Perspectives For Applications Research Articles

The strapdown shipborne gravimetry for ocean based on SINS/GNSS: comparison and optimization

Abstract The strapdown inertial scalar gravimetry (SISG) has become one of the main methods for measuring marine gravity. SIGS requires specific force, attitude, velocity, and position information for gravimetry. The attitude can be obtained by the strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integration, and the displacement information comes from GNSS or SINS/GNSS. Therefore, based on the different sources of displacement information, shipborne SISG can be divided into two modes. Namely, the gravimetry method of different parameter sources (DPGM) based on displacement information of GNSS and the attitude of SINS/GNSS, as well as the full parameter gravimetry method (FPGM) based on the attitude and displacement information of SINS/GNSS. Since the accuracy of GNSS can meet the requirements for gravimetry, the theoretical studies and applications of DPGM are more common. For FPGM, this idea is more often used in underwater applications (SINS/ doppler velocity log (SINS/DVL), etc.). However, it rarely appears in shipborne applications. Moreover, there is relatively little theoretical analysis on FPGM. In fact, FPGM also has certain application prospects due to the high accuracy. Based on this, this article first provides the measurement model and steps of FPGM, and conducts the theoretical analysis of FPGM. The comparison is made with DPGM from the perspective of error terms. And it is pointed out that FPGM has better potential for anti-interference improvement than DPGM. Then, from the perspective of application, we propose the investigated linear robust Kalman filter (LRKF) based on Huber-M estimation without traversing all dimensions and introduce the horizontal acceleration and bias corrections to improve FPGM. Finally, the optimized FPGM (OFPGM) is obtained. The results of lake and sea tests show that FPGM has certain research value. And OFPGM has higher performance in high dynamic condition and when GNSS occurs interference, which is more suitable for offshore measurement.

A Review of Safe Reinforcement Learning: Methods, Theories, and Applications.

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world applications, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safe control has a long history, the study of safe RL algorithms is still in the early stages. To establish a good foundation for future safe RL research, in this paper, we provide a review of safe RL from the perspectives of methods, theories, and applications. First, we review the progress of safe RL from five dimensions and come up with five crucial problems for safe RL being deployed in real-world applications, coined as "2H3W". Second, we analyze the algorithm and theory progress from the perspectives of answering the "2H3W" problems. Particularly, the sample complexity of safe RL algorithms is reviewed and discussed, followed by an introduction to the applications and benchmarks of safe RL algorithms. Finally, we open the discussion of the challenging problems in safe RL, hoping to inspire future research on this thread. To advance the study of safe RL algorithms, we release an open-sourced repository containing major safe RL algorithms at the link.

Multi-channel wide-angle nonreciprocal thermal radiator with planar heterostructure

From the perspective of application at mid-infrared frequencies, omnidirectional nonreciprocal thermal radiation represents a critical need for effective thermal energy harvesting. In this study, we propose a nonreciprocal thermal radiator (NTR) which can be fabricated with a lithography-free approach. The structure is composed by germanium(Ge)-aluminum nitride(AlN)-Weyl semimetal (WSM) stacks and terminated with a metallic substrate. The results indicate that both the magnitude and the sign of the contrast between emission (e) and absorption (α) can be manipulated. This design achieves multi-channel nonreciprocal stability for transverse magnetic (TM) polarized incident wave over an exceptionally wide angular range. Additionally, the electromagnetic field distributions at the resonance wavelengths have been analyzed to elucidate the underlying principles. Multi-band nonreciprocity under transverse electric (TE) polarized incident wave has also been obtained. The capability of our proposal offers a relatively straightforward method to realize high-performance NTR. This advancement holds potential implications for the development of efficient energy harvesting and conversion devices.

