Highly sensitive temperature sensors based on the fluorescence intensity ratio of dual-emissive lead-free metal halides.

Given that optical thermometers are widely used due to their unique advantages, this study aims to address critical challenges in existing technologies, such as insufficient sensitivity, limited temperature measurement ranges, and poor signal recognition capabilities. Herein, we develop a thermometer based on the fluorescence intensity ratio (FIR) of Sb-doped CsNaInCl (CsNaInCl:Sb). As the temperature increases from 203 to 323 K, the thermally induced transition from triplet to singlet self-trapped excitons (STEs) leads to enhanced 455 nm photoluminescence (PL) from singlet STE recombination.

Advances of immunosensors based on noble metal composite materials for detecting procalcitonin.

Procalcitonin (PCT) is a reliable biomarker for diagnosing and monitoring bacterial infections and sepsis. PCT exhibits good stability both in vivo and in vitro, and its levels drastically increase in response to bacterial infection or inflammatory reactions in the human body, making it a dependable indicator for sepsis diagnosis and monitoring with significant implications for clinical diagnosis and treatment guidance. Currently, immunosensors are widely utilized in PCT detection due to their high sensitivity and low detection limits.

The effects of neurofeedback training on behavior and brain functional networks in children with autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with an unclear pathogenesis to date. Neurofeedback (NFB) had shown therapeutic effects in patients with ASD. In this study,we analyzed the brain functional networks of children with ASD and investigated the impact of NFB targeting the beta rhythm training on these networks.

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.

Ferroelectric Optical Memristors Enabled by Non-Volatile Electro-Optic Effect.

Memristors enable non-volatile memory and neuromorphic computing. Optical memristors are the fundamental element for programmable photonic integrated circuits due to their high-bandwidth computing, low crosstalk, and minimal power consumption. Here, an optical memristor enabled by a non-volatile electro-optic (EO) effect, where refractive index modulation under zero field is realized by deliberate control of domain alignment in the ferroelectric material Pb(MgNb)O-PbTiO(PMN-PT) is proposed.

Multimode Miniature Polarization-Sensitive Metamaterial Absorber with Ultra-Wide Bandwidth in the K Band.

Metamaterial absorbers have gained widespread applications in fields such as sensing, imaging, and electromagnetic cloaking due to their unique absorption characteristics. This paper presents the design and fabrication of a novel K-band polarization-sensitive metamaterial absorber, which operates in the frequency range of 20.76 to 24.

Emotion Recognition Based on a EEG-fNIRS Hybrid Brain Network in the Source Space.

: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to that of unimodal approaches. Nonetheless, there remains a paucity of in-depth investigations analyzing the inherent relationship between EEG and fNIRS and constructing brain networks to improve the performance of emotion recognition. : In this study, we introduce an innovative method to construct hybrid brain networks in the source space based on simultaneous EEG-fNIRS signals for emotion recognition.

Efficient Cow Body Condition Scoring Using BCS-YOLO: A Lightweight, Knowledge Distillation-Based Method.

Monitoring the body condition of dairy cows is essential for ensuring their health and productivity, but traditional BCS methods-relying on visual or tactile assessments by skilled personnel-are subjective, labor-intensive, and impractical for large-scale farms. To overcome these limitations, we present BCS-YOLO, a lightweight and automated BCS framework built on YOLOv8, which enables consistent, accurate scoring under complex conditions with minimal computational resources. BCS-YOLO integrates the Star-EMA module and the Star Shared Lightweight Detection Head (SSLDH) to enhance the detection accuracy and reduce model complexity.

Adltformer Team-Training with Detr: Enhancing Cattle Detection in Non-Ideal Lighting Conditions Through Adaptive Image Enhancement.

This study proposes an image enhancement detection technique based on Adltformer (Adaptive dynamic learning transformer) team-training with Detr (Detection transformer) to improve model accuracy in suboptimal conditions, addressing the challenge of detecting cattle in real pastures under complex lighting conditions-including backlighting, non-uniform lighting, and low light. This often results in the loss of image details and structural information, color distortion, and noise artifacts, thereby compromising the visual quality of captured images and reducing model accuracy. To train the Adltformer enhancement model, the day-to-night image synthesis (DTN-Synthesis) algorithm generates low-light image pairs that are precisely aligned with normal light images and include controlled noise levels.

Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.

Diabetes prediction is an important topic in the field of medical health. Accurate prediction can help early intervention and reduce patients' health risks and medical costs. This paper proposes a data preprocessing method, including removing outliers, filling missing values, and using sparse autoencoder (SAE) feature enhancement.

A stable open-shell peri-hexacene with remarkable diradical character.

[n]Peri-acenes ([n]PA) have attracted great interest as promising candidates for nanoelectronics and spintronics. However, the synthesis of large [n]PA (n > 4) is extremely challenging due to their intrinsic open-shell radical character and high reactivity. Herein, we report the successful synthesis and isolation of a derivative (1) of peri-hexacene in crystalline form.

Highly Sensitive Low-Frequency Acoustic Sensor Based on Functionalized Graphene Oxide.

Developing miniaturized low-frequency acoustic sensors with high sensitivity is crucial for diverse applications, including geological monitoring and aerospace exploration. However, the performance of low-frequency acoustic sensor is constrained by the limited mechanical robustness of traditional sensing films at nanoscale thickness. Here, a functionalized graphene oxide (GO)-based Fabry-Perot (FP) low-frequency sensor is proposed, with characteristics of compact size, resistance to electromagnetic interference high-sensitivity low minimum detectable pressure (MDP), and a high signal-to-noise ratio (SNR).

