Research In Field Research Articles

A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.

Plasma metabolomic characteristics of atrial fibrillation patients with spontaneous echo contrast.

The spontaneous echo contrast (SEC) in patients with atrial fibrillation (AF) indicates a prethrombic state that ultimately progresses into thrombus formation. A comprehensive understanding of specific plasma metabolomics characteristics may protect AF patients from thrombus, particularly in the early stage. Through the investigation of metabolic pathways, we endeavor to uncover the metabolomic characteristics associated with SEC states, and to examine the differential metabolites by which may exert their influence on thrombotic states. Patients with AF were enrolled, and the participants were divided into three groups based on the results of the echocardiogram: non-SEC, low-SEC and high-SEC group. Samples were collected and subjected to non-targeted metabolomics analysis. The analytical process included data quality control, metabolite difference analysis, component analysis, Kegg cluster analysis, etc. RESULTS: Our metabolic phenotype revealed a clear differential metabolic pattern between the SEC and non-SEC. Specifically, we identified 35 and 142 significantly differential metabolites in venous and atrial plasma, respectively, suggesting that SEC may be involved in pervasive metabolic dysregulation and that the degree of metabolic dysregulation in atrial plasma is more severe than that in venous blood. Patients with SEC have a significantly different metabolic pattern compared to those without SEC. Our work promoted the understanding of mechanism of the occurrence and development of SEC, facilitated the screening of the target metabolites for its therapeutic intervention, and provided evidence for the prevention and treatment of SEC or thrombosis in AF. Our work also provided new directions for subsequent research in related fields. In conclusion, our study not only provides a theoretical basis for understanding the occurrence and development of SEC in AF, but also provides recommendations for the daily diet of AF patients with SEC, such as a balanced intake of essential amino acids, avoiding excessive intake of benzoic acid, and intake of appropriate inositol. Not applicable.

Decolonizing the Library: Considering the Movement in the Irish Colonial Contexts

Abstract Decolonization is a concept which has been rapidly gaining traction in the library community, particularly in England, the United States, and Australia. This article addresses the question: “what does decolonizing the library look like in the Irish colonial contexts?” The role which libraries play in reckoning with colonial pasts cannot be understated. This paper will explore what decolonization can look like through Irish libraries both north and south of the border. This will be examined through Irish language revitalization, the presence of oral histories in library collections, and how libraries respond to underserved communities. This study also establishes areas for further research in this nascent field as well as outlining practical steps to implement the concept of decolonizing the library in the Irish contexts. Drawing on qualitative data pulled from interviews this paper will offer the latest information on the Irish library landscape and its relation to the broader decolonization movement. This paper aims to demonstrate the value of using the decolonization framework to address the colonial past which has impacted the way in which the information services function in modern day Ireland and pave the way towards an inclusive and equitable future.

Bending moment calibration for rotational bending fatigue testing machine based on strain measurement

Abstract Rotational bending fatigue testing is an important method to evaluate the fatigue of metal materials under cyclic loading. Specifically, the fatigue strength is evaluated through repeated rotational bending under complex loads with the accuracy affected by the bending moment. Here, a method based on strain measurements is proposed for calibrating the bending moment of rotational bending fatigue testing machines. Specifically, the strain of a metal sample under loading with resistance strain gauges was recorded using a strain acquisition system to measure the bending moment. The positions of strain gauges were designed according to the principle of strain measurements to establish a calibration program. Three metal samples of different sizes were tested on two types of rotational bending fatigue testing machines, and the measurement uncertainty was evaluated and verified. The experimental results showed that the proposed method measured and calibrated the bending moment of rotational bending fatigue testing machines rapidly, conveniently, and accurately. The results also provide a practical reference for research and application in related fields.

Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wearable electronics and smart nanodevices for cancer diagnostics, focusing on their applications, benefits, and challenges. AI-driven wearable devices, such as smartwatches and biosensors, are equipped with machine learning algorithms capable of real-time data analysis, facilitating personalized and proactive healthcare solutions. Similarly, smart nanodevices, which leverage nanotechnology combined with AI, offer unprecedented precision in identifying and monitoring cancer cells, allowing for more accurate diagnostic outcomes. Case studies are presented to demonstrate the effectiveness of these technologies in improving diagnostic accuracy and patient outcomes. The review also highlights the challenges these technologies face, including data privacy, ethical concerns, and technical limitations. Additionally, the paper discusses future directions in AI-driven cancer diagnostics, emphasizing the potential for these innovations to revolutionize early cancer detection, improve treatment strategies, and enhance long-term patient outcomes. Ultimately, this review provides a comprehensive understanding of how AI and advanced technologies are reshaping the landscape of cancer diagnostics and monitoring, offering significant implications for clinical practice and research in the medical field.

