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Thermal neutron capture cross-section for191Ir, ${{\rm{\sigma }}_{{\rm{Ir}}}}$ σ Ir (Eth), is a prominent parameter in producing192Ir radionuclide used in high-dose-rate brachytherapy. However, considerable discrepancies exist in past measurements using the activation method and a derived cross-section data calculation from positive resonance parameters of 191Ir, which were measured using the neutron time-of-flight (TOF) method. Therefore, for the first time, we aimed to measure the ${{\rm{\sigma }}_{{\rm{Ir}}}}$ σ Ir (Eth) accurately using the neutron TOF method. Experiments were performed using a pulsed neutron beam and a NaI(Tl) detector at the Accurate Neutron–Nucleus Reaction Measurement Instrument of the Materials and Life Science Experimental Facility in the J-PARC. The ${{\rm{\sigma }}_{{\rm{Ir}}}}$ σ Ir (Eth) was derived from the capture yield using the pulse height weighting technique. As a result, this study obtained the ${{\rm{\sigma }}_{{\rm{Ir}}}}$ σ Ir (Eth) as 949 ± 80 b. The result agrees with some activation data and the evaluated nuclear data libraries, JENDL-5 and ENDF/B-VIII.0, within its uncertainty of 8.4%. This study provides a data reference for the ${{\rm{\sigma }}_{{\rm{Ir}}}}$ σ Ir (Eth) measurement using the neutron TOF method. [ABSTRACT FROM AUTHOR]

Drawing from the big data archive of Google Trends, we innovatively construct a novel data set, the composite news-based speculation index, to proxy for the role of speculation in the predictability of carbon prices. We employ a theory-based multi-factor predictive framework to test the hypothesis that both emission compliance and emission noncompliance dynamics of the ETS matter in the predictability of carbon prices. We show that speculation is a good predictor of carbon prices. We find the robustness of the forecasting power of speculation in the predictability of carbon prices evident for both in-sample and out-of-sample forecasts and across different forecast horizons. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to establish a template containing a skeletal version of the existing Repository of Didactic Processes Data (RDPD) along with its environment and, by utilizing this system, to develop universal installation scripts enabling the implementation of the repository at any university in Poland. These scripts are used for cloning, meaning the template replication. They are intended to enable the implementation of Repository as a Service (RaaS) launched on demand by the service recipient. Design/methodology/approach: The RDPD skeletal version is developed as a system offering repository functionality for managing data from didactic processes. It does not contain visual identification elements and data. To operate, the skeletal version needs an environment that requires creating a VM on Proxmox and installing the OS and applications needed for RDPD: PostgreSQL, Apache, Tomcat, Solr, and Angular. The RDPD skeletal version and its environment create a template which is cloned by Ansible scripts running sequentially. Ansible scripts also configure the created clone. The university then only needs to customize the system and feed the repository with data. Findings: The university (client) provides identification parameters to generate a virtual machine (VM), ultimately containing the repository. In response, the RDPD system is automatically replicated and configured on a clone of the template in line with the parameters provided. Practical implications: A solution was proposed allowing for the automatic implementation of the RDPD repository at any Polish university. Hence, the provision of a service to generate a ready-to-use version of the RDPD system is significantly convenient for universities. Originality/value: The developed RDPD system, dedicated strictly to storing and managing data generated by a university, can be automatically implemented and configured per request. There are no such solutions, which is the original element of this work. [ABSTRACT FROM AUTHOR]

Data centers (DCs) are highly energy-intensive facilities, where about 30%–50% of the power consumed is attributable to the cooling of information technology equipment. This makes liquid cooling, especially in two-phase mode, as an alternative to air cooling for the microprocessors in servers of interest. The need to meet the increased power density of server racks in high-performance DCs, along with the push towards lower global warming potential (GWP) refrigerants due to environmental concerns, has motivated research on the selection of two-phase heat transfer fluids for cooling servers while simultaneously recovering waste heat. With this regard, a heat pump-assisted absorption chiller (HPAAC) system for recovering waste heat in DCs with an on-chip two-phase cooling loop driven by the compressor is proposed in the present paper and the low GWP hydrofluoroolefin refrigerants, including R1224yd(Z), R1233zd(E), R1234yf, R1234ze(E), R1234ze(Z), R1243zf and R1336mzz(Z), are evaluated and compared against R245fa as server coolant. For the HPAAC system, beginning with the development of energy and economic models, the performance is analyzed through both a parametric study and optimization using the coefficient of performance (COP), energy saving ratio (ESR), payback period (PBP) and net present value (NPV) as thermo-economic indicators. Using a standard vapor compression cooling system as a benchmark, the results indicate that with the evaporation temperature between 50°C and 70°C and the subcooling degree ranging from 5°C to 15°C, R1233zd(E) with moderate compressor suction pressure and pressure ratio is the best refrigerant for the HPAAC system while R1234yf performs the worst. More importantly, R1233zd(E) is also superior to R245fa based on thermo-economic performance, especially under work conditions with relatively lower evaporation temperature as well as subcooling degree. Under the given working conditions, the overall COP, ESR, NPV, and PBP of R1233zd(E) HPAAC with optimum subcooling degree range from 4.99 to 11.27, 25.53 to 64.59, 1.13 to 4.10 × 107 CNY and 5.77 to 2.22 years, respectively. Besides, the thermo-economic performance of R1233zd(E) HPAAC under optimum working conditions in terms of subcooling degree varying with the evaporation temperature is also investigated. [ABSTRACT FROM AUTHOR]

Europe has set a target to become a decarbonised continent by 2050. To achieve this, intervention in buildings is crucial, as they serve as significant energy consumers and greenhouse gas emitters. This intervention encompasses two essential pathways: renovation and digitalisation. The combination of these two aspects gives rise to elements such as the Digital Building Logbook (DBL), a digital data repository expected to enhance the pace and quality of renovation efforts. This paper introduces, for the first time, a European DBL data model with a specific focus on building renovation purposes—the DBrL. It outlines its initial requirements, constituent entities, relationships, and attributes. While acknowledging the need to address issues related to data protection, integration with existing data sources, and connections with Building Information Modelling (BIM) and Geographic Information System (GIS) in subsequent design phases, the study's outcome represents a significant stride in defining this tool. [ABSTRACT FROM AUTHOR]

Organizations of all types are prone to cybersecurity and information security attacks. Non-Profit Organizations (NPOs) and Non-Governmental Organizations (NGOs) are not exempt from using information technology solutions and, thus, have been the recipient victims of cyber attackers. There exist many areas and venues where data are collected to report back annually on the status and numbers of cybersecurity attacks against many sectors of our society. The Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA) catalogs sixteen critical sectors that are considered vital to the United States. However, finding where the NPO and NGO community should reside with regard to a categorized sector is challenging. The cybersecurity incident data collected by many agencies does place a focus on the sixteen sectors. This effort and write-up will focus on the NPO and NGO communities and provide the process followed to create the data repository, categorization of attacks taxonomy, fields captured, outlets, and areas where data that is relevant to historical cybersecurity incidents in these types of agencies is available. In addition, the beginning of a running log and dataset for the NPO and NGO community will emerge to determine if this activity is feasible and can continue. A desired outcome for this effort is to make available a dataset that can be referenced by researchers, students, and leaders investigating cybersecurity risk management and analysis of the NPO and NGO sectors. In addition, this effort was started with the purpose of providing students from varied academic disciplines the opportunity to engage in practical pedagogical cybersecurity and cybercrime activity. [ABSTRACT FROM AUTHOR]

