Aspects Of Application Research Articles

Analysis of p-Type Doping in Graphene Induced by Monolayer-Oxidized TMDs.

Doping is one of the most difficult technological challenges for realizing reliable two-dimensional (2D) material-based semiconductor devices, arising from their ultrathinness. Here, we systematically investigate the impact of different types of nonstoichiometric solid MOx (M are W or Mo) dopants obtained by oxidizing transition metal dichalcogenides (TMDs: WSe2 or MoS2) formed on graphene FETs, which results in p-type doping along with disorders. From the results obtained in this study, we were able to suggest an analytical technique to optimize the optimal UV-ozone (UVO) treatment to achieve high p-type doping concentration in graphene FETs (∼2.5 × 1013 cm-2 in this study) without generating defects, mainly by analyzing the time dependency of D and D' peaks measured by Raman spectroscopy. Furthermore, an analysis of the structure of graphene sheets using TEM indicates that WOx plays a better protective role in graphene, compared to MoOx, suggesting that WOx is more effective for preventing the degradation of graphene during UVO treatment. To enhance the practical application aspect of our work, we have fabricated a graphene photodetector by selectively doping the graphene through oxidized TMDs, creating a p-n junction, which resulted in improved photoresponsivity compared to the intrinsic graphene device. Our results offer a practical guideline for the utilization of surface charge transfer doping of graphene toward CMOS applications.

Study on the zinc porphyrins as potential carbonic anhydrase mimics for promoting CO2 absorption in K2CO3 solution

Zinc porphyrins, boasting a coordination environment akin to that of natural carbonic anhydrase (CA), coupled with excellent structural stability, built-in hydrophobic pocket, and suitable charge of center Zn2+, were considered as potential CA-mimics for CO2 capture. To validate this assumption, three zinc porphyrin representatives, zinc porphin (ZnP), zinc protoporphyrin IX (ZnPP), and zinc meso-tetrakis(N-methylpyridinium-4-yl) porphyrin (ZnTMPyP), were prepared, and tested performance in enhancing CO2 absorption in water and K2CO3 solutions. The results from CO2 absorption experiments indicated that ZnTMPyP with the optimal dosage increased CO2 absorption rate by 44.25 % in water, and ZnPP with the optimal dosage increased the CO2 absorption rate by 63.67 % in 20 wt% of K2CO3 solution, which are respectively 1.53 and 2.07 times of CO2 absorption performance of zinc cyclen (ZnC), the most effective CA-mimic reported. Besides, the zinc porphyrins demonstrated excellent stability and reusability throughout five cycles of CO2 absorption and desorption, which are vital aspects for practical applications. Through the calorimetric method and density functional theory (DFT) calculations, it was confirmed that the prepared zinc porphyrins had similar enzymatic behavior and catalytic CO2 hydration pathway to natural CA. According to the results of characterization and DFT calculations, high dispersion, the lowest charge of the Zn2+ center, and the existence of additional CO2 adsorption sites (propionate ions), endow ZnPP with the highest catalytic activity in the K2CO3 solution.