Morphological Characterization of an Eco Friendly Medi Grade Bio-Polymer Composites Using SEM Analysis

ABSTRACT Background: Medical and Dental Implant Biomaterial Research is focused on the cutting-edge interface of polymer composite material. Ongoing research and development in the bio-material field are focused on novel matrix-filler formulations of polymer composite material with improved properties in near future. Aim: This study focuses on the morphologic characterization and structural analysis of three different volume fraction of novel natural filler basalt reinforced with PEEK polymer. Materials and Methodology: The composite samples are prepared with three different volume fraction of basalt fiber in PEEK matrix, such as PBC1 having 90% PEEK and 10% basalt, PBC 2 having 80% PEEK and 20% basalt, and PBC 3 having 70% PEEK and 30% basalt. The basalt fiber influence in the composite was investigated using Scanning Electron Microscope (SEM) and X- Ray Diffraction (XRD) analysis. The crystalline structure and the grain size were examined by X-Ray diffraction method. Result: The SEM images reveal that PBC2 composite exhibits perfect bonding and less voids compared to pure PEEK, PBC1 and PBC3 composites. Conclusion: The study reports that 80% PEEK and 20% basalt fiber composite shows better structure and grain size compared to other samples with different proportions.

Institutionalist Thought: Ceremonial-Instrumental Dichotomy of the Veblen-Ayres Tradition

Veblen and Ayres’ ceremonial-instrumental dichotomy is considered the analytical core of Old Institutional Theory (OIT). This view offers a distinct perspective for analyzing economic issues. The object of economics is studied at the level of cultural generalization, not individual generalization (as neoclassicists do). Human behavior is understood as a process of cumulative adaptation to contextual circumstances. Thus, understanding the meaning of the dichotomy – of doing things and making social distinctions, and of its application to illuminate modern economic problems – deserves attention. This systematic literature review describes the main theoretical, analytical, methodological, and empirical contributions to the ceremonial-instrumental dichotomy of the Veblen-Ayres tradition. To this end, articles on the topic published in the last 46 years were reviewed, which resulted in the analysis of 45 articles, which can be classified as a) conceptual b) methodological; c) empirical, and d) literature reviews. This body of analysis provided insights that lead to inferring the possibility of research in different fields of social sciences.

Mapping the trajectories of digital technology innovation in the construction industry: an integrated SPNP and Louvain approach

