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Engineering microbial consortia for distributed signal processing
Identifying the factors and causes of homelessness tendency: a qualitative study in Tehran
Abstract Homelessness is a major social challenge worldwide, with negative consequences for health and social welfare. This qualitative study, using conventional content analysis, aimed to identify factors contributing to homelessness in Tehran, Iran, in 2023. Seventeen semi-structured interviews were conducted with homeless individuals in neighborhoods and shelters. Participants were purposefully selected to ensure maximum diversity. The data were analyzed using the Granheim and Lundman method. The factors and reasons for the tendency towards homelessness can be classified into two levels: individual and social-environmental level. The subcategories at the individual level included: drug addiction, negative childhood experiences, and behavioral/psychological problems. Social‑environmental subcategories included social and communication issues, family conflicts and violence, and unsafe living environments. Homelessness has multiple causes at both micro and macro levels. Reducing its burden requires policymakers and social planners to address both individual and social factors through intersectional cooperation and targeted planning.
Self‐Assembled Azo‐Benzothiazole Mesogens: Align the Structure and Follow the Heat to Uncover Anisotropic Photothermal Pathways
ABSTRACT Understanding the relationship between molecular structure and anisotropic photothermal behavior is essential for developing light‐responsive materials with directionally controllable behavior. In this study, we report the design of a thiazole‐functionalized azobenzene mesogen, PB‐ABT, which exhibits efficient photothermal conversion under 405 nm light. The introduction of an electron‐rich thiazole moiety induces intramolecular charge transfer (ICT), enhancing π–π stacking and facilitating non‐radiative decay pathways essential for thermal energy generation. Comparative studies with a symmetric reference compound (PB‐Azo) reveal that PB‐ABT exhibits stronger intermolecular interaction, more stable self‐assembly, and significantly improved anisotropic photothermal behavior. The identification of a clear correlation between molecular arrangement and directional photothermal dissipation provides answers to the long‐standing limitations of isotropic behavior and the unclear mechanisms in organic photothermal systems. These anisotropic photothermal properties originate from two key factors: anisotropic surface morphology and direction‐dependent non‐radiative dissipation occurring along π–π stacking pathways, demonstrating a direct link between molecular arrangement and macroscopic heat generation. This work provides a molecular‐level strategy for constructing 405 nm‐responsive organic materials with both optical and thermal anisotropy, offering new insight into the design of aligned photothermal systems for advanced encryptable applications.
Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing
Urbanisation and human capital shape Saudi Arabia’s energy–emissions nexus through direct rather than moderating channels
Beyond Intrinsic Limits: Polarization‐Sensitive Carrier Dynamics Amplify Subtle Intrinsic Optical Anisotropy into Giant Linear Dichroism
ABSTRACT Filterless polarization‐resolved photodetection is attractive for compact optoelectronic systems, but its polarization contrast is fundamentally constrained by the low intrinsic absorption anisotropy of semiconductors. Here, we introduce polarization‐sensitive carrier dynamics (PSCD), a design principle that shifts polarization discrimination from static absorption anisotropy to the time evolution of photocarrier populations. We implement PSCD in a nonvolatile floating‐gate phototransistor by integrating a molecularly thin p‐type organic single crystal with a two‐dimensional covalent organic framework floating gate. Orthogonal linearly polarized light generates different photogeneration rates in the anisotropic crystal, which induce distinct optical‐erasure kinetics and temporally divergent recovery of the channel hole population. Consequently, the absorption linear dichroism ratio (LDR) of 3.4 is dynamically amplified by over four orders of magnitude, reaching a transient photocurrent LDR of 3.6 × 10 4 under optimized bias conditions. The device further maintains strong polarization discrimination under partially polarized illumination, achieving a transient photocurrent LDR of 9.8 × 10 2 at a degree of linear polarization of 0.3. These results establish time‐domain amplification of weak optical anisotropy through carrier‐dynamic modulation as a strategy for filterless, high‐contrast polarimetric sensing in compact optoelectronic platforms.
