Creation Of Database Research Articles

Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations

Introduction. Computer vision is widely used for semantic segmentation of Earth remote sensing (ERS) data. The method allows monitoring ecosystems, including aquatic ones. Algorithms that maintain the quality of semantic segmentation of ERS images are in demand, specifically, to identify areas with phytoplankton, where water blooms— the cause of suffocation — are possible. The objective of the study is to create an algorithm that processes satellite data as input information for the formation and checking of mathematical models of hydrodynamics, which are used to monitor the state of water bodies. Various algorithms for semantic segmentation are described in the literature. New research focuses on enhancing the reliability of recognition — often using neural networks. This approach is modified in the presented work. To develop the direction, a new set of information from open sources and synthetic data are proposed. They are aimed at improving the generalization ability of the model. For the first time, the contour area of the phytoplankton population is compared to the database — and thus the boundary conditions are formed for the implementation of mathematical models and the construction of boundary-adaptive grids.Materials and Methods. The set of remote sensing images was supplemented with the author's augmentation algorithm in Python. Computer vision segmented areas of phytoplankton populations in the images. The U-Net convolutional neural network (CNN) was trained on the basis of NVIDIA Tesla T4 computing accelerators.Results. To automate the detection of phytoplankton distribution areas, a computer vision algorithm based on the U-Net CNN was developed. The model was evaluated by the calculated values of the main quality metrics related to segmentation tasks. The following metric values were obtained: Precision = 0.89, Recall = 0.88, F1 = 0.87, Dice = 0.87, and IoU = 0.79. Graphical visualization of the results of CNN learning on the training and validation sets showed good quality of model learning. This is evidenced by small changes in the loss function at the end of training. The segmentation performed by the model turned out to be close to manual marking, which indicated the high quality of the proposed solution. The area of the segmented region of the phytoplankton population was calculated by the area of one pixel. The result obtained for the original image was 51202.5 (based on information about the number of pixels related to the bloom of blue-green algae). The corresponding result of the modeling was 51312.Discussion and Conclusion. The study expands theoretical and practical knowledge on the use of convolutional neural networks for semantic segmentation of space imagery data. Given the results of the work, it is possible to assess the potential for automating the process of semantic segmentation of remote sensing data to determine the boundaries of phytoplankton populations using artificial intelligence. The use of the proposed computer vision model to obtain contours of water bloom due to phytoplankton will provide for the creation of databases — the basis for environmental monitoring of water resources and predictive modeling of hydrobiological processes.

An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS

Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, and the availability of portable hand-held devices, such as mobile personal computers (PCs), smartphones, and tablets. Numerous software packages have been developed to collect, combine, organise, visualise, publish, and share field data with enhanced spatial accuracy and minimal post-field processing. However, many of these tools are not open-source or are not made available to the geoscientific community, remaining specific to given mapping projects or organisations.In this contribution we introduce GEOL-QMAPS, an open-source, QGIS-based solution promoting digital geological mapping in a harmonised, comprehensive and flexible way. It can be used in the field with a tablet PC or via the QGIS-based QField app on iOS or Android mobile devices, enabling synchronisation with desktop QGIS and the creation of field databases. Designed as a general solution, the GEOL-QMAPS solution consists of a QGIS field data entry template and a custom QGIS plugin, both available on free-access online repositories. The plugin allows for the adaptation of dictionaries (i.e., lists of attributes describing geological features), initially set to international nomenclatures, to the guidelines of different mapping projects. The solution also facilitates the loading and consultation of relevant legacy geodatasets (e.g., preexisting field data, geochemical, geophysical maps or punctual datasets). A fact map, created from field data collected across the Archean Sula-Kangari greenstone belt in Sierra Leone, demonstrates the solution's advantages in terms of post-field processing and raw field data sharing.

Priority radiomic parameters for computed tomography of head and neck malignancies: A systematic review

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed. AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data. MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted. RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters. CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.