Research on the optimization of proportions for synchronous grouting material in composite muck during shield tunneling

To tackle the challenge of onsite resource reuse of composite muck, which relies on a shield tunneling section in the case of Nanjing Metro Line 7, this paper examines the effects of component substitution and varying proportions of factors on slurry properties through laboratory tests and multiobjective modeling. From a practical application perspective, the potential of using composite muck as a synchronous grouting material for shield tunneling was explored. In reference to the common mixed strata encountered in shield tunneling construction in the Nanjing area, a general slurry mixing ratio suitable for various mixed strata conditions was derived. The research results indicate the following optimized mixing ratios for the composite muck slurry: a water-to-binder (cement + fly ash) ratio of 0.76, a cement-to-fly ash ratio of 0.53, a binder-to-soil (silty clay + silty fine sand + river sand) ratio of 0.67, a clay (i.e., silty clay)-to-sand (i.e., silty fine sand + river sand) ratio of 0.4, and a substitution ratio (i.e., silty fine sand/silty fine sand + river sand) of 0.2. This slurry is feasible for practical applications and can reduce construction costs by 38.13%. The obtained general mixing ratio for the composite muck slurry, suitable for various mixed stratigraphic conditions, essentially meets onsite construction requirements (the bleeding rate is controlled within a low range of 1.1–3.1%, and the maximum compressive strength reaches up to 6.76 MPa at 28 days), with reasonable costs. It streamlines construction by eliminating the need for onsite screening of muck, allowing for the direct onsite reuse of composite muck in shield tunneling.

Discourse Strategy Analysis on English Translation of Modern Chinese Essays Based on Framing Theory

The English translation of modern Chinese essays provides a platform for the world to showcase the Chinese spirit. Therefore, it is necessary to conduct in-depth research on the underlying discourse strategies of the English translation of modern Chinese essays. Based on the framing theory proposed by Lakoff, this study adopts a combination of qualitative and quantitative methods to analyze surface frames and deep frames of the English translation of modern Chinese essays. Analyses show that among a total of 1074 metaphorical words, there exist four main metaphorical surface frames: people frame, nature frame, journey frame and object frame. These surface frames activate four deep frames: the pursuit of personal qualities, nostalgia for hometown and family, criticism of social reality and the struggle for the country. This study provides a new application perspective and promotes future research on the English translation of modern Chinese essays, and could help readers to interpret the main values embodied in Selected Modern Chinese Essays 1, enhancing their understanding and appreciation of these literary works.

LAMP Reaction in Plant Disease Surveillance: Applications, Challenges, and Future Perspectives

Movements of plant pathogenic microorganisms in uncontaminated areas occur today at an alarming rate, driven mainly by global trade and climate change. These invaders can trigger new disease outbreaks able to impact the biodiversity and economies of vast territories and affect a variety of ecosystem services. National and supranational regulatory deficiencies, such as inadequate quarantine measures and ineffective early pathogen detection at ports of entry, exacerbate the issue. Thus, there is an urgent need for accurate and rapid diagnostic tools to intercept invasive and nonindigenous plant pathogens. The LAMP (Loop-mediated isothermal AMPlification) technique is a robust, flexible tool representing a significant advance in point-of-care (POC) diagnostics. Its user-friendliness and sensitivity offer a breakthrough in phytosanitary checks at points of entry (harbors and airports), for disease and pest surveillance at vulnerable sites (e.g., nurseries and wood-processing and storage facilities), and for territorial monitoring of new disease outbreaks. This review highlights the strengths and weaknesses of LAMP, emphasizing its potential to revolutionize modern plant disease diagnostics.

Application Perspectives of Induction Surfaces in CAD, Smooth Connection of Concentric Spherical Surfaces

Application Perspectives of Induction Surfaces in CAD, Smooth Connection of Concentric Spherical Surfaces

Noninvasive brain–computer interfaces for children with neurodevelopmental disorders: Attention deficit hyperactivity disorder and autism spectrum disorder

