Explore the vast range of titles available.

Terahertz (THz) spin dynamics and vanishing stray field make antiferromagnetic (AFM) materials the most promising candidate for the next-generation magnetic memory technology with revolutionary storage density and writing speed. However, owing to the extremely large exchange energy barriers, energy-efficient manipulation has been a fundamental challenge in AFM systems. Here, we report an electrical writing of antiferromagnetic orders through a record-low current density on the order of 10 6 A cm −2 facilitated by the unique AFM-ferromagnetic (FM) phase transition in FeRh. By introducing a transient FM state via current-induced Joule heating, the spin-orbit torque can switch the AFM order parameter by 90° with a reduced writing current density similar to ordinary FM materials. This mechanism is further verified by measuring the temperature and magnetic bias field dependences, where the X-ray magnetic linear dichroism (XMLD) results confirm the AFM switching besides the electrical transport measurement. Our findings demonstrate the exciting possibility of writing operations in AFM-based devices with a lower current density, opening a new pathway towards pure AFM memory applications. Electrical manipulation of antiferromagnetic order is crucial for future memory devices, but existing switching schemes require a large current density. Here, the authors achieve record low current density switching in FeRh by taking advantage of its antiferromagnetic to ferromagnetic phase transition.

Noise and vibration signal classification can be applied to fault diagnosis in mechanical and electronic systems such as electric vehicles. Traditional signal classification technology uses signal time and frequency domain characteristics as the identification basis. This study proposes a technique for visualizing sound signals using the Wigner–Ville distribution (WVD) method to extract vibration signal characteristics and artificial neural networks as the signal classification basis. A brushless motor is used as the machinery power source to verify the feasibility of this method to classify different signal vibration characteristics. In this experimental work, six states in various brushless motor revolutions were deliberately designed for measuring vibration signals. The brushless motor vibration signal is imaged using the WVD analysis method to extract the vibration signal characteristics. Through the WVD method, the brushless motor data is converted, and the YOLO (you only look once) deep coiling machine neural method is used to identify and classify the brushless motor WVD images. The Wagener analysis method parameters and recognition rates are discussed, thereby improving accurate motor fault diagnostic capabilities. This research provides a method for fault diagnosis that can be accurately performed without dismantling the brushless motor. The proposed approach can improve the reliability and stability of brushless motor applications.

This study proposes a digital twin-based CPR compression measurement system (DTCMS) architecture for real-time monitoring of CPR compression. The system combines a load cell, an inertial measurement unit (IMU), a LabVIEW acquisition platform, and a CNN module to capture multi-modal motion characteristics during CPR repetitive compression training. A calibration-aware sensor fusion framework synchronizes heterogeneous signals, reduces drift, and enhances robustness under high-frequency operation. Real-time data acquisition, latency-controlled transmission, and digital twin visualization enable synchronized physical-virtual interaction. Experimental results demonstrate high accuracy ( > 0.99), stable repeatability (coefficient of variation: CV < 3.5%), and reliable dynamic tracking. The compression depth error was maintained within ±1.5 mm, and synchronization latency remained below 0.2 s. Results confirm the proposed DTCMS architecture as a robust solution for real-time biomechanical monitoring and digital twin-based interactive systems. Compared with conventional single-sensor CPR monitoring systems, the proposed framework improves synchronization stability and sensing robustness through calibration-aware multi-sensor fusion.

Background Blood is a vital medical resource that is sourced from primarily nonremunerated donations. As Taiwan faces an aging population, increasing medical demands pose new challenges to blood resource management. Trend analysis can improve blood supply chain management and allocate blood resources more efficiently and cost-effectively. Methods A retrospective review was conducted from 2011–2023 in a teaching hospital to examine the utilization and wastage of blood products. Data were collected from hospital information system and blood wastage reports, categorized by cause, and analyzed via SAS software version 9.3. Results The hospital released 424,197 units of blood products, with leukocyte-reduced red blood cells accounting for 140,971 units. The highest annual wastage rate was 0.29% in 2011, and the annual average was 0.08%. Fresh frozen plasma (36.3%), platelet concentrates (15.2%), and cryoprecipitate (14.3%) were the most frequently discarded. Major causes of wastage included excessive or inappropriate orders (22.3%), inability to reissue blood before expiration (16.7%), and incorrect orders (14.6%). Platelet wastage was caused primarily by improper storage, while thawed plasma could not be repurposed. Utilization of leukocyte-reduced blood products has increased, leading to a significant reduction in transfusion reactions from 1.8% in 2011 to 0.6% in 2023. Conclusions The study highlights that blood wastage often stems from human error or improper storage conditions. The leading cause of platelet product disposal is incorrect storage temperatures, along with the inability to repurpose thawed plasma for plasma products. Importantly, the increased adoption of prestorage leukocyte-reduced blood products has significantly mitigated transfusion-related reactions. We emphasize that continuous education for healthcare professionals and enhanced inventory monitoring are critical to minimize wastage and enhance transfusion quality.

This study introduces a novel platform, the Artificial Intelligence of Things Experimental Device Platform (AIoTEDP), to evaluate the durability of eyelash extensions under various environmental factors, including temperature, wind speed, and compression frequency. The experiment employs a three-factor full factorial design, utilizing LabVIEW to collect and analyze independent variables. The retention rate of eyelash extensions is the dependent variable for evaluating the durability. The proposed AIoTEDP regulates thermostats, stepper motors, and heating fans to simulate real-world eyelash extension usage conditions. Quantitative analyses are performed through visual assessments and image recognition technologies. The experimental results indicate that high temperatures and strong winds significantly reduce the durability of eyelash extensions. However, moderate bending damage (3000 repetitions) still allows for sufficient retention. This study validates the practicality and accuracy of the proposed AIoTEDP, showcasing its potential for innovative cosmetic testing systems to assess eyelash extension durability.

