Spectral correction strategy based on beam shaping with image-assisted calibration for enhancing LIBS accuracy.

This dataset presents the first open-access collection of near-infrared hyperspectral imaging (NIR-HSI) data for the optical identification of textiles, with a focus on supporting research in sensor-based textile sorting and recycling. The dataset comprises hyperspectral images, RGB photographs, and detailed metadata, including fibre composition and colour, for 71 post-industrial textile samples, collected in Finland. Over 11 million spectra are included in the hyperspectral images, with more than 6 million annotated, providing a robust foundation for machine learning and data analysis. In addition, we provide a single representative NIR spectra and RGB value for each sample in order to accommodate classic spectroscopic analysis. Used garments were sourced from a partner company specializing in end-of-life textile management, with ground truth information on fibre composition obtained from suppliers. Small pieces of each garment were measured using Specim SWIR 3 hyperspectral camera and photographed with high-resolution mobile phone camera (Samsung Galaxy A52). The dataset is organized into folders containing raw and processed data, including ENVI-format hyperspectral images, RGB images, as well as CSV files with mean spectra, mean RGB values, and sample metadata. An example Python script is provided to facilitate data access and processing. Potential reuse scenarios include classification of textiles by material or colour, prediction of natural fibre content, image segmentation, algorithm development for spectral classification, and use as a reference spectral library. The dataset's comprehensive structure and open availability address the limitations of previous research, which often relied on small or non-public datasets, and is intended to accelerate advances in optical identification technologies for textile recycling.

Considerations and practical recommendations for identifying perimenopause in longitudinal research.

This data article presents a dataset of miniplant-scale reverse water-gas shift (rWGS) experiments conducted in a heated fixed-bed reactor under systematically varied operating conditions. The dataset contains processed measurements including reactor temperature, molar fractions of CO2, CO, H2, CH4, and derived quantities such as CO2 conversion and CO selectivity. The experiments cover a wide parameter space, including gas hourly space velocities of 8000, 14,000 and 20,000 h-1 with temperatures between 550 and 950 °C (increment of 50 K), and H2:CO2 feed ratios of 2:1, 2.5:1 and 3:1. The dataset presents the steady-state values and links to the reproductible data processing step, based on a prior study, enabling Fairness of all steps from the initial measurements to the final processed variables. The processing workflow includes calibration of gas analysis signals, smoothing, dry-gas calculation, and uncertainty estimation. These data provide value for validating mechanistic kinetic models, benchmarking computational fluid dynamics (CFD) reactor simulations, training machine learning models including physics-informed machine learning frameworks, and supporting thermodynamic model assessments. All raw and processed data are made publicly available in a long-term repository, ensuring FAIR access and enabling reuse by the scientific community.

This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.

Privacy-aware data processing and fair model trading protocols among un-trusted participants

Contemporary implant dentistry aims to achieve long-term biological, esthetic, and functional successful outcomes. Thus, instrumental to this aim is designing an integrated system in which the supported prosthesis & components, anatomical and phenotypical tissue characteristics, and the fixture act synergistically to maintain peri-implant tissue stability and health. Central to these goals is (1) the definition of a patient-optimized three-dimensional (3D) implant position during comprehensive treatment planning and (2) the intraoperative transfer of the plan to the final implant position. Preoperative planning is supported by digital imaging, primarily via tomographic and surface scans of the patient's anatomy, and subsequent data processing in dedicated planning software, allowing for comprehensive case evaluation. The digital treatment plan which initiates computer-assisted implant surgery (CAIS), can be executed by means of non-guided or guided implant surgery approach; the latter involving static, dynamic, and robotic techniques. This white paper aims to provide a comprehensive overview of the required resources and workflows involved in digital implant treatment planning and subsequent implant placement using non-guided and static CAIS approaches.

Rapid characterization of heavy metals in soil using a novel integrated strategy for near-infrared spectroscopy models.

The value of artificial intelligence combined with multimodal data analysis in tumor immunotherapy and targeted therapy.

Mitochondrial DNA (mtDNA) alterations are increasingly associated with Parkinson's disease (PD), particularly due to their role in oxidative stress. However, the contribution of mtDNA insertions and deletions (INDELs) to PD remains poorly understood, particularly in genetically admixed populations such as Brazilians. To explore this, we sequenced the complete mtDNA from blood samples of 179 admixed individuals from the Brazilian Amazon (104 people with PD and 75 controls). Data processing included FastQC, MultiQC, FastP, BWA, and mtDNA-Server 2. We identified a significantly higher burden of mtDNA INDELs in PD compared with controls in Complex I genes (OR=9.23; 95% CI: 2.22-63.55; FDR=0.044). Differences in heteroplasmy levels were also observed in the ATP6, ND4, and ND5 genes. Importantly, we discovered seven new PD-associated INDELs (m.13763_13763delinsCCA, m.13885_13885delinsCTG, m.13888_13890delinsT, m.13767_13769delinsC, m.13810_13812delinsG, m.13813_13813delinsGCA, and m.13764_13764delinsCAT) that are particularly more frequent among individuals harboring uniparental lineages of Native American origin. Our findings report novel mtDNA INDELs, particularly in Complex I, which may contribute to PD susceptibility and highlight the importance of investigating mitochondrial genomic variation in underrepresented populations. These associations should be interpreted as preliminary, and further longitudinal studies with independent cohorts are required to confirm these observations.

