Practical Capability Research Articles

Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China

ObjectivesTo investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies.MethodsA prospective survey was distributed to 1339 Chinese radiologists or interns via an online platform from October 2023 to May 2024. The questionnaire covered respondent characteristics, opinions on using GPT-like technologies (in clinical practice, training and education, environment and regulation, and development trends), and their attitudes toward these technologies. Logistic regression was conducted to identify underlying factors associated with the attitude.ResultsAfter quality control, 1289 respondents (median age, 37.0 years [IQR, 31.0–44.0 years]; 813 males) were surveyed. Most of the respondents (n = 1223, 94.9%) supported adoption of GPT-like technologies. Based on the acceptance level of GPT-like technologies, the respondents were 3 (0.2%), 29 (2.2%), 352 (27.3%), 677 (52.5%), and 228 (17.7%) from low to high acceptance degrees. Multivariable analysis revealed significant associations between positive attitudes towards GPT-like technologies and their acceptance: writing papers and language polishing (odds ratio [OR] = 1.99; p < 0.001), influence of colleagues using such technologies (OR = 1.77; p = 0.007), government regulation introduction (OR = 2.25; p < 0.001), and enhancement of decision support capabilities (OR = 2.67; p < 0.001). Sensitivity analyses confirmed these results for different acceptance thresholds (all p < 0.001).ConclusionsChinese radiologists or interns generally support GPT-like technologies due to their potential capabilities in clinical practice, medical education, and scientific research. They also emphasize the need for regulatory oversight and remain optimistic about their future medical applications.Critical relevance statementThis study highlights the broad support among Chinese radiologists for GPT-like technologies, emphasizing their potential to enhance clinical decision-making, streamline medical education, and improve research efficiency, while underscoring the need for regulatory oversight.Key PointsThe impact of GPT-like technologies on the radiology field is unclear.Most Chinese radiologists express the supportive adoption of GPT-like technologies.GPT-like technologies’ capabilities at research and clinic prompt the attitude.Graphical

Modeling and control of a sperm-inspired robot with helical propulsion

Efficient propulsion has been a central focus of research in the field of biomimetic underwater vehicles. Compared to the prevalent fish-like reciprocating flapping propulsion mode, the sperm-like helical propulsion mode features higher efficiency and superior performance in high-viscosity environments. Based on the previously developed sperm-inspired robot, this paper focuses on its dynamic modeling and depth control research. The helical propulsion performance of the sperm-inspired robot is analyzed by resistance-theory-based force analysis, followed by the application of Kirchhoff rod theory to determine the helical waveform parameters. The dynamic model of the sperm-inspired robot is established using the Kirchhoff equation, and its validity is verified through experiments. To enhance the practical application capability of the sperm-inspired robot, this study develops an active disturbance rejection control depth controller for roll-spin coupling motion based on the constructed dynamics model. The effectiveness of the controller is thoroughly validated through a combination of simulation and experiment. Experimental results demonstrate the excellent depth control ability of the robot, with an average depth error controlled within 0.19 cm. This superior performance lays a foundation for the future application of our robot in underwater operations.

Reconfigurable Manufacturing Systems: Enhancing Efficiency via Product Family Optimization

In response to the increasing complexity of modern production environments driven by heightened competition, customer demands, and sustainability goals, this work presents a focused methodology for forming product families within reconfigurable manufacturing systems (RMSs). Unlike traditional approaches, our method emphasizes the practical implementation of the Analytic Hierarchy Process (AHP) and the Average Linkage Clustering (ALC) algorithm to optimize RMS configurations. By evaluating specific comparison criteria—such as assembly sequence, machining sequence, components, production tools, and production demand—we aim to enhance resource utilization and adaptability to market changes. The proposed methodology enables a systematic assessment of RMS performance tailored to diverse product requirements. A detailed example of machining systems demonstrates the use of machining sequence and tool usage as primary criteria, showcasing the practical application and decision-making capabilities of the approach. This work contributes to the field by providing a structured framework for decision-making in RMS, facilitating efficient and precise product family formation to meet evolving manufacturing demands.

Highly scalable solid-core inhibited-coupling fiber-based plasmonic refractive index sensor.

