System Platform Research Articles

Unifying Community Efforts on Registration and Data Rights

At the Smithsonian National Museum of Natural History (SI NMNH), the Assistant Registrar and Informatics Manager in the Department of Paleobiology (Paleo) have identified key linkages between registering collections (documentation for specimens and data through acquisitions, loans and disposals) and management of digitized collections, especially for usage right designation. Both roles require extensive collaboration on a shared framework for managing data related to the provenance of fossil specimens and rights associated with any derivative digital assets. This eliminated a disconnect in overlapping responsibilities: those typically siloed in museum registration practices (e.g., provenance data, intellectual property rights) and the data typically siloed in collections management and digitization workflows (e.g., data quality, digital asset management, and usage rights). Paleo shifted to a new practical approach and mindset focused on communal thinking. This propelled implementation of data standard practices and laid the building blocks for a shared community perspective on digitizing paleontological collections. Since 2020, Paleo established processing workflows for digital asset requests of the fossil collections (new digital asset creations vs. use of existing digital assets) and baseline understandings about rights of digital assets. These products led to a comprehensive effort to outline SI NMNH’s options to designate usage rights for digital assets (Table 1) and distribute basic understandings about Smithsonian Terms of Use and Smithsonian Open Access Policy (SI OA) across SI NMNH. Paleo explored this topic further by engaging in virtual meetings, conferences, presentations and informal discussions with the collections management staff and data managers across SI NMNH and externally with domestic and international groups such as the Paleo Data Working Group (PDWG), Society for the Preservation of Natural History Collections (SPNHC) Registrars Group, and Biodiversity Information Standards (TDWG), to develop data standards initiatives for loans, permits and sensitive data (Fig. 1). Through regular community engagement, Paleo forged a concise, yet evolving, perspective on digitizing collections, which aligned both registration and informatics data management needs within the department and museum (Fig. 2). This multi-professional perspective enables the promotion of shared community knowledge and understanding about data rights. The primary purpose is to balance consistent goals, current needs, and long-term outcomes with the flexibility to adapt our practices based on institution-specific policies, new research methods, and varying domestic and international regulations and laws. The notable outcomes from almost four years of work are highlighted and incorporated into Paleo's departmental policy and documentation requirements for processing and fulfilling digital asset creation or use requests workflows and distribution of this information to the public website for visitors and borrowers. However, there are still persisting challenges related to registering and managing these digital assets. There are immediate and future obstacles such as: limitations in effective tracking methods to record creation of digital assets, use of digital assets, and designation of what usage rights and when the decision was made; gaps in community practice, policy and regulations at the institutional level and globally; past practices in workflows and staff structures or lack thereof, which have left digital assets in limbo without means of accessing the data or documentation to identify the appropriate use options and rights. We see a need to continue asking questions. How are varying policies across institutions globally, impacting compliance concerns in registration for sharing sensitive information with other research institutions? How have or will new research techniques and opportunities impact the need for flexibility in practices and decisions? Are there alternative ways to utilize a Collections Management Systems (CMS) or use other platforms to track, store and manage the digital assets? How have others engaged in community discussions to address overlapping needs for data standards and registration of digitized collections? These identify significant inconsistencies across the community that need further discussion with subject-matter experts before effective and comprehensive decisions in data standards for managing digitized collections can be made. Ultimately, there are outstanding questions and challenges at all stages for registering, digitizing and managing digital assets of natural history collections. We continue to unveil nuances in thinking about this topic and find the need for structures of shared knowledge built and actively engaged in by a professionally diverse community. The utilization of subject-matter experts encourages collaboration and alternative perspectives to standardize operations and policy while expanding community efforts and awareness.

