ABSTRACT Embedding data inputs into the HK framework, this study first theoretically investigates how data inputs enhance export competitiveness in manufacturing through the mechanism of alleviation in misallocation. Subsequently, the empirical tests are conducted using data from China’s manufacturing sector for the period 2003–2022. This study finds, first, that data inputs significantly improve the export competitiveness of manufacturing, which also depends on the amount of data inputs in industries, as this effect is significantly larger in industries with high data factor intensity compared to those with low data factor intensity. However, the effect of data inputs on export competitiveness varies with the sources and types of data inputs, as well as geographic regions. Furthermore, although alleviation in misallocation is an important channel through which data inputs promote the export competitiveness of manufacturing, it’s the alleviation of capital misallocation that works, rather than the alleviation of labor misallocation.

The digitization of medical data and advances in interoperability have opened opportunities for research studies to use more comprehensive, longitudinal patient data from multiple sources. As patients often interact with many providers and payers over time, collecting data across organizations may have critical implications for accuracy and bias in study results. US policy has promoted exchanging health information among providers, payers, and patients, but less attention has focused on facilitating data collection for research, which presents unique challenges. This study aimed to identify and evaluate existing and emerging approaches for collecting comprehensive provider and payer data for research in the United States, with the goals of informing researchers of possible methods and generating evidence to inform policy initiatives. Our focus was on electronic approaches to data aggregation for studies requiring patient consent. We conducted a landscape analysis through interviews with subject matter experts (SMEs). SMEs were selected based on expertise. We created a list of evaluation criteria, identified existing and emerging approaches, and described the benefits and limitations of each approach by applying the evaluation criteria. We interviewed SMEs until saturation was achieved. Data collection was limited to the United States. A total of 20 SMEs helped identify 8 distinct approaches: (1) general-purpose smartphone app, (2) commercial app, (3) research community app, (4) structured data export, (5) Trust Exchange Framework and Common Agreement Individual Access Service, (6) regional study query, (7) national study query, and (8) aggregated data source. Participant-mediated exchange approaches (1-5) leveraged patients' right of access. Three approaches leveraged existing data exchange services (5-7). To evaluate these approaches, we identified 12 criteria, including perspectives of participants, research teams, and broader stakeholders. Each approach had benefits and limitations; no single approach emerged as superior for all use cases. While currently available approaches for participant-mediated exchange bypass the need for complex governance arrangements, they are limited by participant burden, effort needed by research teams, and data gaps, especially from payers. Some regional health information exchanges and aggregated data sources address governance challenges and can provide services such as preparing analytic datasets but are restricted to specific locations and/or data-source coverage. National networks currently do not allow queries for research and confront challenges in establishing trust and enforcing compliance with data-sharing requirements among network sites. Collecting comprehensive health data from multiple providers and payers in the United States is a complex and evolving process. The suitability of an approach may vary based on the needs of a study. Given the numerous barriers and the lack of a clear dominant method, further exploration and benchmark comparisons of all identified approaches are necessary. Ongoing public policy efforts will likely play an important role in progress.

The Immune Epitope Database (IEDB,iedb.org) is a freely available resource that catalogs experimentally defined immune epitopes and – if available – the immune receptors that recognize them. Currently, the IEDB records ∼185, 000 T cell receptors and ∼5, 000 B cell receptors/antibodies with experimentally verified epitope specificity. Because these receptor data were manually curated from ∼3, 300 references spanning decades, nomenclature inconsistencies present challenges for computational analyses and user queries. To support integrated analysis of the entire dataset, we revised the IEDB receptor data standardization and validation pipeline to flag and correct inaccuracies. Anomalous receptors from over 800 studies were flagged for re-curation. The updated receptor dataset shows greater conformity through consistent gene nomenclature formatting and harmonized CDR sequence delimitation. Taking advantage of the increased receptor data consistency, the IEDB web interface was expanded to include receptor search features directly on the homepage, support V/J gene and species options in the refined receptor search, and allow direct data export in the Adaptive Immune Receptor Repertoire (AIRR) format. We anticipate that the improved receptor data quality will simplify bioinformatics analyses, and facilitate integration of IEDB data into cross-repository data resources, such as the AIRR Knowledge Commons.