Objective: The prevalence of overweight and obesity among beneficiaries of the Military Health System (MHS) is 41.6% and 30.5%, respectively. This incurs significant medical, fiscal, and military readiness costs. It is not currently known how the utilization of antiobesity medications (AOMs) within the MHS compares with that in the Veterans Health Administration or the private sector. Our aim was to assess the utilization of AOMs within the MHS. Methods: A cross‐sectional study was conducted using data gathered from the MHS Data Repository and the inclusion of all adult TRICARE Prime and Plus beneficiaries ages 18 to 64 years who were prescribed at least one TRICARE‐approved AOM during the years 2018 to 2022. Results: The total study population included 4,414,127 beneficiaries, of whom 1,871,780 were active‐duty service members. The utilization of AOMs among the eligible population was 0.56% (0.44% among active‐duty personnel). Liraglutide was the most‐prescribed AOM (36% of the total). Female sex, age greater than or equal to 30 but less than 60 years, and enlisted or warrant officer rank were all associated with statistically significant higher odds of receiving AOMs. Conclusions: Comparable with the US private sector, the MHS significantly underutilizes AOMs, including among active‐duty service members, despite coverage of AOMs since 2018. [ABSTRACT FROM AUTHOR]

Background: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Methods: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24–68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. Results: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. Limitations: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. Conclusions: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance. [ABSTRACT FROM AUTHOR]

Aims. We investigate the dynamical and physical structures of bright-rimmed clouds (BRCs) in a nearby H II region. We focused on carbon- and oxygen-bearing species that trace photon-dominated regions (PDRs) and warm molecular cloud surfaces in order to understand the effect of UV radiation from the exciting stars on the cloud structure. Methods. We mapped four regions around the most prominent BRCs at scales of 4–10 arcmin in the H II region IC 1396 (IC 1396A, B, D, and E) in [C II] 158 µm with (up)GREAT on board SOFIA. IC 1396 is predominantly excited by an O6.5V star. Toward IC 1396A, we also observed [O I] 63 µm and 145 µm. We combined these observations with JCMT archive data, which provide the low-J transitions of CO, 13CO, and C18O. All spectra are velocity-resolved. Results. The line profiles in the four mapped regions show a variety of velocity structures, which we investigated in detail for all observed emission lines. IC 1396B and D show clearly distinct velocity components that overlap along the line of sight. We find no clear sign of photoevaporating flows in the [C II] spectra, although the uncertainty in the location of the BRCs along the line of sight makes this interpretation inconclusive. Our analysis of the [13C II] emission in IC1396 A, which has the best signal-to-noise ratio, suggests that the [C II] is likely mostly optically thin. The heating efficiency, measured by the ([C II]+[O I] 63 µm)/far-infrared intensity ratio, is higher in the northern part of IC 1396A than in the southern part, which may indicate a difference in the dust properties of the two areas. Conclusions. The complex velocity structures identified in the BRCs of IC 1396, which is apparently a relatively simple H II region, highlight the importance of velocity-resolved data for disentangling different components along the line of sight and thus facilitating a detailed study of the dynamics of the cloud. We also demonstrate that the optically thin [13C II] and [O I] 145 µm emission lines are essential for a conclusive interpretation of the [C II] 158 µm and [O I] 63 µm line profiles. [ABSTRACT FROM AUTHOR]

The ISLAS2020 field campaign during February and March 2020 set out to obtain a unique dataset describing the Arctic water cycle using stable water isotope (SWI) observations. Our observation strategy focused on measuring evaporation, deposition, and precipitation, all of which are commonly sub-grid scale processes in numerical weather and climate models. Uncertain parameterizations for these processes can lead to compensating errors, which can go unnoticed; however, evaporation and precipitation can also be investigated with SWIs, as they are an integrated tracer for processes that atmospheric moisture has undergone. The campaign can be divided into two efforts: the primary field experiment in Ny-Ålesund focused on evaporation and deposition, and the larger precipitation collection network around the Nordic Seas. The primary field experiment lasted three weeks, from 23 February to 15 March 2020, with temperatures reaching below −30 °C. During these weeks, we obtained near-surface, high-resolution (approx. 20 cm) SWI profiles at two deployment sites. Using a newly developed profiling system, we measured SWI gradients in the lowermost 5 and 2 m over fjord water and snow-covered tundra, respectively. These profiles are complemented by fiber-optic distributed sensing (FODS) columns and nearby meteorological stations. The FODS columns supply continuous, high-resolution (2 cm or finer) temperature profiles above both locations, whereas the meteorological stations provide information on wind speed and direction. We also made a short deployment to the Zeppelin mountain observatory (472 m a.s.l) for measurements of the isotopic signal in the free-troposphere. Additionally, numerous water samples from the snowpack in and around Ny-Ålesund were taken, in addition to daily fjord water samples from Kongsfjorden. These samples provide the context for the surface conditions under which profiles were collected. Isotopic connections on the synoptic scale are achieved by linking Ny-Ålesund observations with precipitation sampling at locations across the European Arctic, namely Longyearbyen, Tromsø, Andenes, Ålesund, and Bergen. The resulting dataset provides comprehensive insight into the Arctic hydrological cycle and can facilitate the study of phase change processes and transport of water vapour into and out of the Svalbard region. Datasets from the field campaign are publicly available at the PANGAEA data repository (doi.pangaea.de/10.1594/PANGAEA.971241 , Seidl et al., 2024). [ABSTRACT FROM AUTHOR]

Nowadays, the web has emerged as an enormous multimedia data resource. Social media platforms are becoming the mass producers of user-generated multimedia content. Web search engines usually organize media-specific information, such as text, images, video, etc., in specialized data repositories (verticals), providing easy access to multimedia content. Web search engines have become efficient in retrieving data across various distinct verticals. However, users still need help formulating queries to retrieve the relevant multimedia results from verticals. Novice users often issue short-length ambiguous queries due to a lack of domain knowledge or query formulation experience, resulting in the retrieval of irrelevant results. The query formulation itself is a time-consuming process for the users. We presented an end-to-end deep-learning automatic query recommendation approach to address the associated issues. The proposed method autonomously extracts the domain knowledge using pseudo-relevance feedback, transforms it into a unified text-to-text summary, and assists users in generating non-ambiguous and well-balanced query recommendations. The proposed system employs Google's real-time dataset and is compared to the Google search engine. The evaluation consists of empirical and usability perspectives. The empirical evaluation of shorter query formulation and reformulation time obtained 89% accuracy scores in automated query recommendation. The usability testing (N=37) reveals 85.4% usefulness & ease-of-use, and "A" category proposed system usability. [ABSTRACT FROM AUTHOR]