Didactic Innovation in Mathematics Using Technologies

The continuous progress of society requires all the actors to be dynamic and constantly updated, both in terms of monitoring as well as in the need to adapt. Regarding education, no matter how updated we are in relation to the contents, didactic strategies, and technological resources, we are still inevitably forced to adapt to new paradigms, leading us to reformulate traditional teaching methods. In this context, the contribution of e-learning platforms emerges, where teachers and students have at their disposal new means of empowering the teaching and learning process, since these platforms can be defined as a virtual teaching support environment. Currently, our students, in general, have many difficulties in problem-solving and mathematical reasoning, which often reflects in the performance of very simple tasks, with a growing disinterest towards Mathematics. It is also possible to argue that our students' "poor preparation" is a contributing factor in their failure (many did not attend Mathematics in Secondary Education and/or have not studied for several years, as is the case of those who entered through special contingents, as the “aged 23 and over”). In order to promote a distinct, alternative and supportive pathway that may help to solve this issue, a project on the Moodle learning management system (LMS) platform has been developed with the goal of assisting students in their study and the development of their skills in a way that is directly related to the curricular units (CU) in the scientific field of Mathematics, in an online learning environment. To increase students’ engagement into their learning process in a collaborative environment, a flipped model was employed into a Financial Mathematics/Financial Calculus Course (FCC). This paper's primary goal is to examine the effects of implementing the flipped classroom paradigm on students' performance, learning, and classroom instruction. The results from employing this approach have indicated a beneficial effect on the overall academic progress of students.

Influencing factors analysis of asymmetry in knee adduction moment among patients with unilateral knee osteoarthritis

BackgroundThe knee adduction moment(KAM) of both lower limbs in patients with unilateral knee osteoarthritis(KOA) exhibits asymmetry during walking, but the factors influencing this asymmetry remain unclear. This study aimed to explore the influencing factors of KAM asymmetry in patients with unilateral KOA.MethodsA total of 148 patients with unilateral medial compartment KOA were selected for this retrospective study, and general data such as gender, age, and duration of disease were collected. The hip-knee-ankle (HKA) angle, degree of pain, and knee-extension muscle strength on the affected side were assessed through radiographic outcomes, the visual analog scale(VAS), and the Biodex isokinetic system. The peak KAM of both lower limbs was analyzed using a BTS motion-capture system and force platform. The asymmetry index(ASI) of KAM was calculated, and the patients were further categorized into the KAM symmetry group(ASI value ≤ 10%) and the KAM asymmetry group(ASI value>10%).Binary logistic regression analysis was employed to analyze the factors influencing the asymmetry of KAM.Results90 patients were categorized into the KAM asymmetry group, representing 60.8% of the cohort. A significant difference in the ASI value of KAM was observed between the two groups. Correlation analysis identified nine factors, including sex, age, and BMI, that were positively correlated with the ASI value of KAM. In contrast, knee-extension muscle strength and per-capita monthly household income were negatively correlated with the ASI value of KAM. Regression analysis revealed that being female(OR = 1.752), older age(OR = 2.472), increased BMI(OR = 1.535), larger varus angle(OR = 3.965), higher VAS score(OR = 2.617), Kellgren-Lawrence(K-L) grade IV(OR = 4.474), history of knee joint trauma(OR = 5.684), and living in a rural location(OR = 1.554) increased the risk of KAM asymmetry. Conversely, increased knee-extension muscle strength(OR = 0.758) and a per-capita monthly household income of 3000 ~ 6000 yuan(OR = 0.814) decreased the risk of KAM asymmetry.ConclusionFemale gender, older age, increased BMI, larger varus angle, higher VAS score, K-L grade IV, history of knee joint trauma, and living in a rural location are identified as risk factors for KAM asymmetry. Conversely, increased knee-extension muscle strength and a per-capita monthly household income of 3000 ~ 6000 yuan serve as protective factors against this asymmetry.

Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

Abstract. With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s.

