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The valorization of agricultural wastes such as pumpkin peel generated from the foodprocessing industry through thermochemical conversion offers sustainable solutions forboth waste management and carb…

The valorization of agricultural wastes such as pumpkin peel generated from the foodprocessing industry through thermochemical conversion offers sustainable solutions forboth waste management and carbon cycling. This study aims to evaluate the physicochemicalproperties and environmental impacts of charcoals produced from pumpkin peelwaste (PPW), without the use of chemicals or pre-washing. In this context, pumpkin peelhydrochar (PPH) was produced by hydrothermal carbonization (HTC) and pumpkin peelbiochar (PPB) by pyrolysis. The systems were modeled according to a pilot-scale scenariobased on the processing of 100 kg of PPW, and the functional unit was defined as theprocessing of this amount. The properties of the products were determined by variousphysicochemical characterization techniques, and environmental impacts were analyzedusing Life Cycle Assessment (LCA). The results showed that PPH has a higher specificsurface area (16.35 m2 g−1) than PPB (9.80 m2 g−1), as well as a higher carbon content(76.18% for PPH and 66.07% for PPB). Furthermore, the environmental impact of PPH(16.42 kg CO2-equivalent/FU) is lower than that of PPB (32.33 kg CO2-equivalent/FU).Based on the obtained physicochemical properties, the potential of both materials as soilconditioners has been evaluated. The lower environmental impact values suggest thatPPH may be a more advantageous alternative in terms of sustainability. However, thisevaluation is not based on direct soil application experiments, and further applied studiesare needed to confirm this potential.

The valorization of agricultural wastes such as pumpkin peel generated from the foodprocessing industry through thermochemical conversion offers sustainable solutions forboth waste management and carb…

The valorization of agricultural wastes such as pumpkin peel generated from the foodprocessing industry through thermochemical conversion offers sustainable solutions forboth waste management and carbon cycling. This study aims to evaluate the physicochemicalproperties and environmental impacts of charcoals produced from pumpkin peelwaste (PPW), without the use of chemicals or pre-washing. In this context, pumpkin peelhydrochar (PPH) was produced by hydrothermal carbonization (HTC) and pumpkin peelbiochar (PPB) by pyrolysis. The systems were modeled according to a pilot-scale scenariobased on the processing of 100 kg of PPW, and the functional unit was defined as theprocessing of this amount. The properties of the products were determined by variousphysicochemical characterization techniques, and environmental impacts were analyzedusing Life Cycle Assessment (LCA). The results showed that PPH has a higher specificsurface area (16.35 m2 g−1) than PPB (9.80 m2 g−1), as well as a higher carbon content(76.18% for PPH and 66.07% for PPB). Furthermore, the environmental impact of PPH(16.42 kg CO2-equivalent/FU) is lower than that of PPB (32.33 kg CO2-equivalent/FU).Based on the obtained physicochemical properties, the potential of both materials as soilconditioners has been evaluated. The lower environmental impact values suggest thatPPH may be a more advantageous alternative in terms of sustainability. However, thisevaluation is not based on direct soil application experiments, and further applied studiesare needed to confirm this potential.

Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)

This article examines Applied Behavior Analysis (ABA) therapy as one of the most effective and scientifically grounded methods used in working with children with Autism Spectrum Disorder (ASD). The ar…

This article examines Applied Behavior Analysis (ABA) therapy as one of the most effective and scientifically grounded methods used in working with children with Autism Spectrum Disorder (ASD). The article analyzes the theoretical foundations and core principles of ABA therapy, its main techniques (Discrete Trial Training, Natural Environment Teaching, and others), and provides practical recommendations for special educators and parents. The advantages and limitations of this methodology are also discussed. The findings have practical significance for the effective application of ABA therapy in special education institutions and at home. 

This record is part of the CHM_PRE_SSP data collection and contains bias-corrected annual and monthly precipitation data in GeoTIFF format. For the complete dataset description, methodology, file stru…

This record is part of the CHM_PRE_SSP data collection and contains bias-corrected annual and monthly precipitation data in GeoTIFF format. For the complete dataset description, methodology, file structure, and usage notes, please refer to the main record: CHM_PRE_SSP, a new bias-corrected daily CMIP6 precipitation projection dataset over the Chinese mainland. References 1. Hu, J., Miao, C., Fan, X., Ji, J., Su, J., & Wu, Y. CHM_PRE_SSP, a new bias-corrected daily CMIP6 precipitation projection dataset over the Chinese mainland. Scientific Data (Submitted).2. Hu, J., Miao, C., Su, J., Zhang, Q., Gou, J., & Sun, Q. (2025). An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates. Earth System Science Data, 17(8), 3987–4004. https://doi.org/10.5194/essd-17-3987-2025. Authors and contacts Jinlong Hu (hujl98@mail.bnu.edu.cn) Chiyuan Miao (miaocy@bnu.edu.cn)

Measurement harness and results for the paper: Open or Frontier? A Cost- and Energy-Aware Benchmark of Large Language Models for Software Vulnerability Detection Patrick Deininger and Wolfgang Sl…

Measurement harness and results for the paper: Open or Frontier? A Cost- and Energy-Aware Benchmark of Large Language Models for Software Vulnerability Detection Patrick Deininger and Wolfgang Slany. Submitted to MDPI Computers.