Background:Brain–computer interface (BCI)-mediated neurofeedback training (BCI-NFT) has emerged as a highly promising treatment in the field of neurorehabilitation. Many previous studies have demonstrated the efficacy of BCI techniques in clinical rehabilitation, but children are largely neglected in BCI research. Purpose:This systematic review aimed to synthesize existing studies from technical and clinical application perspectives to identify the current state of research on noninvasive brain–computer interface (NBCI) technology in children with two major neurodevelopmental disorders, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Methods:Five relevant electronic databases were searched (PubMed, Web of Science, the Cochrane Library, Embase, and the Cumulative Index of Nursing and Allied Health Literature). The publication dates ranged from the inception of each database to June 2024. Randomized controlled trials (RCTs) investigating the use of NBCI technology in children with ASD or ADHD were included. Manual searches of the clinical trial registry platforms and the reference lists of reviews related to the study topic were also conducted. Two independent reviewers performed the literature screening, data extraction, and risk of bias assessment. Results:A total of 24 RCTs involving 1998 children with ASD or ADHD were included in this systematic review. With respect to input brain signals, functional magnetic resonance imaging (fMRI) (4.2%), electroencephalography (EEG) combined with fMRI (4.2%), and EEG combined with galvanic skin response (GSR) sensors (4.2%) were utilized in one study each. Seven studies employed EEG combined with electrooculogram (EOG) (29.1%), and the remaining fourteen studies used EEG alone (58.3%). Compared with those of the controls, significant improvements in both behavioral aspects and brain activity in patients were observed in eleven studies (45.8%). NBCI technology has a positive effect on both the behavioral and brain activity levels of children with ASD or ADHD, while it still faces challenges in the paediatric population, particularly in terms of signal processing and the unique cognitive and physiological developmental stages of children, which may complicate the application of these technologies in this population. Conclusion:It demonstrated that there has a high potential for NBCI application in the field of neurodevelopmental disorders. Future research should focus on developing advanced machine learning algorithms to improve neural signal decoding capabilities and on creating child-appropriate application paradigms to explore the long-term efficacy of these algorithms.

Synthesis-Structure-Property of High-Entropy Layered Oxide Cathode for Li/Na/K-Ion Batteries.

Increasing demand for rechargeable batteries necessitates improvements in electrochemical performance. Traditional optimal approaches such as elemental doping and surface modification are insufficient for practical applications of the batteries. High-entropy materials (HEMs) possess stable solid-state phases and unparalleled flexibility in composition and electronic structure, which facilitate rapid advancements in battery materials. This review demonstrates the properties of HEMs both qualitatively and quantitatively, and the mechanisms of their enhancement on battery properties. It also illustrates the progress in high-entropy layered oxide cathode materials (HELOs) for lithium/sodium/potassium ion batteries (LIBs/SIBs/PIBs) in the perspectives of synthesis, characterization and application, and elucidating the synthesis-structure-property relationship. Furthermore, it outlines future directions for high-entropy strategies in battery study: precise synthesis control, understanding of reaction mechanisms through structural characterization, elucidation of structure-performance correlations, and the computational and experimental methods integration for rapid screening and analysis of HEMs. The perspective aims to inspire researchers in the development of high-performance rechargeable batteries.

SPHERICAL GRAPHENE FILM GROWN ON CARBIDE FOR SENSOR MATRIX

Graphene has amazing applications for sensors due to its excellent performances like high strength and good conductivity, but the transfer issue is in the way of its application perspective. Direct growth of spherical graphene films (SGFs) on cemented carbide may offer a good avenue for various applications in sensor technology, especially for electrochemical sensors. Four common methods for graphene preparation are chemical stripping, chemical vapor deposition (CVD), metal catalysis, and laser fabrication; and subject to transfer issues during usage. In order to overcome this limitation, the fabrication of in-situ growth of SGFs on carbide is proposed as a solution for constructing sensor matrices. This review explores various in-situ SGFs and their potential applications in sensors. The findings presented here shed light on transfer-free graphene with controllable structures that can serve as excellent candidates for sensor matrices.

Harnessing Extrinsic Dissipation to Enhance the Toughness of Composites and Composite Joints: A State-of-the-Art Review of Recent Advances.