Refractory inclusions in Bennu samples indicative of outer Solar System accretion
Elevated FT4 is associated with prevalent stroke in euthyroid adults: a cross‑sectional study
Resolving Abrikosov vortex entry in superconducting nanostring resonators via displacement-noise spectroscopy in cavity optomechanics
Edgeefficient human activity recognition using a quantized patchbased transformer
Abstract Human Activity Recognition (HAR) systems using deep learning have shown significant promise; however, deploying such models on edge computing systems remains challenging due to constraints in inference latency, memory footprints and computational capacity. This study proposes a lightweight, end-to-end patch-based Transformer architecture designed for efficient HAR for resource constrained edge environments, evaluated through an architectural ablation study and multiple quantization strategies using TensorFlow Lite. We conducted extensive experiments by varying patch lengths and Transformer encoder depths in order to evaluate the impact on classification accuracy, latency and model complexity. The results demonstrate that moderate temporal patching achieves an effective balance between temporal representation learning and computational efficiency. Experiments have been conducted on two widely open-source benchmark datasets, WISDM and PAMAP2, where raw sensor signals are pre-processed through segmentation, normalization, and overlapping windowing before being fed into a compact Transformer model with patch embedding and multi-head self-attention for feature extraction. The trained models are converted into FP32, FP16, INT8 dynamic range, and INT8 full formats to evaluate trade-offs between different metrics such as accuracy, model size, and inference latency. The baseline model achieves 96.77% accuracy on WISDM and 98.48% on PAMAP2, with consistently high macro F1-scores. Among all quantization variants, TFLite-INT8-Dynamic reduced model size by 95.55% and 89.80% for WISDM and PAMAP2, respectively and drastically improved inference latency by around 99% for both datasets with minimal accuracy degradation. These findings demonstrate that the proposed patch-based Transformer model for HAR has achieved an effective balance between recognition performance and computational efficiency, which indicates its strong deployment feasibility for resource constrained edge environments through TensorFlow Lite benchmarking and can provide a scalable solution for edge intelligence applications.
Convergent genomic trajectories shape adaptation to life on land across animal lineages
Construction and mechanism of histone deacetylation related gene prognosis model in esophageal carcinoma
Wavelength-encoded neuromorphic inference enabled by microcavity MoS2 photodetector arrays
Dual-mode deep reinforcement learning for safety-oriented MASS collision avoidance
Human imprints on global riverfronts
Trust as a human value and driver of organizational behavior in innovative companies across international rankings
Learning ordinality-aware multimodal representations for composite materials design
Visual fidelity–driven quality assessment of medical image translation
Abstract Automated and reliable image quality assessment (IQA) is essential for safe use of medical image synthesis in critical applications like adaptive radiotherapy, treatment planning, or missing-modality reconstruction, where unnoticed generative artifacts may adversely affect outcomes. We evaluated image-to-image translation quality by coupling large-scale visual quality assessment with explainable automated IQA modeling. Adversarial diffusion-based framework, SynDiff, was applied to four cross-modality synthesis tasks, including three inter-MR and a CBCT-to-CT translation. Using four-fold cross-validation, ten reference-based and eight no-reference IQA metrics were computed for all synthesized images. Visual IQA ratings were independently collected from thirteen raters using predetermined protocol and specialized image viewer enabling blinded, randomized six-point Likert scoring. Auto-Sklearn was employed to learn ensemble regression models mapping IQA metrics to visual consensus ratings, with separate models trained on reference-based and no-reference metrics. The models closely reproduced distribution and ordering of ratings, typically within ±0.5 Likert points. Reference-based models achieved higher agreement with visual ratings than no-reference models ( $$R^2$$ 0.75 vs. 0.59, resp.), although the latter remained unbiased and informative. Explainability analyses indicated that metrics quantifying structural similarity or fidelity (e.g., anatomical boundary preservation) and intensity-based contrast relationships between tissues were the strongest predictors. Overall, the results demonstrate that ensemble regression models can provide transparent, scalable, and clinically meaningful quality control for generative medical imaging.