Scientific Research Data Research Articles

Open Science in Agricultural Economics

Abstract We provide a ‘big picture’ of what Open Science is and what benefits, limitations, and risks it entails for agricultural economists. We show that Open Science comprises various aspects, such as, the accessibility of science, transparency of scientific processes, open and FAIR research data and code, and openness in teaching and education. We identify potential benefits of Open Science for individual researchers and the public, as well as adoption barriers. We highlight that public benefits of a wide-spread uptake of Open Science practices still remain unexplored. We share best practice examples for key aspects of agricultural economic research, i.e., primary data collection and analysis, optimization and simulation models, use of replication packages and an Open Science Community. Assessing the author guidelines of twelve Agricultural Economics journals for their Open Science practices, we find that data citation and transparency are considered important in many journals already, whereas replication, pre-registration or results-blind review are encouraged but rarely enforced. It also becomes evident that the journals differ in terms of how strictly they enforce their open-science guidelines. We close by providing recommendations for researchers, journal editors, policy makers, universities, research institutes and funding agencies to better align public benefits with private incentives.

A weakly supervised model for incomplete multimodal MRI synthesis with tumor-aware approach.

Magnetic resonance images (MRIs) are a valuable tool in the study of brain tumors, and multimodal sequences provide unique insights into different aspects of brain tumors. However, in clinical practice, missing modalities are often encountered due to various factors. This makes it difficult to obtain comprehensive and reliable information related to brain tumors. The purpose of this work is to develop an algorithm for the synthesis of missing MRI modality with high precision, and to center on generating accurate tumor-related information to offer more data for clinical diagnosis. A novel weakly supervised MRI synthesis model named TAM-DAM-GAN has been proposed, which integrates tumor-aware and detail adjustment mechanisms to enhance the quality of tumor generation. The tumor-aware mechanism leverages weak label information to guide the network to classify images based on crucial information in local structures, thereby compelling the generative network to identify and highlight the learning of local tumor regions. The detail adjustment mechanism utilizes a discriminator to create attention maps at the pixel level in real-time. These maps are then used to modify the loss weight, which in turn adjusts the details that are generated. Generation quality of four tasks (T1-to-T2, T2-to-T1, T1-to-FLAIR, and FLAIR-to-T1) was evaluated. Experiments on the BRATS2015 dataset show that the proposed approach is superior in both qualitative and quantitative measures. Taking FLAIR-to-T1 as an example, TAM-DAM-GAN improves PSNR of tumor region from 18.556 to 20.576 compared to baseline. Also, using real FLAIR data with generated T1 data boosts tumor segmentation accuracy by 10% compared to using only real FLAIR data. This finding will be conducive to enhancing the accuracy of cross-modality synthesis in incomplete multimodal MRI, especially for tumor regions, thereby providing more dependable and comprehensive data for clinical diagnosis and scientific research.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

Online homework assignments: A case study for 5<sup>th</sup> grade of primary school in Greece

There is a growing body of scientific literature and research data recognizing the value of using online homework. Online homework may transform the way students learn outside the classroom, offering a dynamic and interactive approach to reinforcing learning objectives. This paper explores the concerns of educators arising from the integration of the online homework assignment and presents an educational intervention for 5<sup>th</sup> grade students. The aim of the intervention was to engage students in online homework as a supplementary material to the educational process. Results from the evaluation of the intervention are presented and the benefits of the intervention are highlighted. Although there are various technological, pedagogical and social challenges that need to be addressed, online homework can contribute to a more positive learning environment. Teachers who assign online homework seem to help enhance students’ intrinsic learning motivation so that students may achieve better learning outcomes.

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.

Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process. This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative. This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05. The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant. The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended.