The concentration of populations in large cities has resulted in significant challenges affecting residents' quality of life, particularly mobility and environmental pollution. Addressing these issues is crucial for enhancing environmental, social, and economic wellbeing. This study proposes leveraging Open Data repositories to identify critical points in urban infrastructure for promoting accessible, sustainable, and healthy mobility. We hypothesize that by analyzing and optimizing urban infrastructure based on available data, it is possible to mitigate the negative impacts of urbanization on mobility and environmental quality. A data-driven tool, incorporating data visualization, exploratory analyses, and classification and clustering algorithms, was employed to develop a system that not only presents data intuitively but also offers insights and recommendations for improvement. The findings, based on a case study in Barcelona, are transferable to other cities worldwide, offering valuable insights for urban planning professionals in future city improvement projects. While the city of Barcelona serves as a case study, the methodology is transferable to other cities worldwide. [ABSTRACT FROM AUTHOR]

The cross section for the neutron-induced fission of 237Np nuclei in the neutron energy range of 0.3–500 MeV has been measured using the time-of-flight spectrometer of the GNEIS neutron complex at the Petersburg Nuclear Physics Institute, National Research Center Kurchatov Institute. The 237Np(n, f) fission cross section has been measured with respect to the 235U(n, f) fission cross section and fission fragments have been detected by low-pressure position-sensitive multiwire proportional counters. The data obtained have been compared to the previous experimental results and to estimates from various evaluated data libraries. [ABSTRACT FROM AUTHOR]

Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83–99% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green–Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is characterized by progressive episodic memory dysfunction. A prominent hallmark of AD is gradual brain atrophy. Despite extensive research on brain pathology, the understanding of spinal cord pathology in AD and its association with cognitive decline remains understudied. We analyzed serial magnetic resonance imaging (MRI) scans from the ADNI data repository to assess whether progressive cord atrophy is associated with clinical worsening. Cervical cord morphometry was measured in 45 patients and 49 cognitively normal controls (CN) at two time points over 1.5 years. Regression analysis examined associations between cord atrophy rate and cognitive worsening. Cognitive and functional activity performance declined in patients during follow-up. Compared with controls, patients showed a greater rate of decline of the anterior–posterior width of the cross-sectional cord area per month (− 0.12%, p = 0.036). Worsening in the mini-mental state examination (MMSE), clinical dementia rating (CDR), and functional assessment questionnaire (FAQ) was associated with faster rates of cord atrophy (MMSE: r = 0.320, p = 0.037; CDR: r = − 0.361, p = 0.017; FAQ: r = − 0.398, p = 0.029). Progressive cord atrophy occurs in AD patients; its rate over time being associated with cognitive and functional activity decline. [ABSTRACT FROM AUTHOR]

Background: Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a computational method is urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets. Results: We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, the ARI and NMI values of SCANN increase by 14% and 11% over those of scHiCluster respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN increase by 63% and 88% over those of scHiCluster respectively. These findings show that SCANN is highly accurate in predicting the type of independent cell samples using single-cell Hi-C data. Conclusions: SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types. [ABSTRACT FROM AUTHOR]

Since foreign object intrusion is a cause of train accidents, railway image classification neural network is established to recognize any input railway images for intelligence control. With the cutting-edge technology and increasing data repositories emanating from Internet of Things (IoT) for driving system, Federated Learning (FL) has emerged as a prominent solution for training machine learning model in the context of distributed devices. Currently, federated learning can balance efficiency and security through differential privacy methods. In recent years, a noise adding mechanism in local differential privacy has provided a strict privacy guarantee for federated learning. However, two problems arise in the rail-driving system. On one hand, an attacker can still extract sensitive information from summation of data disturbed by zero mean noise generated by the existed noise adding mechanism. In this case, privacy concerns limit the publication of data in the IoT for driving system. On the other hand, a large variance of the noise makes the estimated average of the parameters of the model biased, which has an impact on the process of developing and refining Artificial Intelligence (AI) systems. To address these issues, we propose a federated learning framework of nonzero mean noise addition mechanism in differential privacy of deep networks based on fully homomorphic encryption and greedy average block Kaczmarz method, which is viewed as an improved AI application of foreign object detection in rail-driving system. Instead of zero mean noise, the nonzero mean noise to original data is determined locally according to the correlation between the local and global data distributions by a novel interior product calculation. As the means are different among the clients and unpredictable for attackers, the variance of the noise can be small while eliminating undue influence brought by a large variance of the zero mean noise in AI applications. Using fully homomorphic encryption and greedy average block Kaczmarz method, a denoising weighting aggregation strategy is derived without decryption of the mean to guarantee the privacy of individual device, while getting the real parameters on server. Moreover, the weight is generated completely randomly for safety. Meanwhile, the weight is associated with the heterogeneity of local data distribution across parties, balancing the trade-off between privacy loss and model performance. Experiments show that the proposed method provides a higher level of accuracy than the existed federated learning algorithms using the data collected by a number of devices, while it guarantees a better privacy during communications in driving system for multi-party machine learning tasks. [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening (LCS) using low-dose computed tomography (LDCT) is a strategy for early-stage diagnosis. The implementation of LDCT screening in countries with a high prevalence/incidence of tuberculosis (TB) is controversial. This systematic review and meta-analysis aim to identify whether LCS using LDCT increases early-stage diagnosis and decreases mortality, as well as the false-positive rate, in regions with a high prevalence of TB. Methods/Design: Studies were identified by searching BVS, PUBMED, EMBASE, and SCOPUS. RCT and cohort studies (CS) that show the effects of LDCT in LC screening on mortality and secondary outcomes were eligible. Two independent reviewers evaluated eligibility and a third judged disagreements. We used the Systematic Review Data Repository (SRDR+) to extract the metadata and record decisions. The analyses were stratified by study design and incidence of TB. We used the Cochrane "Risk of bias" assessment tool. Results: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) were used. Thirty-seven papers were included, referring to 22 studies (10 RCTs and 12 cohorts). Few studies were from regions with a high incidence of TB (One RCT and four cohorts). Nonetheless, the evidence is compatible with European and USA studies. RCTs and CS also had consistent results. There is an increase in early-stage (I-II) diagnoses and reduced LC mortality in the LCDT arm compared to the control. Although false-positive rates varied, they stayed within the 20 to 30% range. Discussion: This is the first meta-analysis of LDCT for LCS focused on its benefits in regions with an increased incidence/prevalence of TB. Although the specificity of Lung-RADS was higher in participants without TB sequelae than in those with TB sequelae, our findings point out that the difference does not invalidate implementing LDCT LCS in these regions. Trial registration: Systematic review registration Systematic review registration PROSPERO CRD42022309581. [ABSTRACT FROM AUTHOR]

