Original Data Sources Research Articles

Sequential Manipulation Against Rank Aggregation: Theory and Algorithm.

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.

Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach

Machine learning has become increasingly important in biomechanics. It allows to unveil hidden patterns from large and complex data, which leads to a more comprehensive understanding of biomechanical processes and deeper insights into human movement. However, machine learning models are often trained on a single dataset with a limited number of participants, which negatively affects their robustness and generalizability. Combining data from multiple existing sources provides an opportunity to overcome these limitations without spending more time on recruiting participants and recording new data. It is furthermore an opportunity for researchers who lack the financial requirements or laboratory equipment to conduct expensive motion capture studies themselves. At the same time, subtle interlaboratory differences can be problematic in an analysis, due to the bias that they introduce. In our study, we investigated differences in motion capture datasets in the context of machine learning, for which we combined overground walking trials from four existing studies. Specifically, our goal was to examine whether a machine learning model was able to predict the original data source based on marker and GRF trajectories of single strides, and how different scaling methods and pooling procedures affected the outcome. Layer-wise relevance propagation was applied to understand which factors were influential to distinguish the original data sources. We found that the model could predict the original data source with a very high accuracy (up to >99%), which decreased by about 15 percentage points when we scaled every dataset individually prior to pooling. However, none of the proposed scaling methods could fully remove the dataset bias. Layer-wise relevance propagation revealed that there was not only one single factor that differed between all datasets. Instead, every dataset had its unique characteristics that were picked up by the model. These variables differed between the scaling and pooling approaches but were mostly consistent between trials belonging to the same dataset. Our results show that motion capture data is sensitive even to small deviations in marker placement and experimental setup and that small inter-group differences should not be overinterpreted during data analysis, especially when the data was collected in different labs. Furthermore, we recommend scaling datasets individually prior to pooling them which led to the lowest accuracy. We want to raise awareness that differences in datasets always exist and are recognizable by machine learning models. Researchers should thus think about how these differences might affect their results when combining data from different studies.

Danish and Swedish National Data Collections for Cancer – Solutions for Radiotherapy

Collecting large amounts of radiotherapy (RT) data from clinical systems is known to be a challenging task. Still, data collections outside the original RT systems are needed to follow-up on the quality of cancer care and to improve RT. This paper aims to describe how RT data is collected nationally in Denmark and Sweden for this purpose and gives an overview of the stored information in both countries' national data sources.Although both countries have clinical national quality registries with broad coverage and completeness for many cancer diagnoses, some were initiated already in the seventies, and less than one in ten includes quantitative information on RT to a level of detail useful for more than basic descriptive statistics. Detailed RT data can, however, be found in Denmark's DICOM Collaboration (DcmCollab) database, initiated in 2009 and in Sweden's quality registry for RT launched in 2023 (SKvaRT). Denmark has collected raw DICOM data for all patients enrolled in clinical trials, with files being directly and automatically transferred to DcmCollab from the original data sources at each RT centre. Sweden collects aggregated RT data into SKvaRT for all patients undergoing RT in Sweden, with DICOM files being transferred and selected alpha-numeric variables forwarded via a local intermediate storage database (MIQA) at each hospital. In designing their respective solutions, both countries have faced similar challenges regarding which RT variables to collect and how to technically link clinical systems to their data repositories. General lessons about how flexibility currently is balanced with storage requirements and data standards are presented here together with future plans to harvest real-world RT data.

Psychological and pharmacological treatments of intermittent explosive disorder: a meta-analysis protocol

IntroductionIntermittent explosive disorder (IED) is characterised by recurrent, sudden episodes of impulsive aggression that are disproportionate to the provocation. The condition’s management remains challenging due to the variability in treatment...