Pushing health data access boundaries - adapting NHS governance for 3 challenges: clinical free-text, safe commercial access and AI development

This presentation will demonstrate expansions to TRE governance frameworks developed to facilitate safe data access in three advanced areas: commercial access, clinical free-text, and innovation with AI/Machine Learning. NHS research governance frameworks - originally set up for clinical trials and expanded into the TRE and secondary data use space – are not an immediate fit for advanced techniques in data science research. Over 2 years, public, expert and user engagement approaches were utilised to support developing frameworks within DataLoch, a Health and Social Care TRE and data access service in South-East Scotland. Surveys, workshops and continuous user and data controller feedback allowed the testing of tools and assessments to adapt traditional NHS governance structures. These frameworks utilise the Five Safes framework to make NHS data controllers supportive of innovative research in the public and health service’s benefit. The presentation will summarise the challenges faced for one TRE and researchers working to support these areas, and the frameworks that are now are being tested within an NHS Scotland Health Board. The development and implementation demonstrates that governance structures can be adapted collaboratively and paves the way for more flexible TRE governance to allow the use of NHS patient data to remain legally and ethically robust while supporting advanced techniques of analysis.

Psycho-semantic analysis of job advertisements as a communicative tool of educational marketing

Introduction. The relevance of the study of job advertisements for teachers and psychologists is associated with the need to find tools for regulating communication within the modern educational environment with dynamic, constantly changing working conditions. The publication of employer requirements sets the direction for changing the criteria for the success of the specialist, which changes the entire educational environment. The purpose of the study: to identify the categorical semantic structure of job advertisements for teachers (using the example of the "Infrastructure of Scientific Research Data" platform). Materials and methods. The object of the study was a corpus of teacher job vacancies from the "Infrastructure of Scientific Research Data" platform, published in 2023 in 83 regions of Russia (5561 vacancies). The job postings included descriptions of candidate requirements, information about the offered salary, and the region in which the vacancy was available. As a comparison group, a corpus of psychologist job vacancies (2076 vacancies) was considered. The following methods were used in the study: 1) Latent Dirichlet Allocation (LDA) method for identifying and analyzing the structure of requirements presented in teacher and psychologist job vacancies; 2) Term Frequency-Inverse Document Frequency (TF-IDF) method for categorical semantic analysis of the structure of teacher and psychologist job vacancies and description of the semantic framework (frame) of the job description; 3) Descriptive statistical methods for analyzing the needs of professionals in helping professions in different regions of Russia and the conditions offered to them. The results of the study. The regions of the Russian Federation with the greatest need for teachers and psychologists have been identified. Based on the statistical significance in the collection of text documents, the most demanded skills of teachers by employers are identified: methodological development and design of an individual educational route (0.167), work with children with disabilities (0.144), organization of the educational process (0.089). For vacancies of psychologists, the most significant were: work on investigative actions (0.319), methodical work in an educational organization (0.187), psychological and pedagogical support for successful development and training (0.129). The main meanings governing professional pedagogical activity are more focused on meta-skills based on the values of inclusion and continuous improvement of education, as well as on flexible skills (Soft skills) that increase competitiveness in various fields of pedagogical activity. In the vacancies of psychologists, professional skills (Hard skills) are relevant, applicable in specific areas, which somewhat narrows the scope of professional implementation. Conclusion. The results of the empirical study allow for considering job vacancy advertisements as one of the communicative tools of educational marketing. Analyzing the significant professional skills from the employer's perspective will enable the development of recommendations for the development of professional communication for teachers, which can be considered in the system of continuous professional education, as well as in regulating interaction within the educational environment. Automated analysis of job vacancies can become a successful interactive diagnostic tool for educational marketing, necessary for the development of the educational space and the improvement of the competitiveness of educational institutions.