Objective Materials and Methods Results Conclusion This project demonstrates the feasibility of connecting medical imaging data and features, SARS‐CoV‐2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID‐19‐related morbidities. The N3C curated a rich collection of aggregated and de‐identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID‐positive patients, seen at hospitals across the United States. Medical imaging data and variant samples are important data modalities used in the study of COVID‐19.Imaging data and features are hosted on the Cancer Imaging Archive (TCIA), and sequenced variant samples are analyzed and stored at the NIH GenBank. The University of Arkansas for Medical Sciences (UAMS) published the first COVID‐19 data set of 105 patients on TCIA and 37 patients on GenBank. We developed a process to link imaging and genomic variants and N3C EHR data through Privacy Preserving Record Linkage (PPRL) using de‐identified cryptographic hashes to match records associated with the same individuals without using patient identifiers.The PPRL techniques were piloted using clinical and imaging data sets provided by UAMS. Developed software components and processes executed properly, and linked data were returned and processed for visualization.Linking across clinical data sources at the patient level provides opportunities to gain insights from data that may not be known otherwise. The PPRL prototype and the pilot serve as a model to link disparate and diverse data repositories to enhance clinical research. [ABSTRACT FROM AUTHOR]

Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes. [ABSTRACT FROM AUTHOR]

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines. [ABSTRACT FROM AUTHOR]

To align with the global goal of keeping temperature below 2 ∘ C, a market-based initiative, "Emissions Trading System" (ETS), has been developed to mitigate climate change. However, while the carbon allowances traded at the ETS are mostly held and traded by polluting companies, financial actors engage in "speculation", activities that might be detrimental to the functioning of the ETS have also invested in the ETS. By drawing from the big data archive of Google Trends, we construct a news-based speculation index to proxy for the role of speculation in the dynamics of carbon pricing. Given our preliminary finding of inherent volatility and the mixed-frequency nature of the dataset, we employ the GARCH-MIDAS econometric technique to test the hypothesis that an all-inclusive framework that reflects the emission compliance and emissions non-compliance dynamics of the ETS is the most accurate approach to modeling carbon prices. We show that higher speculation in the ETS fosters higher long-term volatility in carbon prices, that speculation is a good predictor of carbon prices, and that its positive impact on carbon price returns makes the ETS an attractive investment opportunity. We provide a data-driven framework upon which the growing debate about whether the behavior of the non-compliance emission actors in the ETS endangers or benefits the functioning of the ETS can be evaluated empirically. [ABSTRACT FROM AUTHOR]

Effective cross-border management of invasive alien species is dependent on robust data sharing among responsible authorities. This need is particularly pronounced for species like aquatic rodents, such as muskrat and coypu, which rely on interconnected hydrographic networks for dispersal. The LIFE MICA project sought to enhance crossborder cooperation between the Netherlands, Germany (Lower Saxony), and Belgium (Flanders) by centralising data on the occurrence and management of invasive alien rodents. Project partners from seven institutions collaborated to develop innovative detection and management methods, such as smart camera trapping, eDNA analysis, and smart life traps for muskrat and coypu. Prior to the LIFE MICA project, management and occurrence data for muskrats and coypus in a substantial part of northwest Europe were fragmented in isolated data repositories. To address this issue, the project adopted an open data approach based on the Global Biodiversity Information Facility (GBIF), resulting in the publication of nearly one million new muskrat and coypu observations dating from 1987 to the present day. This initiative represents a significant step towards improved collaboration and data sharing in the management of invasive alien rodents across a wide geographic area in northwestern mainland Europe. [ABSTRACT FROM AUTHOR]

In derivative pricing and hedging, the consistency between the price and Greek surfaces (i.e., the Greek surfaces can be obtained by differentiating the price surface) is important in stabilizing the balance sheet and reducing the hedging cost. To build consistent surfaces of the price and Greeks for real-time decisions, we propose to use the gradient-enhanced stochastic kriging method, based on the data collected through extensive simulation experiments conducted when the market is closed. In addition to the naturally guaranteed consistency, we prove that the constructed price and Greek surfaces are more accurate than those constructed separately using stochastic kriging. Besides the consistency between the price and Greeks, we show that the partial differential equation relation between the price and Greeks, implied by the famous Feynman-Kac formula, can also be used to further improve the accuracy of the constructed surfaces. The numerical studies show that our proposed metamodeling methods work well for derivative pricing and hedging. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72161160340, 72293562, 72121001, 72031006, and 72171060]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0292) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0292). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This paper focuses on developing effective algorithms for solving a bilevel program. The most popular approach is to replace the lower-level problem with its Karush-Kuhn-Tucker conditions to generate a mathematical program with complementarity constraints (MPCC). However, MPCC does not satisfy the Mangasarian-Fromovitz constraint qualification (MFCQ) at any feasible point. In this paper, inspired by a recent work using the lower-level Wolfe duality (WDP), we apply the lower-level Mond-Weir duality to present a new reformulation, called MDP, for bilevel program. It is shown that, under mild assumptions, they are equivalent in globally or locally optimal sense. An example is given to show that, different from MPCC, MDP may satisfy the MFCQ at its feasible points. Relations among MDP, WDP, and MPCC are investigated. On this basis, we extend the MDP reformulation to present another new reformulation (called eMDP), which has similar properties to MDP. Furthermore, to compare two new reformulations with the MPCC and WDP approaches, we design a procedure to generate 150 tested problems randomly and comprehensive numerical experiments show that MDP has quite evident advantages over MPCC and WDP in terms of feasibility to the original bilevel programs, success efficiency, and average CPU time, whereas eMDP is far superior to all other three reformulations. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 12071280 and 11901380]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0108) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0108). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0025). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Typically, within facility location problems, fairness is defined in terms of accessibility of users. However, for facilities perceived as undesirable by communities hosting them, fairness between the usage of facilities becomes especially important. Limited research exists on this notion of fairness. To close this gap, we develop a series of optimization models for the allocation of populations of users to facilities such that access for users is balanced with a fair utilization of facilities. The optimality conditions of the underlying nonconvex quadratic models state the precise balance between accessibility and fairness. We define new classes of fairness and a metric to quantify the extent to which fairness is achieved in both optimal and suboptimal allocations. We show that a continuous relaxation of our central model is sufficient to achieve a perfect extent of fairness, while a special case reduces to the classical notion of proportional fairness. Our work is motivated by pervasive ecological challenges faced by the waste management community as policymakers seek to reduce the number of recycling centers in the last few years. As a computational case study, applying our models on data for the state of Bavaria in Germany, we find that even after the closure of a moderate number of recycling centers, large degrees of access can be ensured, provided that the closures are conducted optimally. Fairness, however, is impacted more, with facilities in rural regions shouldering larger loads of visiting populations than those in urban regions. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Computer resources and support provided by the Erlangen Regional Computing Center are gratefully acknowledged. B. Singh was partially financially supported by the Bavarian State Ministry for Science and Art (Bayerisches Staatsministerium für Wissenschaft und Kunst) under the Competence Network for Scientific High Performance Computing in Bavaria. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0308) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0308). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Nash equilibrium is arguably the most fundamental concept in game theory, which is used to analyze and predict the behavior of the players. In many games, there exist multiple equilibria, with different expected payoffs for the players, which in turn raises the question of equilibrium selection. In this paper, we study the NP -hard problem of identifying a socially optimal Nash equilibrium in two-player normal-form games (called bimatrix games), which may be represented by a mixed integer linear program (MILP). We characterize the properties of the equilibria and develop several classes of valid inequalities accordingly. We use these theoretical results to provide a decomposition-based reformulation of the MILP, which we solve by a branch-and-cut algorithm. Our extensive computational experiments demonstrate superiority of our approach over solving the MILP formulation through feeding it into a commercial solver or through the "traditional" Benders' decomposition. Of note, our proposed approach can find provably optimal solutions for many instances. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms. Funding: This work was supported by the National Institute of Dental and Craniofacial Research [Grant R01DE028283]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agreements ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0072) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0072). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization's internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0037). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A group of bacterial proteases, the Pro‐Pro endopeptidases (PPEPs), possess the unique ability to hydrolyze proline‐proline bonds in proteins. Since a protease's function is largely determined by its substrate specificity, methods that can extensively characterize substrate specificity are valuable tools for protease research. Previously, we achieved an in‐depth characterization of PPEP prime‐side specificity. However, PPEP specificity is also determined by the non‐prime‐side residues in the substrate. To gain a more complete insight into the determinants of PPEP specificity, we characterized the non‐prime‐ and prime‐side specificity of various PPEPs using a combination of synthetic combinatorial peptide libraries and mass spectrometry. With this approach, we deepened our understanding of the P3‐P3′ specificities of PPEP‐1 and PPEP‐2, while identifying the endogenous substrate of PPEP‐2 as the most optimal substrate in our library data. Furthermore, by employing the library approach, we investigated the altered specificity of mutants of PPEP‐1 and PPEP‐2. Additionally, we characterized a novel PPEP from Anoxybacillus tepidamans, which we termed PPEP‐4. Based on structural comparisons, we hypothesized that PPEP‐4 displays a PPEP‐1‐like prime‐side specificity, which was substantiated by the experimental data. Intriguingly, another putative PPEP from Clostridioides difficile, CD1597, did not display Pro‐Pro endoproteolytic activity. Collectively, we characterized PPEP specificity in detail using our robust peptide library method and, together with additional structural information, provide more insight into the intricate mechanisms that govern protease specificity. [ABSTRACT FROM AUTHOR]