High-Linearity Dual-Parallel Mach–Zehnder Modulators in Thin-Film Lithium Niobate

Microwave photonic (MWP) systems are inseparable from conversions of microwave electrical signals into optical signals, and their performances highly depend on the linearity of electro-optic modulators. Thin-film lithium niobate (TFLN) is expected to be an ideal platform for future microwave photonic systems due to its compact size, low optical loss, linear electro-optic effect, and high bandwidth. In this paper, we propose a TFLN modulator with a low voltage–length product (VπL) of 1.97 V·cm and an ultra-high-linearity carrier-to-distortion ratio (CDR) of 112.33 dB, using a dual-parallel Mach–Zehnder interferometer configuration. It provides an effective approach to fully suppress the third-order intermodulation distortions (IMD3), leading to 76 dB improvement over a single Mach–Zehnder modulator (MZM) in TFLN. The proposed TFLN modulator would enable a wide variety of applications in integrated MWP systems with large-scale integration, low power consumption, low optical loss, and high bandwidth.

Assessment of a Structurally Modified Alternanthera Mosaic Plant Virus as a Delivery System for Sarcoma Cells

The virions of plant viruses and their structurally modified particles (SP) represent valuable platforms for recombinant vaccine epitopes and antitumor agents. The possibility of modifying their surface with biological compounds makes them a tool for developing medical biotechnology applications. Here, we applied a new type of SP derived from virions and virus-like particles (VLP) of Alternanthera mosaic virus (AltMV) and well-studied SP from Tobacco mosaic virus (TMV). We have tested the ability of SP from AltMV (AltMV SPV) and TMV virions also as AltMV VLP to bind to and penetrate Ewing sarcoma cells. The adsorption properties of AltMV SPV and TMV SP are greater than those of the SP from AltMV VLP. Compared to normal cells, AltMV SPV adsorbed more effectively on patient-derived sarcoma cells, whereas TMV SP were more effective on the established sarcoma cells. The AltMV SPV and TMV SP were captured by all sarcoma cell lines. In the established Ewing sarcoma cell line, the effectiveness of AltMV SPV penetration was greater than that of TMV SP. The usage of structurally modified plant virus particles as a platform for drugs and delivery systems has significant potential in the development of anticancer agents.

ДЕРЖАВНА КОНЦЕПЦІЯ «РОЗУМНОГО МІСТА» ТА «РОЗУМНОГО СЕЛА» ЯК ПРІОРИТЕТ НАЦІОНАЛЬНОЇ ЕКОНОМІКИ АЗЕРБАЙДЖАНУ

Along with the “smart city”, it is planned to implement the “smart village” project in post-conflict areas. In international practice, Smart Village means solving the main problems facing the rural population and, more importantly, creating new economic opportunities in rural areas. “Smart village” technologies are digitization, thinking beyond the boundaries of the village, a new format of cooperation between the village and adjacent regions. We can also block the possibility of a cycle of decline by applying smart village technologies in liberated areas. Also, the use of “smart village” technologies will create conditions for the widespread dissemination of social innovations, micro and small start-up businesses among the rural population of these territories. In conclusion, smart city and smart village, the application of technology can transform the Karabakh region into a tourism pearl created by the unity of artificial intelligence and the natural beauty of the world, and at the same time an innovative start. The aim of the article is analysis and characteristics of the state concept of "Smart city" and "Smart village" as a priority of the national economy of Azerbaijan. Methods used in the study: comparative analysis, gradient, detailing and generalization, rating and grouping, etc. The hypothesis of the study. One of the current areas of scientific research is the sphere of living environment, which from the field of Smart Homes is currently developing into the field of Smart City, Smart Transport System, etc. Presentation of the main material. Currently, there is a rapid change in information and communication technologies (ICT). The emerging changes affect network technologies, computing and communication devices themselves, as well as data processing. As a result, information technologies are used in more and more areas of human life and economic life. Today, cities have become a major force in economic development and are central to production, consumption networks, defining social and economic relationships, and now provide a significant share of the gross domestic product of many countries. Cities have come to play a major role in national, regional and global development. The quality of people's lives depends on them. Therefore, today, more than ever, they are subject to special requirements, such as the availability of accessible urban infrastructure, high mobility, safety of urban areas, environmental friendliness, and developed urban self-government. For example, there is a whole direction that studies the growth of urban settlements, population concentration, and socio-economic changes. This direction is called urbanization. In 2008, the urban population was equal to the rural population. According to UN forecasts, by 2050, 85% of the population will live in cities. Thus, governments and city authorities are faced with new tasks that must not only solve a whole range of emerging problems, but also carry out a radical transformation of cities. The important point is a detailed analysis, understanding of these problems, as well as the ability to consider various solution options. All of the listed problems as a result of active urbanization are the main triggers and forced processes of urban development and their transformation into “Smart Cities”. The originality and practical significance of the study. The main goal of Smart Village projects around the world is to prevent rural-to-urban migration. In other words, conditions must be created for residents of rural areas so that all the services that people can get in the city are easily accessible in villages. At the same time, it is necessary to create more income and employment opportunities for rural residents. The third point is the creation of full-fledged conditions to ensure access of rural residents to public services. In other words, in the Smart Village concept, the difference between rural and urban areas has practically disappeared in terms of service, profit generation, and use of public services. Conclusions. According to the Smart Village concept, process electronization expands production capabilities. “Smart Village” is an automated, centrally managed and jointly managed system of the entire platform, from irrigation to tillage, harvesting, and also involves the use of the most advanced technologies and technology transfer. Integration of this system into other areas of rural infrastructure – alternative energy sources and a modern transport system - will complete the Smart Village concept.