Problems and Prospects of Administrative Prejudice in the Criminal Law of Russia

Introduction. The emergence and then increase during the current decade of the number of corpus delicti with administrative prejudice in the acting Criminal Code of the Russian Federation (hereinafter referred to as the Code, the Criminal Code of the Russian Federation) proves the good result achieved by the legislators in implementation of this legal construct of the Special part of the Code, as well as the efficiency of this tool in fulfilling the current policy of our state referring to the criminal law. Every year a number of such kind of corpus delicti in the Code is only increasing, which indicates the correctness of the policy taken by the legislators more than ten years ago on integrating it into the Code. The aim of the study is to identify the features and problems in the legislative design of the corpus delicti with the administrative prejudice in the Russian criminal law and to propose ways to optimize the corpus delicti of particularly this kind of offences.Materials and Methods. The methodological basis of the research are the provisions of the national criminal law, for which the corpus delicti with administrative prejudice are foreseen, studied both in historical and present day aspects, criminal laws of the near-abroad countries, as well as the scientific works of the researchers studying this topic. The following methodological tools have been used during the research: the general scientific, as well as some specific scientific methods, i.e., dialectical, system analysis, legalistic, comparative legal and other methods.Results. The historical and present day aspects of the administrative prejudice application in design of corpus delicti, the features of design of the corpus delicti with administrative prejudice and various variants of prejudice integrated by the legislators in the acting criminal law of the Russian Federation have been analysed. The legislative examples of the administrative prejudice application in some of the Criminal Codes of the CIS countries have been provided. The authors’ own standpoint regarding the presence of the corpus delicti with administrative prejudice in the Criminal Code of the Russian Federation has been expressed. The authors’ conclusions and suggestions on optimisation and expansion of such norms in the acting criminal law of the Russian Federation have been made. The tendency for the further increase of the number of corpus delicti with administrative prejudice in the acting Criminal Code of the Russian Federation has been supported.Discussion and Conclusion. The conducted research is considered important for the Russian juridical science due to the classification of the prejudices presented by the authors, which implies the availability of "classical" (simple) and "double" (complex) administrative prejudice in the corpus delicti. The amendments proposed by the authors to a number of provisions of the Special part of the Code, in particular, to Articles 180, 213, 330 of the Criminal Code of the Russian Federation about including the administrative prejudice, as a mandatory feature of the objective side of the above corpus delicti, are recommended to be used in further research of the corpus delicti with administrative prejudice

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Progress in Promising Semiconductor Materials for Efficient Photoelectrocatalytic Hydrogen Production.

Photoelectrocatalytic (PEC) water decomposition provides a promising method for converting solar energy into green hydrogen energy. Indeed, significant advances and improvements have been made in various fundamental aspects for cutting-edge applications, such as water splitting and hydrogen production. However, the fairly low PEC efficiency of water decomposition by a semiconductor photoelectrode and photocorrosion seriously restrict the practical application of photoelectrochemistry. In this review, the mechanisms of PEC water decomposition are first introduced to provide a solid understanding of the PEC process and ensure that this review is accessible to a wide range of readers. Afterwards, notable achievements to date are outlined, and unique approaches involving promising semiconductor materials for efficient PEC hydrogen production, including metal oxide, sulfide, and graphite-phase carbon nitride, are described. Finally, four strategies which can effectively improve the hydrogen production rate-morphological control, doping, heterojunction, and surface modification-are discussed.

Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects.

The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.

An Optimized Inversion Method for Hyperspectral Image Fusion Based on a Hue–Intensity–Saturation, Wavelet, and Trust-Region Conjugate Gradient Method

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application.

An updated review of few-layer black phosphorus serving as a promising photocatalyst: synthesis, modification and applications.

Semiconductor photocatalysts represent a potential strategy to simultaneously solve the global energy shortage and environmental pollution, and black phosphorus (BP) has gained widespread applications in photocatalysis due to its high hole mobility, strong light trapping capabilities, and adjustable band gap. Nevertheless, the original material exhibits unsatisfactory photocatalytic activity in terms of low carrier separation efficiency, weak environmental stability, and difficult to control layer thickness. The following review briefly presents the fundamental characteristics and extensively discusses the synthesis methods and modification strategies for few-layer black phosphorus (FL-BP). Furthermore, various applications of composite photocatalysts derived from FL-BP such as water splitting, pollutant degradation, the carbon dioxide reduction reaction (CO2RR), phototherapy, bacterial disinfection, N2 fixation, and hydrogenation reactions are reviewed. Finally, the opportunities and challenges for the development and further investigation of advanced FL-BP-based photocatalysts are also presented.

Shining a light on liver health: advancements in fluorescence-enhanced enzyme biosensors for early disease detection.