This study examines consortia academic research data repositories, and their curators, across five global regions. Using an ad-hoc sample, we explore the roles of curators in research data repositories and their perceptions of skills and training, Research Data Management (RDM) practices, and the limitations, strengths, and opportunities they encounter in doing their work. Furthermore, our study analyses the roles of academic research repositories and the degree to which the FAIR principles are embedded in their strategic approaches. Our findings indicate that although numerous repositories are managed through collaborations among higher education bodies and research organisations, in certain nations, the development of research data repositories is in its early stage, often led by a single entity and having limited representation. Additionally, curators face challenges when dealing with the submission of datasets by researchers, who often provide insufficiently clear information for data reuse. We argue that despite the efforts to align data repositories with FAIR principles, substantial progress is required in educating researchers on their effective utilisation. [ABSTRACT FROM AUTHOR]

Study Objectives: The Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System contains individual-patient-level traumatic brain injury (TBI) data, which when combined, allows for the examination of rates and outcomes for key subpopulations at risk for developing sleep disturbance. Methods: This proof-of-concept study creates a model system for harmonizing data (i.e., combining and standardizing data) across FITBIR studies for participants with and without a history of TBI to estimate rates of sleep disturbance and identify risk factors. Results: Three studies were eligible for harmonization (N = 1753). Sleep disturbance was common among those with a history of mild TBI (63%). Individuals with mild TBI were two to four times more likely to have sleep disturbance compared to those with no history of TBI. Conclusions: This study established methods, harmonization code, and meta-databases that are publicly available on the FITBIR website. We demonstrated how the harmonization of FITBIR studies can answer TBI research questions, showing that associations between TBI and sleep disturbance may be influenced by demographic factors. [ABSTRACT FROM AUTHOR]

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools. [ABSTRACT FROM AUTHOR]

Long‐term ecological data are of paramount importance to document the effects of global changes on biodiversity and dynamics of populations and communities. The site of Bandia, 70 km southeast of Dakar in western Senegal, has been the scene of numerous ecological studies since the 1970s. In the frame of projects led by researchers of the Institut de Recherche pour le Développement (IRD), rodent populations were monitored at various periods using capture‐mark‐recapture (CMR) protocols on trapping grids that yielded important datasets on population dynamics and ecology of the main species present. Among them, the Guinea Multimammate Rat Mastomys erythroleucus proved to represent the dominant species. Thus, CMR data were collected on M. erythroleucus between (i) November 1975–March 1981, (ii) January 1983–October 1986, (iii) January 1997–April 2001, and (iv) June 2007–June 2012. Raw data from the 1975–1981 period were not available, but those from the three other periods are now in the IRD data repository DataSuds at https://doi.org/10.23708/YEA5AR. They represent 2556 (re)captures of 1296 M. erythroleucus individuals. They include the identity of each animal captured with some biological attributes (sex, weight at first capture, and reproductive activity), exact date and point of capture (via a trap‐specific code) at each trapping occasion, and additional comments that may help to interpret the data. This dataset concerning one of the most widespread rodent species of the Sahelo‐Sudanian bioclimatic belt provides information that can be used to address various questions such as outbreak prediction or effects of climate change. The complete data set for this abstract published in the Data Article section of the journal is also available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2024-05.1/jalter-en. [ABSTRACT FROM AUTHOR]

This paper describes a catalog of simultaneous observations of the Earth's magnetosheath by ESA's Cluster and NASA's MMS missions. The catalog is built from a visual inspection of summary plots provided by the two missions complemented by an analysis of high‐resolution magnetic field data. The catalog includes 117 events when Cluster 4 and MMS 4 crossed simultaneously the magnetosheath between January–April, 2017–2021. The dynamical and turbulent features of the magnetosheath are strongly influenced by θBn, the angle between the interplanetary magnetic field (IMF) and the shock normal direction. To facilitate such investigations, we also determine the bow shock geometry for each event based on two different approaches: (a) a minimum variance analysis of in‐situ magnetic field measurements, and (b) a geometrical approach which considers a bow shock model parameterized by OMNI data. A description of spacecraft trajectory during each event is also provided. Additional data describe the relative distances between Cluster 4 and MMS 4, a classification of each event as either quasi‐parallel or quasi‐perpendicular, and the distribution of events per magnetospheric flank. The time intervals for the Cluster ‐ MMS conjunctions are included in the catalog, as well as all associated figures and tables discussed in this paper are made available through an independent online data repository, and can be freely downloaded and used by any interested researcher. Key Points: We use in‐situ measurements from Cluster 4 and MMS 4 to build a catalog/database of simultaneous observations of the Earth's magnetosheathEmpirical models and minimum variance analysis of the magnetic field allow for an estimation of the bow‐shock orientation for each eventWe also provide details on the relative spacecraft position and an analysis of the bow‐shock geometry [ABSTRACT FROM AUTHOR]