Advancements in plasmonic sensing require simultaneous detection capability that ensures large-scale detection with reduced losses. In this work, we propose a new solid-core fiber-based refractive index (RI) sensor with an ultra-broad detection range. The proposed fiber consists of a relatively simple single-ring cladding with six circular tubes in which the light is guided in the core based on the inhibited-coupling (IC) mechanism. The sensing performance is investigated using extensive finite-element modeling (FEM) through the combination of IC and surface plasmon resonance (SPR) sensing technology. Our results show a low loss of <3 dB/cm across the RI detection range from 1 to 1.60, peaking the wavelength sensitivity (WS) of 3000 nm/RIU and figure of merit (FOM) of 120 RIU-1. Our study also includes the investigation of the fabrication tolerance, fiber bending, and the use of alternative plasmonic materials, providing insights into the practical implementation capability of the proposed sensor. Our findings highlight the potential of the proposed sensor in emerging applications such as detecting air pollutants, biochemical substances, and DNA, paving the way towards bio-sensing within a lab-on-a-chip platform.

Core-Satellite Gold Nanoparticle@Silver Nanocluster Nanohybrids for Milk Allergen β-Lactoglobulin Detection Using the Electrochemical Aptasensor.

Detecting β-lactoglobulin (β-Lg) with high sensitivity and selectivity is an urgent requirement due to nearly 80% of milk anaphylaxis, such as respiratory tract, skin urticaria, and gastrointestinal disorders, being caused by β-Lg. An ultrasensitive β-Lg electrochemical aptasensor utilizing core-satellite gold nanoparticle@silver nanocluster (AuNPs@AgNCs) nanohybrids as electrocatalysts was developed. First, β-Lg aptamer was anchored on gold electrodes and AuNPs to obtain high selectivity. Aptamer-cDNA-AuNPs conjugation was attached to the functionalized electrode with β-Lg as a "bridge" through the target-ligand interaction. Second, DNA-templated AgNCs were introduced via the hybridization of DNA templates oligonucleotide with cDNA anchored on AuNPs. The formed AuNPs@AgNCs nanohybrids showed enhanced catalytic performance toward the silver deposition reaction. This strategy is demonstrated by determining the oxidation current of produced silver nanoparticles (AgNPs) surrounding AuNPs by β-Lg. A detection limit of 0.87 fg/mL and a linear range of 0.001-1000 pg/mL were obtained. Finally, β-Lg content in food products was analyzed successfully, and RSD of 2.44-8.33% was obtained. The recovery of 87.54-113.70% and RSD of 0.95-9.29% was obtained for standard addition experiments. This proposed aptasensor exhibits excellent sensitivity, selectivity, reproducibility, and stability and has good practical application capability for complex food matrices.

Consumer-grade wearable devices in arrhythmia diagnostics for clinicians: where we are and where we are going.

Atrial arrhythmias, including atrial fibrillation (AF), are a major contributor to cardiovascular morbidity and mortality. Early detection and effective management are critical to mitigating adverse outcomes such as stroke, heart failure, and overall mortality. Wearable devices have emerged as promising tools for monitoring, detecting, and managing atrial arrhythmias near-continuously. This comprehensive analysis explores these wearable technologies' current role and capabilities for clinicians' daily practice. Despite challenges related to data accuracy, privacy, patient compliance, and integration with healthcare systems, ongoing advancements hold significant promise for the future. Continued research and development are essential to fully realize the potential of wearables in improving clinical outcomes for patients with atrial arrhythmias.

Can Low Structural Anisotropy Produce High Optical Anisotropy? Anomalous Giant Optical Birefringent Effect in PI4AlI4 in Focus.