E-Counseling : Mobile-Based Specialization System in Vocational Schools

Many students who will continue their studies to the high school level are confused about choosing a major. This is because most students do not understand the majors at the vocational level, making it difficult for students to enter majors according to the talents and interests of each student. This research is a continuation of previous research that has developed web-based applications. With a majors specialization system using the Inventory Personal Survey Test model system which was built as a majors selection system at the SMK level, mobile access is needed to make it easier for students to access this service via an application on a smartphone.This system is designed using Android Studio and the database creation using MYSQL. The results of the specialization that has been carried out show that the system can be used to conduct online counseling and makes it easier for counselors to collect and analyze counseling results.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

IPUMS Multigenerational Longitudinal Panel

Over the past five years, the IPUMS Multigenerational Longitudinal Panel (MLP) has constructed a massive longitudinal population panel by linking together American censuses, surveys, administrative sources, and vital records spanning the period from 1850 to the present. MLP is designed as a general-purpose resource for studying longitudinal processes and long-run social and economic change. By linking individuals across generations from birth through death using data from multiple sources, MLP enables reproducible research on shifting patterns of life course patterns and intergenerational processes. The large scale of the MLP database allows researchers both to study particular communities and small dispersed populations and to conduct big studies spanning many places and periods. Data that allow investigators to examine simultaneously the broad sweep of time and fine spatial detail will yield new insights into ongoing transformations of demographic behavior. The presentation will describe the creation of the IPUMS MLP database and the current MLP linking strategy. We then detail our plans for expansion and improvement of MLP over the next five years, including the incorporation of additional data sources, the development of a “linkage hub” to connect MLP with other major record linkage efforts, and the refinement of our technology and dissemination efforts. We will conclude by describing a few early examples of MLP-based research.

Immersive Photorealistic Three-Dimensional Neurosurgical Anatomy of the Cerebral Arteries: A Photogrammetry-Based Anatomic Study.

Neurosurgeons need a profound knowledge of the surgical anatomy of the cerebral arteries to safely treat patients. This is a challenge because of numerous branches, segments, and tortuosity of the main blood vessels that supply the brain. The objective of this study was to create high-quality three-dimensional (3D) anatomic photorealistic models based on dissections of the brain arterial anatomy and to incorporate this data into a virtual reality (VR) environment. Two formaldehyde-fixed heads were used. The vessels were injected with radiopaque material and colored silicone and latex. Before the dissections, the specimens were computed tomography scanned. Stratigraphical anatomic dissection of the neck and brain was performed to present the relevant vascular anatomy. A simplified surface scanning method using a mobile phone-based photogrammetry application was used, and the data were incorporated into a VR 3D modeling software for post-processing and presentation. Fifteen detailed layered photorealistic and two computed tomography angiography-based 3D models were generated. The models allow manipulation in VR environment with sufficient photographic detail to present the structures of interest. Topographical relevant anatomic structures and landmarks were annotated and uploaded for web-viewing and in VR. Despite that the VR application is a dedicated 3D modeling platform, it provided all necessary tools to be suitable for self-VR study and multiplayer scenarios with several participants in one immersive environment. Cerebral vascular anatomy presented with photogrammetry surface scanning method allows sufficient detail to present individual vessel's course and even small perforating arteries in photorealistic 3D models. These features, including VR visualization, provide new teaching prospects. The whole study was done with simplified algorithms and free or open-source software platforms allowing creation of 3D databases especially useful in cases with limited body donor-based dissection training availability.

