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Allosteric PROTACs: Expanding the Horizon of Targeted Protein Degradation
Deep learning for construction waste detection using ConvNeXt V2 EMA attention and WIoU v3 loss
Topology-Informed Design of Circularly Locked DNAzymes Enables Orthogonally Controlled Gene Regulation
Behaviorally informed deep reinforcement learning for portfolio optimization with loss aversion and overconfidence
Integration of Photoresponsive Single-Molecule Bithermoelectric Devices
Development of a carbon fiber-based microextraction sample preparation patch for the detection of 21 organochlorine pesticides from water
Abstract The widespread use of organochlorine pesticides (OCPs) in agricultural practices has led to its accumulation in water bodies, causing significant environmental and health risks due to its persistence and toxicity. To enable effective monitoring, carbon fiber-based thin-film solid-phase microextraction (TF-SPME) patch were developed using a uniform divinylbenzene (DVB) coating applied exploiting an automatic film applicator. These patch were tailored for the extraction and quantification of 21 OCPs from water matrices. Extraction parameters such as extraction time, temperature variation study, desorption time and solvent profile were optimized using water samples spiked with a standard mixture of the 21 OCPs across a concentration range of 100–900 ng/mL. The DVB-coated carbon fiber TF-SPME patch exhibited excellent extraction efficiency, achieving limits of detection ~ 0.3–1.5 ng/mL when analyzed with a triple quadrupole gas chromatography-mass spectrometer (GC–MS/MS). Calibration curves with regression equations were established for each pesticide to support rapid and reliable quantification. The method aligns with green analytical chemistry principles due to its minimal solvent consumption, low waste production, and energy-efficient operation. Greenness assessment tools such as AGREE, GAPI, and BAGI confirmed the method’s environmental compatibility and user safety. This DVB-coated carbon fiber TF-SPME platform presents a sensitive, robust, and eco-friendly approach for the routine monitoring of organochlorine pesticides in water resources, crucial for protecting ecological balance and public health.
Alternative explanation for how celestial objects generate large-scale magnetic fields
An optimized substitution box generator based on cubic pell curves and its application in image encryption
Abstract In today’s digital landscape, safeguarding confidential data from cyber threats and unauthorized breaches is more crucial than ever. A key component in modern cryptographic systems is the substitution box (S-box), which ensures data security through complex transformations. Designing S-boxes with high nonlinearity and computation efficiency is still challenging. In this paper, we propose a novel S-box generator based on cubic Pell curves. The key idea of our generator is to utilize the randomness of the points over the cubic Pell curves by using their binary strings. The optimized S-boxes are obtained by performing swapping operations on initial S-boxes that ensure high nonlinearity. Cryptographic evaluations including nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), differential approximation probability (DAP), linear approximation probability (LAP) and algebraic complexity (AC) demonstrate the strength of our method. The proposed S-boxes achieve optimal nonlinearity (108), outperform existing schemes in speed and security, and pass NIST randomness tests. Image encryption results demonstrate the robustness of the generated S-boxes against statistical attacks, further validating their cryptographic strength.
Synthesis and Computational Analysis of Uranium(III)-Pnictogen Bonds
Multistage treatment of industrial ethylene glycol (EG) effluent: integrating chemical extraction, coagulation/precipitation, and decolouration for enhanced wastewater remediation
Abstract Industrial wastewater containing high concentrations of ethylene glycol (EG) represents a major treatment challenge due to its high solubility, elevated chemical oxygen demand, and limited removal by conventional treatment systems. In this study, a multistage treatment strategy is proposed to overcome the demonstrated limitations of an existing industrial wastewater treatment plant for EG removal. The approach integrates solvent-assisted phase separation, coagulation–precipitation, and nanomaterial-based polishing. An external solvent-assisted phase separation step was applied as a pretreatment stage, achieving substantial reduction of the organic load (≈ 75–80% COD removal) and enabling partial recovery of an EG-rich fraction through association-driven co-extraction mechanisms rather than classical liquid–liquid extraction. Subsequent coagulation–precipitation removed suspended and colloidal matter, while tertiary polishing using nano zero-valent aluminum (nZVAl) achieved complete decoloration (100%). Kinetic analysis indicated that color removal followed Avrami-type behavior, reflecting a heterogeneous and multistep adsorption mechanism. Pilot-scale validation using real industrial wastewater confirmed the robustness of the proposed system. A preliminary techno-economic screening showed that the multistage process can operate at a net treatment cost comparable to conventional high-strength industrial wastewater treatment systems, with solvent recovery and partial EG reuse contributing to operational cost reduction rather than direct profit. Overall, the proposed framework provides a practical and scalable upgrade for industrial EG-laden wastewater treatment.
Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning
Prevalence, types, and demographic characteristics associated with major life changes following psychedelic use
Catalyst Metamorphosis: In Situ Oxidation of Diphosphines in Palladium-Catalyzed Regioselective and Enantioselective Heck Reactions
DNA conjugation on functionalized plastic surfaces for sequential, iterative single molecule sequencing
Electron scattering on carbon monoxide: An optimization of target molecular orbitals
An accurate description of target molecular orbitals is essential for modeling the electron–molecule scattering process. Here, we devise a framework for optimizing target molecular orbitals by numbering and weighting state-averaged molecular configuration wave functions automatically according to the experimental parameters to investigate low-energy electron scattering from carbon monoxide using the ab initio R-matrix method. Its main feature is the ability to provide optimal target molecular orbitals in terms of various specific elastic and inelastic scattering processes. Agreement with the available measurements and previous calculations is mostly excellent. The good description of the electronic dipole moment for the CO molecule plays a key role in determining the rotational excitation and elastic scattering results. The electronic excitation energies contribute to the accuracy of electronic excitation cross sections, with a low root-mean-square error of only 0.06 Å2. This study may pave a promising pathway for enhancing the study of electron–molecule scattering.
Enantioselective Radical Addition of Carboxylic Acids to Imines through Cooperative Copper/Acridine Catalysis
Object-guided contrastive language-image pre-training for zero-shot target recognition
Abstract Target recognition is critical for security systems, but traditional Visual-Language Models (VLMs) like CLIP suffer from limited training data semantics, poor background suppression, and inflexible multi-resolution features. To address these, we propose Object-Guide CLIP (OG-CLIP), integrating three core enhancements: Knowledge graph-driven data augmentation : A 5000-category military knowledge graph and 1M image-text pairs via multi-source acquisition and knowledge-infused prompts. Target-centered ROI module : Fuses SAM 2-generated masks with ViT features to focus on discriminative regions and suppress background noise. Adaptive MRL : Resolves traditional MRL’s rigid granularity via 128D–1024D continuous features, dynamic dimension weighting, and cross-granularity semantic alignment. Experiments on 99 target categories (military aircraft, warships, civilian targets) show OG-CLIP achieves 84.28% mean Accuracy (mAcc), 11.36 percentage points higher than baseline CLIP. Ablation confirms contributions of each component, and OG-CLIP excels in complex scenarios. The proposed framework offers a scalable and adaptable vision-language modeling approach for military recognition, with future work focusing on dataset expansion and model lightweight optimization.
“Ensemblization” of density functional theory
Density functional theory (DFT) has transformed our ability to investigate and understand electronic ground states. In its original formulation, however, DFT is not suited to addressing (e.g.) degenerate ground states, mixed states with different particle numbers, or excited states. All these issues can be handled, in principle exactly, via ensemble DFT (EDFT). This Perspective provides a detailed introduction to and analysis of EDFT, in an in-principle exact framework that is constructed to avoid uncontrolled errors and inconsistencies that may be associated with ad hoc extensions of conventional DFT. In particular, it focuses on the “ensemblization” of both exact and approximate density functionals, a term that we coined to describe a rigorous approach that lends itself to the construction of novel approximations consistent with the general ensemble framework, applicable to practical problems where traditional DFT tends to fail or does not apply at all. In particular, symmetry considerations and ensemble properties are shown to enable each other in shaping a practical DFT-based methodology that extends beyond the ground state and, in doing so, highlights the need to look outside the standard ground state Kohn–Sham treatment.