PurposeDespite the continuous development and application of new digital technologies in the construction industry, there has been little research on digital technology trajectories in the construction industry. The study addresses the issue faced by the construction industry in exploring digital technology trajectories: how to comprehensively identify and analyse digital technology pathways across multiple technology fields in the construction industry.Design/methodology/approachFirstly, the digital technology patent identification and classification method based on text mining is used to identify digital technology patents and construct a digital technology innovation network. Second, the main path of the digital technology innovation network is identified with the help of SPNP. Then, the subpaths of the digital technology innovation network are identified with the help of the Louvain algorithm and SPNP. Finally, starting from the technology nodes where the main path and subpaths intersect, the technological similarity of the paths is analysed to explore the evolutionary characteristics of the technology trajectories. In light of this, the developed method is applied to the global construction industry patent dataset to analyse the trajectories of digital technologies.FindingsThe technological innovation path in the construction industry starts with construction materials and gradually expands to intelligence, automation and digital data processing technology. Equipment and devices with electronic digital data processing capabilities as well as improvements in green building technologies and user experience-enhancing technologies, may be the future of the construction industry. With the increasing demand for green buildings and intelligent buildings, the direction of digital technology innovation in the construction industry is gradually tilted towards these areas. In addition, influenced by geographic and economic factors, there is a spatial clustering effect of digital technology innovation in the construction industry.Research limitations/implicationsFuture research should analyse in depth the performance of different countries and regions in digital technology innovation and explore the root causes, motivations and influencing factors behind it, such as the policy environment, the level of the economy and the investment in research and development. Exploring the reasons affecting digital technology innovation can help formulate more targeted policies and promote cooperation and exchange of digital technology innovation in the global construction industry. Meanwhile, to solve the problems of overly broad IPC categorization and the difficulty of accurately describing cross-field innovations, combining IPC co-occurrence networks with patent citation networks is an effective strategy. This strategy can track technologically interrelated patents and provide more specific contents to know the advantages and challenges of the construction industry in the field of digital technology innovation.Practical implicationsThe study has practical implications for the construction industry. The identification of digital technology innovation trajectories provides valuable insights for industry firms and research institutes. It helps them understand the current and future directions of digital technology in construction, enabling them to stay at the forefront of technological advancements. The findings highlight the importance of focusing on areas such as solar energy utilisation, green energy, intelligence, automation and data applications. This knowledge can guide firms in developing new building materials, incorporating digital information technologies and enhancing user experiences. The study’s results can inform strategic decision-making, technology adoption and innovation management in the construction sector.Social implicationsThe social implications of this study are significant for various stakeholders. The identification of digital technology innovation trajectories in the construction industry highlights the potential benefits for society. The focus on green energy, intelligent buildings and enhanced user experiences aligns with the increasing demand for sustainability, energy efficiency and comfortable living environments. These technological advancements can contribute to reducing environmental impact, improving quality of life and promoting sustainable development. The findings can inform policymakers, urban planners and architects in shaping regulations, designing sustainable cities and creating buildings that prioritize energy efficiency and user well-being. Ultimately, the study’s social implications aim to foster a more sustainable and livable built environment.Originality/valueAn identification method integrated with SPNP and the Louvain algorithm is developed to map digital technology innovation trajectories in the construction industry. This study helps to reveal the trajectories of digital technology innovation, provides new perspectives, insight and ideas for research in related fields and has great potential for applications in practice to promote the innovation and development of the construction industry.

Development of the Recreational Sports Programme to Enhance the Emotional Intelligence of Students at Fuzhou Institute of Technology

Background and Aim: Under the influence of the novel coronavirus epidemic, has brought a serious negative impact on the emotional intelligence of college students. To improve the emotional intelligence of college students, a leisure sports program was developed and designed. The objectives of this study are:1) To understand the current situation of college students participating in recreational sports in Fuzhou University of Technology; 2) Develop recreational sports programs to improve the emotional intelligence of Fuzhou University of Technology students; 3) Evaluate the effectiveness of the program. Materials and Methods: A questionnaire was administered to 730 college students of Fuzhou Institute of Technology; interviews were conducted with 20 freshmen, 5 sports experts, 5 school physical education teachers, and 5 school leaders; 15 freshmen males, and 15 freshmen females were subjected to an 8-week experiment; the data obtained from the questionnaires and interviews were analyzed using descriptive statistics by using the SPSS software and a sample t-test was conducted on the data from the experiment. Results: 1) Students of Fuzhou Institute of Technology tended to exercise twice a week; The strength tends to be low strength and medium and low strength. Each time is less than 1 hour; The types are yoga, basketball, jogging, and Taichi Chuan. 2) The FITT-SFD recreational sports program was developed. 3) T-tests before and after the experiment showed that the average values of emotional intelligence before and after the experiment were 110.2 and 137.8, and EI was significantly improved. Conclusion: The experimental results show that this program has significant improvement in the dimensions of improving emotional perception, self-emotional management, others' emotional management, and emotional application. The experimental data verify the effectiveness of the research and development of recreational sports programs in improving students' emotional intelligence and provide empirical support for the promotion of research in related fields of emotional intelligence.

Exploring Biomineralization Processes Using In Situ Liquid Transmission Electron Microscopy: A Review.

Liquid transmission electron microscopy (TEM) is a newly established technique broadly used to study reactions in situ. Since its emergence, complex and multifaceted biomineralization processes have been revealed with real-time resolution, where classical and non-classical mineralization pathways have been dynamically observed primarily for Ca and Fe-based mineral systems in situ. For years, classical crystallization pathways have dominated theories on biomineralization progression despite observations of non-traditional routes involving precursor phases using traditional- and cryo-TEM. The new dynamic lens provided by liquid TEM is a key correlate to techniques limited to time-stamped, static observations - helping shift paradigms in biomineralization toward non-classical theories with dynamic mechanistic visualization. Liquid TEM provides new insights into fundamental biomineralization processes and essential physiological and pathological processes for a wide range of organisms. This review critically reviews a summary of recent in situ liquid TEM research related to the biomineralization field. Key liquid TEM preparation and imaging parameters are provided as a foundation for researchers while technical challenges are discussed. In future, the expansion of liquid TEM research in the biomineralization field will lead to transformative discoveries, providing complementary dynamic insights into biological systems.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.