Dataset of in-situ meteorological measurements for urban wind energy assessment in the southern region of the Dominican Republic.

A dual-signal amplification rapid detection platform integrating ERA and lateral flow immunoassay.

Dual-branch enhanced U-Net algorithm for multi-source separation and multiple suppression in seismic data processing

An adaptive Laplacian-based smoothing method for LiDAR data processing in space

In 2002 a UK audit was performed by the Nuclear Medicine Software Quality Group of filtered back projection (FBP) software, designed to evaluate the quantitative characteristics of single-photon emission computed tomography (SPECT). Subsequently, the use of FBP has reduced in common practice, with most guidelines now recommending and using iterative reconstruction. This study aimed to audit ordered-subset expectation-maximisation (OSEM) algorithms in clinical use, acting on the same input data. A computational phantom was devised to evaluate the effect of sphere diameter, position and activity concentration along with an assessment of uniformity and resolution. Additional sections were implemented to evaluate the recovery in photopoenic areas and of small lesions adjacent to active structures. SPECT projections were created from the phantom and placed in the Digital Imaging and Communications in Medicine (DICOM) structures of acquired data from three SPECT camera manufacturers. Resultant projections were reconstructed via five platforms and quantitative measures from the above sections compared. Across all measures it was found that there was excellent agreement among platforms offering similar reconstruction methods. One platform was found to not offer the ability to perform a true 'pencil-beam' OSEM reconstruction and results varied with different manufacturer data supplied. While there are differences in how reconstruction platforms process data from different manufacturers, these differences were generally small, with results from the one wide-beam reconstruction method having the largest variation. It would be advisable that users implementing sensitivity-based quantitative SPECT should derive factors for the various combinations of acquisition and reconstruction platforms at their disposal.

The rapid development of digital technologies and the emergence of the digital economy have significantly transformed managerial decision-making processes. Digitalization affects all areas of enterprise activity, including data collection, processing, and analysis, which directly influences the speed, quality, and effectiveness of managerial decisions. Modern managers increasingly rely on digital tools, analytical platforms, decision support systems, artificial intelligence (AI), and large datasets, enabling more informed, timely, and flexible decision-making. These capabilities are essential for maintaining competitiveness in dynamic and uncertain business environments. This article analyzes the transformational features of managerial decision-making under digitalization, emphasizing theoretical foundations and practical applications. Effective managerial decisions are determined not only by their formulation but also by successful implementation, which ensures achievement of both strategic and operational objectives. Key contemporary aspects include digitalization and automation, real-time data-driven analysis, scenario modeling, AI integration, flexibility, and adaptability. These features enhance optimization, responsiveness, and overall managerial efficiency, particularly in enterprises such as bakeries, where production, financial, marketing, and personnel management must respond to fluctuating markets, regulatory changes, and consumer expectations.The classification of managerial decisions is considered with respect to management levels, problem complexity, personnel involvement, functional orientation, and decision-making conditions. Digital technologies facilitate process automation, data integration, collective decision-making, and transparency, enabling managers to make well-grounded, efficient, and adaptive choices. The study demonstrates that incorporating digital tools and AI in decision-making improves performance, strengthens competitiveness, and supports sustainable development of modern enterprises.

ABSTRACT This survey study focused on mathematics instruction by Finnish lower secondary special needs and special class teachers with students in need of additional support. Teachers reported their teaching of and preparation for teaching, (a) thinking skills and methods, (b) numbers and mathematical operations, (c) algebra, (d) functions, (e) geometry, and (f) data processing, statistics, and probability. Teachers also reported their use of research-based instructional approaches (i.e., explicit instruction, self-monitoring, mathematics discourse, real-world problems, graduated instructional sequence, strategy instruction, grouping strategies) and the reasons if an approach was not used. Results indicated that about 90% of teachers feel prepared or very prepared to teach mathematical topics, except for data processing, statistics, and probability. Teachers use some aspects of explicit instruction always or often. However, other instructional approaches, including incorporating mastery learning, scaffolding instruction, and teaching problem representation and solution, are implemented sometimes or never. Additional results and implications are provided.