Variable-energy cocktail beam technology for investigating synergistic damage in nuclear materials on LEAF platform

This study addresses the critical issue of synergistic radiation damage in structural materials of fusion reactors, focusing on the interaction between the displacement defects and transmutation-produced hydrogen and helium. These effects are simulated and investigated by employing the advanced multi-beam ion implantation capabilities of the LEAF (Low Energy high intensity highly charged ion Accelerator Facility) platform. Firstly, high-intensity cocktail beams, such as “4He+ and 56Fe14+" and “4He+ and 58Ni15+", are generated and characterized successfully. Then, a complex radiation environment is mimicked within the fusion reactors by applying variable-energy irradiation. Secondly, similar penetration depths for different ions, which are crucial for studying synergistic effects, are obtained by precisely controlling the energy of the cocktail beams through the innovative energy modulation system of the LEAF platform. Finally, the post-irradiation analyses, performed by using the transmission electron microscopy (TEM) and nanoindentation, revealed distinct microstructural changes and alterations in material properties, providing insights into the degradation mechanisms under irradiation. This work not only generates diverse and high-intensity “cocktail” ion beams but also achieves rapid energy switching of the beams. Further, the work is expected to pave the way for the implementation of a novel multi-beam irradiation technique in advanced heavy-ion linear accelerators, and also to provide innovative experimental methods and technical support for studying the synergistic effects of nuclear materials.

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

AbstractWe introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

On-the-Go Automated Break Recommendation for Stress Avoidance during Highway Driving

Continuous cab driving is considered a highly stressful job, although drivers often ignore the stress. Taking a break from manual driving or transferring the control to another driver to release the stress would be an easy, intuitive solution, although the challenge is to detect the driving stress while the trip is going on. As driving stress depends on multiple diverse environmental and affective features, we, in this article, develop a novel assistive system, SmartHalt , which continuously senses the driving environment (such as road type, congestion, driver’s driving pattern) and then utilizes a spatial time series model of the driving environment with a deep learning framework to predict whether the driver will be stressed while on the trip. The model also considers the personality traits of the drivers along with the spatio-temporal features to differentiate the impact of stress on the driving behavior for different drivers and recommends taking a break soon before the driving behavior drops below a critical level. A thorough analysis of the model over seven different drivers for a 10 month-long experiment over 204,871 km of driving data reveals that the proposed approach can significantly improve driving behavior by recommending a driving break at proper times. Following the recommendation by SmartHalt improves the driving score by ≈50% and reduces the number of driving offenses by ≈50%. SmartHalt can help develop advanced driving assisting system (ADAS) platforms that understand the affective states of the driver and thus can be helpful for semi-autonomous driving environments for effective driver-vehicle interactions.