This repository contains the example dataset for the protocol paper titled smFISH-on-Chip: Candida albicans biofilm growth and gene expression analysis by Single-molecule Fluorescence In Situ Hybridiz…

This repository contains the example dataset for the protocol paper titled smFISH-on-Chip: Candida albicans biofilm growth and gene expression analysis by Single-molecule Fluorescence In Situ Hybridization. AbstractCandida albicans is a major opportunistic fungal pathogen whose pathogenicity relies on extensive phenotypic plasticity and dynamic transitions between morphological and lifestyle states. Although many regulators of morphogenesis have been identified, the molecular mechanisms by which local cues are integrated remain poorly understood, partly due to limitations in preserving spatial context during gene expression analysis. Here, we present an smFISH-on-Chip protocol that couples controlled C. albicans growth in microfluidic devices with single-molecule mRNA imaging and quantification. This approach enables time-lapse tracking, maintains native spatial organization, and supports high-resolution quantification of endogenous mRNAs in intact yeast, pseudo-hyphal, and hyphal cells. We demonstrate the method’s robustness by measuring the expression and spatial localization of the cell cycle mRNA CLB2, and of the hyphal tip-localized mRNA HWP1. We benchmark smFISH-on-Chip against our established smFISH-on-Coverslip approach to compare RNA spot detection efficiency. We provide analysis pipelines for spot identification and subcellular mRNA localization analysis. Altogether, this platform provides a powerful tool for dissecting how microenvironmental cues and community architecture shape the expression of localized and non-localized mRNAs, and in turn, fungal behavior.ContentFISH_data:Contains raw microscopy data, processed data and analysis code.In the folder FISH/analysis/CLB2 and FISH/analysis/HWP1 you can find ipynb files to replicate the performed analysis. Growth_videos_and_adherence_testingContains images used to make Figure 2 and 3.3D print designs:Additionally, both .stl and .3mf files for 3D printed tools used in this protocol are published. It should be noted that prints were performed using PLA-CF from bambulab, especially for the 3D printed caps this is important as the pin that is used to close of the connector is more robust when printed with PLA-CF as opposed to standard PLA.The 3D platforms for holding the microfluidic chips and eppendorfs were adapted from the designs of https://makerworld.com/nl/models/417346-eppendorf-tube-holder-6x3?from=search#profileId-426433. 

Cost-aware machine learning pipeline for predicting horizontal permeability in a heterogeneous carbonate reservoir from wireline logs and core data. Implements the four-phase workflow reported in the …

Cost-aware machine learning pipeline for predicting horizontal permeability in a heterogeneous carbonate reservoir from wireline logs and core data. Implements the four-phase workflow reported in the accompanying manuscript: data preparation, model selection, exhaustive cost-aware feature selection over all 8,192 subsets with Pareto frontier construction and SHAP analysis, and nested leave-one-well-out generalization. The field data are proprietary and are not included; a synthetic surrogate dataset generator is provided so that the pipeline can be run end to end.

This record contains raw data related to article "Concordance Between Human and AI-Based clinical follow-up after Total Hip Arthroplasty for prioritizing outpatient services" Abstract PurposeTo evalua…

This record contains raw data related to article "Concordance Between Human and AI-Based clinical follow-up after Total Hip Arthroplasty for prioritizing outpatient services" Abstract PurposeTo evaluate concordance between human clinical grading and a multimodal artificial intelligence (AI) virtual follow-up model for prioritising outpatient follow-up after total hip arthroplasty (THA). MethodsThis concordance study compared human reference grading with AI model predictions in 603 THA follow-up cases. The AI system integrated radiographic deep-learning outputs with machine-learning branches based on clinical and comorbidity data. Agreement and classification performance were assessed using cross-tabulation, observed agreement, Cohen’s kappa, sensitivity, specificity, predictive values, likelihood ratios, odds ratio, and AUC with 95% confidence intervals. ResultsHuman grading classified 583 cases as normal and 20 as abnormal, whereas the AI model classified 586 as normal and 17 as abnormal. Cross-tabulation showed 582 true negatives, 16 true positives, one false positive, and four false negatives, corresponding to five discordant cases. Observed agreement was 99.2%, and Cohen’s kappa was 0.86 (standard error 0.04; Z = 21.21; p < 0.001). Sensitivity was 80.0% (95% confidence interval [CI] 56.3–94.3), specificity was 99.8% (95% CI 99.0–100.0), positive predictive value was 94.1%, negative predictive value was 99.3%, and ROC area was 0.90. ConclusionThe AI-supported virtual follow-up model showed excellent concordance with human clinical follow-up and very high specificity in detecting abnormal cases. However, four false-negative cases indicate that the tool should support, rather than replace, clinician-led surveillance.

This infopack is about the installation of a new turbine at the King Plains wind farm (Oklahoma, USA), assessment of energy production, and verification that the loads do not exceed design limits.