Wind loading on gable and multi-span roof buildings: Comparison between field monitoring, wind tunnel experiments, and design code provisions

The wind loading provisions in the National Building Code of Canada (NBCC) have not been updated for decades despite developments in wind tunnel methods since the original source data. This study aims to review the Nbcc 2020 provisions compared to wind tunnel experiments, field measurements, and the ASCE 7–22 provisions for single and multi-span gable roof buildings. Field measurements and wind tunnel tests are used to collect data for an isolated building and later for a multi-span gable. The pressure coefficients from the field measurements were mostly higher than the wind tunnel, which is consistent with previous studies. The results provided evidence that NBCC provisions for single-gabled roofs (with slope 7∘<α≤27∘) and multi-span gabled roofs (with slope 10∘<α≤30∘) are currently underestimated. New provisions are suggested for the corner (c), edge (s), and field (r) zones of the single-span gabled roof and for gable-end-edge (s’) zone of the multi-span. It is suggested to merge Zone s into Zone c to form a new perimeter Zone p for the single gabled roof and to merge Zone s’ into Zone c for the multi-span gabled roof. The proposed changes correct the current provision underestimation and add simplicity for easier construction.

ANALYSIS OF PARAPRASHING STRATEGIES IN THESIS OF EXEMPLARY STUDENTS’ ENGLISH EDUCATION STUDY PROGRAM UNIVERSITAS MUHAMMADIYAH KOTABUMI

There are many strategies of avoiding plagiarism in academic writing, paraphrase is the most important one. Students often struggle hard with paraphrasing their citation properly and effectively. Furthermore, to address these challenges it is important for student to apply some strategies of paraphrase in their writing. The objectives of this research is to describe the paraphrasing strategies applied by students as well as the the most frequently strategies in thesis. This study uses qualitative research that examines 30 paraphrase from three thesis of first exemplary students English Education Study Program Universitas Muhammadiyah Kotabumi who graduated in 2021-2023. The category of paraphrasing strategies from Pietrick in Injai were used in this study as well as instruments for collecting data. The instrument used was document analysis. Data analysis was carried out in several stages; identifying data, finding the original source of data, analyzing the data, and interpreting the data. The findings show that there are three paraphrasing strategies applied in students theses. The three strategies were changing the structure and grammar of original text, changing the word of original text, and changing the structure of ideas. The researcher also found two combination strategies used by students, namely; changing the structure and grammar of original text and changing the word of original text, and also changing the word of original text and changing the structure of ideas. Then, of the five strategies, changing the word of original text is the most frequently strategies applied by students.

Dynamically documenting archaeological excavations based on 3D modeling: a case study of the excavation of the #3 fossil of hominin cranium from Yunxian, Hubei, China

Documenting tangible cultural heritage using 3D modeling techniques is gradually becoming an indispensable component of archaeological practice. The 3D modeling techniques based on photogrammetry and LiDAR scanning enable high-accuracy and high-realistic reconstruction of archaeological sites, and have been proven a powerful tool for documenting archaeological excavations. However, dynamically documenting an ongoing excavation using these techniques is still considered tedious, time-consuming, expensive, and dependent on expertise. Moreover, the application of 3D modeling techniques in archaeological excavations still faces some technical challenges, such as modeling with multi-source and multi-scale data, fusing local models at different times into a whole, achieving fast modeling while GPU workstations are not available in the field, and evaluating the quality of 3D models. As a result, there are still very few archaeological teams deeply engaged in dynamic documentation with 3D modeling techniques, and traditional drawing sketches and taking photographs still dominate. In these senses, documenting the archaeological excavation at the Yunxian Man site (located in Hubei, China) is an invaluable opportunity for exploration and practice. Archaeologists determined to conduct dynamically documenting at the beginning of the 6th excavation project for the site, and established a rotation system to reconcile physical excavation with digital preservation. Through repeated practice and communication, we proposed a workflow and pursued several new methods to enhance the feasibility of dynamically documenting, and obtained 4D models of the ongoing archaeological excavations. In 2022, the Yunxian Man site unearthed the most intact fossil of hominin cranium from about one million years ago in the Eurasian continent, preserving important and scarce anatomical features of early humans in Asia. As the original taphonomic context of the fossil corroded away during physical excavations, the digital documentation consisting of 4D models serves as permanent original data source in subsequent archaeological research. Moreover, we obtained cross-scale 3D models from geographical environment to archaeological site, excavation area, and cultural remains, and all of these 3D models are in an actual, unified coordinate framework. Thus, we can contribute to multidisciplinary cross-collaborative research through data sharing. Considering that digital documentations serve a great value in archaeological research, this paper focuses on sharing the workflow and methods to facilitate digital preservation for more archaeological projects.