Tetrahedral halides with broad transparency and large second harmonic effects have the potential to serve as mid-infrared wide-bandgap materials with balanced nonlinear-optical (NLO) properties. However, their regular tetrahedral motifs tend to exhibit low optical birefringence (Δn < 0.03) due to limited structural anisotropy, which constrains their practical phase-matched capability. A significant challenge in halide structural chemistry and material exploration is to enhance the Δn of tetrahedral halides while maintaining their balanced properties. The question of whether tetrahedra with low structural anisotropy can produce high optical anisotropy remains unanswered. In this study, in addition to the conventional strategy of enhancing Δn by increasing structural anisotropy, we identify a previously unreported strategy of enhancing Δn by increasing electronic anisotropy. This novel strategy model unprecedentedly enhances the Δn of an existing tetrahedral halide, PI4AlI4, to a degree not achievable by conventional techniques, with the value reaching up to 0.31@1 μm. The anomalous increase in Δn is achieved by anisotropic charge redistribution resulting from a unique charge transfer effect between the anionic and cationic tetrahedral motifs. First-principles analysis provides further corroboration of the detailed chemical bonding electronic distributions and charge transfer illustrations. It is predicted that analogous AsI4AlI4 and TeI4ZnI4 will exhibit a greater propensity for giant Δn (> 0.5@1 μm). This finding will greatly enrich the structural chemistry of sp3-hybridized tetrahedra and provide seminal ideas for the design and modulation of highly birefringent structures.

A training framework for multi-professional advanced level practice in non-surgical oncology: The journey through development and consultation to consensus.

The Non-Surgical Oncology (NSO) workforce, like many healthcare specialities, faces critical personnel shortages. With a 21% deficit in consultant oncologists anticipated by 2028, alongside vacancies in key roles such as therapeutic radiographers. To address these challenges, innovative workforce strategies are necessary, including the diversification of skills and the creation of advanced career pathways for healthcare professionals such as nurses, pharmacists, and allied health professionals. Advanced Clinical Practitioners (ACPs) and Advanced Practitioners (APs) have emerged as pivotal in bridging workforce gaps; however, their training lacks standardisation. This study utilised a multiphase design to gain consultation and consensus on the suitability of a newly developed NSO training framework adapted from the Royal College of Radiologists and Royal College of Physicians' joint curricula. Applying a modified Delphi approach, seen to be beneficial in curriculum design, and considering the diversity of professions and roles across the speciality, was used to develop an online survey which was distributed across the NSO healthcare sector. The survey aimed to gain consensus on the framework's alignment with clinical and educational needs and identify barriers to its implementation. On reviewing the NSO training framework, the majority of the panel members (n=46/51; 90.2%) felt the capabilities appropriately mapped to the 2017 MPF.7 When asked about the clarity of the overall introduction and the introduction to the CiPs an overall positive response rate was noted (n=50/51; 98%). There was a slightly lower level of consensus around the clarity of the generic and core oncology CiPs, and slightly less agreement in the speciality-specific capabilities. Following the first survey (round one), the responses were reviewed, and it was identified that overall, in eight out of ten of the questions agreement between panel members had exceeded the level of agreement established by the researchers. As a result, the decision was made to cease the surveys and analyse the results in full as a single round. Addressing the projected workforce shortages in oncologists and other key non-surgical oncology roles, this study contributes a framework adapted from established curricula, ensuring an equitable and high-standard educational pathway for trainees. The multi-phase process revealed consensus for the framework's alignment with clinical and educational standards, particularly its focus on capabilities in practice (CiPs), though highlighted inconsistencies in supervision and assessment. The NSO framework is essential for ensuring equitable training, improving workforce retention, and enhancing patient care outcomes.

A Model For Developing Students' Labor Upbringing Technologies In The Context Of Traditional Karakalpak Folk Pedagogy

This article explores the development of students' labor upbringing technologies within the framework of traditional Karakalpak folk pedagogy. The study highlights the importance of connecting historical roots with modern pedagogical approaches to foster creativity, independence, and initiative in students. A model was developed and implemented to promote students' active participation in labor activities, emphasizing the acquisition of professional skills, motivation, and creative problem-solving abilities. The research outlines criteria for assessing students’ labor activity, such as motivation, cognitive skills, practical capabilities, methodological understanding, and personal creativity. By integrating traditional Karakalpak crafts into educational activities, the study underscores the value of preserving cultural heritage while preparing students for modern technological and professional demands. Findings demonstrate the model’s effectiveness in fostering students’ creativity, independence, and adaptability, thus contributing to their holistic development and readiness for future professional challenges.

