature by expanding understanding of the application of the Random Forest algorithm in various contexts, including forest basal area prediction, supply chain management, and backorder prediction. In conclusion, the Random Forest algorithm has great potential to improve e-commerce inventory management, opening up opportunities for broader application in the digital business world. Proactive adoption of these algorithms can have a positive impact on operational efficiency, customer satisfaction, and a company's competitiveness in an ever-evolving market.

Traditional Chinese Medicine in Regulating Tumor Microenvironment.

Tumor microenvironment (TME) is a complex and integrated system containing a variety of tumor-infiltrating immune cells and stromal cells. They are closely connected with cancer cells and influence the development and progression of cancer. Traditional Chinese medicine (TCM) is an important complementary therapy for cancer treatment in China. It mainly eliminates cancer cells by regulating TME. The aim of this review is to systematically summarize the crosstalk between tumor cells and TME, and to summarize the research progress of TCM in regulating TME. The review is of great significance in revealing the therapeutic mechanism of action of TCM, and provides an opportunity for the combined application of TCM and immunotherapy in cancer treatment.

Evaluating consumer 3D printing nozzles as a low cost alternative for mesophase pitch-derived carbon fiber production

Synthetic fibers, such as Kevlar fibers, SiC fibers, and carbon fibers, are essential components for constructing high performance structures. Whether for engineering, sports, energy storage (batteries and supercapacitors), or aerospace applications, fiber microstructure plays a critical role in fiber properties and functionalities. However, studying fiber nozzle configurations and spinning parameters to achieve the desired microstructure remains challenging, costly, and time consuming. Here, mesophase pitch-derived fibers were used as an example to demonstrate that low cost, commercially available 3D printer nozzles can “print” fibers. Four different nozzles were used to “print” fibers and the effects of their features on fiber properties were observed and compared to other lab spun and commercial pitch-derived CF. A longer orifice length resulted in higher modulus fiber whereas a larger draw-down ratio yielded a stronger fiber. The findings provide a new opportunity for 3D printer hardware application and open opportunities for developing low-cost fibers.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Enhancing performance and stability of LiNi0.8Co0.1Mn0.1O2 cathode via Na/Y dual doping for Lithium-Ion batteries

Nickel-rich layered oxide cathode materials face limitations in practical applications due to trade-offs between capacity and structural stability. As more Li+ is extracted during charging, the layered structure of nickel-rich materials becomes increasingly fragile. To address this, a double doping strategy with Na and Y is proposed in this study to enhance the strength of the layered structure. The research findings demonstrate that Na doping enlarges the spacing between Li layers in the crystal structure, while Y doping improves the reversibility of the H2-H3 phase transition but narrows the Li layer spacing in nickel-rich materials. Na and Y double doping, the advantages of Na in enlarging the Li layer, and Y in improving the H2-H3 phase transition can be harnessed simultaneously. This double doping strategy ensures that the laminar structure of the cathode material remains stable even when it is highly detached, effectively solving the trade-off between the capacity and structural stability of nickel-rich laminar cathode materials. The application in LiNi0.8Co0.1Mn0.1O2 shows that the capacity and cycle life of cathode materials can be significantly improved after Na/Y double doping. These findings open up opportunities for practical applications of nickel-rich cathode materials in lithium-ion batteries.

Wettability and Bactericidal Properties of Bioinspired ZnO Nanopillar Surfaces.

Nanomaterials of zinc oxide (ZnO) exhibit antibacterial activities under ambient illumination that result in cell membrane permeability and disorganization, representing an important opportunity for health-related applications. However, the development of antibiofouling surfaces incorporating ZnO nanomaterials has remained limited. In this work, we fabricate superhydrophobic surfaces based on ZnO nanopillars. Water droplets on these superhydrophobic surfaces exhibit small contact angle hysteresis (within 2-3°) and a minimal tilting angle of 1°. Further, falling droplets bounce off when impacting the superhydrophobic ZnO surfaces with a range of Weber numbers (8-46), demonstrating that the surface facilitates a robust Cassie-Baxter wetting state. In addition, the antibiofouling efficacy of the surfaces has been established against model pathogenic Gram-positive bacteria Staphylococcus aureus (S. aureus) and Gram-negative bacteria Escherichia coli (E. coli). No viable colonies of E. coli were recoverable on the superhydrophobic surfaces of ZnO nanopillars incubated with cultured bacterial solutions for 18 h. Further, our tests demonstrate a substantial reduction in the quantity of S. aureus that attached to the superhydrophobic ZnO nanopillars. Thus, the superhydrophobic ZnO surfaces offer a viable design of antibiofouling materials that do not require additional UV illumination or antimicrobial agents.

Folding State within a Hysteresis Loop: Hidden Multistability in Nonlinear Physical Systems.

Identifying hidden states in nonlinear physical systems that evade direct experimental detection is important as disturbances and noises can place the system in a hidden state with detrimental consequences. We study a cavity magnonic system whose main physics is photon and magnon Kerr effects. Sweeping a bifurcation parameter in numerical experiments (as would be done in actual experiments) leads to a hysteresis loop with two distinct stable steady states, but analytic calculation gives a third folded steady state "hidden" in the loop, which gives rise to the phenomenon of hidden multistability. We propose an experimentally feasible control method to drive the system into the folded hidden state. We demonstrate, through a ternary cavity magnonic system and a gene regulatory network, that such hidden multistability is in fact quite common. Our findings shed light on hidden dynamical states in nonlinear physical systems which are not directly observable but can present challenges and opportunities in applications.

Hydrophobic Solid Photothermal Slippery Surfaces with Rapid Self-repairing, Dual Anti-icing/Deicing, and Excellent Stability Based on Paraffin and Etching.

Ice and snow disasters have greatly affected both the global economy and human life, and the search for efficient and stable anti-icing/deicing coatings has become the main goal of much research. Currently, the development and application of anti-icing/deicing coatings are severely limited due to their complex preparation, structural fragility, and low stability. This work presents a method for preparing hydrophobic solid photothermal slippery surfaces (SPSS) that exhibit rapid self-repairing, dual anti-icing/deicing properties, and remarkable stability. A photothermal layer of copper oxide (CuO) was prepared by using chemical deposition and etching techniques. The layer was then impregnated with stearic acid and solid paraffin wax to create a hydrophobic solid photothermal slippery surface. This solves the issue of low stability on superhydrophobic surfaces caused by fragile and irretrievable micro/nanostructures. In addition, the underlying photothermal superhydrophobic surface provides good anti-icing/deicing properties even if the paraffin on the surface evaporates or is lost during operation. The findings indicate that when subjected to simulated light irradiation, the coating's surface temperature increases to 80 °C within 12 min. The self-repair process is completed rapidly in 170 s, and at -15 °C, it takes only 201 s for the ice on the surface to melt completely. The surface underneath the paraffin exhibited good superhydrophobic properties, with a contact angle (CA) of 154.1° and a sliding angle (SA) of 6.8° after the loss of paraffin. Simultaneously, the surface's mechanical stability and durability, along with its self-cleaning and antifouling properties, enhance its service life. These characteristics provide promising opportunities for practical applications that require long-term anti-icing/deicing surfaces.