Background: Delayed graft function (DGF) is a common early complication after kidney transplantation (KT) and is associated with various long‐term adverse outcomes. Despite numerous studies on hemodynamic management, the optimal hemodynamic goals during KT remain unclear. In this retrospective study, we aimed to investigate if three mean artery pressure (MAP) thresholds (≤75, 80, and 85 mmHg) that were commonly used in clinical practice were associated with DGF in adult patients undergoing KT. Methods: We extracted de‐identified data on adult patients who underwent deceased donor KT from our Discovery Data Repository. DGF was defined as the requirement for dialysis within the first 7 days after transplantation. Three MAP thresholds (≤75, 80, and 85 mmHg) and the duration of pressure below the three thresholds were recorded. Multivariable logistic analysis was used to identify risk factors for DGF. Results: We included 2301 adult KT patients. The mean age was 52.5±12.9 years and 59% were male. DGF occurred in 1066 patients (46.3%). Patients frequently experienced MAP ≤75, 80, and 85 mmHg (approximately 70%, 80%, and 90% of patients experienced 10 min of MAP ≤75, 80, and 85 mmHg, respectively). Patients with DGF spent significantly longer durations below the three MAP thresholds during surgery compared with those without DGF. Further analysis revealed that the minimal time spent on MAP ≤75, 80, and 85 mmHg that were significantly associated with DGF were 6, 23, and 37 min, respectively. After adjusting for non‐hemodynamic risk factors (age, basiliximab administration, and urine output), prolonged exposure to the three MAP thresholds remained significant predictors for DGF (for MAP ≤75 mmHg, OR 1.257, 95% CI 1.017–1.554, p = 0.034; MAP ≤80 mmHg, OR 1.220, 95% CI 1.018–1.463, p = 0.031; MAP ≤85 mmHg, OR 1.253, 95% CI 1.048–1.498, p = 0.013). Conclusion: Prolonged exposure to the three common MAP thresholds (≤75, 80, and 85 mmHg) occurred frequently during adult deceased donor KT and was associated with DGF. [ABSTRACT FROM AUTHOR]

Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science and library sciences are needed to support these research systems and promote high‐calibre research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centres that host skilled experts who approach research with best practices in mind and domain‐specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long‐term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency and long‐term sustainability. We hope this work contributes to—and provides shared language for—a conversation on a broader framework beyond metrics that can be used to advocate for needed resources. [ABSTRACT FROM AUTHOR]

Biodiversity science is in a pivotal period when diverse groups of actors – including researchers, businesses, national governments, and Indigenous Peoples – are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. The management of these repositories, often called biodiversity data portals, can serve either to redress or to perpetuate the colonial history of biodiversity science and current inequities. Both researchers and Indigenous Peoples are implementing new strategies to influence whom biodiversity data portals recognise as salient participants in data management and use. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Data Principles. Actors use these principles to influence the governance of biodiversity data portals. 'Fit-for-use' data is a social status provided by groups of actors who approve whether the data meets specific purposes. Advocates for the FAIR and CARE Principles use them in a similar way to institutionalise the authority of different groups of actors. However, the FAIR Principles prioritise the ability of machine agents to understand the meanings of data, while the CARE Principles prioritise Indigenous Peoples and their data sovereignty. Together, FAIR and CARE illustrate a broader emerging strategy for institutionalising international norms for digital repositories about who they should recognise as having a formal role in determinations of the fitness-for-use of data. [ABSTRACT FROM AUTHOR]

Both academics and the IT industry are now researching the Internet of Things and software-defined networks. They have received a number of criticisms in the SD-IoT due to their novelty. One of the 5 G technologies that makes it possible to construct complex, controllable, economical, and adaptive networks is software-defined networking (SDN). In contrast, edge computing (EC) uses data from sensors, network switches, or other devices to automatically do analytical computing rather than waiting for the data to be sent back to a centralised data repository. This article offers a study on feature selection using an ensemble of filter methods to create a lightweight IDS for SD-IoT edge devices that support OpenFlow in order to defend against such attacks. To create the ensemble of filter methods, three filter-based methods, namely Pearson's correlation coefficient (PCC), mutual information (MI), and Fisher's score, have been used. The features selected by this ensemble is sent to the ensemble of classifiers called stack of the classifiers that comprises of support vector machine (SVM) and K-nearest neighbour (KNN) at level '0' and logistic regression (LR) at level '1'. To check the effectiveness of the selected features, stack of the classifiers and individual classifiers are trained and tested with 'All' and 'Selected' features, and then their performances are compared. Two datasets, the BoT-IoT dataset and the TON-IoT dataset, were utilised to complete this work. The performance is compared under some performance measuring metrics, namely recall, accuracy, FAR, F1, precision, CKC, and prediction time. It has been discovered that classifiers perform better when trained with selected features rather than all the features. Also, it is discovered that stack of the classifiers with chosen features outperforms all individual classifiers, hence it is chosen for deployment in OpenFlow enabled edge devices of the SD-IoT data plane where it can identify and counteract threats in real-world settings. This offers the SD-IoT distributed attack detection approach. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The welfare of calves is important to both farmers and consumers. Practices that increase the proportion of calves born alive and enable them to thrive through to weaning contribute to improved sustainability. Stillbirths (SB) are calvings where the calf dies at birth or within 24 h after birth. Preweaning mortality (PWM) refers to calves that die after the first day of life but before weaning based on termination data. Both SB and PWM are binary traits characterized by low heritability. Data collection for these traits is incomplete, compared with traits such as milk yield in cows. Despite these challenges, genetic variation can be measured and used to produce breeding tools, such as EBVs, to reduce calf mortality over time. The aim of this study was to compare the performance of various linear models to predict SB and PWM traits in Holstein and Jersey cattle and evaluate their applicability for industry-wide use in the Australian dairy industry. Calving records from around 2.25 million Holstein and Jersey dams were obtained from DataGene's Central Data Repository from the year 2000 onward, to calculate genetic parameters. About 7% of calves were recorded as SB in the period from 2000 to 2021 (n = 1.48 million calvings). The prevalence of PWM was much lower than SB during the same period at 2% (n = 0.89 million calves). Genetic parameters were estimated for SB direct, SB maternal, and PWM using bivariate linear models with calving ease (CE) as the second trait in the model. The heritability of these calf traits was low and varied between 1% and 5% depending on the breed, trait, and model. In Holstein cattle, heritabilities were 2% for PWM and SB direct and 1% for SB maternal, whereas in Jersey cattle heritabilities were 5% for PWM, 2% for SB direct, and 1% for SB maternal. The genetic trends for both SB direct and SB maternal in Holstein cattle indicate improvement in both traits, whereas there was no apparent increase or decrease in PWM in the past 2 decades. The coefficient of genetic variation for SB direct and PWM was between 11.7% and 23.0% in Holstein and Jersey cattle, demonstrating considerable genetic variation in calf survival traits as a first step to using genetic selection to increase the proportion of calves born alive and calves weaned. A focus on improved calf and calving recording practices is expected to increase the reliability of genetic predictions. [Display omitted] [ABSTRACT FROM AUTHOR]