The molecular structure, electronic properties, and decomposition mechanism of FOX-7 under external electric field were calculated based on density functional theory.

Based on the density functional theory (DFT), we analyzed the changes of the FOX-7 molecule under external electric field (EEF) from multiple perspectives, including molecular structure, electronic structure, decomposition mechanism, frontier molecular orbitals (FMOs), and density of states (DOS). The results revealed that as the intensity of the positive EEF increased, the detonation performance of the FOX-7 molecule was significantly enhanced, while its thermal stability was also improved. This discovery challenges the traditional concept that explosives are inherently dangerous under external electric fields and provides new insights for research in related fields. To further explore the impact of EEF on the thermal stability of FOX-7, we conducted a thorough analysis of the mechanism by which EEF affects the decomposition process. Our findings indicate that applying a positive EEF significantly increases the energy required to overcome intramolecular hydrogen transfer and C-NO2 bond rupture, while having a relatively minor effect on the nitro isomerization process. This observation further demonstrates that the appropriate application of a positive EEF can enhance the detonation performance of FOX-7 without compromising its thermal stability. Further research revealed that as the intensity of the positive EEF increased, the electronegativity of the nitro group gradually enhanced, leading to an increase in the electronegativity of the oxygen atoms within it. This made the oxygen atoms more prone to participating in chemical reactions. This phenomenon also explains why the energy barrier required for nitro isomerization in FOX-7 gradually decreases as the intensity of the positive EEF increases. Based on the density functional theory (DFT), the structural optimizations were performed both under applied EEF and without EEF at the B3LYP/6-311G (d, p) level. All optimized results were converged and exhibited no imaginary frequencies. Based on the optimized structures, single-point energy calculations were further conducted at the B3LYP/def2-TZVPP level. Subsequently, analyses of molecular structure, electronic structure, decomposition mechanism, frontier molecular orbitals, and density of states were carried out.

Hyperbranched Thermosensitive Polymer-AuNP Composite Probe for Temperature Colorimetric Detection

Temperature detection is particularly important in the medical and scientific fields. Although there are various temperature detection methods, most of them focus on broad temperature detection, and basic research in specific fields, especially the detection of subtle temperature changes (32–34 °C) during wound infection, is still insufficient. For this purpose, a novel colorimetric temperature sensing probe is designed in this paper, which can quickly and intuitively respond to small temperature changes within a specific range through color changes. In this paper, hyperbranched polyethyleneimine (HPEI) was modified by isobutyrylation to prepare hyperbranched temperature-sensitive polymer (HPEI-IBAm). And it was combined with gold nanoparticles (AuNPs) prepared by a sodium citrate reduction method to construct an HPEI-IBAm-AuNP colorimetric probe. The probe exhibits excellent stability, even at salt concentrations of up to 12 g/L, thanks to the abundant amino functional groups and the large steric hindrance effect unique to HPEI-IBAm. In particular, the temperature detection range of the probe is precisely locked within 32–34 °C, enabling it to respond quickly and accurately to small temperature changes of only 2 °C. This feature is perfectly suited to the practical needs of temperature detection in infected wounds. The linear fitting coefficient of the temperature response is as high as 0.9929, ensuring the accuracy of the test results. The detection performance of the probe remained highly consistent over 10 cycles, fully proving its excellent reusability and durability. In addition, a flexible colorimetric sensor was prepared by combining the probe with polydimethylsiloxane (PDMS) film. This sensor is capable of rapidly detecting human skin temperature in real time, achieving an accuracy of 99.07% to 100.61%. It can provide a possible solution to the challenges of delayed and difficult temperature detection caused by different body parts and uneven surfaces, among others. This demonstrates its extensive practical value and potential, and it is expected to be further applied in the monitoring of wound infections.