Innovation capability and R&amp;D performance of organizations: moderating role of industry–academic knowledge transfer

Purpose This study aims to examine how an organization’s innovation capability could influence research and development (R&amp;D) performance. It also investigates if industry–academic knowledge transfer has a moderating relationship between organizational innovation capability and exploration and exploitative innovation in improving the R&amp;D performance of the organizations. Design/methodology/approach Based on the literature and dynamic capability view, a conceptual model was developed and then validated using the partial least squares-structural equation modeling technique considering 387 responses from academicians and industry personnel. Findings The study found that industry–academic knowledge transfer has a significant moderating impact toward improving innovation capability, organizations’ R&amp;D performance and exploration innovation. However, it has an insignificant moderating impact on improving innovation capability and exploitative innovation. Practical implications Organizational innovation capability is characterized by both exploratory and exploitative innovation. Both types of innovation support the R&amp;D performance of an organization. Also, organizations that closely work with academic institutions could gain significant R&amp;D knowledge from academic expertise. This study provides food for thought for the academic community as well as industry policymakers. Originality/value There are significant opportunities for academic institutions to gain practical knowledge from industry which can help them to accelerate their R&amp;D activities. However, transferring knowledge between industry and academia has challenges related to intellectual property, patents and so on. Not much research has been conducted in this area. Thus, the proposed research model is unique and adds to the existing literature.

Management control systems and dynamic capabilities as antecedents to financial performance

PurposeThe resource-based theory (RBT) explains that the management control system (MCS) represents a resource that complements dynamic capabilities by steering employees toward strategies, leading to superior performance. This study aims to identify the impacts of the MCS and dynamic capabilities such as entrepreneurship, innovation, organizational learning and market orientation on financial performance.Design/methodology/approachThe research sample included 200 managers from grain-producing agribusiness organizations in Brazil. Partial least squares structural equation modeling (PLS-SEM) was used to conduct a symmetric analysis, while fuzzy set qualitative comparative analysis (fsQCA) was applied for an asymmetric analysis.FindingsIn the symmetric analysis, the linear association model did not fully explain how the use of MCSs and dynamic capabilities drive financial performance. However, the findings indicate that diagnostic and interactive MCSs positively affect dynamic capabilities. In the asymmetric analysis, the results suggest that the high financial performance of the analyzed organizations can be achieved without considering the need for Entrepreneurship or Innovation.Research limitations/implicationsThe study broadens and complements the interpretive scope of previous studies and allows for comparisons.Practical implicationsMCSs must be used in a balanced and synergistic manner (diagnostic and interactive), with an emphasis on monitoring goals and discussions with managers. This is important and contributes to developing internal capabilities, which are embodied in decisions and actions, potentially impacting financial performance. These results are crucial for Brazilian agribusiness organizations, as they provide actionable insights into how the strategic use of MCSs can directly enhance financial performance. By revealing that the balanced and synergistic use of diagnostic and interactive MCSs fosters dynamic capabilities such as innovation and market orientation, the study offers practical guidance for managers aiming to boost organizational effectiveness. This research not only informs management practices but also positions Brazilian agribusinesses to remain competitive in a global market.Originality/valueThis study has generated new discussions based on possible combinations and interactions between MCSs and dynamic capabilities to predict high organizational performance. In practical terms, MCSs and dynamic capabilities can be designed in various manners to reach optimal performance levels.

P-n-Type LaCoO3/NiFe LDH Heterostructures for Enhanced Photogenerated Carrier-Assisted Electrocatalytic Oxygen Evolution Reaction.

The oxygen evolution reaction (OER) poses a significant kinetic challenge for various critical energy conversion and storage technologies including electrocatalytic water splitting and metal-air batteries. In this study, a LaCoO3/NiFe layered double hydroxide (LDH) catalyst was synthesized through the in situ growth of n-type NiFe LDH on the surface of the p-type LaCoO3 semiconductor, resulting in a p-n heterostructure for a photogenerated carrier-assisted electrocatalytic OER (PCA-eOER). The alignment of their band structures facilitates the formation of an internal electric field at the heterojunction interface, which promotes the creation of oxygen vacancies and enhances electron transport. Under illumination, the expanded visible-light absorption range and built-in electric field work synergistically to improve the generation and separation of photogenerated carriers. Meanwhile, the accumulation of photogenerated holes on the surface of NiFe LDH results in an enhancement in the concentration of high-valent active metal sites, resulting in a boost in the PCA-eOER efficiency. The LaCoO3/NiFe LDH has achieved an overpotential of 260 mV at the current density of 10 mA cm-2, 50 mV lower than in the absence of illumination. In addition, LaCoO3/NiFe LDH was assembled into an alkaline water electrolyzer and zinc-air batteries (ZABs), showing excellent practical application capability. We explored the application of LaCoO3 in a PCA-eOER, which provides a concept for designing PCA-eOER catalysts and advancing the development of perovskite-based catalysts for clean energy conversion technology.

