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Advanced Molecular Tweezers Effectively Target Membranes Lacking Choline Headgroups for Broad-Spectrum Antiviral Efficacy
Modeling, environmental and economic analysis of drying of orange slice in an automatic indirect mixed mode solar dryer
Abstract This study presents an indirect mixed-mode solar dryer (IMMSD) integrated with a photovoltaic system with an automatic, temperature-responsive control system that automatically switches between natural and forced convection, enhancing efficiency and reducing energy use. Unlike previous fixed systems, it prevents over-drying and spoilage. The cost-effective, solar-powered design suits off-grid communities. By integrating drying kinetics with economic and environmental assessments, the system supports sustainability goals. The study also examines how slice thicknesses and tray positions within the drying room affect orange drying kinetics. Then, the IMMSD was used to dry orange slices under real conditions at Aswan University, Egypt, in January 2025, aiming to evaluate performance across different slice thicknesses (4, 6, and 8 mm) and tray positions (lower, middle, and upper levels). The results demonstrated that orange slices with a 4 mm thickness dried on the lower tray reached the final moisture content (MC) more quickly than thicker slices (8 mm) dried on the middle and upper trays. The 4 mm slices achieved the lowest final MC at approximately 12.5%, with a 48% reduction in drying time compared to 8 mm slices on the upper tray. The effective moisture diffusivity (D eff ) ranged from 4.5 × 10⁻⁸ to 15 × 10⁻⁸ m²/s. Additionally, five semi-theoretical models—Midilli, Modified Midilli I and II, Aghbashlo, and Henderson–Pabis—provided the best fit for modeling the drying behavior of orange slices. The environmental analysis showed that the energy required for moisture evaporation ( $$\:{E}_{at}$$ ) increased with slice thickness, reaching 916.56, 1372.31, and 1839.76 kWh for 4, 6, and 8 mm slices, respectively. The 8 mm slices yielded the highest annual dried product output, net CO₂ reduction (90.72 tons), and the shortest energy payback time (0.85 years). Carbon credits ranged from 2179.29 to 4535.76 USD. Economically, the IMMSD required a low capital cost of 700 USD and annual costs of 996.12 USD, generating yearly savings of up to 14,015.9 USD, that reduced the payback period of investigation up to only about one month.
Short-term effect of PM2.5 exposure on pediatric neurological outpatient visits in Shijiazhuang China 2013–2021
Siderophore–Pt(IV) Conjugates as Tools to Probe Cytoplasmic Cargo Delivery to Gram-Negative Bacteria
Molecular characterization of recessively inherited ataxic and neuropathic disorders in consanguineous Pakistani families
Construction of Three-Dimensional Covalent Organic Frameworks for Photocatalytic Synthesis of 2,3-Dihydrobenzofuran Derivatives
SMILES-based QSAR analysis of carbamate derivatives targeting butyrylcholinesterase
Correction to “Enantioselective Synthesis of 2,3-Disubstituted Azetidines via Copper-Catalyzed Boryl Allylation of Azetines”
Lanatoside C ameliorates DSS-induced colitis with improved intestinal barrier integrity and reduced M1 macrophage polarization
Delocalization versus Coherence under Vibrational and Environmental Disorder in Photoexcited Supramolecular Aggregates
Genome wide data recover hierarchical genetic structure and help define conservation units for the threatened Asian Houbara
Abstract The Asian Houbara Bustard ( Chlamydotis macqueenii ), a partially migratory bird from the western and Central Asian steppes, is listed as vulnerable on the IUCN Red List. This study reassesses the species’ genetic structure using modern genomics to identify evolutionary significant units (ESUs). Following the generation of a de novo reference assembly and resequencing data (114 birds, 10 locations), we integrated genetic results, migratory behaviour, and geography to identify eight hierarchically structured ESUs: four near range edges (Yemen, Mongolia, Eastern Kazakhstan, Israel) and four within the central range (Central-Eastern, Central-Western, North Iran, South Iran). Low genetic diversity and recent inbreeding make ESUs on the range periphery (Israel, Mongolia, Yemen) the most genetically threatened, consistent with the central-marginal hypothesis. ESUs do not cluster according to their migrant/non-migrant status. Geographic distance significantly shaped genetic structure, with longitudinal separation (isolation-by-distance along an east–west axis) emerging as the strongest predictor of differentiation, particularly among high-latitude migrant populations. Our findings underscore the importance of integrating genomic, geographic and behavioural criteria to define intraspecific units that effectively address the conservation needs of widespread species with complex evolutionary dynamics.
Oxidative Peptide Backbone Cleavage by a HEXXH Enzyme during RiPP Biosynthesis
A hybrid machine learning approach for detecting DDoS attacks in software-defined networks
Abstract Software-Defined Networking (SDN) introduces programmability and centralized control to modern networks, but this flexibility also exposes both the controller and data plane to severe threats such as Distributed Denial of Service (DDoS) attacks. Effective early detection of these attacks requires SDN-aware traffic features that capture the unique behavior of OpenFlow-based environments. This study presents a machine-learning framework for distinguishing benign and malicious traffic using a dataset constructed directly from an SDN testbed employing a Ryu controller and Open vSwitch. Flow and port-level statistics were periodically collected through OpenFlow monitoring messages, enabling the extraction of new SDN-specific features tailored for DDoS detection. A hybrid classification model that integrates the Random Forest (RF) with XGBoost (XGB) Classifier is proposed to enhance detection performance. The hybrid RF-XGB model demonstrates clear superiority over individual classifiers, achieving an accuracy of 99.36% and exhibiting near-perfect discrimination in ROC AUC and confusion matrix evaluations. These results confirm that combining SDN based feature engineering with ensemble learning provides a highly effective and reliable approach for early DDoS detection in programmable networks.
Hydrophobic Metal–Organic Frameworks Enable Superior High-Pressure Ammonia Storage through Geometric Design
Machine learning approach for wheat variety identification using single-seed imaging
Manipulating Terminal Iron-Hydroxide Nucleophilicity through Redox
In vitro characterization and in ovo embryotoxicity assessment of a triazol-5-one derivative in broiler embryos
Global temperature anomaly prediction by using additive twin LSTM networks
Abstract Due to the complexity of climate systems, data-driven modeling based on observed time series data is essential for predicting future climatic trends. This study aims to improve the long-term global temperature anomaly forecast performance of Long Short-Term Memory (LSTM) based neural network models. Although several LSTM variants and hybrid architectures have been suggested for time series data prediction problems, the long-term forecast performance of these models may not be satisfactory in practice. To address solution of these problems, firstly, authors focused on evaluating the forecast performance of models and suggested performance and test assessment procedures. Secondly, authors suggest an Additive Twin LSTM (AT-LSTM) model that can improve the forecast performance for the global temperature anomaly. Our test on the Berkeley Global Temperature Anomaly dataset demonstrates that the proposed AT-LSTM can improve performance relative to conventional LSTM variants in long-term forecasting. Authors observed that global temperature trend projections of the AT-LSTM models for 20 years in future are consistent with expectations of climate organizations and projections in other works. The AT-LSTM models forecasted an average of 1.415 °C with ± 0.073 °C error in the year 2042 and this indicates the strong potential of major climate changes in the near future of Earth.