Interfaces play a critical role in modern structures, where integrating multiple materials and components is essential to achieve specific functions. Enhancing the mechanical performance of these interfaces, particularly their resistance to delamination, is essential to enable extremely lightweight designs and improve energy efficiency. Improving toughness (or increasing energy dissipation during delamination) has traditionally involved modifying materials to navigate the well-known strength-toughness trade-off. However, a more effective strategy involves promoting non-local or extrinsic energy dissipation. This approach encompasses complex degradation phenomena that extend beyond the crack tip, such as long-range bridging, crack fragmentation, and ligamentformation. This work explores this innovative strategy within the arena of laminated structures, with a particular focus on fiber-reinforced polymers. This review highlights the substantial potential for improvement by presenting various strategies, from basic principles to proof-of-concept applications. This approach represents a significant design direction for integrating materials and structures, especially relevant in the emerging era of additive manufacturing. However, it also comes with new challenges in predictive modeling of such mechanisms at the structural scale, and here the latest development in this direction is highlighted. Through this perspective, greater durability and performance in advanced structural applications can beachieved.

Biotechnology of groundwater purification for water supply systems, using Gallionella and Lepthothrix ferrobacteria

Aim. The aim of the study was to consider the possibility of using the consortia of chemolithoautotrophic ferrobacteria from Gallionella genus and heterotrophic bacteria from Lepthothrix genus for the biological method of groundwater purification. Methods. The photocolorimetric method to determine the concentrations of ammonium and iron ions, the titrimetric method to determine the hydrocarbon and total alkalinity, the method of determining the permanganate oxidizability using the Kubel method, the potentiometric method to determine the values of рН and Еh, the electronic microscopy using the X-ray spectral analysis of matrix structures of bio-minerals, microbiological and statistical methods. Results. The main technological parameters of the water deironing process were defined as follows: the filtration velocity of the bioreactor – 7–11 m/h, and of the filters – 3.5–5 m/h; the filter-cycle duration – 48 h. It was found that the application of the two-stage technology of biological deironing in the bioreactor and filters provided for the possible removal of Fe2+ compounds up to 5.0 mg/cdm, ammonium nitrogen — up to 1.5 mg/cdm, soluble organic substances by PO – up to 6.0 mg O2/cdm. It was determined that the optimal parameters for the process of biological purification of neutral groundwaters, containing increased concentrations of Fe2+ cations were as follows: рН 7.0–7.2; hydrocarbon alkalinity 2.5–2.2 mmol/cdm; content of soluble oxygen – 1.5–2.0 mg/cdm. The ability of concentrated (Dos 200 mg/cdm) matrix structures of Gallionella and Lepthothrix ferrobacteria to remove Cr6+ ions from natural groundwaters was determined. The study found no considerable differences in the efficiency of applying disinfectants, produced using polyhexamethylene guanidine chloride (PHMGchl) or polyhexamethylene biguanidine chloride (PHMBchl). In concentrations of 0.25–0.5 %, they effectively disinfect pathogenic microorganisms, including Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. The same was found true for the bacteria, most common in the systems of technical reverse water supply, the systems of water circulation, plant watering, and fire tanks, including Aeromonas hydrophila, Aeromonas salmonicida, Pseudomonas sp., E. coli, Flavobacterium columnare, Micrococcus lysodeikticus. Conclusions. This was the first study on the possibility of applying the biotechnology of groundwater purification from excessive amounts of iron in conditions of uneven hydraulic burden, notable for the water supply systems of rural areas and most agricultural enterprises in the north-western and northern regions of Ukraine. The specificities and perspectives of PHMGchl application in the systems of water preparation were studied. It was found that from the standpoint of safety and efficiency, the use of the water deironing processes involving iron bacteria was reasonable in the systems of water consumption and technical water supply, and the disinfection of water using PHMG was possible only in the second case, due to some toxicity of the preparation. The optimal parameters for the process of biological purification of neutral groundwaters, containing increased concentrations of Fe2+ cations were determined.

Current status and future direction of cancer research using artificial intelligence for clinical application.

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI-equipped software as a medical device (AI-SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data-driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI-based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology.

Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives.

Wearable biosensors are a fast-evolving topic at the intersection of healthcare, technology, and personalized medicine. These sensors, which are frequently integrated into clothes and accessories or directly applied to the skin, provide continuous, real-time monitoring of physiological and biochemical parameters such as heart rate, glucose levels, and hydration status. Recent breakthroughs in downsizing, materials science, and wireless communication have greatly improved the functionality, comfort, and accessibility of wearable biosensors. This review examines the present status of wearable biosensor technology, with an emphasis on advances in sensor design, fabrication techniques, and data analysis algorithms. We analyze diverse applications in clinical diagnostics, chronic illness management, and fitness tracking, emphasizing their capacity to transform health monitoring and facilitate early disease diagnosis. Additionally, this review seeks to shed light on the future of wearable biosensors in healthcare and wellness by summarizing existing trends and new advancements.