Active sensing of industrial fluid degradation via electro-mechanical impedance of a submerged piezoelectric wafer

Abstract Industrial fluids, such as lubricants and cutting compounds, are ubiquitous in diverse mechanical transmission systems and manufacturing scenarios. Throughout the operational lifespan, these fluids are vulnerable to degradation and contamination, rendering change of mechanical properties and loss of performance, therefore causing detrimental effects on subordinate machineries and product quality. To address such a concern, this article proposes an electro-mechanical impedance spectroscopy for the real-time monitoring of industrial fluids via a structure-fluid interactive piezoelectric sensor. To unravel the intricacies of the interactive mechanism between industrial fluids and the piezoelectric sensory device, an analytical model was established, with the influence of the fluid processed as an additional inertial load and dissipative viscosity. Meanwhile, finite element analysis was performed to scrutinize the nuanced influence of the fluid with fluid-structural interaction boundary condition and spring–damper dissipative elements. Parametric studies were conducted to evaluate the impedance spectral features arising from the fluid properties alterations. A damage index based on the amplitude and frequency shift of the impedance spectra can readily serve as a robust quantifier of fluid degradation severity. Ultimately, experimental tests were performed to validate against the analytical and finite element models. The glycerol–water-mixed fluid was utilized to verify the sensor’s sensitivity on density and viscosity alteration of the fluid. Two industrial fluids, gearbox lubricant and cutting fluid under various service periods, were employed to demonstrate the practical monitoring capability. This article culminates with summary, concluding remarks, and suggestions for future work.

Miniature lab-made electrochemical biosensor: A promising sensing kit for rapid detection of E. coli in water, urine and milk

A novel, rapid production methodology for laboratory-made carbon electrodes (LCE) employing cost-effective and readily available materials has developed in the present work. The LCE presents superior electrochemical characteristics compared to commercially available screen-printed carbon electrodes (SPCE). Furthermore, this research has demonstrated the performance of readily accessible, highly sensitive, and portable biosensors for on-site detection of E. coli in aqueous samples. Silver nanoparticles (AgNPs) were successfully electrodeposited onto the LCE (Ag-LCE) using the electrochemical method at optimised parameters. The E. coli-specific aptamer was conjugated with AgNPs, and uncoated Ag-LCE surfaces were blocked with a BSA (BSA-Apt-Ag-LCE). The developed BSA-Apt-Ag-LCE biosensor was characterised and validated for the successful detection of E. coli in aqueous samples using cyclic voltammetry (CV). A linear correlation was obtained for sensor response in the 3.4 × 101 to 3.4 × 106 CFU/ml bacterial concentration as ΔIpa = 5.71 log C + 2.91 with R2 = 0.987. BSA-Apt-Ag-LCE biosensors have a limit of detection of 34 CFU/ml and a response time of 15 min, indicating their prompt and practical on-site identification capabilities. The proficient detection of E. coli in diverse aqueous samples, substantiated by its consistent reproducibility as indicated by the relative standard deviation (RSD) value of a maximum of 1.71 %, is a compelling validation of the biosensor's efficacy and reliability. The proposed biosensor exhibited selectivity towards E. coli and was found stable even after being stored at 4 °C for four weeks.

The importance of data management for the payments industry

We are in the throes of a digital revolution. Although this has been taking place for several decades, the advent of modern digital technologies, machine learning and artificial intelligence has accelerated this transformation. Data impacts all facets of our lives, both personal and professional. It influences everything we do. Embracing digital transformation is not a ‘nice-to-have’, but a mandatory capability to survive in today’s competitive world. The payment industry is no stranger to this transformation. Every step of the payment process is governed by a complex and intricate series of transactions, critically dependent on accurate and timely data. As the speed and velocity of these transactions increase, data is the fuel and data management is the discipline that ensures a trusted and efficient process. In this paper, the best practice capabilities of data management will be explored in their relationship to the payment process. The paper explores the intricacies of the payment process, highlighting how effective data management at each step — from transaction initiation to reconciliation — ensures efficiency, security and customer satisfaction. The efficient movement of funds across the payment process fuels a vibrant global economy. By adhering to industry standards and leveraging advanced data management techniques, the payments industry can navigate the complexities of a digital ecosystem, providing a reliable and trusted payment environment and position it for continued improvement and innovation.