Oral Health Surveillance System in India: Need and Proposal

Public health surveillance is described as the ongoing systematic collection, analyzing, and interpreting of the data, closely integrated with the transmission of these data to public health practitioners, doctors, and policymakers responsible to prevent and manage disease as well as injury. Both developed and developing nations are becoming aware of the significance of information from efficient surveillance systems for allocating resources and evaluating programs. Over the past few decades, there was a significant change in oral health profiles around the world. Indicators of oral health have improved in many countries, but not in India. An organized system for providing, monitoring, and evaluating oral health care services is needed in India. Draft National oral health policy 2021 has mentioned specific objectives: (a) by 2025, establish baseline data on the burden of oral diseases in the nation, (b) by 2030, the mortality and morbidity from dental and oro-facial diseases reduce to 15%, (c) By 2025, community-based awareness initiatives and practices for oral health will be covered by the healthcare system by a factor of 50%, and by 2030, by a factor of 70%, (d) the creation of an electronic database at district-level on components of the health system by 2025. A well-defined oral health surveillance system in India will serve the purposes (a) to provide standardized recording procedures, standards, and techniques to collect, analyze and interpret the oral health data at the state and central levels (b) to prioritize oral diseases or conditions that will be the focus of oral health surveillance or disease elimination, (c) to digitally monitor and evaluate the progress of nation’s oral health care programs.

Meta-analysis of the diagnostic value of functional magnetic resonance imaging for distinguishing unresponsive wakefulness syndrome/vegetative state and minimally conscious state.

Unresponsive wakefulness syndrome/vegetative state (UWS/VS) and minimally conscious state (MCS) are considered different clinical entities, but their differential diagnosis remains challenging. As a potential clinical tool, functional magnetic resonance imaging (fMRI) could detect residual awareness without the need for the patients' actual motor responses. This study aimed to investigate the diagnostic value of fMRI for distinguishing between UWS/VS and MCS through a meta-analysis of the existing studies. We conducted a comprehensive search (from the database creation date to November. 2023) for relevant English articles on fMRI for the differential diagnosis of UWS/VS and MCS. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of fMRI in distinguishing between UWS/VS and MCS. The statistical I 2 test was used to assess heterogeneity, and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Ten studies were included in the meta-analysis. The pooled sensitivity and specificity were 0.71 (95% CI 0.62-0.79) and 0.71 (95% CI 0.54-0.84), respectively. The fMRI for the differential diagnosis of UWS/VS and MCS has a moderate positive likelihood ratio (2.5) and a relatively low negative likelihood ratio (0.40). Additionally, SROC curves showed that the AUC was 0.76 (95% CI 0.72-0.80). Functional magnetic resonance imaging has a good performance in the differential diagnosis of UWS/VS and MCS, and may provide a potential tool for evaluating the prognosis and guiding the rehabilitation therapy in patients with disorders of consciousness.

A Variant-Centric Analysis of Allele Sharing in Dogs and Wolves.

Canines are an important model system for genetics and evolution. Recent advances in sequencing technologies have enabled the creation of large databases of genetic variation in canines, but analyses of allele sharing among canine groups have been limited. We applied GeoVar, an approach originally developed to study the sharing of single nucleotide polymorphisms across human populations, to assess the sharing of genetic variation among groups of wolves, village dogs, and breed dogs. Our analysis shows that wolves differ from each other at an average of approximately 2.3 million sites while dogs from the same breed differ at nearly 1 million sites. We found that 22% of the variants are common across wolves, village dogs, and breed dogs, that ~16% of variable sites are common across breed dogs, and that nearly half of the differences between two dogs of different breeds are due to sites that are common in all clades. These analyses represent a succinct summary of allele sharing across canines and illustrate the effects of canine history on the apportionment of genetic variation.

Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF – Multilayer Perceptron (MLP), EBF – Function Tree (FT), EBF – Alternating decision tree (ADtree), EBF – Adaptive Boosting (AdaBoost), EBF – Random SubSpace (RSS), EBF – Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15–21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance.