Metal-mining extractivism is on the defensive in Honduras. Following the 2009 coup against President Manuel Zelaya, all signs pointed towards an expansion of metal-mining: radical neoliberalization; authoritarian governance; mining policy reform; hundreds of new concessions; domestic and transnational capital interest; and violence with impunity against environmentalists. Despite this, no post-coup metal-mining projects were exporting by the end of 2024. Relying on 47 interviews conducted in Honduras, GIS analysis, and export data, I argue that the strategic relations between capital, the state, and territorial movements undermined capitalist hegemony around mining, thereby undermining stability for mining capital. Pro-mining interests could generate neither material compromises nor compelling discourses necessary to construct hegemony in the territories containing mining concessions. Instead, mining companies opted for force over consent-building strategies. Resistance movements filled the discursive gap, weaving anti-mining discourses into in-depth territorial organizing. The state, debilitated by neoliberal reforms and lacking sufficient autonomy from capital, could not impose hegemony-supporting compromises between companies and communities. As hegemony faltered, movements disrupted mining projects, generating investment risk that caused metal-mining stagnation. This research shows that organized, ordinary people are central to impeding extractivism; impunity does not equal powerlessness of resistance; and coercion can undermine, rather than support, capitalist and extractivist hegemony.

Advances in automated image classification, together with near-global imaging coverage of the Martian surface, have enabled detailed characterization of the spatial distribution of pitted cones, providing a foundation for systematic investigations of their morphological diversity. Concurrently, the continued acquisition of high-resolution Martian imagery over the past decades has allowed photogrammetrically derived digital elevation models (DEMs) to enhance the accessibility, precision, and overall robustness of morphological analyses. However, the number of identified Martian pitted cones causes systematic, manual morphological measurements to be highly labour-intensive and, consequently, impractical for large datasets. To address this challenge, we present a command-line open-source MarsCONE software toolbox, designed to perform automatic cone-morphology detection, and to compute the morphological parameters of Martian pitted cones using High Resolution Imaging Science Experiment (HiRISE)-derived DEMs. The toolbox is built on as a suite of Python tools and Jupyter notebooks, and performs key processing steps including data preparation and transect generation according to user-defined configurations (Generator), signal extraction and landform-point morphology detection (Finder), and cross-transect aggregation with uncertainty handling and data export (Analyzer). This enables MarsCONE to analyze hundreds of pitted cones in a single batch, providing fully reproducible and systematic results within seconds and at minimal computational cost. Consequently, the MarsCONE toolbox improves reproducibility, reduces manual workload, and facilitates large-scale comparative studies of pitted cones across Mars, thereby supporting a more robust understanding of the geological processes governing their formation.

A municipality-level, monthly dataset of disappearance cases and outcomes in Mexico from the National Registry of Disappeared Persons, 2015\u20132025

ABSTRACT Urban functional specialization (UFS) is increasingly organized in a value-chain-oriented manner across cities and may shape firms’ ability to sustain export products in foreign markets. This paper examines how UFS affects export product survival through three chain-based mechanisms – industrial-chain coordination, supply-chain stabilization, and innovation-chain integration. Using text mining and machine learning, we construct a novel value-chain-oriented measure of UFS based on the headquarters – subsidiary networks of Chinese listed firms. Employing a complementary log–log model and firm product-level export data, we find that UFS significantly extends export product survival through the three proposed mechanisms. Moreover, firm AI adoption positively moderates UFS’s impact on export product survival. These findings highlight the importance of cross-city functional specialization for export resilience and demonstrate how firms’ digital capability strengthens the benefits of specialized urban systems.

Training in responsible and ethical conduct of research (RECR) is essential for fostering a strong research culture. We surveyed faculty, students, and research staff at R1/R2 and primarily undergraduate institutions (PUIs) to assess practices in mentoring, administrative support, grant writing, and project management. While many institutions provide formal education in research ethics and grant writing, major gaps persist in mentoring, grant implementation, and project management. R1/R2 faculty reported the greatest institutional support, yet disparities remain across research integrity areas, including human and animal subject protocols. Students were generally satisfied with training but showed limited awareness of RECR foundations, such as data lifecycle management and export compliance. Findings underscore the need for more consistent, comprehensive research training-especially at PUIs.