Early detection of liver diseases holds paramount importance in optimizing treatment outcomes and prognosis, thereby significantly enhancing the likelihood of recovery while mitigating the risk of progression to liver cancer. Liver diseases encompass a spectrum of conditions, each potentially manifesting distinct enzymatic profiles. Monitoring these enzymes in situ facilitates timely intervention and therapeutic management. In recent years, the field of biosensor technology has witnessed remarkable advancement, owing to strides in biomedicine and computational sciences. Biosensors have garnered widespread utility across medical and biological domains, spanning the detection of disease biomarkers, drug release tracking, ion imaging, and fluorescence imaging within living organisms. These applications have markedly enhanced imaging resolution and have the potential to refine disease diagnosis accuracy for clinicians. A pivotal aspect in the successful application of this technology lies in the construction of fluorescence probes adept at swiftly and selectively identifying target enzymes by amalgamating liver disease enzymes with fluorescence probe technology. However, research in this niche area remains relatively scarce. Building upon this foundational understanding, the present review delineates the utilization of biosensors in the early diagnosis of liver disease. Serving as a theoretical framework, this review envisages the development of high-performance biosensors tailored for the early detection of liver cancer. Furthermore, it offers insights into the potential of biosensor technology to progress and broaden its practical applications, thus contributing to the advancement of diagnostic methodologies in liver disease management.

Benchmarking the Robustness of Instance Segmentation Models.

This article presents a comprehensive evaluation of instance segmentation models with respect to real-world image corruptions as well as out-of-domain image collections, e.g., images captured by a different set-up than the training dataset. The out-of-domain image evaluation shows the generalization capability of models, an essential aspect of real-world applications, and an extensively studied topic of domain adaptation. These presented robustness and generalization evaluations are important when designing instance segmentation models for real-world applications and picking an off-the-shelf pretrained model to directly use for the task at hand. Specifically, this benchmark study includes state-of-the-art network architectures, network backbones, normalization layers, models trained starting from scratch versus pretrained networks, and the effect of multitask training on robustness and generalization. Through this study, we gain several insights. For example, we find that group normalization (GN) enhances the robustness of networks across corruptions where the image contents stay the same but corruptions are added on top. On the other hand, batch normalization (BN) improves the generalization of the models across different datasets where statistics of image features change. We also find that single-stage detectors do not generalize well to larger image resolutions than their training size. On the other hand, multistage detectors can easily be used on images of different sizes. We hope that our comprehensive study will motivate the development of more robust and reliable instance segmentation models.

Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis.

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models and frameworks, and a thorough assessment of performance metrics employed in the surveyed studies. Additionally, it includes a comparative analysis between imitation learning techniques and conventional control methodologies in the realm of robotics. The findings derived from this survey unveil profound insights into the applications of imitation learning in agricultural robotics. These methods are highlighted for their potential to significantly improve task execution in dynamic and high-dimensional action spaces prevalent in agricultural settings, such as precision farming. Despite promising advancements, the survey discusses considerable challenges in data quality, environmental variability, and computational constraints that IL must overcome. The survey also addresses the ethical and social implications of implementing such technologies, emphasizing the need for robust policy frameworks to manage the societal impacts of automation. These findings hold substantial implications, showcasing the potential of imitation learning to revolutionize processes in agricultural robotics. This research significantly contributes to envisioning innovative applications and tools within the agricultural robotics domain, promising heightened productivity and efficiency in robotic agricultural systems. It underscores the potential for remarkable enhancements in various agricultural processes, signaling a transformative trajectory for the sector, particularly in the realm of robotics and autonomous systems.