The Corporate Chief of Staff: Strategic Leadership Influence From Outside the Spotlight

For decades, scholars of strategic leadership have explored the degree to which senior executives and the team of top managers influence firm outcomes. However, one growing quasi-executive role in corporate settings, the chief of staff (COS), has been almost entirely ignored. Our paper presents a novel, foundational exploration of the COS role, including primary job functions and challenges, characteristics and backgrounds of incumbents, and relevance for strategic leadership and upper echelons theory. We synthesize three original sources of data — 2,500 LinkedIn COS profiles, surveys of 108 COSs, and in-depth interviews of 13 current and former COSs — to identify COS tasks and functions that interface with and influence the CEO, the top management team (TMT), and the broader organization. Though not typically considered a member of the TMT, we find compelling evidence for their ‘behind the scenes’ relevance to strategic leadership processes and outcomes. We then lay out a future research agenda comprising four domains: COS influence on CEO attention and psychological states, COS influence on TMT and organizational processes and outcomes, characteristics and conditions of organizations with a COS, and long-term career trajectories of COSs.

FetalAI: A deep learning web-based application for predicting birthweight from prenatal ultrasound measurements

FetalAI is a web-based application that utilizes a longitudinal learning procedure that integrates data augmentation and deep learning as back-end, with a purpose of predicting an newborn’s projected birthweights and the associated risks of excessive fetal growth conditions, including large-for-gestational-age (LGA) and macrosomia. This article provides an end-to-end overview of FetalAI’s development process, which includes data sourcing and preprocessing, back-end model design and training, application output interpretation, and user interactions. We begin by describing the original training data sources and their mathematical structures. We then outline the data engineering pipeline that acquires and pre-processes user input. After data sourcing and pre-processing, we provide details on the back-end model, which applies mixed-effects models for data augmentation and pre-trained recurrent neural networks (RNNs) to forecast birthweight sequences. After back-end, we discuss the front-end development of FetalAI, with an emphasis on creating the graphical user interface (GUI) using Streamlit and the application’s ability to handle corner cases and anomalous inputs. Our article concludes by highlighting the key functionalities and potential use cases of FetalAI, as well as outlining future directions for research and development in this domain. FetalAI represents a significant step forward in the application of deep learning techniques to improve prenatal care and support clinical decision-making.

A bi-fidelity DeepONet approach for modeling hysteretic systems under uncertainty

This study proposes a multi-fidelity paradigm for developing surrogates of degrading hysteretic systems under uncertainty through the use of deep operator networks (DeepONets). Instead of attempting to directly train a DeepONet on the original response, this study adopts a residual modeling approach wherein the DeepONet is trained on the discrepancy between the original (high-fidelity) data source and a relatively simpler (low-fidelity) representation of the system. Within these examples, a conventional Bouc-Wen model is treated as a “low-fidelity” representation given that it is free of any further assumptions about the nonlinear behavior, while the “high-fidelity” data is generated from different structures with various forms of complex hysteretic behavior. The results of this study show that the proposed multi-fidelity approach consistently outperforms standard surrogates trained on only the original datasets considering a variety of systems with unknown parameters. The results also show that the difference in performance grows as training data becomes more scarce, a critical consideration for many real-world engineering systems, and that the proposed multi-fidelity approach maintains its performance edge even when controlling for training time and noise in the training data.