D-PRISM: a global survey-based study to assess diagnostic and treatment approaches in pneumonia managed in intensive care

BackgroundPneumonia remains a significant global health concern, particularly among those requiring admission to the intensive care unit (ICU). Despite the availability of international guidelines, there remains heterogeneity in clinical management. The D-PRISM study aimed to develop a global overview of how pneumonias (i.e., community-acquired (CAP), hospital-acquired (HAP), and Ventilator-associated pneumonia (VAP)) are diagnosed and treated in the ICU and compare differences in clinical practice worldwide.MethodsThe D-PRISM study was a multinational, survey-based investigation to assess the diagnosis and treatment of pneumonia in the ICU. A self-administered online questionnaire was distributed to intensive care clinicians from 72 countries between September to November 2022. The questionnaire included sections on professional profiles, current clinical practice in diagnosing and managing CAP, HAP, and VAP, and the availability of microbiology diagnostic tests. Multivariable analysis using multiple regression analysis was used to assess the relationship between reported antibiotic duration and organisational variables collected in the study.ResultsA total of 1296 valid responses were collected from ICU clinicians, spread between low-and-middle income (LMIC) and high-income countries (HIC), with LMIC respondents comprising 51% of respondents. There is heterogeneity across the diagnostic processes, including clinical assessment, where 30% (389) did not consider radiological evidence essential to diagnose pneumonia, variable collection of microbiological samples, and use and practice in bronchoscopy. Microbiological diagnostics were least frequently available in low and lower-middle-income nation settings. Modal intended antibiotic treatment duration was 5–7 days for all types of pneumonia. Shorter durations of antibiotic treatment were associated with antimicrobial stewardship (AMS) programs, high national income status, and formal intensive care training.ConclusionsThis study highlighted variations in clinical practice and diagnostic capabilities for pneumonia, particularly issues with access to diagnostic tools in LMICs were identified. There is a clear need for improved adherence to existing guidelines and standardized approaches to diagnosing and treating pneumonia in the ICU.Trial registration As a survey of current practice, this study was not registered. It was reviewed and endorsed by the European Society of Intensive Care Medicine.Graphical abstract

Hollow transition metal chalcogenides derived from vanadium-based metal organic framework for hybrid supercapacitors with excellent energy-density and stability

In-situ synthesized hollow transition metal chalcogenides have gained significant attention on account of their excellent electrochemical properties. Here, Ni-doped V-MOF (V(Ni)-MOF) nanorod arrays as precursor are first grown on nickel foam (NF). Subsequently, the nanorod arrays are converted into V(NiCo)-OH hollow nanotube arrays with cross-linked nanosheets by Co2+ etching. Finally, V(NiCo)-OH/NF is converted into V(NiCo)-X/NF (X = O, S and Se) by annealing or ion exchange. Due to the unique morphology of hollow nanotube arrays with cross-linked nanosheets and synergistic effect of multi-metal components, the V(NiCo)-Se/NF achieves an outstanding specific capacity (1806.7 C g−1 at 1 A g−1), which is higher than that of V(NiCo)-O/NF (1208.3 C g−1) and V(NiCo)-S/NF (1558.4 C g−1). In addition, the capacity retention rate is 91.7 % (at 10 A g−1 after 10, 000 cycles). Utilizing V(NiCo)-Se/NF (positive) and activated carbon/NF (negative), the hybrid supercapacitor (HSC) achieves an impressive high energy density of 114.8 Wh kg−1 (at 679.5 W kg−1). Moreover, two HSCs in series can power the LED and stopwatch, and keep working for more than 60 min, displaying good practical application capabilities.