Electrocardiographic Diagnosis of Right Ventricular Hypertrophy: a Literature Review

The development of right ventricular hypertrophy (RVH) is a consequence of a large group of diseases that lead to increased pressure or volume load on the right ventricle. Electrocardiography (ECG) is a commonly used diagnostic method in clinical practice. This method allows for the identification of RVH signs in the early stages of examination. During the 20th century, more than 30 diagnostic criteria were developed. Several of these criteria are associated with worse prognosis, including increased mortality. Authors of international guidelines recommend the verification of criteria in local populations. Such studies have not been published in Russia, but according to data obtained from foreign researchers, the diagnostic properties of ECG criteria can vary significantly. In most cases, the criteria are characterized by low sensitivity and high specificity, which complicates early diagnosis of conditions leading to RVH development. It is proposed to increase the sensitivity of the method by using all the developed criteria or sets that include some of them. However, this approach may reduce the specificity of ECG diagnosis of RVH, and the selection of criteria for inclusion in diagnostic algorithms is not always based on scientific research data. This article analyzes the history of the development of RVH criteria, research data on their diagnostic capabilities, and ways to improve diagnostic algorithms.

Проблемы преступности лиц, больных наркоманией, состоящих на учете уголовно-исполнительных инспекций

The article is devoted to the study of drug addiction among convicts who have been sentenced without isolation from society, suspended or replaced with something else - without isolation from society, and have also been granted a deferment of serving their sentence or parole. Purpose: to review and evaluate the results of K.V.’Luttseva s dissertation research on the topic “Crime of drug addicts registered with penal inspections: characteristics, determinants, prevention” in the context of the modern development of the drug situation. Research methods: the basis was the dialectical method of understanding drug addiction as a complex socio-legal phenomenon based on current areas of legal regulation and scientific research data. A critical and problematic analysis of K.V.’Luttseva s dissertation was carried out, expressed in the author’s opinion on the problem of criminality of drug addicts registered with penal inspections. Conclusions: it is proved that there are significant features in the content of the study of crime among drug addicts registered with penal inspections, as well as the need for adequate and coordinated prevention. Scientific and practical significance: the article has scientific significance, providing a comprehensive overview of current problems in the context of combating recidivism among drug addicts. The practical significance lies in the study of strategies based on modern theories, which can serve as the basis for improving legislation and practical interaction in the field of countering drug addiction.

A fair multi-attribute data transaction mechanism supporting cross-chain

Reliable storage and high-speed data networks enable individuals to access high-quality Internet of Things (IoT) data for scientific research through global transactions. Blockchain technology provides transparency for institutions to securely store and manage IoT data, while cross-chain transaction mechanisms facilitate the flow of IoT data. However, fairness issues may arise when it comes to cross-chain transactions of IoT data. This paper proposes a mechanism for multi-attribute data transactions to support cross-chain. The solution utilizes Vickrey–Clarke–Groves (VCG) auction, Paillier, Intel SGX, and other technologies to design a secure and equitable data seller selection scheme. The scheme ensures that the selection process for data sellers is both informed and private. Additionally, we generate a key pair for each attribute in the dataset to produce the corresponding attribute data signature. The dataset’s legitimacy is verified through batch verification to ensure that the data seller’s purchased attributes align with their requirements. The exchange of crypto assets and private keys between data sellers and buyers is designed to achieve fair payment. Our research suggests that the scheme meets the necessary security standards, and simulation results confirm its feasibility and effectiveness.

Integrating Generative AI in Data Science Programming: Group Differences in Hint Requests

Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners’ use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N=45) and those without AI assistance (N=44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.

RLclean: An unsupervised integrated data cleaning framework based on deep reinforcement learning