A Novel Method for Technology Roadmapping: Nanorobots

In the dynamic field of robotics engineering, nanorobot technology has witnessed rapid advancements. Developing a technology roadmap is essential for quickly identifying the trends and key technological aspects of nanorobotics from an array of multi-source data. Traditional research methods, such as Delphi surveys, bibliometrics, patent analysis, and patent paper citation analyses, often fail to capture the rich semantic information available. Moreover, these approaches generally provide a unidimensional perspective, which restricts their capacity to depict the complex nature of technological evolution. To overcome these shortcomings, this paper introduces a novel framework that utilizes the ALBERT method combined with multi-source data for critical theme extraction. It integrates varied data sources, including academic papers and patents, to explore the interrelation within the nanorobot technology roadmap. The methodology begins with text feature extraction, clustering algorithms, and theme mining to identify dominant technological themes. Subsequently, it applies semantic similarity measures to connect multiple themes, employing a “multi-layer ThemeRiver map” for a visual representation of these inter-layer connections. The paper concludes with a comprehensive analysis from both the technological research and industrial application perspectives, underscoring the principal developmental themes and insights of nanorobot technology, and projecting its future directions.

The Role of Positron Emission Tomography/Computed Tomography in the Management of Differentiated Thyroid Cancer: Current Applications and Future Perspectives.

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid carcinoma, is the most common thyroid malignancy and typically has a favourable prognosis when detected early. Positron emission tomography/computed tomography (PET/CT) has emerged as a valuable imaging modality, integrating metabolic and anatomical data. Although PET/CT is not usually part of the initial diagnostic process due to DTC's indolent nature and low metabolic activity, it plays an essential role in selected clinical scenarios. This includes identifying recurrence in patients with elevated thyroglobulin (Tg) levels and negative radioactive iodine (RAI) scans, evaluating metastatic disease, and guiding treatment in advanced cases. As the use of PET/CT evolves in oncology, this review explores its application in regard to staging, detection of recurrence, and follow-up in terms of managing DTC while also evaluating potential challenges that may occur in the future. The review also considers emerging radiotracers and the theragnostic potential of PET/CT.

Exploration of M2 macrophage membrane as a biotherapeutic agent and strong synergistic therapeutic effects in ischemic stroke

The macrophage-derived membrane is widely applied in the targeting nanocarrier for its inflammatory tendency and long circulation ability due to the preservation of membrane protein. Few studies reported the application of macrophage membranes as biotherapeutic agents. To verify the ability of macrophage membrane as a biotherapeutic agent, ischemic stroke was selected as the model disease. Inspired by the features of macrophages infiltrating the ischemic core and the talent of M2 macrophages in modulating the inflammatory microenvironment, an M2 macrophage membrane (M2M)-disguised poly lactic-co-glycolic acid nanoparticles loaded with baicalin (BA) (M2M@BANPs) is developed. The results in vivo and in vitro indicate that M2M@BANPs could efficiently and actively target ischemic brain tissue and accumulate in microglia and neurons due to the coating of M2M. Furthermore, M2M and M2M@BANPs exhibit significant therapeutic effects in salvaging brain tissue damage and neurological functional recovery by reprogramming microglia from M1 to M2, reducing neutrophil infiltration and inhibiting neuronal apoptosis. Together, our fabrication provides a new insight and an applicative perspective for M2M in the therapy of ischemic stroke.