Recognition of self and nonself is important for outcrossing organisms, and different mating types establish the barrier against self-mating. In the unicellular ciliate T. thermophila, mating type determination requires complex DNA rearrangements at a single mat locus during conjugation to produce a type-specific gene pair (MTA and MTB) for 1 of 7 possible mating types. Surprisingly, we found that decreased expression of the DNA breakage-repair protein Ku80 at late stages of conjugation generated persistent selfing phenotype in the progeny. DNA analysis revealed multiple mating-type gene pairs as well as a variety of mis-paired, unusually arranged mating-type genes in these selfers that resemble some proposed rearrangement intermediates. They are found also in normal cells during conjugation and are lost after 10 fissions but are retained in Ku mutants. Silencing of TKU80 or TKU70-2 immediately after conjugation also generated selfing phenotype, revealing a hidden DNA rearrangement process beyond conjugation. Mating reactions between the mutant and normal cells suggest a 2-component system for self-nonself-recognition through MTA and MTB genes.

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry.

Background Ovarian carcinoma (OC) is a fatal malignancy, with most patients experiencing recurrence and resistance to chemotherapy. In contrast to hematogenous metastasizing tumors, ovarian cancer cells disseminate within the peritoneal cavity, especially the omentum. Previously, we reported omental crown-like structure (CLS) number is associated with poor prognosis of advanced-stage OC. CLS that have pathologic features of a dead or dying adipocyte was surrounded by several macrophages is well known a histologic hallmark for inflammatory adipose tissue. In this study, we attempted to clarify the interaction between metastatic ovarian cancer cells and omental CLS, and to formulate a therapeutic strategy for advanced-stage ovarian cancer. Methods A three-cell (including OC cells, adipocytes and macrophages) coculture model was established to mimic the omental tumor microenvironment (TME) of ovarian cancer. Caspase-1 activity, ATP and free fatty acids (FFA) levels were detected by commercial kits. An adipocyte organoid model was established to assess macrophages migration and infiltration. In vitro and in vivo experiments were performed for functional assays and therapeutic effect evaluations. Clinical OC tissue samples were collected for immunochemistry stain and statistics analysis. Results In three-cell coculture model, OC cells-derived IL-6 and IL-8 could induce the occurrence of pyroptosis in omental adipocytes. The pyroptotic adipocytes release ATP to increase macrophage infiltration, release FFA into TME, uptake by OC cells to increase chemoresistance. From OC tumor samples study, we demonstrated patients with high gasdermin D (GSDMD) expression in omental adipocytes is highly correlated with chemoresistance and poor outcome in advanced-stage OC. In animal model, by pyroptosis inhibitor, DSF, effectively retarded tumor growth and prolonged mice survival. Conclusions Omental adipocyte pyroptosis may contribute the chemoresistance in advanced stage OC. Omental adipocytes could release FFA and ATP through the GSDMD-mediate pyroptosis to induce chemoresistance and macrophages infiltration resulting the poor prognosis in advanced-stage OC. Inhibition of adipocyte pyroptosis may be a potential therapeutic modality in advanced-stage OC with omentum metastasis.

The general tool of fluorescent PET (photoinduced electron transfer) sensors/switches – a molecular design principle with engineering features – is outlined, with the aid of frontier orbital energy diagrams. Fluorophores such as anthracene, 1,3‐diaryl‐Δ2‐pyrazolines and 4‐amino‐1,8‐naphthalimides are employed within this system, alongside receptors such as amines, carboxylates, crown ethers and amino acids. This tool appealed to a multinational corporation for building a medical analyzer for electrolytes such as Na+, K+, Ca2+ and gases like CO2, which became a commercially successful application. Finally, the tool was a springboard for chemistry to cross into computer science. The field of molecular logic can elucidate how molecules inside us handle information. Molecular examples of the simplest logic gates such as YES, NOT, OR, AND are described. A case of a human‐level computation – visual edge detection – is also included. Serendipity, in the form of personal encounters, gave birth to the generalization of fluorescent PET (photoinduced electron transfer) sensors and to their application in the OPTI blood gas and electrolyte analyzer. The serendipitous realization that a sensor can also be operated as a switch gave birth to the field of molecular logic‐based computation, which now has contributions from chemists, molecular biologists and other life scientists.

Large and permanent porosity is the primary concern when designing metal-organic frameworks (MOFs) for specific applications, such as catalysis and drug delivery. In this article, we report a MOF Co11(BTB)6(NO3)4(DEF)2(H2O)14 (1, H3BTB = 1,3,5-tris(4-carboxyphenyl)benzene; DEF = N,N-diethylformamide) via a mixed cluster secondary building unit (SBU) approach. MOF 1 is sustained by a rare combination of a linear trinuclear Co3 and two types of dinuclear Co2 SBUs in a 1:2:2 ratio. These SBUs are bridged by BTB ligands to yield a three-dimensional (3D) non-interpenetrated MOF as a result of the less effective packing due to the geometrically contrasting SBUs. The guest-free framework of 1 has an estimated density of 0.469 g cm−3 and exhibits a potential solvent accessible void of 69.6% of the total cell volume. The activated sample of 1 exhibits an estimated Brunauer-Emmett-Teller (BET) surface area of 155 m2 g−1 and is capable of CO2 uptake of 58.61 cm3 g−1 (2.63 mmol g−1, 11.6 wt % at standard temperature and pressure) in a reversible manner at 195 K, showcasing its permanent porosity.