Bibliographic Culture as a Factor of Security of Trusted Public Internet

The article deals with the problem of security of search in modern public computer-network resources of reliable, complete sources, their qualitative bibliographic description. The relevance is due to the “digital transformation”, the rapid spread of computer-network technologies since the 2010s to all sectors, as well as everyday life; a sharp and significant increase in the number and variety of electronic databases (libraries); turning the majority of humanity into daily active users. Objective international and national diseases of growth of standardization, streamlining, structuring of databases, development of indexing systems and search public network services, means of verification of electronic documents have manifested themselves.Since 2014 and even later from 2022, the problem was exacerbated by the reduction of international cooperation in computer-network technologies, anti-Russian sanctions of unfriendly countries, violation of Russian legislation by global digital corporations, leapfrogging strengthening of cybercrime and information confrontation in the Internet. The possibilities of attackers who, by exploiting vulnerabilities, reduced the access of mass users to reliable electronic documents in cross-border Internet communications increased. The opportunities caused by the relativistic understanding of truth, fabrication and broadcasting of falsified information, fake news, disinformation, use of new means of “social engineering” (fraud, manipulation of consciousness) that threaten citizens and national interests of Russia have increased.The novelty of the study lies in the integrated application of theoretical and cultural gnoseology (culture of cognition) and methodology of information security (countering deliberate attempts to mislead). The comparative analysis revealed significant differences between Russian state electronic libraries, document management systems, registers and services and non-state, as well as foreign public Internet resources and services. The result of the study is the conclusion about the importance of the prevalence of essential interests of mass users of Russian public libraries and electronic services. Recommendations on the preferability of applying this experience in the creation and modernization of Russian public non-state digital databases, libraries, services, as well as the development of standardization on its basis are formulated.

Jazz Trio Database: Automated Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation

Recent advances in automatic music transcription have facilitated the creation of large databases of symbolic transcriptions of improvised music forms including jazz, where traditional notated scores are not normally available. In conjunction with music source separation models that enable audio to be “demixed” into separate signals for multiple instrument classes, these algorithms can also be applied to generate annotations for every musician in a performance. This can enable the analysis of interesting performer-level and ensemble-level features that have often been difficult to explore. To this end, we introduce Jazz Trio Database (JTD), a dataset of 44.5 h of jazz piano solos accompanied by bass and drums, with automatically generated annotations for each performer. These annotations consist of onset, beat, and downbeat timestamps, alongside MIDI for the piano soloist. Suitable recordings, broadly representative of the “straight-ahead” jazz style, were identified by scraping user-based listening and discographic data; source separation models were applied to isolate audio for each performer in the trio; annotations were generated by applying appropriate algorithms to both the separated and the mixed audio sources. Onset annotations generated by the pipeline achieved a mean F-measure of 0.94 when compared with ground truth annotations. We conduct several analyses of JTD, including with relation to swing and inter-performer synchronization. We anticipate that JTD will be useful in a variety of music information–retrieval tasks, including artist identification and expressive performance modeling. We have made JTD, including the annotations and associated source code, available at https://github.com/HuwCheston/Jazz-Trio-Database

Osteoinductive biomaterials: Machine learning for prediction and interpretation

Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivotal in analyzing osteoinductivity and accelerating new material design. However, challenges include creating a comprehensive database of osteoinductive materials and dealing with low-quality, disparate data. As a standard for evaluating the osteoinductivity of biomaterials, ectopic ossification has been used. This paper compiles research findings from the past thirty years, resulting in a robust database validated by experts. To tackle issues of limited data samples, missing data, and high-dimensional sparsity, a data enhancement strategy is developed. This approach achieved an area under the curve (AUC) of 0.921, a precision of 0.839, and a recall of 0.833. Model interpretation identified key factors such as porosity, bone morphogenetic protein-2 (BMP-2), and hydroxyapatite (HA) proportion as crucial determinants of outcomes. Optimizing pore structure and material composition through partial dependence plot (PDP) analysis led to a new bone area ratio of 14.7 ± 7 % in animal experiments, surpassing the database average of 10.97 %. This highlights the significant potential of ML in the development and design of osteoinductive materials. Statement of significanceThis study leverages machine learning to analyze osteoinductive biomaterials, addressing challenges in database creation and data quality. Our data enhancement strategy significantly improved model performance. By optimizing pore structure and material composition, we increased new bone formation rates, showcasing the vast potential of machine learning in biomaterial design.