Colon cancer is genetically heterogeneous, necessitating standardized genomic analyses for cross-cohort comparisons. While The Cancer Genome Atlas (TCGA)-colon adenocarcinoma (COAD) is a widely used dataset, its comparability to other ethnically different populations remains unclear. This study systematically compares the genomic characteristics of TCGA-COAD and ChangKang, a Chinese colon cancer cohort, using an identical data-processing pipeline to minimize methodological biases. Whole-exome sequencing data from both cohorts were uniformly processed to analyze five key genomic features: tumor mutation burden (TMB), microsatellite instability (MSI), significantly mutated genes, mutational signatures, and copy number variation (CNV). Samples were classified into hypermutated and non-hypermutated subgroups for further comparisons. The TCGA-COAD cohort exhibited a higher overall TMB, driven by a greater proportion of hypermutated samples. However, the hypermutated subgroup of the ChangKang cohort included more ultramutated cases with POLE exonuclease domain mutations, leading to a higher subgroup TMB. MSI was more prevalent in TCGA-COAD, while significantly mutated gene frequencies varied, with lower APC and ACVR2A mutation rates in the ChangKang cohort. CNV patterns were largely similar, though CNV frequencies were higher in TCGA-COAD. Despite differences in subgroup distributions and mutation frequencies, the overall genomic characteristics of colon cancer remain consistent between these ethnically different cohorts. This suggests that cross-population analyses are feasible when standardized processing methods are applied. This study provides a systematic, unbiased comparison of TCGA-COAD and the Chinese ChangKang cohort, demonstrating that the genomic characteristics remain largely consistent across ethnically distinct populations.

To systematically map Irish national health datasets and policy frameworks relevant to children and young people (0-24years) and appraise their readiness for quality improvement, equity monitoring, and interoperable reuse. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI)-guided scoping review will synthesise peer-reviewed and grey literature. Datasets will be benchmarked using World Health Organization Data Quality Review (DQR) domains, Findable, Accessible, Interoperable, Reusable (FAIR) principles, European Medicines Agency registry guidance and PROGRESS-Plus equity stratifiers. Outputs will be synthesised into structured matrices (national catalogue, key quality indicators and availability-variability layer) and an equity heat map. The review will characterise heterogeneity in coverage, coding, governance, equity stratification and linkage-readiness across perinatal, hospital, registry, surveillance and community datasets, identifying priority gaps for standardisation. Within the context of healthcare quality assurance, the synthesis will enable evidence-informed benchmarking across clinical domains, from perinatal outcomes to chronic disease management. The integration of DQR and FAIR appraisals will allow Irish health agencies to identify datasets that meet international standards of reliability, completeness and accessibility. Simultaneously, mapping PROGRESS-Plus variables will reveal where data gaps perpetuate inequities, informing targeted data-collection reforms. The resultant framework will provide a replicable model for how nations can align data governance with the continuous quality-improvement cycle central to the International Journal of Pharmaceutical Quality Assurance's mission - linking structure (data quality), process (data use) and outcomes (policy and patient benefit). This review will generate a decision-ready catalogue of Irish paediatric and young people's health datasets, highlighting strengths, gaps and opportunities for improvement. By appraising data quality, equity stratifiers and linkage readiness, it will provide actionable recommendations for standardisation and governance. Policymakers can use the outputs to align datasets with international best practice, clinicians can advocate for inclusion of outcome and patient-reported measures and researchers can identify priority areas for secondary analysis and linkage studies - supporting safer, fairer, and more effective child health services in Ireland. Strengthening child health data systems has direct societal benefits by enabling more equitable, transparent and evidence-based policy. By mapping available datasets and assessing equity stratifiers, this review will highlight gaps in capturing determinants such as ethnicity, deprivation and disability. Addressing these gaps will allow more accurate monitoring of health inequalities and ensure that vulnerable groups are not overlooked in service planning. The outputs will support a culture of accountability, inform public debate on data use and contribute to building a learning health system that promotes fairness, inclusivity and trust in healthcare for children and young people. This protocol delivers the first integrated, decision-ready framework to benchmark paediatric data ecosystems against international quality, equity and stewardship standards, enabling learning health systems and policy-relevant data governance in Ireland and comparable Organisation for Economic Co-operation and Development settings.

In 2021, China's first Mars rover Zhurong successfully landed on the southern Utopia Planitia (109.925°E, 25.066°N) and began its exploration. The low-frequency channel (15-95 MHz) of Mars Rover Penetrating Radar (RoPeR) has capability to explore the Martian subsurface down to approximately 100 m, making it possible to investigate the past geological activities. Here, we provide a RoPeR low-frequency channel dataset that have undergone pre-processing, partial processing, and complete processing to users and can be directly applied for analysis, interpretation and inversion. The dataset also contains the information of 76 subsurface dipping reflectors that dip toward the northern lowlands at depths of 10-35 m. These reflectors show inclinations ranging from 6° to 20° with an average value of 14.5° and standard deviation of 2.9°. This dataset can promote further research on the formation and evolution of Martian ancient ocean.