Ethnobotanical survey of medicinal plants used in north-central Morocco as natural analgesic and anti-inflammatory agents

For centuries, the Moroccan population has relied on herbs as medicine to treat a variety of diseases, especially inflammation and pain-related ones. To the best of our knowledge, no survey had ever been conducted to address this subject in the Fez-Meknes region of Morocco. Thus, a survey was conducted of 544 interviewees, using a semi-structured ethnopharmacological survey designed with “Why-How” questions about plants used, their vernacular names, parts used, mode of preparation, and mode of administration. Fidelity level (FL), relative frequency of citation (RFC), frequency of citation (FC), informant consensus factor (ICF), and family importance value (FIV) were calculated. A total of 104 plant species belonging to 49 families used for inflammatory and pain treatment were documented. Lamiaceae (16 species) was the most used family and Curcuma longa L. (RFC=0.069) was the most frequently prescribed by local traditional healers and herbalists. Leaves were the most used part for herbal remedies, appearing in 30.8 % of preparations. Decoctions and infusions were the most popular preparation methods with percentages of 38.3 % and 19.2 %, respectively. Inflammations and pain in the digestive system had the largest widespread affections (IFC= 0.729) in the Fez-Meknes region. The findings of this study uncovered a reliable and original source of ethnomedicinal data pertaining to plants used to treat inflammation and inflammatory pain in the Fez-Meknes region, which could serve as a credible source of knowledge to determine new-based phytomedicines.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

Predicting an Optimal Virtual Data Model for Uniform Access to Large Heterogeneous Data

ABSTRACT The growth of generated data in the industry requires new efficient big data integration approaches for uniform data access by end-users to perform better business operations. Data virtualization systems, including Ontology-Based Data Access (ODBA) query data on-the-fly against the original data sources without any prior data materialization. Existing approaches by design use a fixed model e.g., TABULAR as the only Virtual Data Model — a uniform schema built on-the-fly to load, transform, and join relevant data. While other data models, such as GRAPH or DOCUMENT, are more flexible and, thus, can be more suitable for some common types of queries, such as join or nested queries. Those queries are hard to predict because they depend on many criteria, such as query plan, data model, data size, and operations. To address the problem of selecting the optimal virtual data model for queries on large datasets, we present a new approach that (1) builds on the principal of OBDA to query and join large heterogeneous data in a distributed manner and (2) calls a deep learning method to predict the optimal virtual data model using features extracted from SPARQL queries. OPTIMA — implementation of our approach currently leverages state-of-the-art Big Data technologies, Apache-Spark and Graphx, and implements two virtual data models, GRAPH and TABULAR, and supports out-of-the-box five data sources models: property graph, document-based, e.g., wide-columnar, relational, and tabular, stored in Neo4j, MongoDB, Cassandra, MySQL, and CSV respectively. Extensive experiments show that our approach is returning the optimal virtual model with an accuracy of 0.831, thus, a reduction in query execution time of over 40% for the tabular model selection and over 30% for the graph model selection.