Theoretical and methodological aspects of didactic information environments design and application at higher education settings

Theoretical and methodological aspects of didactic information environments design and application at higher education settings

Research progress of ultracold ion source

<sec>Nanobeam is an advanced technology for preparing charged ion beams with spot diameters on a nanometer scale, and mainly used for high-resolution and high-precision ion beam analysis, ion beam fabrication and ion beam material modification research. The nanobeam devices play an important role in realizing material analysis, micro/nano fabrication, microelectronic device manufacturing and quantum computing. The high-quality ion source is one of the key components of nanobeam device, the performance of which directly affects the resolution and precision of the nanobeam system. However, the traditional ion source used in this system is limited to available ionic species, large energy spread and complex structure. These issues hinder their ability to meet emerging application scenarios that require multi-ion types and high resolution. This emphasizes the importance of creating newion sources as soon as possible.</sec><sec>With the development of laser cooling technology, ultracold ions with temperatures in the range of mK or even μK can be obtained based on photoionization of cold atoms and laser cooling of ions. The typical characteristics of low temperature and easy operation greatly promote the emergence of ultracold ion sources. The ultracold ions exhibit extremely small transverse velocity divergence, which can significantly enhance the brightness and emittance quality parameters of the ion source, bringing great opportunities for innovating nano-ion beam technology. Therefore, the research on ultracold ion sources is of great significance for achieving high-quality ion sources with higher brightness, smaller size, lower energy dispersion, more diverse ion species, and simplified structure. Here, we introduce the important achievements in basic research and application technology development of magneto-optical trap ion sources, cold atomic beam ion sources, and ultracold single ion sources from the aspects of preparation principles, generation methods, and typical applications, and review the recent research progress of ultracold ion sources. Finally, we provide an outlook on the future development and application prospects of ultracold ion sources.</sec>

Surface functionalization of hydroxyapatite nanoparticles for biomedical applications.

This review completely covers the various aspects of hydroxyapatite (HAp) nanoparticles and their role in different biological situations, and provides the surface and interface contents on (i) hydroxyapatite nanoparticles and their hybridization with organic molecules, (ii) surface designing of hydroxyapatite nanoparticles to provide their biocompatibility and photofunction, and (iii) coating technology of hydroxyapatite nanoparticles. In particular, we summarized how the HAp nanoparticles interact with the different ions and molecules and highlighted the potential for hybridization between HAp nanoparticles and organic molecules, which is driven by the interactions of the HAp nanoparticle surface ions with several functional groups of biological molecules. In addition, we highlighted the studies focusing on the interfacial interactions between the HAp nanoparticles and proteins for exploring the enhanced biocompatibility. Such studies focus on how these interactions affect the hydration layers and protein adsorption. However, the hydration layer state involves diverse molecular interactions that can alter the shape of the adsorbed proteins, thereby affecting cell adhesion and spreading on the surfaces. We also summarized the relationship between the surface properties of the HAp nanoparticles and the hydration layer. Furthermore, we spotlighted the cytocompatible photoluminescent probes that can be developed by designing HAp/organic nanohybrid structures. We then emphasized the importance of photofunctionalization in theranostics, which involves the integration of diagnostics and therapy based on the surface design of the HAp nanoparticles. Furthermore, the coating techniques using HAp nanoparticles and HAp nanoparticle/polymer composites were outlined for fusing base biomaterials with biological tissues. The advantages of HAp/biocompatible polymer composite coatings include the ability to effectively cover porous or irregularly shaped surfaces while controlling the thickness of the coating layer, and the addition of HAp nanoparticles to the polymer matrix improves the mechanical properties, increases the roughness, and forms the morphologies that mimic bone nanostructures. Therefore, the fundamental design of hydroxyapatite nanoparticles and their surfaces was suggested from various aspects for biomedical applications.

An efficient high-gain bidirectional interleaved boost converter for PV integration to DC microgrid.

