The Chaste Gaze: Long Thick Eyelashes as a Symbol of Divine Chastity-From Ancient Greece to Now.

This narrative review explores the evolving symbolism of long, thick eyelashes from classic antiquity to the present, positioning them as cultural markers of virtue, beauty, and identity. Sparked by the author's observation of the finely sculpted eyelashes of the Charioteer of Delphi (circa 470 BCE), this study traces how eyelashes transitioned from signs of divine chastity to tools of performative beauty. In ancient Greece, eyelashes embodied sōphrosynē-moral restraint and inner harmony-rather than sensuality.

Revisiting Kalos: Beauty, Irony, and the Plastic Surgeon's Eye.

This study explores a rare double-head vase from 6th-century BCE Akanthos, featuring 2 contrasting profiles-a pale-skinned Greek woman and a dark-skinned male with exaggerated features-each accompanied by a kalos inscription. While the inscriptions appear to assert symmetrical beauty ("Eronassa is very beautiful"; "Timyllos is as beautiful as this face"), philological and iconographic analysis reveals layers of irony. The woman's face reflects classical ideals, whereas the male's physiognomy (eg, bimaxillary protrusion) signals parody.

What Modern Aesthetic Surgeons Learn From Winckelmann's Ideal of "Noble Simplicity and Quiet Grandeur".

In an age when aesthetic surgery is often driven by algorithms, celebrity trends, and consumer demand, Johann Joachim Winckelmann's 18th-century ideal of "noble simplicity and quiet grandeur" offers a timely counterpoint. This essay revisits Winckelmann's reflections on ancient Greek sculpture-notably his interpretation of the Laocoön-to explore how restraint, balance, and silent dignity can inform contemporary aesthetic surgical practice. Through the lens of classic ideals, it argues that true beauty is not found in exaggeration, but in proportion, discipline, and the preservation of anatomic harmony.

Safety and efficacy of hairy scalp donors in thick split-thickness skin grafting: Healing and complications at donor sites.

Background: We aimed to investigate how the thickness of removed skin grafts affects the healing time at the donor site, with a focus on the depth of the donor wound and the normal thickness of the scalp.

Methods: We examined the outcomes of the donor sites of thick split-thickness skin grafts using hairy scalp skin in 102 Korean patients. We measured the thickness of the scalp donor skin using preoperative ultrasonography, histometric thickness of normal scalp skin in 61 patients, and histometric thickness of the thickest part of the grafted skin after surgery.

Integrated Estimation of Stress and Damage in Concrete Structure Using 2D Convolutional Neural Network Model Learned Impedance Responses of Capsule-like Smart Aggregate Sensor.

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed.

Enhanced mechanical and damping properties of Cu-Modified Al-Ni eutectic alloys: Solid-solution and precipitation hardening effects.

The study aimed to analyze the mechanical properties, precipitation strengthening, and microstructure of Al-Ni-Cu alloys to understand their enhanced characteristics. Additionally, the damping behavior was examined using a dynamic mechanical analyzer across a continuous heating temperature range with frequencies from 0.5 to 15 Hz.

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses.

Characterization of TJS119 and its biocontrol potential against insect pathogens.

Introduction: The white-spotted flower chafer (), which is widely distributed in Asian countries, is traditionally used in oriental medicine. However, its larvae are prone to severe damage by green muscardine disease (caused by ) during breeding. The aim of this study was to characterize TJS119, which has been isolated from freshwater, and investigate its potential as a biocontrol agent against in insects.

Plasma Surface Treatment of Cu Current Collectors for Improving the Electrochemical Performance of Si Anodes.

The practical utilization of Si electrodes is hindered by their substantial volume expansion during alloying and dealloying processes, which causes mechanical damage and separation from Cu current collectors. To alleviate the problem of Si composite detachment from Cu current collectors, the surface of the Cu current collectors is modified using atmospheric oxygen plasma. Plasma treatment improves the wetting ability of the Cu current collectors and, consequently, the coating quality of the Si electrodes.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring.

Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface.

A wireless impedance monitoring system, called SSeL-Pi, is designed to have cheap, mobile, and handy practical features as compared to wired commercial impedance analyzers. A Raspberry Pi platform impedance sensor node is designed to measure signals at a low-frequency range of up to 100 kHz. The low-frequency impedance measurement via the proposed node has been combined with a new PZT interface technique for measuring local responses sensitive to structural damage.

Inflammation promotes synucleinopathy propagation.

The clinical progression of neurodegenerative diseases correlates with the spread of proteinopathy in the brain. The current understanding of the mechanism of proteinopathy spread is far from complete. Here, we propose that inflammation is fundamental to proteinopathy spread.

Dendrite Suppression by Lithium-Ion Redistribution and Lithium Wetting of Lithium Zeolite Li(AlSiO) in Liquid Electrolytes.

Lithium metal is considered a next-generation anode material for high-voltage, high-energy-density batteries; however, its commercialization is limited because of dendrite formation during charging, which leads to short-circuiting and fire. Li metal is coated with a lithium zeolite Li(AlSiO) (bikitaite - BKT) for dendrite suppression. The BKT-coated Li metal anode exhibits enhanced cycle performance for both Li/LMO (over 982 cycles) and Li/Li cells (over 2000 h at 0.

TNF-α promotes α-synuclein propagation through stimulation of senescence-associated lysosomal exocytosis.

Cell-to-cell propagation of α-synuclein is thought to be the underlying mechanism of Parkinson's disease progression. Recent evidence suggests that inflammation plays an important role in the propagation of protein aggregates. However, the mechanism by which inflammation regulates the propagation of aggregates remains unknown.

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable.

Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage.

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar-aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members.

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone.

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage.

Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. 2020, , 3382.

The authors wish to make the following correction to this paper [...

Piezoelectric Sensor-Embedded Smart Rock for Damage Monitoring in a Prestressed Anchorage Zone.

In this paper, a piezoelectric sensor-embedded smart rock is proposed for the electromechanical impedance monitoring of internal concrete damage in a prestressed anchorage zone. Firstly, a piezoelectric sensor-embedded smart rock is analyzed for impedance monitoring in concrete structures. An impedance measurement model is analyzed for the PZT (lead zirconate titanate)-embedded smart rock under compression in a concrete member.

N-acetylcysteine reduce the stress induced by cold storage of platelets: A potential way to extend shelf life of platelets.

The room temperature storage used for platelets worldwide leads to platelet storage lesion (PSL) and risk of bacterial growth, limiting platelet shelf life and safety in transfusion. Thus, there is a need for an alternative storage method that can serve as effective temperature storage for platelet concentrates (PCs). In the previous investigation, we have shown that N-acetylcysteine (NAC) is a potential candidate for an additive solution to retain platelet characteristics during cold storage for up to 5 days.

Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms.

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals.

Comparing recombinant MPB70/SahH and native 20-kDa protein for detecting bovine tuberculosis using ELISA.

Bovine tuberculosis (bTB) is a zoonosis caused by Mycobacterium bovis. Test-and-cull protocols and gross pathological examinations of abattoir animals as well as milk pasteurization have been implemented to prevent the spread of tuberculosis from animals to humans worldwide. Despite the importance of precise and rapid diagnostic tests, conventional methods including intradermal skin tests and γ-interferon assays are limited by the high rate of false-negative results for cattle in the late infectious stage and due to laborious and time-consuming procedures.

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model.