Deployment of AI-driven automated quality control of whole-slide images in a large tertiary cancer center

ABSTRACT This article examines the evolution of, and controversies surrounding, China's outbound data transfer regime. Taking the Measures for Security Assessment of Data Exports and the Provisions on Promoting and Regulating Cross-Border Data Flows as key inflection points, it traces the regime through three stages. Before the Measures took effect, Chinese law established the architecture for security assessment but provided limited operational detail, while China pursued cross-border data governance through agreements and initiatives. After the Measures were issued, three compliance pathways—security assessment, standard contractual clauses, and certification—were operationalised. However, practical frictions emerged, including a low trigger threshold for security assessment and uncertainty over certain mechanisms' applicability. With the Provisions' entry into force, filing requirements were eased, exempted scenarios were clarified, and pilot free trade zones were authorised to formulate negative lists. The 2025 Measures for Certification of Personal Information Export further consolidated the toolkit and signalled institutional maturation. Nevertheless, uncertainties remain. These include the indeterminate scope of important data and slow catalogue compilation; overlap and divergence among free trade zone negative lists in sectoral coverage and threshold design; difficulties in aligning domestic rules with digital trade frameworks such as the CPTPP and DEPA; and, in international cooperation, the need to navigate fragmented rulemaking and foreign restrictions on data flows. The article argues that China's regime is best understood not as a static set of rules, but as a tiered toolkit dynamically calibrated amid tensions between security, development, and openness, while facing challenges of legal certainty, coordination, and interoperability.

This paper introduces the Type II Exponentiated Half-Logistic Exponential (TIIEHLEx) distribution, a three-parameter model designed for enhanced flexibility in modeling positive-valued data. We derive the distribution’s core mathematical properties, including the probability density function, cumulative distribution function, and quantile function. A key focus of this work is the evaluation of fourteen non-Bayesian estimation methods to identify the most robust approach for parameter estimation. Through a comprehensive simulation study, we demonstrate that the Minimum Spacing Absolute Distance Estimator (MSADE) consistently outperforms other methods, yielding the lowest bias and mean square error across various sample sizes. The practical utility of the TIIEHLEx model is illustrated using March precipitation and insurance service exports data. Goodness-of-fit tests, including AIC and Kolmogorov-Smirnov statistics, reveal that the TIIEHLEx distribution provides a superior or highly competitive fit compared to several well-known heavy-tailed distributions, particularly in capturing complex hazard rate profiles.

The World Health Organization's Integrated Care for Older People (ICOPE) program is an evidence-based and user-friendly approach that supports healthy aging. Despite its demonstrated value, most healthcare professionals remain unfamiliar with its application in clinical settings. Structured training in the ICOPE approach could significantly enhance its adoption and contribute to promoting healthy aging. This study developed within the framework of the European project JA PreventNCD and aims to describe the protocol of a strategy for the implementation of ICOPE framework in Greece. This protocol describes a prospective longitudinal study integrating both educational and research components. During the Implementation Phase - Theoretical Education, approximately 75 healthcare professionals will be recruited to complete training in the ICOPE approach. In the subsequent Implementation Phase - Practical Training and Research, trained professionals will recruit at least 200 older adults aged ≥ 60years with a Katz Index ≥ 5. They will conduct ICOPE Step 1 screenings and Step 2 assessments, followed by individualized care planning and referrals. Older adults will be assessed twice annually over a 24-month period. Trainees will serve as field researchers, recording assessment data and uploading it to a specifically developed digital platform that supports standardized data entry, longitudinal monitoring, and pseudonymized data export for analysis. The study is expected to enhance healthcare professionals' competencies in early detection of intrinsic capacity and functional decline and promote consistent use of person-centred assessment frameworks in primary healthcare. Biannual monitoring will generate the first national dataset on intrinsic capacity trajectories among Greek older adults. Findings will inform the feasibility, acceptability, and sustainability of ICOPE in Greece and provide evidence to support future scale-up and policy adoption. This protocol represents the first structured effort to embed the WHO ICOPE model within the Greek healthcare system. By integrating professional training, community-based assessment, and digital documentation, the initiative aims to strengthen person-centred care, prevent dependency, and align national ageing policies with international standards.

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work.