Thermoelectric Gel Enabling Self-Powered Facial Perception for Expression Recognition and Health Monitoring.

By analyzing facial features to perform expression recognition and health monitoring, facial perception plays a pivotal role in noninvasive, real-time disease diagnosis and prevention. Current perception routes are limited by structural complexity and the necessity of a power supply, making timely and accurate monitoring difficult. Herein, a self-powered poly(vinyl alcohol)-gellan gum-glycerol thermogalvanic gel patch enabling facial perception is developed for monitoring emotions and atypical pathological states.

Revealing the key role of interfacial oxygen activation over CoMnO@MnO in the catalytic oxidation of acetone.

The accumulation of intermediate products on the catalyst surface caused by insufficient oxygen activity is an important reason for the poor activity of catalysts towards oxygenated volatile organic compounds (OVOCs). CoMnO@MnO heterogeneous catalysts were fabricated to decipher the interfacial oxygen activation mechanism for efficient acetone oxidation. Experimental and theoretical explorations revealed that oxygen vacancies were easily formed at the interface.

A Room-Temperature Terahertz Photodetector Imaging with High Stability and Polarization-Sensitive Based on Perovskite/Metasurface.

Terahertz (THz) polarization detection facilitates the capture of multidimensional data, including intensity, phase, and polarization state, with broad applicability in high-resolution imaging, communication, and remote sensing. However, conventional semiconductor materials are limited by energy band limitations, rendering them unsuitable for THz detection. Overcoming this challenge, the realization of high-stability, room-temperature polarization-sensitive THz photodetectors (PDs) leveraging the thermoelectric effect of Cs(FAMA)Pb(IBr) (CsFAMA)/metasurfaces is presented.

A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF-gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning-based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes.

A Kinetically Stabilized Dianthraceno[2,3-a:3',2'-h]-s-Indacene: Stable Kekulé Diradical Polycyclic Hydrocarbon with Triplet Ground State.

High-spin polycyclic hydrocarbons (PHs) hold significant potential in organic spintronics and organic magnets. However, their synthesis is very challenging due to their extremely high reactivity. Herein, we report the successful synthesis and isolation of a kinetically blocked derivative (1) of dianthraceno[2,3-a : 3',2'-h]-s-indacene, which represents a rare persistent triplet diradical of a Kekulé PH.

PCBM Constructing Heterojunction for Efficient CsPbI Perovskite Quantum Dot Solar Cells.

CsPbI perovskite quantum dots (PQDs) have emerged as promising photovoltaic materials for third-generation solar cells, owing to their superior optoelectronic properties. Nevertheless, the performance of CsPbI PQD solar cells is primarily hindered by low carrier extraction efficiency, largely due to the insulative ligands. In this study, we introduced a semiconductor molecule, [6,6]-phenyl C butyric acid methyl ester (PCBM), onto the surfaces of CsPbI PQDs as surface ligands to enhance photogenerated charge extraction.

A butterfly metasurface with efficient multi-functional polarization conversion operating in the Ku-Ka band.

A highly efficient and multi-functional butterfly polarization conversion metasurface is proposed for the Ku-Ka frequency range, designed to reduce the radar cross-section. The suggested converter enables dual frequency bands linear-to-cross (LX) and linear-to-circular (LC) polarization transformations. The efficiency of cross-polarization conversion exceeds 90% over the frequency ranges of 14.

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We design and develop an electronic nose system containing a bionic chamber that imitates the nasal-cavity structure of humans and canines.

Utilization of Electric Fields to Modulate Molecular Activities on the Nanoscale: From Physical Properties to Chemical Reactions.

As a primary energy source, electricity drives broad fields from everyday electronic circuits to industrial chemical catalysis. From a chemistry viewpoint, studying electric field effects on chemical reactivity is highly important for revealing the intrinsic mechanisms of molecular behaviors and mastering chemical reactions. Recently, manipulating the molecular activity using electric fields has emerged as a new research field.

MST-YOLO: Small Object Detection Model for Autonomous Driving.

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures.

Using SiO-Supported MnO@FeO Composite to Catalytically Decompose Waste Drilling Fluids Through Fenton-like Oxidation.

Waste drilling fluids produced from oil extraction can cause serious harm to the ecological environment; thus, the treatment of waste drilling fluids is urgent and important to ensure the sustainability and development of the oil extraction. In this work, we used the Fenton-like reaction method to degrade waste drilling fluids with SiO-supported MnO@FeO composite material as a catalyst in the presence of HO. During the Fenton-like reaction process, the MnO@FeO interface exhibits exceptional activity by facilitating the production of ·OH species with high activity and strong oxidizing properties, which degrade the organic substances in the waste drilling fluids into smaller inorganic molecules, thereby reducing its COD value.

Multitask-Based Anti-Collision Trajectory Planning of Redundant Manipulators.

During performing multiple tasks of a redundant manipulator, the obstacles affect the sequential order of task areas and the joint trajectories. The end-effector is constrained to visit multiple task areas with an optimal anti-collision path, while the joints are required to move smoothly and avoid predefined obstacles. A special encoding genetic algorithm (SEGA) is proposed for multitask-based anti-collision trajectory planning.