Abstract LB141: Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium

Abstract Introduction: The promise of achieving cancer control both nationally and globally rest on rigorous data collection, data harmonization and data sharing to catalyze scientific knowledge and understanding to more quickly advance our progress to addressing cancer disparities. In response, the LIFE Project aimed to establish a cancer-focused robust, functional platform for data harmonization and sharing across national and international studies with a focus on low-resourced settings and LMIC. Methods: Established in 2022, the integrated database platform hosts data collected from the ongoing Jamaican LIFE and US CAP3 projects. Both studies focus on investigating biologic and socioecologic risk factors for non-communicable chronic diseases (NCDs) and cancers. Participants self-reported data via REDCap. A one-to one mapping of the variables from both questionnaires was conducted and stored in the integrated REDCap file. An automated REDCap Application Programming Interface (API) script weekly imports the new entries into the integrated database. To ensure data accuracy, on a weekly schedule, data importation and data integration are monitored and assessed by random sampling of original source data entries and the corresponding entries in the integrated database. Any additional errors in data integration are resolved via consultations with the programming team. Results: As of June 2023, 3,369 participants were enrolled - 2,561 enrolled from the LIFE study and 808 from the CAP3 study. Overall, the combined participants were 89% Afro-Caribbean or African American, and comprised 40% males with a median age of 54 years and 60% females with median age being 51 years. For the LIFE study, median age is 50 years, and for the CAP3 the median age is 55. The prevalence of self-reported health conditions are as follows: diabetes - 13% LIFE, 13% CAP3; hypertension - 37% LIFE, 19% CAP3; dyslipidemia - 15% LIFE, 21% CAP3. Conclusion: With the continuing need for precision public health, there is a dire need to address disparities through innovative ways that promote comprehensive data linkage and analyses. This platform can be used in mapping individual cohort data with demonstrated success for the LIFE and CAP3 studies. Our web-based query tool to interface that interfaces with our REDCap database also allows us to query the available variables in both the separate and combined cohort databases and will facilitate hypothesis development. The impact of our study goes beyond the data harmonization, and robust data integration platform that links the LIFE and CAP3 studies. This allows for US comparative studies. Novel. We will also have the ability to harmonize with large US cohort studies, as well as interrogate research questions examining commonalities and differences among African Americans, Afro-Caribbean Americans and Afro-Caribbeans in the Caribbean. These comparative studies will help to elucidate the causes increased health risks, and accelerate the science to remedy the global Black NCD and cancer disparities. Citation Format: Camille C. R. Ragin, Janeil Williams, Olga Tchuvatkina, Joette Mckenzie, Kimlin Ashing, Marshall Tulloch-Reid. Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB141.

DRC-EDI: An integrity protection scheme based on data right confirmation for mobile edge computing

As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.

Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke. retrospective cohort study. We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse). We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population. Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p<0.0001), and a higher prevalence of chronic obstructive pulmonary disease (COPD: 8.5% vs 4.7%, p<0.0001), prior ischemic stroke (1.7% vs 0.1%, p<0.001), and prior venous thromboembolism (VTE: 8.2% vs 1.5%, p<0.0001). They also had a longer length of stay (8 days [IQR 3-16] vs 6 days [IQR 3-13], p = 0.011), and higher costs associated with their stroke encounters: $11,498 (IQR $4,440 -$20,668) in the cancer cohort vs $8,084 (IQR $3,947 -$16,706) in the non-cancer cohort (p = 0.0061). A multivariable logistic regression model identified 5 predictors for recurrent ischemic stroke in the cancer cohort using synthetic data; 3 of the same predictors identified using real patient data with similar effect measures. Summary statistics between synthetic and original datasets did not significantly differ, other than slight differences in the distributions of frequencies for numeric data. We demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.

The Attitude of The Chinese and Vietnamese Ruling Class Towards Western Astronomy From the 16th to the 18th Centuries

From the 16th century to the 18th century, in order to realize the goal of promoting its propaganda and strengthening the influence of Christianity in China and Vietnam, Western missionaries, especially Jesuit missionaries, applied the method of “missionary academic” and “missionary bibliography” thoroughly, effectively and creatively. As a result, Western science and technology in general, and astronomy in particular, were gradually introduced into these two countries. The ruling class in China and Vietnam still had a few critical and negative reactions to such moves, but in general openness to and active acceptance of Western astronomical achievements were always the dominant attitudes of emperors, kings and mandarins in these two countries.On the basis of taking advantage of the original historical data sources and research achievements of Chinese, Vietnamese and international scholars, and at the same time combining the application of the research method of historical science along with other approaches, especially the comparative research method, this article will thoroughly analyse the attitude of the ruling class of China and Vietnam in receiving Western astronomy. This article also clarifies the main cause for the difference in the reception of Western astronomy by emperors, kings and mandarins in these two countries, thereby making a contribution to the study of the history of East-West cultural exchanges in China and Vietnam in the 16th, 17th and 18th centuries.