The void spot assay (VSA) is a widely used, noninvasive method for evaluating urinary behavior in rodents, but existing analysis tools are limited in scope, throughput, or accessibility. We developed Spoti-find, a stand-alone, open-source VSA image analysis application that introduces novel, biologically meaningful metrics including void circularity, distance to paper edge, and volume-based binning into primary, micro-, and nanovoids. Designed with usability in mind, Spoti-find features a graphical interface that enables manual or semiautomated spot identification, adjustable thresholds, and streamlined data export without the need for coding expertise. We validated Spoti-find across diverse datasets, showing strong interuser consistency, sensitivity to known phenotypes in aging and disease models, and accuracy in capturing novel parameters while demonstrating high agreement with existing tools. By capturing behavioral context and spatial morphology in voiding patterns, Spoti-find expands the interpretive power of VSA and provides a flexible, user-friendly platform for phenotyping urinary dysfunction in preclinical studies.NEW & NOTEWORTHY The VSA is a popular, noninvasive method for studying rodent urinary behavior, but current tools lack flexibility and require extensive manual quality control. We developed Spoti-find, an open-source application introducing novel parameters like void circularity, edge proximity, and volume-based binning. With a user-friendly interface requiring no coding, Spoti-find enables manual and semiautomated analysis with high consistency and transparency.

Virtual reality (VR) is increasingly influencing how researchers study human behavior. Yet, running large-scale VR experiments remains a challenge. The software is complex, capturing data from many sources at once is difficult, and analysis at scale takes significant effort. This paper reviews 67 open-source, research-oriented toolkits that support some or all stages of running VR studies, including the planning, design and management of experiments, collecting and synchronizing data, analyzing and visualizing results, and even running studies remotely or across different types of devices. We group existing peer-reviewed tools into five categories and highlight trends in the field. We discuss Unity's strong influence, the ongoing balance between easy-to-use interfaces and flexible coding approaches, the increased adoption of Lab Streaming Layer (LSL) for time-synchronized data capture, and growing interest in real-world and AI-supported analysis. Based on this overview, we offer practical recommendations to make VR research more consistent and sustainable, such as using shared formats for data and events, ensuring accurate timing and replay, and developing data exports that remain usable over time. We also compile a maintenance snapshot, which highlights technological and operational risks. We conclude with a roadmap targeting reproducibility, including considerations for a cross-platform VR experiment description language and distributed, replayable datasets. This review aims to help researchers choose and combine tools more effectively and give developers directions for supporting a more robust and reliable VR research ecosystem.

This study investigates whether the Belt and Road Initiative (BRI) mitigates the impact of exchange rate volatility on China’s exports to BRI partner countries across different technology levels. A GARCH(1,1) model is employed to estimate exchange rate volatility from monthly exchange rates using bilateral export data at the SITC 5-digit level from the UN Comtrade database for the years 2006–2022. Export goods are divided into three categories: high-, medium-, and low-technology manufacturers; resource-based manufacturers; and primary products. The results imply that the BRI weakens the US dollar and mitigates the negative effects of RMB exchange rate volatility on high- and medium-tech exports. Furthermore, the policy increases China's exports in all fields of technology. The findings also reveal that China's exports increased during both the COVID-19 outbreak and the global financial crisis of 2008. The findings demonstrate that the BRI can help mitigate the negative effects of currency rate volatility and increase export stability when the global economy is uncertain.

Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a need for a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To bridge this gap, we introduce the first open-source benchmarking platform, named MToP, for EMT. MToP incorporates over 50 MTEAs, more than 200 MTO problem cases with real-world applications, and over 20 performance metrics. Based on these, we provide benchmarking recommendations tailored for different MTO scenarios. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 50 popular single-task evolutionary algorithms to address MTO problems. Notably, we release extensive pre-run experimental data on benchmark suites to enhance reproducibility and reduce computational overhead for researchers. MToP features a user-friendly graphical interface, facilitating results analysis, data export, and schematic visualization. More importantly, MToP is designed with extensibility in mind, allowing users to develop new algorithms and tackle emerging problem domains. The source code of MToP is available at: https://github.com/intLyc/MTO-Platform