The design of a power electronic interface for high voltage difference DC buses is a key aspect in DC microgrid applications. A multi-port non isolated interleaved high-voltage gain bidirectional converter, which facilitates bidirectional power transfer and islanded operation in a DC microgrid, is presented in this paper. The forward high-voltage transfer ratio is achieved using a voltage multiplier circuit, and the high-gain step-down power conversion is performed using a resonant power module. A novel power transfer selection algorithm is proposed to control power flow among the interfaces of the RES, ESS, and DC grid converters, which utilizes the net power difference as the basis for switching the converter. The proposed converter is simulated for a 24 V PV source, 12 V battery, and 400 V DC grid interface using MATLAB/SIMULINK. A 200 W hardware prototype is implemented. The simulation results for voltages, currents, and power flow among RES, ESS, and microgrid DC bus proved an excellent voltage regulation, efficient power conversion, and a feasible duty cycle range with high voltage gain. These observations are validated through equivalent experimental results. A comparison is made regarding achieved gain, component sizing, achievable power transfer modes, efficiency, and control complexity with existing converters for DC microgrid applications. The presented topology proved to be a better interface with multiple-mode support with high efficiency.

Roman law in the jurisprudence of Russian courts

In modern Russian judicial practice, the justification of court decisions by normative provisions extracted from all that is collectively embraced by the concept of “soft law” is becoming increasingly widespread. In this article, this phenomenon is revealed in the aspect of the practical application by Russian courts of certain provisions of Roman law. Considering specific examples of this kind, the authors, through doctrinal reflection, seek to identify common reasons that encourage domestic courts to turn to the mentioned source of “soft law” and come to the conclusion that modern Russian civil law in a sense repeated the path that was traversed by the civil law of imperial Russia. If, back in the middle of the 19th century, the judiciary could not even think of looking for a source of inspiration anywhere other than a literal interpretation of the text of the law, then with the beginning of the “Great Reforms” of the 1860s, the unsuitability of such an approach for servicing the rapidly developing turnover was revealed, and since the legislator could not keep up with its development, the main work of adapting positive law to changing relations was accepted the courts have assumed much broader discretionary powers. Since the possibilities of the domestic doctrine to meet the needs of the law enforcement officer in understanding and developing Russian law turned out to be very limited, judges found themselves in demand for the European civilian tradition based on Roman law, familiarity with which prompted them to independent judicial law-making. In many ways, similar processes are observed in post-Soviet Russia, a visible manifestation of which is the expansion of “soft law” into law enforcement practice, primarily through an appeal to the analogy of law and teleological interpretation. One of the facets of this process is the revival of interest in the legacy of Roman law and the Romanistic tradition. In this sense, unexpected parallels are found with the development of the common law.

Continuous flow synthesis of metal nanowires: protocols, engineering aspects of scale-up and applications.

This review comprehensively covers the translation from batch to continuous flow synthesis of metal nanowires (i.e., silver, copper, gold, and platinum nanowires) and their diverse applications across various sectors. Metal nanowires have attracted significant attention owing to their versatility and feasibility for large-scale synthesis. The efficacy of flow chemistry in nanomaterial synthesis has been extensively demonstrated over the past few decades. Continuous flow synthesis offers scalability, high throughput screening, and robust and reproducible synthesis procedures, making it a promising technology. Silver nanowires, widely used in flexible electronics, transparent conductive films, and sensors, have benefited from advancements in continuous flow synthesis aimed at achieving high aspect ratios and uniform diameters, though challenges in preventing agglomeration during large-scale production remain. Copper nanowires, considered as a cost-effective alternative to silver nanowires for conductive materials, have benefited from continuous flow synthesis methods that minimize oxidation and enhance stability, yet scaling up these processes requires precise control of reducing environments and copper ion concentration. A critical evaluation of various metal nanowire ink formulations is conducted, aiming to identify formulations that exhibit superior properties with lower metal solid content. This study delves into the intricacies of continuous flow synthesis methods for metal nanowires, emphasizing the exploration of engineering considerations essential for the design of continuous flow reactors. Furthermore, challenges associated with large-scale synthesis are addressed, highlighting the process-related issues.