Surface and bulk characterization of magnetic multilayers formed within a single layer FeRh by hydrogen ion irradiation

In modern spintronics, magnetic multilayers, such as a ferromagnet/antiferromagnet (FM/AFM) heterostructure, are essential for achieving novel and practical capabilities, including the exchange bias effect, spin-transfer torque, tunneling magnetoresistance, etc. As these functions are determined by the exchange interaction or the carrier transport across the interface, it is critical to optimize the magnetic interface usually formed between different materials with the chemical mismatch as well as the structural discontinuity. Here, we explored the potential of FeRh for creating the FM/AFM multilayer within a single material by exploiting its tunability of the temperature-dependent AFM-FM transition with a hydrogen ion irradiation. We investigated bulk and surface magnetic states separately based on a magneto-optical Kerr effect and magnetization-induced second-harmonic generation, respectively, and could reveal that FeRh can host the FM (surface)/AFM (bulk) magnetic multilayer within a single layer of FeRh even at room temperature prepared with a hydrogen ion dose of 2.0×1015 H+/cm2. As the FM and AFM states are stabilized with a well-defined spatial separation as manifested by the exchange bias effect, we expect the FeRh-based FM/AFM bilayer to alleviate limitations arising from the interfaces formed by otherwise different materials.

USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability to process and interpret large volumes of data. The implementation of AI for biomarker analysis opens new opportunities in personalized medicine, offering more accurate and individualized approaches to disease diagnosis and treatment. The relevance of this review stems from the need to systematize recent advances in AI application for biomarker analysis, which is critical for early diagnosis and prediction of chronic non-communicable diseases (NCDs). Material and methods. The analysis of peer-reviewed scientific publications and reports from leading research centers over the past five years was conducted. Studies on the application of AI algorithms for analyzing genomic, proteomic, and metabolomic biomarkers were reviewed, including machine learning methods and deep neural networks. Special attention was paid to the integration of multi-marker panels for improving the accuracy of diagnosis and prediction of cardiovascular, digestive, respiratory, endocrine system diseases, as well as oncological and neurodegenerative pathologies. Results. The application of AI has significantly increased the sensitivity and specificity of diagnostics, especially in complex cases requiring analysis of multiple disease parameters. The effectiveness of AI has been demonstrated in early diagnosis of lung, breast, and colorectal cancer, prediction of cardiovascular complications and NCDs progression, including diabetes mellitus and Alzheimer’s disease. AI’s significant contribution to the discovery of new biomarkers, optimization of personalized treatment, and improvement of therapeutic strategies has been noted. Conclusion. The use of AI in biomarker analysis has become a significant breakthrough in medical diagnostics, particularly in oncology, cardiology, and neurodegenerative diseases. The technology allows integration of data about various biomarkers and contributes to creating more accurate models for disease diagnosis and prediction. Further development is associated with technology advancement and overcoming ethical and regulatory barriers, which will expand AI capabilities in clinical practice.

Lessons Learnt Delivering a Novel Infectious Diseases National Training Programme to Timor‑Leste's Primary Care Workforce.

Background and Objectives: Timor‑Leste is a lower‑middle‑income country in Southeast Asia. To control the significant local threat from infectious diseases, it is imperative to strengthen the knowledge and practice capabilities of the primary care workforce. Methods: We report and reflect on the development and delivery of a national training programme in infectious diseases called the Advancing Surveillance and Training to Enhance Recognition of Infectious Diseases (ASTEROID) programme, developed by the medical non‑governmental organisation (NGO) Maluk Timor and other Timorese stakeholders. The 1-week training course delivered by local doctors is multi‑modal, combining lectures with educational videos, interactive sessions and a mobile application. The ongoing training was delivered to every Timorese municipality in the participants' place of work and involved 540 healthcare professionals from 37 facilities. Training covered infectious diseases most relevant to the Timorese workforce, and focused on disease detection, management, prevention (including infection prevention and control issues) and notification. Findings: Multiple choice question (MCQ) assessment during the training has shown an average improvement in test scores from 45% to 64%, improving to 71% and 79% at 3- and 12-month follow‑up respectively. The programme has been well‑received, with participants appreciating the use of local specialists in video content, the tailoring of content to the local context and the variety of educational methods. Difficulties have been faced when it comes to delivering adequate content in a week‑long format to a workforce which has not previously received significant professional development. Conclusions: This approach could provide a model for delivering training to national healthcare workforces in low- and middle‑income countries (LMIC) and could be further refined on the basis of the lessons detailed here.