Workarounds produce pseudo-data quality: Insights from case studies.

This research explores how workarounds can affect organisational data quality. It uses a theory-building approach from four case studies in Indonesian organisations responsible for producing managerial reports. This study finds that workarounds produce pseudo-data quality when original data sources are inaccessible. Employees tend to re-interpret data pragmatically to rectify pressing data needs, producing pseudo-data quality. Even though pseudo-data quality might not reflect actual transactions, it emphasises data consistency across available data sources. Therefore, it helps produce acceptable managerial reports. This research's theoretical insights are helpful for unit managers to mitigate pseudo-data quality's negative consequences, such as financial loss and damage to reputation.

Helicopter Aerial Work: Technology to Meet Growing Needs in Critical Missions

Helicopters provide aerial services such as firefighting, medical transport, agriculture, and construction for critical industries that form the backbone of modern society. These "aerial work" applications comprise the majority of the current market for civilian helicopters and include a diverse set of mission types and flight profiles; in many cases, aerial work applications are meeting growing needs driven by the climate crisis and energy transition. Through a series of more than 180 interviews with operators, pilots, and users across helicopter aerial work, this paper — the largest contemporary study of its kind — surveys the challenges that the industry sees for itself. Part I analyzes and summarizes our original primary source data to provide both qualitative and quantitative assessments of the needs of this community. Part II presents a case study for aerial firefighting, where a growing demand driven by record-breaking climatic changes is straining the existing air crews, aircraft fleet, and firefighting technology. Many issues identified in firefighting are endemic problems that beset other neighboring applications within aerial work such as public services, energy infrastructure, construction, and agriculture; technologies that have the potential to have a significant impact on re ghting are discussed. Part III is an analysis of technology and how it might better meet the needs of helicopter aerial work. We identify characteristics such as compatibility, reliability, and application specialization that are valued by helicopter aerial work customers and operators. In the backdrop of unprecedented venture capital investment and technology development in new types of vertical takeoff and landing aircraft for AAM, this paper's core nding is that the needs of helicopter aerial work are not being met: a venture capital culture prioritizes innovation that can create new markets over solving challenges in existing ones. Innovation for commercial air transport missions that operate under different market, environmental, and operational conditions does not "trickle down" to aerial work. We identify a need for increased collaboration between technologists and the aerial work community and for a more thoughtful approach towards investment and development to meet the growing challenges of the industry.

基于InVEST模型的2000–2020年乌鲁木齐碳储量分布数据集

Terrestrial ecosystem is an important part of the global carbon cycle research. As the global carbon cycle can fix CO2 in the atmosphere in soil and vegetation, regional carbon storage is deeply affected by the changes of land use. Urumqi, the capital of Xinjiang Uygur Autonomous Region, is the political, economic and cultural center of the whole region. It is also an important central city and international inland port area in northwest China. With various land cover types, the city is suitable for the study of carbon sequestration in terrestrial ecosystems. The rational layout of its economic and ecological construction has a very important impact on the construction of the Silk Road Economic Belt and the overall layout of China’s westward opening. Based on the data input requirements of the InVEST model carbon storage calculation module, we collected land use data, meteorological data and carbon density data of Urumqi in 2000, 2010 and 2020 to assess the regional carbon storage. The original data source is reliable, and the data processing and calculation strictly follow the professional operating procedures to ensure the accuracy of the data results. In this study, we corrected carbon density data based on meteorological data, which is more compatible with the facts of the study area and can help to improve the accuracy of carbon storage calculation on InVEST model. This study can provide some scientific data reference for the future economic and ecological construction pattern of Urumqi.