Leveraging Self-Attention Mechanism for Deep Learning in Hand-Gesture Recognition System

This research addresses the complex challenge of recognizing hand gestures irrespective of the user's body posture, a crucial issue in medical treatment for people with speech impairments and human-machine interfaces where precise gesture interpretation is vital. The aim is to engineer an advanced hand gesture recognition system, effective across various body positions and camera viewpoints. A novel flexible camera arrangement was employed, integrating a CNN-Transformer hybrid model, leveraging the strengths of Convolutional Neural Networks and the self-attention mechanism of Transformers. Developed using Python and the PyTorch deep learning framework, the system focuses on sophisticated image processing techniques. A thorough literature review on gesture recognition systems and multi-view analysis was conducted to inform the development. The system demonstrated exceptional accuracy in recognizing hand gestures in diverse body postures and from multiple camera perspectives, significantly outperforming existing methods. It marked a significant advancement in decoding complex gestures, a key aspect for medical applications and intricate human-machine is primarily interactions. This beneficial for people with speech impairments, rehabilitation, and in human-machine interfaces, poised to revolutionize patient care and enhance interaction with advanced machinery and computer systems.

Research on Computer-aided Innovation and Entrepreneurship Education Model and Algorithm

Abstract With the rapid development of science and technology, computer-aided technology has been widely used in the field of education, but in the field of innovation and entrepreneurship education (Hereinafter referred to as IEE), there are still problems such as uneven distribution of resources and the disconnection between teaching content and actual needs. This paper puts forward a new model framework of computer-aided IEE, which is student-centered and practice-oriented. Through clear teaching objectives, rich course content, diverse teaching methods, and a scientific evaluation system, students’ innovative consciousness and entrepreneurial ability are comprehensively improved. The course covers innovative thinking, business models, market research, and other theoretical knowledge, and introduces virtual reality (VR) and augmented reality (AR) technologies to simulate the real entrepreneurial process and provide a practical platform. In the aspect of algorithm application, the research combines the content recommendation algorithm and collaborative filtering recommendation algorithm, constructs accurate user portraits by analyzing students’ learning behavior data, and matches them according to the characteristics of learning resources to recommend personalized learning resources for students. At the same time, the learning path optimization algorithm is developed, and the personalized and efficient learning path is planned by using the shortest path algorithm in graph theory to ensure that students can master the required knowledge in the shortest time. The experimental part designed a one-semester teaching experiment, and compared the differences between the experimental group and the control group in academic performance, learning participation, and satisfaction. The results show that the computer-aided IEE model significantly improves students’ academic performance, learning participation, and satisfaction, which verifies the effectiveness of the model. The research on the model and algorithm of computer-aided IEE proposed in this paper provides new ideas and methods for IEE, and helps to cultivate more high-quality talents with innovative thinking and entrepreneurial ability.

Advancements in enzyme immobilization on magnetic nanomaterials: toward sustainable industrial applications.

Enzymes are widely used in biofuels, food, and pharmaceuticals. The immobilization of enzymes on solid supports, particularly magnetic nanomaterials, enhances their stability and catalytic activity. Magnetic nanomaterials are chosen for their versatility, large surface area, and superparamagnetic properties, which allow for easy separation and reuse in industrial processes. Researchers focus on the synthesis of appropriate nanomaterials tailored for specific purposes. Immobilization protocols are predefined and adapted to both enzymes and support requirements for optimal efficiency. This review provides a detailed exploration of the application of magnetic nanomaterials in enzyme immobilization protocols. It covers methods, challenges, advantages, and future perspectives, starting with general aspects of magnetic nanomaterials, their synthesis, and applications as matrices for solid enzyme stabilization. The discussion then delves into existing enzymatic immobilization methods on magnetic nanomaterials, highlighting advantages, challenges, and potential applications. Further sections explore the industrial use of various enzymes immobilized on these materials, the development of enzyme-based bioreactors, and prospects for these biocatalysts. In summary, this review provides a concise comparison of the use of magnetic nanomaterials for enzyme stabilization, highlighting potential industrial applications and contributing to manufacturing optimization.