A novel 3D uniformity measurement method in mechanical stirring systems

AbstractAccurate assessment of mixing uniformity is crucial in industrial mixing processes. This study proposes an evaluation method for three‐dimensional (3D) mixing processes that combines dual‐camera positioning and point pattern density fluctuation (PD) based on disordered hyperuniformity. This study employs a positioning method using dual‐camera to achieve precise capture and reconstruction of tracer particles in 3D space. The 3D reconstruction data is then evaluated for mixing performance using the PD method. A relationship model between |k| and time I values with mixing time was established for λ = 1. The results indicate that mixing time decreases with the increase of |k| and decreases with the decrease of I values. To ensure the accuracy of the PD method, feasibility analysis was conducted using conductometry. Additionally, the superiority of the PD method was validated by comparing it with the 3D‐Q method. The impact of bottom height of stirring paddle and motor speed on mixing effect were also investigated. This study establishes a fundamental groundwork and theoretical framework for optimizing parameters of stirring systems and assessing 3D mixing uniformity. It also offers important references and insights for engineering practices and theoretical research in related fields.

The 100 most-cited articles in hypothermic brain protection journals: a bibliometric and visualized analysis

IntroductionA bibliometric analysis is used to assess the impact of research in a particular field. However, a specialized bibliometric analysis focused on hypothermic brain protection has not yet been conducted. This study aimed to identify the 100 most-cited articles published in the field of hypothermic brain protection and analyze their bibliometric characteristics.MethodsAfter screening articles from the Web of Science citation database, complete bibliographic records were imported into Python for data extraction. The following parameters were analyzed: title, author’s name and affiliation, country, publication year, publication date, first author, corresponding author, study design, language, number of citations, journal impact factors, keywords, Keywords Plus®, and research topic.ResultsThe 100 articles were published between 1990 and 2016. The citation frequency for each publication ranged from 86 to 470. Among the 100 articles, 73 were original articles, 18 were review articles, 8 were clinical articles, and 1 was editorial material. These papers were published in 37 journals, with the Journal of Cerebral Blood Flow and Metabolism being the most prolific with 15 papers. Eighteen countries contributed to the 100 publications, 51 of which were from United States institutions. In addition, the keywords in the Sankey plot indicated that research in the field of hypothermic brain protection is growing deeper and overlapping with other disciplines.DiscussionThe results provide an overview of research on hypothermic brain protection, which may help researchers better understand classical research, historical developments, and new discoveries, as well as providing ideas for future research.

Frequency Selective Surfaces: Design, Analysis, and Applications

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field.

Vision-Based Drone Detection in Complex Environments: A Survey

The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged as a hot topic of research. However, current reviews of vision-based drone detection are less focused on algorithmic summarization and analysis. For this reason, this survey aims to comprehensively review the latest methods for vision-based drone detection in complex environments, with the goal of providing a more thorough reference and guidance for related research. We first explore the imaging and drone characteristics in complex environments and summarize the main challenges of visual UAV detection. Then, we summarize the existing solutions for the main challenges. Finally, we systematically organize and introduce the commonly used datasets and evaluation metrics and conduct experiment comparisons based on the representative methods. We not only reveal the current development status of visual UAV detection but also analyze the deficiencies in current research. On this basis, we further look forward to future research directions and possible breakthroughs, with a view to providing useful insights for further research and development in related fields.

The impact of SARS-CoV-2 (COVID-19) pandemic on educational and professional growth of young Italian epileptologists: a survey of the Young Epilepsy Section-Italian chapter.

In March 2020, the World Health Organization declared the coronavirus-related disease SARS-CoV-2 infection pandemic. Italy was one of the most affected countries and managed the emergency also by a health care reorganization. The Education and Career Development Task Force of the Young Epilepsy Section-Italy (YES-I) designed a survey to assess the impact of the pandemic on the training and work of young epileptologists (< 40years). Fifty-three responses were collected: 45.3% were resident, 9.4% PhD students and the remainder specialists. Clinical activity changed for most (83%) during the pandemic. Educational activity at epilepsy centers was reduced for 35.8% of the survey participants, while 30.2% of research projects involving patients participation were stopped to switch mainly to COVID-19-related research. For 73.6% of survey participants, attending online courses and congresses was easier in terms of cost and organization, although for 50.9% the level of training was lower in quality. In contrast, 58.5% rated the webinars organized by YES-I very educational. Less than 50% of the clinicians used telemedicine in the pandemic period and continue to use it. Despite several positive aspects of virtual medicine, a small number (32.1%) of our interviewees were satisfied from telemedicine and few of them (30.2%) reported that it led to improvement of clinical practice. Our survey showed that the pandemic has had a negative impact on training, research and clinical activity in the epilepsy field; moreover, it underlined the critical aspects of virtual communication methods in order to improve its use for the future.