Application of a Discrete AlGaN/GaN HEMT Sensor for in‐situ Regenerative Soot Particulate Detection

AbstractAn in‐situ regenerative soot particulate matter sensor for automotive exhaust systems based on a discrete AlGaN/GaN high electron mobility transistor (HEMT) was developed and tested, where charge‐sensitive AlGaN/GaN HEMT was utilized to detect the signal variations caused by the presence of soot particulate matters, and the micro‐hotplate serves as soot deposition, while the self‐heating unit was used for sensing and in‐situ regeneration. The two parts were connected for electrical signal transmission. Soot particulate were generated by a laminar diesel flame with an adsorption rate of 23.29 μg cm−2 min−1 on the sensor surface, resulting in 1.32% sensing response after 20 s deposition and reached saturation after a 12 minute deposition of soot. After soot deposition, the sensor was successfully regenerated when a voltage of 2 V was applied to the micro‐hot plate, causing in‐situ heat‐up to reach 600°C for 30 seconds. Moreover, benefiting from the advantage of discrete structure, the sensor exhibited consistent and stable response characteristics even after undergoing more than 10 consecutive regeneration cycles, and the replaceable micro‐hotplate could further extend the service life of the sensor. These results show that the discrete AlGaN/GaN HEMT sensor can be a promising soot particulate sensing platform for automotive exhaust systems.

Feasibility of Employing mHealth in Delivering Preventive Nutrition Interventions Targeting the First 1000 Days of Life: Experiences from a Community-Based Cluster Randomised Trial in Rural Bangladesh.

An Android platform-based customised app and web-linked system was developed to aid in implementing selected nutrition interventions by community health workers (CHWs) in a community-based cluster randomised trial (c-RCT) in rural Bangladesh. Here, we describe the architecture of the intervention delivery system, and explore feasibility of employing mHealth as CHWs' job aid, employing a mixed-method study design covering 17 visits per mother-child dyad. We analysed CHWs' real-time visit information from monitoring and documentation data, and CHWs' qualitative interviews to explore the advantages and barriers of using mHealth as a job aid. Intervention coverage was high across the arms (>90%), except around the narrow perinatal period (51%) due to mothers' cultural practice of moving to their parents' homes and/or hospitals for childbirth. CHWs mentioned technical and functional advantages of the job aid including device portability, easy navigability of content, pictorial demonstration that improved communication, easy information entry, and automated daily scheduling of tasks. Technical challenges included charging tablets, especially in power cut-prone areas, deteriorated battery capacity over continuous device usage, unstable internet network in unsupportive weather conditions, and device safety. Nevertheless, onsite supervision and monitoring by expert supervisors remained important to ensure intervention quality. With appropriate training and supervision, CHWs utilised the tablet-based app proficiently, attaining high coverage of long-term visits. mHealth was thus useful for designing, planning, scheduling, and delivering nutrition interventions through CHWs, and for monitoring and supervision by supervisors. Therefore, this application and job aid can be adopted or replicated into the currently developing national health systems platform for improving coverage and quality of preventive maternal and child nutrition services. In addition, continuous supportive supervision by skilled supervisors must be accompanied to ensure CHWs' task quality. Finally, future studies should rigorously assess undesirable health and environmental effects of mHealth before and after mainstreaming, effective interventions addressing device-induced health hazards should be designed and scaled up, and effective e-waste management must be ensured.