Abstract India’s marine biodiversity remains unevenly documented, with fragmented and inconsistently accessible observations limiting their use for long-term assessment and conservation planning. To address this gap, we developed OceanEyes, a citizen-science mobile application designed for standardised, high-resolution documentation of marine biodiversity across India’s EEZ. The platform integrates Darwin Core-compliant data structures with a two-tier expert validation system, ensuring scientific accuracy and interoperability with global repositories. Unlike existing platforms, OceanEyes is specifically tailored to marine ecosystems, incorporating domain-specific metadata (e.g., habitat, depth, substrate) and enabling offline data collection in low-connectivity coastal regions. A comparative analysis demonstrates that OceanEyes bridges the gap between global generalist platforms and region-specific applications by combining marine focus, standardisation, expert validation, and integration with OBIS via IndOBIS through standardised data export. Initial deployment (November 2023–March 2026) recorded 539 users, with ~ 74% from India, and peak engagement of 232 active users, stabilising at 60–100 users, indicating sustained participation. These results demonstrate the platform’s usability and scalability for participatory marine monitoring. By combining citizen participation with rigorous quality control and FAIR-compliant data workflows, OceanEyes provides a scalable and scientifically robust framework for marine biodiversity documentation, with direct relevance to marine spatial planning and national commitments under the Convention on Biological Diversity and SDG 14.

Purpose This article examines the impact of the U.S.–China trade war on the quality and quantity of China's agricultural exports. While most current studies have focused on the effects of China's retaliatory tariffs on U.S. agriculture, this study investigates the reverse channel, focusing on how escalating U.S. tariffs affected Chinese agricultural exports to the U.S. We highlight a previously overlooked dimension by examining not only changes in export value and volume but also product quality in response to the trade war, offering new insights into the broader implications of trade policy uncertainty. Design/methodology/approach We employ a difference-in-differences approach using bilateral monthly HS 8-digit-level export data from 2017 to 2021, combined with U.S. tariff schedules. Export quality is measured using a structural demand estimation following Khandelwal (2010) and Fan et al. (2015). We assess treatment effects on export quantity, f.o.b. price and product quality by comparing treated exports (to the U.S.) with control exports (to other countries). Extensive robustness checks and heterogeneity analyses are conducted to validate the results across products and provinces. Mechanism analysis explores whether quality downgrading occurred through product exit at the extensive margin. Findings We reveal a hidden export margin that is rarely discussed in the literature: the trade war not only reduced export value but also lowered the export quality of Chinese agricultural products, with no significant impact on export prices. Exporters responded by shifting to lower-quality varieties rather than reducing prices, indicating quality downgrading as a hidden margin of adjustment to trade war. The negative effects were more prominent in animal products, while provinces with higher export sophistication, agricultural orientation, or policy support experienced smaller negative impacts on exports in response to the trade war. Originality/value This study contributes to the literature by uncovering a quality dimension of adjustment in response to trade war tariffs, which has been largely overlooked in previous research. While prior studies emphasize price and volume effects, our findings highlight quality downgrading as a strategic and non-price response to rising trade costs. By focusing on the agricultural sector, which is often vulnerable yet underexplored in trade war analyses, the article provides important insights for policymakers and exporters navigating future trade uncertainties, emphasizing the importance of maintaining product quality as part of export resilience strategies.

Very-low-frequency (VLF) underwater acoustic monitoring is a primary means for monitoring specialized underwater explosions. In view of the limited availability of dedicated off-the-shelf VLF underwater acoustic monitoring equipment and related technologies, this study investigates embedded VLF underwater acoustic acquisition techniques and develops an embedded acquisition device for VLF signals generated by specialized underwater explosions. The device adopts a modular architecture that separates the sensor from the controller, and integrates a low-noise transimpedance amplification and differential conditioning circuit, a high-resolution ADS1285 analog-to-digital converter, an STM32F429 control core, and an eMMC-based storage system. Multi-node time consistency is achieved through GNSS timing combined with a PPS synchronization mechanism, while cyclic recording and plug-and-play data export are implemented using the FATFS file system and USB MSC mode. In underwater explosion experiments, the device successfully recorded the initial shock-wave arrival, bubble pulsation signals, and long-range VLF underwater acoustic signals, thereby demonstrating the capability of the system for low-noise acquisition, reliable time synchronization, and long-term autonomous monitoring. The results indicate that the device can provide long-term stable acquisition and storage capabilities, meeting the engineering requirements of VLF underwater acoustic monitoring, explosion effect assessment, and underwater acoustic experiments, and offering key technical support for the development of VLF underwater acoustic monitoring equipment.