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Merging Redox and Nonredox Catalysis by a Dinuclear Nickel Catalyst: Enantioselective Hydroalkoxylation of Enamides with Alcohols
A lightweight hash-based quantum-resistant bidirectional authentication protocol for IoT devices integrating physical unclonable functions
Abstract IoT devices face mounting security threats: tightly bounded computing budgets on one side, the imminent arrival of quantum adversaries on the other. We present a bidirectional authentication protocol that pairs Physical Unclonable Functions with hash-based primitives so that no long-term key ever sits in device-side non-volatile memory. The scheme is best characterised as Grover-tolerant rather than fully post-quantum: hash one-wayness incurs a quadratic-only slowdown under quantum search, and PUF unclonability adds a physical, non-cryptographic uniqueness assumption — we do not claim parity with NIST-standardised lattice or code-based schemes. Mutual authentication is achieved with conditional forward secrecy and weak unlinkability against external eavesdroppers, while delivering 24.3 ms latency, 15.7 mJ per-authentication energy, and a 136-byte exchange on ESP32. ProVerif symbolic verification, complemented by a sketched QROM reduction with explicit PUF-leakage modelling, covers replay, MITM, impersonation, and physical-capture adversaries. Environmental stressing from 0 °C to 70 °C confirms practical reliability. We deliberately omit ephemeral key exchange and anonymous credentials — the price of doing so is the conditional and partial (rather than full) flavours of forward secrecy and anonymity, which we make explicit throughout.
Engineering a Transmembrane Receptor for Coacervate-Based Artificial Cells
Consumer-oriented CSR practices and food safety perceptions: a stakeholder and signaling theory perspective
Abstract Consumer concerns about food safety have intensified in recent years, as transparency and accountability in the food sector have become essential to maintaining public trust. This study examines how consumer-oriented corporate social responsibility (CSR) practices influence food safety perceptions, emphasizing the mediating role of customer satisfaction and the moderating role of loyalty. Drawing on stakeholder theory and signaling theory, we developed and tested a model using survey data from 498 chain restaurant consumers in China. Partial least squares structural equation modeling (PLS-SEM) results show that consumer/product safety CSR, ethical procurement and supply chain CSR, and environmental/social CSR all positively affect perceived food safety, although the effect of environmental/social CSR is relatively weaker. Moreover, customer satisfaction mediates the relationships between consumer/product safety CSR and perceived food safety, and between ethical procurement and supply chain CSR and perceived food safety, while customer loyalty strengthens the impact of satisfaction on safety perceptions. These findings contribute to consumer behavior and CSR literature by uncovering the psychological mechanisms through which CSR enhances food safety perceptions. For practitioners, the study highlights how CSR strategies can simultaneously improve satisfaction, strengthen loyalty, and build sustainable consumer trust in food safety.
Steering Hydrogenation Pathways via Construction of Multivalence Cu Electrocatalysts for Boosting Electrocatalytic CO <sub>2</sub> Reduction to CH <sub>4</sub>
Predictors of stroke-associated pneumonia and its influence on 90-day stroke functional outcome
Pd-Catalyzed Facile and Selective B–H Carbonylation Leading to Boron Cluster Carboxylic Acids for Diverse Transformations
AI-driven projection of seasonal agricultural drought using CMIP6 and remote sensing data in Borena Zone, Southern Ethiopia
Abstract In arid and semi-arid countries, accurate agricultural drought characterization is crucial for climate-resilient planning and efficient early warning systems. Therefore, by integrating remote sensing data, CMIP6 projections, and machine learning (ML) models, the study assesses the spatio-temporal dynamics, driving factors, and future evolution of agricultural drought in the Borena Zone, southern Ethiopia. The Vegetation Health Index (VHI) was primarily targeted using several drought-related indicators, such as the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land surface temperature (LST), and Temperature Condition Index (TCI) to detect the drought. Additionally, Shapley Additive Explanations (SHAP) and ML models were utilized to differentiate drought dynamics and performance for two seasons: March-May (MAM) and August-November (ASON). The findings show that the observed distribution of rainfall during both seasons spans from less than 20 mm month⁻¹ in the southern lowlands throughout a large portion of the southern basin, and that LST variation increases to 35.1 °C during MAM and rises to 38.1 °C during ASON, making it greatly susceptible to evapotranspiration. Consequently, the southern zone’s root-zone soil moisture decreases from 0.30 to 0.35 m³ m⁻³ in MAM to 0.25–0.30 m³ m⁻³ in ASON, along with notable decreases in NDVI and MNDWI. The ML models show strong drought prediction performance when targeting VHI; the Receiver Operating Characteristic Area Under the Curve (ROC–AUC) and Cohen’s kappa of XGBoost achieve 0.997, and 0.72 during MAM, and Random Forest achieves approximately 0.988 and 0.78 during ASON, respectively. TCI and NDVI are the most important predictors of drought severity according to SHAP-based feature attribution. Integrating future drought forecasts from a bias-corrected CMIP6 model (CNRM-CM6-1) under SSP2-4.5 and SSP5-8 scenarios indicates a significant expansion and intensification of drought. Mild drought is projected to affect up to 36.2% of the zone under SSP2-4.5, whereas under SSP5-8.5, moderate, severe, and extreme drought are projected to increase by 18.87%, 5.03%, and 3%, respectively, by the late 21st century. These robust results highlight escalating risks to rain-fed agriculture, rangeland productivity, and water resources, underscoring the urgency of machine-learning–based drought early-warning systems and targeted adaptation strategies in the Borena Zone.
Single-Crystalline Twelve-Connected Nanographene-Based Covalent Organic Frameworks
Utility of antifilarial antibodies for post-validation surveillance of lymphatic filariasis in Tonga
Bioorthogonal Gold-Catalyzed Hydrothiolation Leading to Amide Bond Cleavage of Ethynylated Biarylbutanamide Precursors
Differences in canine serum N-glycosylation pattern during infection and pregnancy
Abstract Diagnosis of inflammatory disorders during canine pregnancy is complicated by changes caused by pregnancy. The total glycosylation pattern of body fluids reflects cellular status in health and disease. The aim of the present study was to evaluate the overall canine N-glycan pattern in dogs with infection compared to pregnant dogs. Serum samples from ten dogs with pyometra (bacterial disease), ten dogs with canine infectious respiratory disease, CIRD (primarily viral disease), ten pregnant dogs (day 24–30) and nine healthy non-pregnant dogs were included. A method optimised for N-glycan quantification in canine serum, based on RapiFluor-labelling and subsequent glycan mapping by HILIC-UPLC-FLR-MS was used. The levels of the two dominating glycans, A2G2S2 and FA2, and six glycan groups were evaluated. No significant differences between pregnant dogs and dogs with pyometra were observed. In dogs with CIRD and in healthy non-pregnant dogs, the levels of terminally galactosylated and high mannose glycans were higher, while the levels of glycan A2G2S2 were lower, than in pregnant dogs. The present study provides new insight into the canine N-glycan pattern during infection and pregnancy. Serum N-glycans and N-glycan groups do not seem suitable as biomarkers to differentiate inflammatory changes due to pregnancy from those caused by infections.