Data cleaning, a prerequisite to subsequent data analysis, has always been the focus of data science research. Datasets with errors can severely detract from the quality of downstream analytical results. Unfortunately, despite the proliferation of various data cleaning methods, it remains a time-consuming problem and frequently entails considerable labor expenses. In reality, errors are often heterogeneous and require different solutions. As a result, stand-alone methods often inadequate for addressing dirty data with multiple types of errors, while studies have demonstrated that combining such methods always require human intervention and the result remains unsatisfactory.In this paper, we propose an unsupervised integrated data cleaning framework, namely RLclean. Based on deep reinforcement learning, RLclean takes advantages of multiple data cleaning techniques, enabling it to effectively clean multiple types of errors and achieve satisfactory results. Additionally, it eliminates the need for costly human involvement, as the cleaning strategy is learned by data-driven, which further allows the framework to self-adapt to diverse domains. RLclean mainly consists of two parts: (i) an integrated error detection model that unites multiple techniques to detect different types of errors from multiple views; and (ii) an integrated data repair model that learns the optimal repair operations and repairs dirty data in an unsupervised manner. Extensive experiments on benchmark datasets have demonstrated the superiority of RLclean over state-of-the-art methods.

SODIUM NITRITE (E250) AND ZINC AS MODULATORS OF THE BIOMECHANICAL PROPERTIES OF BLOOD IN SPORTS

Nutritional supplements are often used by athletes to support their performance. However, if there is no nutrient deficiency, supplementation may not improve performance and may even have a detrimental effect on both performance and health. The aim of this study was to review the influence of sodium nitrite (NaNO2) and zinc (Zn) on the hemorheological status of different types of blood cells with application in sports. Sodium nitrite, known as food preservative E250, and zinc are proven modulators of the biomechanical properties of blood, influencing its hematometric and hemorheological properties. Our previous experimental results demonstrated that intraperitoneal injection of NaNO2 in mature rats had a diverse effect on the leukocyte and platelet populations. After the treatment, the number of monocytes and granulocytes decreased significantly, thus increasing the susceptibility to infections. The number of platelets increased, altering the coagulation status of blood. Scientific research data show that the proper intake of NaNO2 and Zn improves glucose metabolism, a key factor in maintaining energy levels during physical activity, muscle strength, and endurance. Zn also exhibits protective effects against bone loss. Excessive intake of zinc for long periods may interfere with other essential elements of metabolism. This review proves that hemorheological parameters such as whole blood viscosity, plasma viscosity, as well as the main hematometric indices are suitable quantitative biological markers for monitoring nitrite and/or zinc supplementation.

A novel integrated quantum-resistant cryptography for secure scientific data exchange in ad hoc networks

The fast advancement of quantum computing poses a substantial challenge to the privacy and security of critical scientific research data. This is because the standard cryptography methods, which have been proven effective in classical computers, are rendered less secure in the face of quantum computing approaches. Previously, numerous endeavors have been made to safeguard confidential information through the utilization of different standards and quantum cryptographic methods. However, there remains a research void with several challenges and limitations, including excessive computational burden, vulnerability to various attacks, and limited hardware compatibility for implementation. We propose a modern hybrid cryptographical approach to secure sensitive data from various attacks and vulnerabilities to address the existing limitations. The suggested standard integrates traditional cryptographic standards with quantum-resistant standards to boost sensitive scientific data privacy and security and address various classical cyber-attacks and critical quantum attacks. For the context of scientific data privacy and security, our work depicts a hybrid standard structure by performing a systematic exploration of current encipherment model challenges and issues such as the investigation of various susceptibilities of mathematical cryptographic models. In this work, we apply lattice-based coding as the outer layer and Advanced Encryption Standard (AES) as the inner layer to improve security and efficacy. The proposed security theorem launches the operational veracity of lattice-based coding in the face of quantum attacks, while a complete investigation of the proposed algorithm efficacy vitrines the enhanced security and scalability of the anticipated hybrid standard transversely diverse input sensitive data volumes. Furthermore, this proposed work offers the security confidence score of the hybrid model by the amalgamation of AES and lattice-based cryptography (LBC), hence guaranteeing strength next to both quantum and traditional computing weaknesses. The investigational results prove the improved efficiency of the proposed hybrid model in contrast to traditional and past quantum-resistant models.