Relationship Between Delay Discounting and Clinical Diabetes Outcomes: A Systematic Review.

To evaluate the evidence on the relationship between delay discounting and clinical diabetes outcomes, identify current measures, and recommend areas for future work. A reproducible search using OVID Medline, PsycINFO, PubMed, Science Direct, and Scopus was conducted. Articles published from database creation up to March 2024 were searched. Medical Subject Heading (MeSH) terms and keywords representing delay discounting and diabetes were used. Outcomes included hemoglobin A1c (HbA1c), LDL, body mass index (BMI), blood pressure, quality of life (QOL), psychosocial factors, self-care behaviors, and diabetes complications. A total of 15 articles met the inclusion criteria and were included for final synthesis. Overall, 14 studies included in this review found a significant relationship between delay discounting and diabetes-related outcomes, such that higher delay discounting is significantly related to worse diabetes outcomes for HbA1c, self-care behaviors, BMI, stress, and quality of life across self-reported measures of delay discounting and delay discounting tasks. Evidence supports the relationship between delay discounting and diabetes-related outcomes and self-care behaviors across measures of delay discounting and type of diabetes. To understand delay discounting as a mechanism driving diabetes outcomes and to develop targeted interventions, additional work using a multidisciplinary approach is needed to validate the construct, identify pathways, and refine intervention approaches that can be tested to improve population health.

Prevalence of poor sleep quality during menopause: a meta-analysis.

Numerous researches have demonstrated that sleep quality deteriorates during menopause, but the prevalence rates vary between studies. The purpose of this meta-analysis was to determine the pooled prevalence of poor sleep quality during menopause and to explore potential moderators of discrepancies in prevalence between studies. Web of Science, PubMed, PsycINFO and Embase databases were comprehensively searched from database creation to June 19, 2024. Studies were included if they focused on menopausal women and provided an estimate of the prevalence of poor sleep quality, which could be derived from the Pittsburgh Sleep Quality Index (PSQI). Totally, 37 studies with 29,284 menopausal women were included in the analysis. A meta-analysis based on a random-effects model revealed that the prevalence of poor sleep quality during menopause was 50.8% (95% CI: 45.4-56.3%). The PSQI score was 6.24 (95% CI: 5.73-6.75). Subgroup analyses indicated that cut-off values of PSQI, menopausal status, age range, depressive symptoms and geographic region moderated the prevalence of poor sleep quality. Meta-regression analyses indicated that quality score was negatively correlated with the pooled prevalence. Poor sleep quality is a prevalent phenomenon among women during menopause. In light of the numerous adverse effects of poor sleep quality on menopausal women, it is of paramount importance to assess sleep quality on a regular basis and to implement effective interventions for menopausal women.

Is Ablation Suitable For Small Renal Masses (SRMs)? A Meta-Analysis

ObjectiveTo compare perioperative and oncology outcomes of ablation and partial nephrectomy in small renal masses (SRMs). MethodsWe conduct this meta-analysis strictly according to the PRISMA standard, and the quality evaluation follows the AMSTAR standard. Four databases, Embase, PubMed, Cochrane Library, and Web of Science, were systematically searched. The search time range is from database creation to November 2023. Stata16 statistical software was used for statistical analysis. Weighted mean difference (WMD) represented continuity variables, odds ratio or relative risk (OR/RR) represented dichotomies variables, and 95% confidence intervals (95%CI) were calculated. ResultsA total of 27 studies, including 6030 patients. Results showed that patients undergoing partial nephrectomy were younger (WMD = -5.45 years, 95%CI [-7.44, -3.46], P < 0.05), had longer operation time (WMD = 64.91 min, 95%CI [44.47, 85.34], P < 0.05), had longer length of stay (WMD = 2.91 days, 95%CI [2.04, 3.78], P < 0.05), and had more estimated blood loss (WMD = 97.76 ml, 95%CI [69.48, 126.04]. P < 0.05), the overall complication rate was higher (OR = 1.84, 95%CI [1.48, 2.29], P < 0.05), the major complication rate was higher (OR = 1.98, 95%CI [1.36, 2.88], P < 0.05), and the recurrence rate was lower (OR = 0.32, 95%Cl [0.20, 0.50], P < 0.05). However, there were no differences between ablation and partial nephrectomy in cancer-specific survival (CSS) (HR = 2.07, 95%CI [0.61, 7.04], P > 0.05), overall survival (OS) (HR = 1.24, 95%CI [0.58, 2.65], P > 0.05), and recurrence-free survival (RFS) (HR = 2.68, 95%CI [0.91, 7.88], P > 0.05). ConclusionPatients undergoing partial nephrectomy are younger, have longer operation time and length of stay, and have higher complication rate. However, there was no significant difference in CSS, OS, and RFS between partial nephrectomy and ablation, but more well-designed, high-quality studies are needed to confirm this.

Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms

Abstract Magnetohydrodynamic (MHD) activity in fusion devices is typically analyzed by examining time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist with various types of noise and complex patterns generated by other events like pellet injection or active diagnostics. Traditionally, identifying MHD modes has been a manual task, making it labor-intensive. To overcome this issue, this study proposes the use of computer vision (CV) algorithms for noise removal and automatic feature extraction. First, the automatic detection of straight-line patterns is achieved by applying the Hough transform. Then, the discrete wavelet transform is proposed to break down spectrograms into sub-images of different scales, removing broadband noise and pellet injection signatures. The multiscale decomposition is subsequently extended to multiple directions using either 2D Fourier transforms or curvelets, achieving a high signal-to-noise ratio in spectrograms and eliminating undesired frequency sweeps of toroidal Alfvén eigenmodes antenna. Once MHD activity is successfully enhanced, a pipeline of algorithms for ridge detection, thresholding and labeling perform a segmentation of the image, automatically labeling individual modes. This study demonstrates the effectiveness of CV algorithms for the identification of MHD modes. The use of such algorithms may potentially help in the analysis process and the creation of large databases of modes.

Coalescence investigations in a small‐scale continuously operated setup for bubble column design

AbstractA novel micro‐channel technique for analyzing the coalescence of bubbles and obtaining relevant information for the creation of a coalescence database is presented. The micro‐channel improves the coalescence investigations by a continuously operated setup, reduces the accumulation of impurities and increases the amount of recorded data. To introduce the new setup, studies with alcoholic, electrolytic aqueous systems and liquid silicone oil as a second liquid are presented, showing the influence of different concentrations. Artificial intelligence has been successfully developed to automate data generation. This approach improves the understanding of bubble coalescence by introducing a reproducible setup. Furthermore, it facilitates the transition to a predictive column design through data‐based decisions and modelling.

Research on Evaluation of City–Industry Integration in Industrial Parks

The original meaning of city–industry integration should be understood as the coordination, balance, reasonable layout, and mutual support between urban production functional areas and service functional areas, which both have urban populations as their core element. The evaluation of city–industry integration in industrial parks can be carried out from two aspects: land–industry integration and residence–industry integration. The secondary indexes of the former mainly include industrial land efficiency and service sector land efficiency, while the secondary indicators of the latter mainly include supporting rail transit and the matching degree between residence and environment. The output value, land use structure, enterprise profile, employment rates, investments, air quality, rail transit system and other data points regarding sample industrial parks were collected by means of geofencing as well as through the creation of an enterprise credit information database and development area yearbook. The Analytic Hierarchy Process (AHP) combined with expert scoring was used to determine the index weights and implement the evaluation of city–industry integration. This study found that city–industry integration in Beijing and the Chengdu Economic and Technological Development Zones is at the forefront of sample industrial parks, and the entropy weight evaluation method verified this evaluation result. The analysis of the benchmark development zone of city–industry integration shows that the Chengdu model and the Beijing model are worthy of reference for growing and mature industrial parks when promoting city–industry integration.