Recent developments in autonomous floor-cleaning robots: a review

Purpose Autonomous floor-cleaning robots (AFCRs) have become increasingly popular due to their ability to provide efficient and effective cleaning without the need for human intervention. These robots can perform various cleaning tasks, such as vacuum cleaning, mopping, scrubbing or sweeping, in domestic or industrial setups. As the use of floor-cleaning robots continues to grow, this paper aims to document key technological advancements. Design/methodology/approach The structure of the present work relies on published research articles excavated from general online research databases such as Google Scholar, Web of Science and Scopus. The authors use a variety of keywords and titles to search for research papers. Finally, 93 research articles are selected for review based on abstracts and key results that match AFCRs. Findings According to market trends, floor-cleaning robots dominate other cleaning areas. This review mainly focuses on five attributes of floor-cleaning robots: design and development of AFCR, complete coverage path planning, the application of machine learning (ML)/deep learning (DL), optimisation strategies for qualitative output and ethnographic studies. It also consists of discussions based on the results of reported technical works. Hence, AFCRs have dominated the market in the past decade and are likely to be more aggressive in the coming years. Originality/value To the best of the authors’ knowledge, only a survey article based on US-granted patents published in 2013 constitutes a review work in the research domain on AFCRs. In 2021, another review conducted a survey on the latest technological advancements in window-cleaning robots. It reviewed in detail the locomotion aspects, control mechanisms, adhesion mechanisms, sensors and actuators required for window-cleaning robots. In 2019, a comprehensive review was published on cleaning robots from a control strategy perspective for domestic applications. Therefore, the authors have crafted this review to understand the evolution of floor-cleaning robots in the past decade.

A comprehensive benchmarking of a U-Net based model for midbrain auto-segmentation on transcranial sonography

Background and objectiveTranscranial sonography-based grading of Parkinson's Disease has gained increasing attention in recent years, and it is currently used for assistive differential diagnosis in some specialized centers. To this end, accurate midbrain segmentation is considered an important initial step. However, current practice is manual, time-consuming, and bias-prone due to the subjective nature. Relevant studies in the literature are scarce and lacks comprehensive model evaluations from application perspectives. Herein, we aimed to benchmark the best-performing U-Net model for objective, stable and robust midbrain auto-segmentation using transcranial sonography images. MethodsA total of 584 patients who were suspected of Parkinson's Disease were retrospectively enrolled from Beijing Tiantan Hospital. The dataset was divided into training (n = 416), validation (n = 104), and testing (n = 64) sets. Three state-of-the-art deep-learning networks (U-Net, U-Net+++, and nnU-Net) were utilized to develop segmentation models, under 5-fold cross-validation and three randomization seeds for safeguarding model validity and stability. Model evaluation was conducted in testing set in three key aspects: (i) segmentation agreement using DICE coefficients (DICE), Intersection over Union (IoU), and Hausdorff Distance (HD); (ii) model stability using standard deviations of segmentation agreement metrics; (iii) prediction time efficiency, and (iv) model robustness against various degrees of ultrasound imaging noise produced by the salt-and-pepper noise and Gaussian noise. ResultsThe nnU-Net achieved the best segmentation agreement (averaged DICE: 0.910, IoU: 0.836, HD: 2.793-mm) and time efficiency (1.456-s). Under mild noise corruption, the nnU-Net outperformed others with averaged scores of DICE (0.904), IoU (0.827), HD (2.941 mm) in the salt-and-pepper noise (signal-to-noise ratio, SNR = 0.95), and DICE (0.906), IoU (0.830), HD (2.967 mm) in the Gaussian noise (sigma value, σ = 0.1); by contrast, intriguingly, performance of the U-Net and U-Net+++ models were remarkably degraded. Under increasing levels of simulated noise corruption (SNR decreased from 0.95 to 0.75; σ increased from 0.1 to 0.5), the nnU-Net network exhibited marginal decline in segmentation agreement meanwhile yielding decent performance as if there were absence of noise corruption. ConclusionsThe nnU-Net model was the best-performing midbrain segmentation model in terms of segmentation agreement, stability, time efficiency and robustness, providing the community with an objective, effective and automated alternative. Moving forward, a multi-center multi-vendor study is warranted when it comes to clinical implementation.

Enhancing Cybersecurity through Artificial Intelligence: Techniques, Applications, and Future Perspectives

This study investigates the role of artificial intelligence (AI) in improving cybersecurity, addressing a vital issue as digital threats become more complex and faster. AI-driven advancements in threat detection, predictive vulnerability assessments, and ethical considerations pave the way for robust and adaptable defenses.