Operational long-term management of a salt cavern for industry targeted green H2 production

Hydrogen and its derivatives such as ammonia are increasingly important for reducing carbon emissions in sectors with limited alternatives. Large production systems are generally preferred to reduce costs and for those, advanced management is a cornerstone to the project economic viability. The present work explores the development of an Energy Management System for an industrial low-emission ammonia production platform. The latter is connected to the electrical grid and consists of 1.5 GW of renewable production units, around 500 MW of electrolyzers and storage facilities including especially a salt cavern for H2 storage. The long-term management of the salt cavern is decisive to enhance the profitability while satisfying industry related constraints such as the carbon content of the produced H2. The literature lacks in addressing both aspects simultaneously. Methodological approaches to tackle the long-term management and industrial constraints are proposed and compared. For the considered platform, it is shown that a strategy based on precalculated cost functions in the long term horizon allows relative gains of 2.5% with respect to a state-of-the-art short term management leading to an annual cost saving of approximately $6.2 million in the platform operational expenses. This translates to a reduction of the gap between the state-of-the-art and the optimal a posteriori solution by about 82%.

Impact of Autoscaling on Application Performance in Cloud Environments

The current paper studies the impact of Autoscaling on application performance in Cloud computing environments. Cloud computing is one of the most encouraging innovations due to its vast applications. Predictive autoscaling is an advanced technique that aims to address the challenges in the autoscaling trends for large-scale systems. To account for the Quality of Service to the customer, features like load balancing according to the workload demand, understanding resource allocation and utilization, and dynamic decision-making are vital to any cloud computing application. This paper interprets these challenges and reviews a meta-reinforcement learning approach for predictive autoscaling in cloud environments. A novel RL-based predictive autoscaling approach on a popular large-scale digital payment platform system, Alipay, is compared with the existing models such as Autopilot and FIRM. The aim is to conduct a detailed analysis of performance metrics before and after autoscaling actions, aiming to identify optimal scaling strategies that minimize response time and maximize resource utilization without over- provisioning.

Development and validation of a relatively accurate gastric cancer high-risk group screening scoring system in urban residents.

Our study aimed to develop a relatively accurate gastric cancer (GC) screening score system for urban residents and to validate the screening efficacy. The present study included a derivation cohort (n = 3406) and a validation cohort (n = 868) of urban residents. Applying the full-stack engineering intelligent system platform of Hualian Health Big Data of Shandong University, the clinical physical examination data of subjects were collected. Univariate and multivariate analyses were used to identify risk factors for GC, and subsequently, an optimal prediction rule was established to create three distinct scoring systems. In the GC-risk scoring system I, age, plateletocrit (PCT), carcinoembryonic antigen (CEA), glucose, albumin, creatinine were independent risk factors of GC, with scores ranging from 0 to 28 and optimal cut-off was 15.5. The second scoring system consisted of age, PCT, RDW-CV, CEA, glucose, albumin, and creatinine, with scores ranging from 0 to 31. The optimal cut-off point was determined to be 15.5. The scoring system III comprise of age, sex, PCT, RDW CV, CEA, glucose, with scores ranging from 0 to 21 and optimal cut-off was 10.5. All three scoring systems demonstrated excellent discrimination for GC, achieving an AUC of 0.884, 0.89, and 0.876, respectively. In external validation, the AUC values were 0.654, 0.658, and 0.714. Notably, the GC-risk scoring system III exhibited the highest screening efficiency. Urban residents benefited from the effective and verified GC-risk scoring systems, which demonstrated excellent performance in identifying individuals with an elevated risk of GC.

Active vibration isolation support platform for double crystal monochromator

The quality of the beamline at a synchrotron light source is directly related to the performance of the double crystal monochromator. This paper presents a designed active vibration isolation support platform for the double-crystal monochromator to meet the higher stability demands of advancing synchrotron light sources. The platform has good decoupling characteristics through the redundant structure of eight actuators mixed with active and passive. A vibration isolation control scheme is designed based on distributed degrees of freedom and a feed-forward feedback composite control scheme to ensure high stiffness of the support platform while achieving full-band vibration isolation. Finally, constructed a verification platform for an active vibration isolation system and verified the effectiveness of the proposed control method through experiments. With the designed active vibration isolation algorithm, the system can achieve full-band vibration isolation within the 1–100 Hz bandwidth.