Introduction Injuries are the leading cause of medical encounters with over 2 million medical encounters for musculoskeletal (MSK) conditions and over 700,000 acute injuries per year. Musculoskeletal injuries (MSKIs) are by far the leading health and readiness problem of the U.S. Military. The Proceedings of the International Collaborative Effort on Injury Statistics published a list of 12 data elements deemed necessary for injury prevention in the civilian population; however, there are no standardized list of common data elements (CDEs) across the DoD specifically designed to study MSKIs in the Military Health System (MHS). This study aims to address this gap in knowledge by defining CDEs across the DoD for MSKIs, establishing a CDE dictionary, and compiling other necessary information to quantify MSKI disease burden in the MHS. Materials and Methods Between November 2022 and March 2023, we conducted an environmental scan of current MSKI data metrics across the DoD. We used snowball sampling with active engagement of groups housing datasets that contained MSKI data elements to determine CDEs as well as information on readiness databases across the DoD containing up-to-date personnel information on disease, hospitalizations, limited duty days (LDDs), and deployability status for all military personnel, as well as MSKI-specific measures from the MHS Dashboard which tracks key performance measures. Results We identified 8 unique databases: 5 containing demographic and diagnostic information (Defense Medical Surveillance System, Medical Assessment and Readiness Systems, Military Health System Data Repository, Person-Data Environment, and Soldier Performance, Health, and Readiness Database); and 3 containing LDD information (Aeromedical Services Information Management System, eProfile, and Limited Duty Sailor Marines Readiness Tracker). Nine CDEs were identified: DoD number, sex, race, ethnicity, branch of service, rank, diagnosis, Common Procedural Terminology coding, and cause codes, as they may be captured in any database that is a derivative of the Military Health System Data Repository. Medical Assessment and Readiness Systems contained most variables of interest, excluding injury/place of region and time in service. The Limited Duty Sailor Marines Readiness Tracker contains a variable corresponding to "days on limited duty." The Aeromedical Services Information Management System uses the "release date" and "profile date" to calculate LDDs. The eProfile system determines LDDs by the difference between the "expiration date" and "approved date." In addition, we identified 2 measures on the MHS Dashboard. One measures the percentage of service members (SMs) who are on limited duty for longer than 90 days because of an MSKI and the other tracks the percentage of SMs that are not medically ready for deployment because of a deployment-limiting medical condition. Conclusions This article identifies core data elements needed to understand and prevent MSKIs and where these data elements can be found. These elements should inform researchers and result in evidence-informed policy decisions supporting SM health to optimize military force readiness. [ABSTRACT FROM AUTHOR]

Introduction Clinical practice guidelines (CPGs) and clinical recommendations (CRs) are developed to aide and guide providers in treating a variety of conditions, including traumatic brain injury (TBI). There is little knowledge on the impact that CPGs and CRs have on provider practice. One TBI recommendation that was able to be tracked in medical record codes was the use of benzodiazepines (BZD). Because of potential for misuse, diversion, addiction, cognitive impairment, and brain healing interference, the DoD and Department of Veterans Affairs (VA) jointly discourage prescribing BZD after TBI. As part of an effort to look at translation of CPG guidance into clinical practice, our objective was to examine the issuance of BZD prescriptions, including dose, type, and prescribing provider, prescribing setting, and primary diagnosis at issuance among U.S. service members with mild Traumatic Brian Injury (mTBI). Materials and Methods Using DoD data sourced from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) databases of the Military Health System (MHS) Medical Data Repository (MDR), we identified all U.S. service members with a first lifetime diagnosis of mTBI from October 1, 2015 to September 30, 2016. Data on prescriptions issued to this group during a period of active treatment for a mTBI were obtained from the Pharmacy Detail Transaction Service (PDTS) databases of the MDR and identified BZD prescriptions based on the American Hospital Formulary Service (AHFS) therapeutic classification system. We validated coding assumptions through structured review of the clinical record contained within the Armed Forces Health Longitudinal Technology Application (AHLTA) of 30 randomly selected cohort members. Results Among U.S. service members, 4.5% filled a BZD prescription while under active medical treatment for a recent mTBI. These service members were more likely female and older when compared to their counterparts not prescribed BZD. Among service members under active treatment for mTBI during the study period, 52.6% (n  = 7,935) filled a prescription; of these, 8.5% (n  = 676) filled a BZD prescription. Of U.S. service members filing a BZD prescription while undergoing active treatment for an mTBI, 64.6% (n  = 437) filled prescriptions for BZD and antidepressants, 54.9% (n  = 371) filled prescriptions for both BZD and NSAIDS, and 42.2% (n  = 285) concurrently filled prescriptions for BZD and opioids. Conclusions This effort to examine the translation of CPG recommendations into practice through evaluation of medical record data indicates that providers are prescribing BZD to patients under active treatment for an acute mTBI. The mTBI CPG recommends that the BZD class of medications be avoided in patients healing from brain injury. However, the team recognizes there are confounding factors that may impact the medications that are prescribed for patients with mTBI. Additional work to understand how CPGs and CRs are received and utilized by providers may elucidate opportunities to close the gap between clinical practice guidance and clinical practice. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing. [ABSTRACT FROM AUTHOR]

Distributed Scatterers Interferometry (DS-InSAR) has been widely applied to increase the number of measurement points (MP) in complex mountainous areas with dense vegetation and complicated topography. However, DS-InSAR method adopts batch processing mode. When new observation data acquired, the entire archived data is reprocessed, completely ignoring the existing results, and not suitable for high-performance processing of operational observation data. The current research focuses on the automation of SAR data acquisition and processing optimization, but the core time series analysis method remains unchanged. In this paper, based on the traditional Sequential Estimator proposed by Ansari in 2017, a Recursive Sequential Estimator with Flexible Batches (RSEFB) is improved to divide the large dataset flexibly without requirements on the number of images in each subset. This method updates and processes the newly acquired SAR data in near real-time, and obtains long-time sequence results without reprocessing the entire data archived, helpful to the early warning of landslide disaster in the future. 132 Sentinel-1 SAR images and 44 TerraSAR-X SAR images were utilized to inverse the line of sight (LOS) surface deformation of Xishancun landslide and Huangnibazi landslide in Li County, Sichuan Province, China. RSEFB method is applied to retrieve time-series displacements from Sentinel-1 and TerraSAR-X datasets, respectively. The comparison with the traditional Sequential Estimator and validation through Global Position System (GPS) monitoring data proved the effectiveness and reliability of the RSEFB method. The research shows that Xishancun landslide is in a state of slow and uneven deformation, and the non-sliding part of Huangnibazi landslide has obvious deformation signal, so continuous monitoring is needed to prevent and mitigate possible catastrophic slope failure events. [ABSTRACT FROM AUTHOR]