A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space.

Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic. We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed. Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%. This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.

FOREWORD AND FAREWELL

As I write my final foreword as Editor-in-Chief of the Journal of Naval Sciences and Engineering (JNSE), it is with deep gratitude and pride that I look back on the transformative progress we have achieved together. I first took on this role with the responsibility of publishing the second issue of 2022, just as the journal transitioned from the Naval Sciences and Engineering Institute to the Turkish Naval Academy (TNA). Now, following my promotion to Rear Admiral (Lower Half) and my appointment as the Deputy General Manager of Naval Shipyards, I prepare to step down, filled with confidence in the journal's future impact. During my tenure, JNSE has not only expanded its reach but also evolved in structure. As the first Editor-in-Chief after this significant transition, I established a structured workflow to ensure the smooth handling of the journal’s workload. This progress would not have been possible without the unwavering support of our esteemed contributors, editors, reviewers, and readers, who have collectively strengthened the journal’s place as a respected publication in naval sciences and engineering. Naval sciences and engineering have always been at the forefront of the Turkish Navy’s priorities, with an increasing emphasis on innovation and self-reliance in defense capabilities. As researchers and engineers, our collective work—through publications like this journal—contributes, even if indirectly, to these larger goals. It is essential to recognize that advancements in naval platforms, weaponry, and defense systems depend heavily on foundational research in science and technology. Every study, every paper, and every breakthrough—however modest it may seem—plays a role in building the knowledge base that supports these ambitious naval goals. In this foreword, I highlight recent and ongoing projects of the Turkish Navy, from initial concepts to construction, to offer future contributors an idea of impactful areas of study. Research in fields like autonomous systems, AI-driven operations, and advanced propulsion technologies can shape the Navy’s future capabilities. As the quote often attributed to Faraday or Franklin reminds us, "What is the use of a newborn baby?" Theoretical research, like this newborn, may not reveal its full utility immediately, yet it holds the potential to make profound impacts over time. (Full foreword available in the PDF version.) As I transition from this role, I am proud of the journal’s achievements. One of the most notable milestones has been the introduction of Digital Object Identifiers (DOIs) for all published papers since the second issue of 2022. This change has allowed for greater visibility, wider dissemination, and immediate online publication, which are crucial in the fast-paced world of research. Additionally, reorganizing the editorial team, including the appointment of key roles such as the Technical Editor, Layout Editor, and Secretariat from the capable staff of the TNA has ensured a streamlined workflow and elevated the journal's standards. The December 2024 issue is a testament to the journal’s interdisciplinary reach. This issue includes four valuable studies: one presenting a robust planning model for irrigation, a topic of relevance as climate change increasingly impacts maritime and agricultural resources; a second, developing a cybersecurity perception scale for maritime industry employees, reflecting the growing importance of cybersecurity in naval operations; a third, a performance analysis of adaptive MIMO techniques for visible light communication, which holds promise for improving naval communication systems; and finally, a paper on thyroid nodule segmentation using ultrasound, which highlights how advances in medical imaging can also improve healthcare aboard naval vessels. These papers, though diverse, reflect the journal's goal of fostering interdisciplinary innovation. As I conclude my tenure as Editor-in-Chief, I want to express my gratitude one last time to the readers, the dedicated editors who served alongside me, and the referees who ensured the quality of the research we published. It has been a privilege to oversee the journal through significant milestones, and I deeply appreciate the effort and commitment of everyone involved. Reflecting on my tenure, I am particularly proud of the journal's growth, both in scope and influence. Over these years, we have not only expanded our reach but have also contributed to shaping the dialogue around the future of naval technology. Though this marks the end of my tenure, I depart with the satisfaction of knowing that we have upheld the highest standards in naval science and engineering research. As I bid farewell, I leave with the hope that the journal continues to inspire and contribute to the advancement of naval sciences. May your journey be swift and your destination secure. Fatih ERDEN, Ph.D. Editor-in-Chief