Making teacher educators’ experiences visible: seeing inside the hopeful and ambiguous dimensions of practice

ABSTRACT The field of teacher education is subject to intense scrutiny and policy reform and within this context, the voices of those working within the field are often marginalised. Drawing on our larger study of teacher educators, we addressed the key research question: “How do those who work in the field of teacher education articulate and represent the nature of their work?.” Using a fusion of arts-based and data science research methods and the theory of practice architectures, we examined the ways that teacher educators’ work is mediated within cultural-discursive, material-economic, and social-political domains. We argue that while ambiguous dimensions exist in teacher educators’ practices, they remain hopeful about their work and find spaces to create alternative sayings, doings and relatings as they prepare teachers for the profession. We contend that using both text-based and visual images facilitates the generation of enhanced meanings to highlight the complexity of teacher educators’ work to those within and outside the profession.

Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.

This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

VICR: A Novel Software for Unbiased Video and Image Analysis in Scientific Research.

In scientific research, objectivity and unbiased data analysis is crucial for the validity and reproducibility of outcomes. This is particularly important for studies involving video or image categorization. A common approach of decreasing the bias is delegating data analysis to researchers unfamiliar with the experimental settings. However, this requires additional personnel and is prone to cognitive biases. Here we describe the Video & Image Cutter & Randomizer (VICR) software (https://github.com/kkihnphd/VICR), designed for unbiased analysis by segmenting and then randomizing videos or still images. VICR allows a single researcher to conduct and analyze studies in a blinded manner, eliminating the bias in analysis and streamlining the research process. We describe the features of the VICR software and demonstrate its capabilities using zebrafish behavior studies. To our knowledge, VICR is the only software for the randomization of video and image segments capable of eliminating bias in data analysis in a variety of research fields.

A dataset of desert plant catalogue in Xinjiang

The dataset of desert flora catalogue in Xinjiang was constructed by consulting monographs such as Chinese Flora, Xinjiang Flora, Chinese Desert Flora, Xinjiang Flora, Xinjiang Tree Chronicles, as well as related documents and major online open databases, aiming to enhance the accuracy and completeness of the data and information included in the catalogue. This dataset comprises 31 families, 150 genera and 360 species of desert plants in Xinjiang, including 355 species of angiosperms and 5 species of gymnosperms. Among them, there are 80 species of plants with medicinal value, 61 species of plants with forage value, and 26 species with economic value, accounting for 22.2%, 16.9% and 7.2% of all plants in the dataset, respectively. This dataset contains the Chinese names, Latin names, aliases and other information of desert plants, which can provide scientific reference and basic data for scientific research on various aspects, such as the growth environment and plant value of desert plants. Moreover, it is of great significance for the conservation and development of desert plant diversity in Xinjiang.

Boundaries of the built environment: defining the significance of the material presence of spatial morphology in social life

Settled societies inhabit environments shaped by building activity. Geographic data in social scientific and geographical research are generally composed of architectural and social categories derived from commonplace lived experience and societal knowledge, thus carrying socio-culturally specific meaning. The mundane pragmatism of such categories conflate spaces and buildings with their use and may obstruct effective comparison. Here I introduce a set of formally redescriptive ontological concepts for built environments that operates on the basis of how differentiation and subdivision constitute distinct occupiable spaces through boundaries. An ontology of the inhabited built environment arises from the application of these socio-spatial and material concepts called ‘Boundary Line Types’ (BLT). I present and photographically illustrate the definitions of the BLTs, which are conceived on a critical realist basis and rooted in a multidisciplinary body of theory concerning the development and inhabitation of built space. Considering inhabited built environments through BLTs foregrounds the emergent logic by which spaces are divided and connected, creating configurations of boundaries as material frames that afford everyday social life. Since BLTs offer transferrable empirical principles from which these material frames emerge, they also enable diachronic and cross-cultural comparative social research. My proposition to approach social scientific built environment research through constitutive material boundaries offers a comparative complement to commonplace and socio-culturally specific spatial categories that compose most geographic data, enabling formal thick redescriptions and the potential for quantitative spatial analysis.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.