We provide a key magnetotelluric section, composed of archived magnetotelluric data along a NW-SE profile in Transdanubia, Hungary. For the interpretation of the key section, observations from raw magnetotelluric data and inversion results were used. In addition, other geophysical-geological information was also considered to confirm the conclusions based on the electrical resistivity sections. All this information was combined to identify the main structural lines and geologic units along the profile. Main structural lines observed on the resistivity sections are the Alpokalja line, Rába line, Balaton line, Kapos line, and Mecsekalja line. Geologic units that can be delineated due to their resistivity contrast include the Lower and Upper Austroalpine Units, the Transdanubian Range Unit, the Mid-Hungarian Megaunit, the Tisza Megaunit and sedimentary rocks filling the sub-basins of the Miocene Pannonian back-arc basin. The inversion results of the transverse magnetic (TM) polarization mode and the phase-depth sections of the raw data were found to be the most suitable for detecting the morphology and identifying the depth of the Pre-Cenozoic basement along the profile. [ABSTRACT FROM AUTHOR]

Context: Integrated omics studies hold a crucial role in improving our understanding of reproductive biology. However, the complex datasets so generated are often only accessible via supplementary data files, which lack the capacity for interactive features to allow users to readily interrogate and visualise data of interest. Aims: The intent of this technical note was to develop an interactive web-based application that enables detailed interrogation of a representative sperm proteome, facilitating a deeper understanding of the proteins identified, their relative abundance, classifications, functions, and associated phenotypes. Methods: We developed a Shiny web application, ShinySperm (), utilising R and several complementary libraries for data manipulation (dplyr), interactive tables (DT), and visualisation (ggplot2, plotly). ShinySperm features a responsive user interface, dynamic filtering options, interactive charts, and data export capabilities. Key results: ShinySperm allows users to interactively search, filter, and visualise sperm proteomics data based on key features (e.g. protein classification, sperm cell domain, presence, or absence at different maturation stages). This application responds live to filtering options and enables the generation of interactive plots and tables, thus enhancing user engagement and understanding of the data. Conclusions: ShinySperm provides a robust platform for the dynamic exploration of epididymal sperm proteome data. It significantly improves accessibility and interpretability of complex datasets, allowing for effective data-driven insights. Implications: This technical note highlights the potential of interactive web applications in reproductive biology and provides a plug and play script for the field to produce applications for meaningful researcher interaction with complex omic datasets. Understanding complex biological data is crucial for advancements in reproductive biology. Our study developed ShinySperm, a web application that allows researchers to easily explore, interrogate, and visualise intricate sperm proteomic data. This tool sets out a blueprint for future research, enabling researchers to bring their intricate datasets to life, allowing others to interact with and interpret more effectively, ultimately driving innovation in reproductive biology. Image by David Skerrett-Byrne. This article belongs to the Collection Frontiers in Reproduction Science – Rising Stars at the Society for Reproductive Biology 2023. [ABSTRACT FROM AUTHOR]

Introduction: Adult respiratory syncytial virus (RSV) burden is underestimated due to non-specific symptoms, limited standard-of-care and delayed testing, reduced diagnostic test sensitivity—particularly when using single diagnostic specimen—when compared to children, and variable test sensitivity based on the upper airway specimen source. We estimated RSV-attributable hospitalization incidence among adults aged ≥ 18 years in Ontario, Canada, using a retrospective time-series model-based approach. Methods: The Institute for Clinical Evaluative Sciences data repository provided weekly numbers of hospitalizations (from 2013 to 2019) for respiratory, cardiovascular, and cardiorespiratory disorders. The number of hospitalizations attributable to RSV was estimated using a quasi-Poisson regression model that considered probable overdispersion and was based on periodic and aperiodic time trends and viral activity. As proxies for viral activity, weekly counts of RSV and influenza hospitalizations in children under 2 years and adults aged 60 years and over, respectively, were employed. Models were stratified by age and risk group. Results: In patients ≥ 60 years, RSV-attributable incidence rates were high for cardiorespiratory hospitalizations (range [mean] in 2013–2019: 186–246 [215] per 100,000 person-years, 3‒4% of all cardiorespiratory hospitalizations), and subgroups including respiratory hospitalizations (144–192 [167] per 100,000 person-years, 5‒7% of all respiratory hospitalizations) and cardiovascular hospitalizations (95–126 [110] per 100,000 person-years, 2‒3% of all cardiovascular hospitalizations). RSV-attributable cardiorespiratory hospitalization incidence increased with age, from 14–18 [17] hospitalizations per 100,000 person-years (18–49 years) to 317–411 [362] per 100,000 person-years (≥ 75 years). Conclusions: Estimated RSV-attributable respiratory hospitalization incidence among people ≥ 60 years in Ontario, Canada, is comparable to other incidence estimates from high-income countries, including model-based and pooled prospective estimates. Recently introduced RSV vaccines could have a substantial public health impact. [ABSTRACT FROM AUTHOR]

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The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. The present study offers an overview of the datacenter technology and optical datacenter with details of its concepts and technologies. First, the optical data center systems are described and the components that describe it are network operations. Then, we detail the specific technologies that are important to data center networks throughout wide-area networks, analyzing them deeply. Finally, we make a comprehensive between some related works with an analysis of the algorithmic challenges faced by these technologies. In addition, we examine the solutions that have been developed to address these challenges and explore the relevant systems and implementation considerations. [ABSTRACT FROM AUTHOR]

We conduct an experiment in a simulated online bookstore to study how consumers respond to display ads with varied content. We study how advertising content influences consumers' decisions. To this end, we create a simulated online bookstore that imitates a real online shopping experience. We then conduct a preregistered and incentive-compatible experiment in which we randomly expose store visitors to display ads, randomizing both advertising exposures and content. We find that ad content plays a major role in shaping advertising effects. Ads that reveal a book's low price consistently increase demand for the advertised book relative to ads that do not reveal price. By contrast, ads that reveal the book's genre induce some consumers to search and buy the book but lead others to reject it without search. We show these polarized responses can increase or decrease the total number of searches and purchases of the advertised book depending on the share of consumers who favor the revealed genre. Our findings suggest that advertisers should carefully choose which product attributes to reveal in their ad copies. We also show that revealing product attributes in ads does not change the total amount of search. Taken together, our results suggest the primary role of ad content is to affect consumers' beliefs about the advertised book rather than to alter the perceived benefits from search. History: Catherine Tucker served as the senior editor for this article. Funding: This work was supported by Amazon Research Award. All authors certify that they have no affiliations with or involvement in an any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0138. The preregistration document is available at https://aspredicted.org/m3jm2.pdf. Our public data repository at https://github.com/ilyamorozov/adContent contains data, codes, and a written tutorial with instructions on how to create a simulated online store. [ABSTRACT FROM AUTHOR]