Concept of the digital state: concept, nature and ontology

The article is devoted to the study of the phenomenon and concept of the digital state. The general concept and meaning of digitization in public administration is revealed. The problems of relevant interpretation and the limits of applicability of this concept to public administration are explained. The author’s concept and definition are presented, explaining the concept, nature and ontology of the digital state. The author suggests to consider the digital state in 4 interpretive projections: 1) as an ecosystem of operational, multi-service and proactive state digital super-services; 2) as an integrated computer-software meta-platform (platform system); 3) as an organizational and technological approach and corresponding paradigm; 4) as the digital ontology of state-building and functioning, public administration and public policy. According to the author’s interpretation, the concept of the “digital state” structurally includes the concepts: 1) the concept of the “digital government”; 2) the “digital democracy” concept; 3) the concept of the “digital justice”; 4) the concept of the “digital electoral technologies”; 5) the concept of the “digital public control over public administration”. The article details the author’s concept of “digital government”. The article is based on application of research methods of analysis and synthesis, induction, deduction and abduction, classification and formalization; through application of research methods, the essential descriptive characteristics of the phenomenon and the concept of the digital state were revealed.

Optimization of energy system of natural gas hydrate offshore platform considering wind power uncertainty

Natural gas hydrate has potentially become a clean energy source in the future. Most natural gas hydrates are stored in the ocean, and the exploitation of deep-sea gas hydrates requires the construction of offshore platforms. Generally, the platform consumes a lot of energy due to its impact on the marine environment. Therefore, the adoption of a reasonable energy equipment operation scheme can effectively reduce the platform operation cost. Compared with the way of directly consuming natural gas to provide power to the platform, introducing wind energy may be cleaner and more economical. However, the uncertainty and volatility of wind power must be carefully considered when it is introduced. The paper establishes a mixed integer linear programming (MILP) model to solve this problem. A flexible distributed energy system framework for the natural gas hydrate mining platform is constructed to design and optimize the equipment scale and the number of wind turbines with the minimum total annual cost of the energy system as the objective function. A case study is conducted by an established model and the results show that access to wind energy can significantly reduce the cost of the energy system by 18.9 % and CO2 emissions by 46.9 %. Meanwhile, the uncertainty of electric energy demand is the most sensitive to the optimization results.

Time from Diagnostic Suspicion to In-Person Evaluation in Skin Cancer Using Teledermatology.

Introduction: Early diagnosis of skin cancer is crucial for improving prognosis. Teledermatology (TD) usage can optimize referrals and reduce waiting times. This study aims to evaluate waiting times at the critical referral nodes in teleinterconsultations that raised suspicion of skin malignancy in the Chilean TD platform of the public health care system. Materials and Methods: A cross-sectional observational study that analyzed asynchronous teleinterconsultations and raised suspicion for skin malignancy following the teledermatologist evaluation was uploaded on the Chilean Ministry of Health's TD platform from January 1 to June 30, 2022. Results: Out of 20,522 teleinterconsultations, 1,853 raised suspicion of skin cancer. Among them, 1,119 patients were assessed by in-person examination, while 669 were still on the waiting list. Response times averaged 3.98 days for TD diagnostic suggestions. Overall referral times averaged 75.98 days from initial teleinterconsultation to the final specialist in-person evaluation. Waiting times showed significant differences among health care services and geographic regions. Discussion: In resource-limited settings, TD serves as a valuable tool to optimize referrals and manage the demand for oncologic dermatological consultation. The long waiting times emphasize the need for targeted interventions, especially in regions with longer delays. Conclusion: While TD has shown to be an effective tool in optimizing referrals, waiting times still exceed international recommendations, even in urban centers. The considerable heterogeneity in referral times within health care services and geographic regions highlights the necessity of establishing standardized referral protocols and explicit deadlines to fulfill teleinterconsultations that raise suspicion of skin malignancy in the Chilean public system.